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coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/include/fst
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/include/fst/script/info.h
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. #ifndef FST_SCRIPT_INFO_H_ #define FST_SCRIPT_INFO_H_ #include <string> #include <tuple> #include <fst/script/arg-packs.h> #include <fst/script/fst-class.h> #include <fst/script/info-impl.h> namespace fst { namespace script { using InfoArgs = std::tuple<const FstClass &, bool, const string &, const string &, bool, bool>; template <class Arc> void PrintFstInfo(InfoArgs *args) { const Fst<Arc> &fst = *(std::get<0>(*args).GetFst<Arc>()); const FstInfo fstinfo(fst, std::get<1>(*args), std::get<2>(*args), std::get<3>(*args), std::get<4>(*args)); PrintFstInfoImpl(fstinfo, std::get<5>(*args)); if (std::get<5>(*args)) fst.Write(""); } void PrintFstInfo(const FstClass &f, bool test_properties, const string &arc_filter, const string &info_type, bool pipe, bool verify); using GetInfoArgs = std::tuple<const FstClass &, bool, const string &, const string &, bool, FstInfo *>; template <class Arc> void GetFstInfo(GetInfoArgs *args) { const Fst<Arc> &fst = *(std::get<0>(*args).GetFst<Arc>()); *(std::get<5>(*args)) = FstInfo(fst, std::get<1>(*args), std::get<2>(*args), std::get<3>(*args), std::get<4>(*args)); } void GetFstInfo(const FstClass &fst, bool test_properties, const string &arc_filter, const string &info_type, bool verify, FstInfo *info); } // namespace script } // namespace fst #endif // FST_SCRIPT_INFO_H_
0
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/script/shortest-distance.cc
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. #include <fst/script/fst-class.h> #include <fst/script/script-impl.h> #include <fst/script/shortest-distance.h> namespace fst { namespace script { void ShortestDistance(const FstClass &fst, std::vector<WeightClass> *distance, const ShortestDistanceOptions &opts) { ShortestDistanceArgs1 args(fst, distance, opts); Apply<Operation<ShortestDistanceArgs1>>("ShortestDistance", fst.ArcType(), &args); } void ShortestDistance(const FstClass &ifst, std::vector<WeightClass> *distance, bool reverse, double delta) { ShortestDistanceArgs2 args(ifst, distance, reverse, delta); Apply<Operation<ShortestDistanceArgs2>>("ShortestDistance", ifst.ArcType(), &args); } REGISTER_FST_OPERATION(ShortestDistance, StdArc, ShortestDistanceArgs1); REGISTER_FST_OPERATION(ShortestDistance, LogArc, ShortestDistanceArgs1); REGISTER_FST_OPERATION(ShortestDistance, Log64Arc, ShortestDistanceArgs1); REGISTER_FST_OPERATION(ShortestDistance, StdArc, ShortestDistanceArgs2); REGISTER_FST_OPERATION(ShortestDistance, LogArc, ShortestDistanceArgs2); REGISTER_FST_OPERATION(ShortestDistance, Log64Arc, ShortestDistanceArgs2); } // namespace script } // namespace fst
0
coqui_public_repos/TTS/TTS/vc
coqui_public_repos/TTS/TTS/vc/configs/freevc_config.py
from dataclasses import dataclass, field from typing import List, Optional from coqpit import Coqpit from TTS.vc.configs.shared_configs import BaseVCConfig @dataclass class FreeVCAudioConfig(Coqpit): """Audio configuration Args: max_wav_value (float): The maximum value of the waveform. input_sample_rate (int): The sampling rate of the input waveform. output_sample_rate (int): The sampling rate of the output waveform. filter_length (int): The length of the filter. hop_length (int): The hop length. win_length (int): The window length. n_mel_channels (int): The number of mel channels. mel_fmin (float): The minimum frequency of the mel filterbank. mel_fmax (Optional[float]): The maximum frequency of the mel filterbank. """ max_wav_value: float = field(default=32768.0) input_sample_rate: int = field(default=16000) output_sample_rate: int = field(default=24000) filter_length: int = field(default=1280) hop_length: int = field(default=320) win_length: int = field(default=1280) n_mel_channels: int = field(default=80) mel_fmin: float = field(default=0.0) mel_fmax: Optional[float] = field(default=None) @dataclass class FreeVCArgs(Coqpit): """FreeVC model arguments Args: spec_channels (int): The number of channels in the spectrogram. inter_channels (int): The number of channels in the intermediate layers. hidden_channels (int): The number of channels in the hidden layers. filter_channels (int): The number of channels in the filter layers. n_heads (int): The number of attention heads. n_layers (int): The number of layers. kernel_size (int): The size of the kernel. p_dropout (float): The dropout probability. resblock (str): The type of residual block. resblock_kernel_sizes (List[int]): The kernel sizes for the residual blocks. resblock_dilation_sizes (List[List[int]]): The dilation sizes for the residual blocks. upsample_rates (List[int]): The upsample rates. upsample_initial_channel (int): The number of channels in the initial upsample layer. upsample_kernel_sizes (List[int]): The kernel sizes for the upsample layers. n_layers_q (int): The number of layers in the quantization network. use_spectral_norm (bool): Whether to use spectral normalization. gin_channels (int): The number of channels in the global conditioning vector. ssl_dim (int): The dimension of the self-supervised learning embedding. use_spk (bool): Whether to use external speaker encoder. """ spec_channels: int = field(default=641) inter_channels: int = field(default=192) hidden_channels: int = field(default=192) filter_channels: int = field(default=768) n_heads: int = field(default=2) n_layers: int = field(default=6) kernel_size: int = field(default=3) p_dropout: float = field(default=0.1) resblock: str = field(default="1") resblock_kernel_sizes: List[int] = field(default_factory=lambda: [3, 7, 11]) resblock_dilation_sizes: List[List[int]] = field(default_factory=lambda: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]) upsample_rates: List[int] = field(default_factory=lambda: [10, 8, 2, 2]) upsample_initial_channel: int = field(default=512) upsample_kernel_sizes: List[int] = field(default_factory=lambda: [16, 16, 4, 4]) n_layers_q: int = field(default=3) use_spectral_norm: bool = field(default=False) gin_channels: int = field(default=256) ssl_dim: int = field(default=1024) use_spk: bool = field(default=False) num_spks: int = field(default=0) segment_size: int = field(default=8960) @dataclass class FreeVCConfig(BaseVCConfig): """Defines parameters for FreeVC End2End TTS model. Args: model (str): Model name. Do not change unless you know what you are doing. model_args (FreeVCArgs): Model architecture arguments. Defaults to `FreeVCArgs()`. audio (FreeVCAudioConfig): Audio processing configuration. Defaults to `FreeVCAudioConfig()`. grad_clip (List): Gradient clipping thresholds for each optimizer. Defaults to `[1000.0, 1000.0]`. lr_gen (float): Initial learning rate for the generator. Defaults to 0.0002. lr_disc (float): Initial learning rate for the discriminator. Defaults to 0.0002. lr_scheduler_gen (str): Name of the learning rate scheduler for the generator. One of the `torch.optim.lr_scheduler.*`. Defaults to `ExponentialLR`. lr_scheduler_gen_params (dict): Parameters for the learning rate scheduler of the generator. Defaults to `{'gamma': 0.999875, "last_epoch":-1}`. lr_scheduler_disc (str): Name of the learning rate scheduler for the discriminator. One of the `torch.optim.lr_scheduler.*`. Defaults to `ExponentialLR`. lr_scheduler_disc_params (dict): Parameters for the learning rate scheduler of the discriminator. Defaults to `{'gamma': 0.999875, "last_epoch":-1}`. scheduler_after_epoch (bool): If true, step the schedulers after each epoch else after each step. Defaults to `False`. optimizer (str): Name of the optimizer to use with both the generator and the discriminator networks. One of the `torch.optim.*`. Defaults to `AdamW`. kl_loss_alpha (float): Loss weight for KL loss. Defaults to 1.0. disc_loss_alpha (float): Loss weight for the discriminator loss. Defaults to 1.0. gen_loss_alpha (float): Loss weight for the generator loss. Defaults to 1.0. feat_loss_alpha (float): Loss weight for the feature matching loss. Defaults to 1.0. mel_loss_alpha (float): Loss weight for the mel loss. Defaults to 45.0. return_wav (bool): If true, data loader returns the waveform as well as the other outputs. Do not change. Defaults to `True`. compute_linear_spec (bool): If true, the linear spectrogram is computed and returned alongside the mel output. Do not change. Defaults to `True`. use_weighted_sampler (bool): If true, use weighted sampler with bucketing for balancing samples between datasets used in training. Defaults to `False`. weighted_sampler_attrs (dict): Key retuned by the formatter to be used for weighted sampler. For example `{"root_path": 2.0, "speaker_name": 1.0}` sets sample probabilities by overweighting `root_path` by 2.0. Defaults to `{}`. weighted_sampler_multipliers (dict): Weight each unique value of a key returned by the formatter for weighted sampling. For example `{"root_path":{"/raid/datasets/libritts-clean-16khz-bwe-coqui_44khz/LibriTTS/train-clean-100/":1.0, "/raid/datasets/libritts-clean-16khz-bwe-coqui_44khz/LibriTTS/train-clean-360/": 0.5}`. It will sample instances from `train-clean-100` 2 times more than `train-clean-360`. Defaults to `{}`. r (int): Number of spectrogram frames to be generated at a time. Do not change. Defaults to `1`. add_blank (bool): If true, a blank token is added in between every character. Defaults to `True`. test_sentences (List[List]): List of sentences with speaker and language information to be used for testing. language_ids_file (str): Path to the language ids file. use_language_embedding (bool): If true, language embedding is used. Defaults to `False`. Note: Check :class:`TTS.tts.configs.shared_configs.BaseTTSConfig` for the inherited parameters. Example: >>> from TTS.vc.configs.freevc_config import FreeVCConfig >>> config = FreeVCConfig() """ model: str = "freevc" # model specific params model_args: FreeVCArgs = field(default_factory=FreeVCArgs) audio: FreeVCAudioConfig = field(default_factory=FreeVCAudioConfig) # optimizer # TODO with training support # loss params # TODO with training support # data loader params return_wav: bool = True compute_linear_spec: bool = True # sampler params use_weighted_sampler: bool = False # TODO: move it to the base config weighted_sampler_attrs: dict = field(default_factory=lambda: {}) weighted_sampler_multipliers: dict = field(default_factory=lambda: {}) # overrides r: int = 1 # DO NOT CHANGE add_blank: bool = True # multi-speaker settings # use speaker embedding layer num_speakers: int = 0 speakers_file: str = None speaker_embedding_channels: int = 256 # use d-vectors use_d_vector_file: bool = False d_vector_file: List[str] = None d_vector_dim: int = None def __post_init__(self): for key, val in self.model_args.items(): if hasattr(self, key): self[key] = val
0
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/script/topsort.cc
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. #include <fst/script/fst-class.h> #include <fst/script/script-impl.h> #include <fst/script/topsort.h> namespace fst { namespace script { bool TopSort(MutableFstClass *fst) { TopSortArgs args(fst); Apply<Operation<TopSortArgs>>("TopSort", fst->ArcType(), &args); return args.retval; } REGISTER_FST_OPERATION(TopSort, StdArc, TopSortArgs); REGISTER_FST_OPERATION(TopSort, LogArc, TopSortArgs); REGISTER_FST_OPERATION(TopSort, Log64Arc, TopSortArgs); } // namespace script } // namespace fst
0
coqui_public_repos/TTS/TTS/tts/layers/bark
coqui_public_repos/TTS/TTS/tts/layers/bark/hubert/hubert_manager.py
# From https://github.com/gitmylo/bark-voice-cloning-HuBERT-quantizer import os.path import shutil import urllib.request import huggingface_hub class HubertManager: @staticmethod def make_sure_hubert_installed( download_url: str = "https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960.pt", model_path: str = "" ): if not os.path.isfile(model_path): print("Downloading HuBERT base model") urllib.request.urlretrieve(download_url, model_path) print("Downloaded HuBERT") return model_path return None @staticmethod def make_sure_tokenizer_installed( model: str = "quantifier_hubert_base_ls960_14.pth", repo: str = "GitMylo/bark-voice-cloning", model_path: str = "", ): model_dir = os.path.dirname(model_path) if not os.path.isfile(model_path): print("Downloading HuBERT custom tokenizer") huggingface_hub.hf_hub_download(repo, model, local_dir=model_dir, local_dir_use_symlinks=False) shutil.move(os.path.join(model_dir, model), model_path) print("Downloaded tokenizer") return model_path return None
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coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/include/fst
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/include/fst/script/fstscript-decl.h
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. // // Forward declarations for the FST and FST script classes. #ifndef FST_SCRIPT_FSTSCRIPT_DECL_H_ #define FST_SCRIPT_FSTSCRIPT_DECL_H_ #include <fst/fst-decl.h> namespace fst { namespace script { class ArcClass; class ArcIteratorClass; class MutableArcIteratorClass; class EncodeMapperClass; class FstClass; class MutableFstClass; class VectorFstClass; class StateIteratorClass; class WeightClass; } // namespace script } // namespace fst; #endif // FST_SCRIPT_FSTSCRIPT_DECL_H_
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coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/include
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/include/fst/generic-register.h
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. #ifndef FST_GENERIC_REGISTER_H_ #define FST_GENERIC_REGISTER_H_ #include <fst/compat.h> #ifndef FST_NO_DYNAMIC_LINKING #include <dlfcn.h> #endif #include <map> #include <string> #include <fst/types.h> #include <fst/log.h> // Generic class representing a globally-stored correspondence between // objects of KeyType and EntryType. // // KeyType must: // // * be such as can be stored as a key in a std::map<>. // * be concatenable with a const char* with the + operator // (or you must subclass and redefine LoadEntryFromSharedObject) // // EntryType must be default constructible. // // The third template parameter should be the type of a subclass of this class // (think CRTP). This is to allow GetRegister() to instantiate and return an // object of the appropriate type. namespace fst { template <class KeyType, class EntryType, class RegisterType> class GenericRegister { public: using Key = KeyType; using Entry = EntryType; static RegisterType *GetRegister() { static auto reg = new RegisterType; return reg; } void SetEntry(const KeyType &key, const EntryType &entry) { MutexLock l(&register_lock_); register_table_.insert(std::make_pair(key, entry)); } EntryType GetEntry(const KeyType &key) const { const auto *entry = LookupEntry(key); if (entry) { return *entry; } else { return LoadEntryFromSharedObject(key); } } virtual ~GenericRegister() {} protected: // Override this if you want to be able to load missing definitions from // shared object files. virtual EntryType LoadEntryFromSharedObject(const KeyType &key) const { #ifdef FST_NO_DYNAMIC_LINKING return EntryType(); #else const auto so_filename = ConvertKeyToSoFilename(key); void *handle = dlopen(so_filename.c_str(), RTLD_LAZY); if (handle == nullptr) { LOG(ERROR) << "GenericRegister::GetEntry: " << dlerror(); return EntryType(); } #ifdef RUN_MODULE_INITIALIZERS RUN_MODULE_INITIALIZERS(); #endif // We assume that the DSO constructs a static object in its global scope // that does the registration. Thus we need only load it, not call any // methods. const auto *entry = this->LookupEntry(key); if (entry == nullptr) { LOG(ERROR) << "GenericRegister::GetEntry: " << "lookup failed in shared object: " << so_filename; return EntryType(); } return *entry; #endif // FST_NO_DYNAMIC_LINKING } // Override this to define how to turn a key into an SO filename. virtual string ConvertKeyToSoFilename(const KeyType &key) const = 0; virtual const EntryType *LookupEntry(const KeyType &key) const { MutexLock l(&register_lock_); const auto it = register_table_.find(key); if (it != register_table_.end()) { return &it->second; } else { return nullptr; } } private: mutable Mutex register_lock_; std::map<KeyType, EntryType> register_table_; }; // Generic register-er class capable of creating new register entries in the // given RegisterType template parameter. This type must define types Key and // Entry, and have appropriate static GetRegister() and instance SetEntry() // functions. An easy way to accomplish this is to have RegisterType be the // type of a subclass of GenericRegister. template <class RegisterType> class GenericRegisterer { public: using Key = typename RegisterType::Key; using Entry = typename RegisterType::Entry; GenericRegisterer(Key key, Entry entry) { RegisterType::GetRegister()->SetEntry(key, entry); } }; } // namespace fst #endif // FST_GENERIC_REGISTER_H_
0
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/bin/fstprint-main.cc
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. // // Prints out binary FSTs in simple text format used by AT&T. #include <cstring> #include <fstream> #include <memory> #include <ostream> #include <string> #include <fst/flags.h> #include <fst/log.h> #include <fst/script/print.h> DECLARE_bool(acceptor); DECLARE_string(isymbols); DECLARE_string(osymbols); DECLARE_string(ssymbols); DECLARE_bool(numeric); DECLARE_string(save_isymbols); DECLARE_string(save_osymbols); DECLARE_bool(show_weight_one); DECLARE_bool(allow_negative_labels); DECLARE_string(missing_symbol); int fstprint_main(int argc, char **argv) { namespace s = fst::script; using fst::script::FstClass; using fst::SymbolTable; using fst::SymbolTableTextOptions; string usage = "Prints out binary FSTs in simple text format.\n\n Usage: "; usage += argv[0]; usage += " [binary.fst [text.fst]]\n"; std::set_new_handler(FailedNewHandler); SET_FLAGS(usage.c_str(), &argc, &argv, true); if (argc > 3) { ShowUsage(); return 1; } const string in_name = (argc > 1 && strcmp(argv[1], "-") != 0) ? argv[1] : ""; const string out_name = argc > 2 ? argv[2] : ""; std::unique_ptr<FstClass> fst(FstClass::Read(in_name)); if (!fst) return 1; string dest = "standard output"; std::ofstream fstrm; if (argc == 3) { fstrm.open(argv[2]); if (!fstrm) { LOG(ERROR) << argv[0] << ": Open failed, file = " << argv[2]; return 1; } dest = argv[2]; } std::ostream &ostrm = fstrm.is_open() ? fstrm : std::cout; ostrm.precision(9); const SymbolTableTextOptions opts(FLAGS_allow_negative_labels); std::unique_ptr<const SymbolTable> isyms; if (!FLAGS_isymbols.empty() && !FLAGS_numeric) { isyms.reset(SymbolTable::ReadText(FLAGS_isymbols, opts)); if (!isyms) return 1; } std::unique_ptr<const SymbolTable> osyms; if (!FLAGS_osymbols.empty() && !FLAGS_numeric) { osyms.reset(SymbolTable::ReadText(FLAGS_osymbols, opts)); if (!osyms) return 1; } std::unique_ptr<const SymbolTable> ssyms; if (!FLAGS_ssymbols.empty() && !FLAGS_numeric) { ssyms.reset(SymbolTable::ReadText(FLAGS_ssymbols)); if (!ssyms) return 1; } if (!isyms && !FLAGS_numeric && fst->InputSymbols()) { isyms.reset(fst->InputSymbols()->Copy()); } if (!osyms && !FLAGS_numeric && fst->OutputSymbols()) { osyms.reset(fst->OutputSymbols()->Copy()); } s::PrintFst(*fst, ostrm, dest, isyms.get(), osyms.get(), ssyms.get(), FLAGS_acceptor, FLAGS_show_weight_one, FLAGS_missing_symbol); if (isyms && !FLAGS_save_isymbols.empty()) { if (!isyms->WriteText(FLAGS_save_isymbols)) return 1; } if (osyms && !FLAGS_save_osymbols.empty()) { if (!osyms->WriteText(FLAGS_save_osymbols)) return 1; } return 0; }
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coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/include/fst
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/include/fst/script/topsort.h
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. #ifndef FST_SCRIPT_TOPSORT_H_ #define FST_SCRIPT_TOPSORT_H_ #include <fst/topsort.h> #include <fst/script/arg-packs.h> #include <fst/script/fst-class.h> namespace fst { namespace script { using TopSortArgs = WithReturnValue<bool, MutableFstClass *>; template <class Arc> void TopSort(TopSortArgs *args) { args->retval = TopSort(args->args->GetMutableFst<Arc>()); } bool TopSort(MutableFstClass *fst); } // namespace script } // namespace fst #endif // FST_SCRIPT_TOPSORT_H_
0
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/extensions
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/extensions/compress/Makefile.am
AM_CPPFLAGS = -I$(srcdir)/../../include $(ICU_CPPFLAGS) if HAVE_BIN bin_PROGRAMS = fstcompress fstrandmod LDADD = libfstcompressscript.la \ ../../script/libfstscript.la \ ../../lib/libfst.la \ -lm $(DL_LIBS) fstcompress_SOURCES = fstcompress.cc fstrandmod_SOURCES = fstrandmod.cc endif if HAVE_SCRIPT libfstcompressscript_la_SOURCES = compress-script.cc libfstcompressscript_la_LDFLAGS = -version-info 10:0:0 libfstcompressscript_la_LIBADD = \ ../../script/libfstscript.la \ ../../lib/libfst.la -lz -lm $(DL_LIBS) endif if HAVE_SCRIPT lib_LTLIBRARIES = libfstcompressscript.la endif
0
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/m4/ltoptions.m4
# Helper functions for option handling. -*- Autoconf -*- # # Copyright (C) 2004-2005, 2007-2009, 2011-2015 Free Software # Foundation, Inc. # Written by Gary V. Vaughan, 2004 # # This file is free software; the Free Software Foundation gives # unlimited permission to copy and/or distribute it, with or without # modifications, as long as this notice is preserved. # serial 8 ltoptions.m4 # This is to help aclocal find these macros, as it can't see m4_define. AC_DEFUN([LTOPTIONS_VERSION], [m4_if([1])]) # _LT_MANGLE_OPTION(MACRO-NAME, OPTION-NAME) # ------------------------------------------ m4_define([_LT_MANGLE_OPTION], [[_LT_OPTION_]m4_bpatsubst($1__$2, [[^a-zA-Z0-9_]], [_])]) # _LT_SET_OPTION(MACRO-NAME, OPTION-NAME) # --------------------------------------- # Set option OPTION-NAME for macro MACRO-NAME, and if there is a # matching handler defined, dispatch to it. Other OPTION-NAMEs are # saved as a flag. m4_define([_LT_SET_OPTION], [m4_define(_LT_MANGLE_OPTION([$1], [$2]))dnl m4_ifdef(_LT_MANGLE_DEFUN([$1], [$2]), _LT_MANGLE_DEFUN([$1], [$2]), [m4_warning([Unknown $1 option '$2'])])[]dnl ]) # _LT_IF_OPTION(MACRO-NAME, OPTION-NAME, IF-SET, [IF-NOT-SET]) # ------------------------------------------------------------ # Execute IF-SET if OPTION is set, IF-NOT-SET otherwise. m4_define([_LT_IF_OPTION], [m4_ifdef(_LT_MANGLE_OPTION([$1], [$2]), [$3], [$4])]) # _LT_UNLESS_OPTIONS(MACRO-NAME, OPTION-LIST, IF-NOT-SET) # ------------------------------------------------------- # Execute IF-NOT-SET unless all options in OPTION-LIST for MACRO-NAME # are set. m4_define([_LT_UNLESS_OPTIONS], [m4_foreach([_LT_Option], m4_split(m4_normalize([$2])), [m4_ifdef(_LT_MANGLE_OPTION([$1], _LT_Option), [m4_define([$0_found])])])[]dnl m4_ifdef([$0_found], [m4_undefine([$0_found])], [$3 ])[]dnl ]) # _LT_SET_OPTIONS(MACRO-NAME, OPTION-LIST) # ---------------------------------------- # OPTION-LIST is a space-separated list of Libtool options associated # with MACRO-NAME. If any OPTION has a matching handler declared with # LT_OPTION_DEFINE, dispatch to that macro; otherwise complain about # the unknown option and exit. m4_defun([_LT_SET_OPTIONS], [# Set options m4_foreach([_LT_Option], m4_split(m4_normalize([$2])), [_LT_SET_OPTION([$1], _LT_Option)]) m4_if([$1],[LT_INIT],[ dnl dnl Simply set some default values (i.e off) if boolean options were not dnl specified: _LT_UNLESS_OPTIONS([LT_INIT], [dlopen], [enable_dlopen=no ]) _LT_UNLESS_OPTIONS([LT_INIT], [win32-dll], [enable_win32_dll=no ]) dnl dnl If no reference was made to various pairs of opposing options, then dnl we run the default mode handler for the pair. For example, if neither dnl 'shared' nor 'disable-shared' was passed, we enable building of shared dnl archives by default: _LT_UNLESS_OPTIONS([LT_INIT], [shared disable-shared], [_LT_ENABLE_SHARED]) _LT_UNLESS_OPTIONS([LT_INIT], [static disable-static], [_LT_ENABLE_STATIC]) _LT_UNLESS_OPTIONS([LT_INIT], [pic-only no-pic], [_LT_WITH_PIC]) _LT_UNLESS_OPTIONS([LT_INIT], [fast-install disable-fast-install], [_LT_ENABLE_FAST_INSTALL]) _LT_UNLESS_OPTIONS([LT_INIT], [aix-soname=aix aix-soname=both aix-soname=svr4], [_LT_WITH_AIX_SONAME([aix])]) ]) ])# _LT_SET_OPTIONS ## --------------------------------- ## ## Macros to handle LT_INIT options. ## ## --------------------------------- ## # _LT_MANGLE_DEFUN(MACRO-NAME, OPTION-NAME) # ----------------------------------------- m4_define([_LT_MANGLE_DEFUN], [[_LT_OPTION_DEFUN_]m4_bpatsubst(m4_toupper([$1__$2]), [[^A-Z0-9_]], [_])]) # LT_OPTION_DEFINE(MACRO-NAME, OPTION-NAME, CODE) # ----------------------------------------------- m4_define([LT_OPTION_DEFINE], [m4_define(_LT_MANGLE_DEFUN([$1], [$2]), [$3])[]dnl ])# LT_OPTION_DEFINE # dlopen # ------ LT_OPTION_DEFINE([LT_INIT], [dlopen], [enable_dlopen=yes ]) AU_DEFUN([AC_LIBTOOL_DLOPEN], [_LT_SET_OPTION([LT_INIT], [dlopen]) AC_DIAGNOSE([obsolete], [$0: Remove this warning and the call to _LT_SET_OPTION when you put the 'dlopen' option into LT_INIT's first parameter.]) ]) dnl aclocal-1.4 backwards compatibility: dnl AC_DEFUN([AC_LIBTOOL_DLOPEN], []) # win32-dll # --------- # Declare package support for building win32 dll's. LT_OPTION_DEFINE([LT_INIT], [win32-dll], [enable_win32_dll=yes case $host in *-*-cygwin* | *-*-mingw* | *-*-pw32* | *-*-cegcc*) AC_CHECK_TOOL(AS, as, false) AC_CHECK_TOOL(DLLTOOL, dlltool, false) AC_CHECK_TOOL(OBJDUMP, objdump, false) ;; esac test -z "$AS" && AS=as _LT_DECL([], [AS], [1], [Assembler program])dnl test -z "$DLLTOOL" && DLLTOOL=dlltool _LT_DECL([], [DLLTOOL], [1], [DLL creation program])dnl test -z "$OBJDUMP" && OBJDUMP=objdump _LT_DECL([], [OBJDUMP], [1], [Object dumper program])dnl ])# win32-dll AU_DEFUN([AC_LIBTOOL_WIN32_DLL], [AC_REQUIRE([AC_CANONICAL_HOST])dnl _LT_SET_OPTION([LT_INIT], [win32-dll]) AC_DIAGNOSE([obsolete], [$0: Remove this warning and the call to _LT_SET_OPTION when you put the 'win32-dll' option into LT_INIT's first parameter.]) ]) dnl aclocal-1.4 backwards compatibility: dnl AC_DEFUN([AC_LIBTOOL_WIN32_DLL], []) # _LT_ENABLE_SHARED([DEFAULT]) # ---------------------------- # implement the --enable-shared flag, and supports the 'shared' and # 'disable-shared' LT_INIT options. # DEFAULT is either 'yes' or 'no'. If omitted, it defaults to 'yes'. m4_define([_LT_ENABLE_SHARED], [m4_define([_LT_ENABLE_SHARED_DEFAULT], [m4_if($1, no, no, yes)])dnl AC_ARG_ENABLE([shared], [AS_HELP_STRING([--enable-shared@<:@=PKGS@:>@], [build shared libraries @<:@default=]_LT_ENABLE_SHARED_DEFAULT[@:>@])], [p=${PACKAGE-default} case $enableval in yes) enable_shared=yes ;; no) enable_shared=no ;; *) enable_shared=no # Look at the argument we got. We use all the common list separators. lt_save_ifs=$IFS; IFS=$IFS$PATH_SEPARATOR, for pkg in $enableval; do IFS=$lt_save_ifs if test "X$pkg" = "X$p"; then enable_shared=yes fi done IFS=$lt_save_ifs ;; esac], [enable_shared=]_LT_ENABLE_SHARED_DEFAULT) _LT_DECL([build_libtool_libs], [enable_shared], [0], [Whether or not to build shared libraries]) ])# _LT_ENABLE_SHARED LT_OPTION_DEFINE([LT_INIT], [shared], [_LT_ENABLE_SHARED([yes])]) LT_OPTION_DEFINE([LT_INIT], [disable-shared], [_LT_ENABLE_SHARED([no])]) # Old names: AC_DEFUN([AC_ENABLE_SHARED], [_LT_SET_OPTION([LT_INIT], m4_if([$1], [no], [disable-])[shared]) ]) AC_DEFUN([AC_DISABLE_SHARED], [_LT_SET_OPTION([LT_INIT], [disable-shared]) ]) AU_DEFUN([AM_ENABLE_SHARED], [AC_ENABLE_SHARED($@)]) AU_DEFUN([AM_DISABLE_SHARED], [AC_DISABLE_SHARED($@)]) dnl aclocal-1.4 backwards compatibility: dnl AC_DEFUN([AM_ENABLE_SHARED], []) dnl AC_DEFUN([AM_DISABLE_SHARED], []) # _LT_ENABLE_STATIC([DEFAULT]) # ---------------------------- # implement the --enable-static flag, and support the 'static' and # 'disable-static' LT_INIT options. # DEFAULT is either 'yes' or 'no'. If omitted, it defaults to 'yes'. m4_define([_LT_ENABLE_STATIC], [m4_define([_LT_ENABLE_STATIC_DEFAULT], [m4_if($1, no, no, yes)])dnl AC_ARG_ENABLE([static], [AS_HELP_STRING([--enable-static@<:@=PKGS@:>@], [build static libraries @<:@default=]_LT_ENABLE_STATIC_DEFAULT[@:>@])], [p=${PACKAGE-default} case $enableval in yes) enable_static=yes ;; no) enable_static=no ;; *) enable_static=no # Look at the argument we got. We use all the common list separators. lt_save_ifs=$IFS; IFS=$IFS$PATH_SEPARATOR, for pkg in $enableval; do IFS=$lt_save_ifs if test "X$pkg" = "X$p"; then enable_static=yes fi done IFS=$lt_save_ifs ;; esac], [enable_static=]_LT_ENABLE_STATIC_DEFAULT) _LT_DECL([build_old_libs], [enable_static], [0], [Whether or not to build static libraries]) ])# _LT_ENABLE_STATIC LT_OPTION_DEFINE([LT_INIT], [static], [_LT_ENABLE_STATIC([yes])]) LT_OPTION_DEFINE([LT_INIT], [disable-static], [_LT_ENABLE_STATIC([no])]) # Old names: AC_DEFUN([AC_ENABLE_STATIC], [_LT_SET_OPTION([LT_INIT], m4_if([$1], [no], [disable-])[static]) ]) AC_DEFUN([AC_DISABLE_STATIC], [_LT_SET_OPTION([LT_INIT], [disable-static]) ]) AU_DEFUN([AM_ENABLE_STATIC], [AC_ENABLE_STATIC($@)]) AU_DEFUN([AM_DISABLE_STATIC], [AC_DISABLE_STATIC($@)]) dnl aclocal-1.4 backwards compatibility: dnl AC_DEFUN([AM_ENABLE_STATIC], []) dnl AC_DEFUN([AM_DISABLE_STATIC], []) # _LT_ENABLE_FAST_INSTALL([DEFAULT]) # ---------------------------------- # implement the --enable-fast-install flag, and support the 'fast-install' # and 'disable-fast-install' LT_INIT options. # DEFAULT is either 'yes' or 'no'. If omitted, it defaults to 'yes'. m4_define([_LT_ENABLE_FAST_INSTALL], [m4_define([_LT_ENABLE_FAST_INSTALL_DEFAULT], [m4_if($1, no, no, yes)])dnl AC_ARG_ENABLE([fast-install], [AS_HELP_STRING([--enable-fast-install@<:@=PKGS@:>@], [optimize for fast installation @<:@default=]_LT_ENABLE_FAST_INSTALL_DEFAULT[@:>@])], [p=${PACKAGE-default} case $enableval in yes) enable_fast_install=yes ;; no) enable_fast_install=no ;; *) enable_fast_install=no # Look at the argument we got. We use all the common list separators. lt_save_ifs=$IFS; IFS=$IFS$PATH_SEPARATOR, for pkg in $enableval; do IFS=$lt_save_ifs if test "X$pkg" = "X$p"; then enable_fast_install=yes fi done IFS=$lt_save_ifs ;; esac], [enable_fast_install=]_LT_ENABLE_FAST_INSTALL_DEFAULT) _LT_DECL([fast_install], [enable_fast_install], [0], [Whether or not to optimize for fast installation])dnl ])# _LT_ENABLE_FAST_INSTALL LT_OPTION_DEFINE([LT_INIT], [fast-install], [_LT_ENABLE_FAST_INSTALL([yes])]) LT_OPTION_DEFINE([LT_INIT], [disable-fast-install], [_LT_ENABLE_FAST_INSTALL([no])]) # Old names: AU_DEFUN([AC_ENABLE_FAST_INSTALL], [_LT_SET_OPTION([LT_INIT], m4_if([$1], [no], [disable-])[fast-install]) AC_DIAGNOSE([obsolete], [$0: Remove this warning and the call to _LT_SET_OPTION when you put the 'fast-install' option into LT_INIT's first parameter.]) ]) AU_DEFUN([AC_DISABLE_FAST_INSTALL], [_LT_SET_OPTION([LT_INIT], [disable-fast-install]) AC_DIAGNOSE([obsolete], [$0: Remove this warning and the call to _LT_SET_OPTION when you put the 'disable-fast-install' option into LT_INIT's first parameter.]) ]) dnl aclocal-1.4 backwards compatibility: dnl AC_DEFUN([AC_ENABLE_FAST_INSTALL], []) dnl AC_DEFUN([AM_DISABLE_FAST_INSTALL], []) # _LT_WITH_AIX_SONAME([DEFAULT]) # ---------------------------------- # implement the --with-aix-soname flag, and support the `aix-soname=aix' # and `aix-soname=both' and `aix-soname=svr4' LT_INIT options. DEFAULT # is either `aix', `both' or `svr4'. If omitted, it defaults to `aix'. m4_define([_LT_WITH_AIX_SONAME], [m4_define([_LT_WITH_AIX_SONAME_DEFAULT], [m4_if($1, svr4, svr4, m4_if($1, both, both, aix))])dnl shared_archive_member_spec= case $host,$enable_shared in power*-*-aix[[5-9]]*,yes) AC_MSG_CHECKING([which variant of shared library versioning to provide]) AC_ARG_WITH([aix-soname], [AS_HELP_STRING([--with-aix-soname=aix|svr4|both], [shared library versioning (aka "SONAME") variant to provide on AIX, @<:@default=]_LT_WITH_AIX_SONAME_DEFAULT[@:>@.])], [case $withval in aix|svr4|both) ;; *) AC_MSG_ERROR([Unknown argument to --with-aix-soname]) ;; esac lt_cv_with_aix_soname=$with_aix_soname], [AC_CACHE_VAL([lt_cv_with_aix_soname], [lt_cv_with_aix_soname=]_LT_WITH_AIX_SONAME_DEFAULT) with_aix_soname=$lt_cv_with_aix_soname]) AC_MSG_RESULT([$with_aix_soname]) if test aix != "$with_aix_soname"; then # For the AIX way of multilib, we name the shared archive member # based on the bitwidth used, traditionally 'shr.o' or 'shr_64.o', # and 'shr.imp' or 'shr_64.imp', respectively, for the Import File. # Even when GNU compilers ignore OBJECT_MODE but need '-maix64' flag, # the AIX toolchain works better with OBJECT_MODE set (default 32). if test 64 = "${OBJECT_MODE-32}"; then shared_archive_member_spec=shr_64 else shared_archive_member_spec=shr fi fi ;; *) with_aix_soname=aix ;; esac _LT_DECL([], [shared_archive_member_spec], [0], [Shared archive member basename, for filename based shared library versioning on AIX])dnl ])# _LT_WITH_AIX_SONAME LT_OPTION_DEFINE([LT_INIT], [aix-soname=aix], [_LT_WITH_AIX_SONAME([aix])]) LT_OPTION_DEFINE([LT_INIT], [aix-soname=both], [_LT_WITH_AIX_SONAME([both])]) LT_OPTION_DEFINE([LT_INIT], [aix-soname=svr4], [_LT_WITH_AIX_SONAME([svr4])]) # _LT_WITH_PIC([MODE]) # -------------------- # implement the --with-pic flag, and support the 'pic-only' and 'no-pic' # LT_INIT options. # MODE is either 'yes' or 'no'. If omitted, it defaults to 'both'. m4_define([_LT_WITH_PIC], [AC_ARG_WITH([pic], [AS_HELP_STRING([--with-pic@<:@=PKGS@:>@], [try to use only PIC/non-PIC objects @<:@default=use both@:>@])], [lt_p=${PACKAGE-default} case $withval in yes|no) pic_mode=$withval ;; *) pic_mode=default # Look at the argument we got. We use all the common list separators. lt_save_ifs=$IFS; IFS=$IFS$PATH_SEPARATOR, for lt_pkg in $withval; do IFS=$lt_save_ifs if test "X$lt_pkg" = "X$lt_p"; then pic_mode=yes fi done IFS=$lt_save_ifs ;; esac], [pic_mode=m4_default([$1], [default])]) _LT_DECL([], [pic_mode], [0], [What type of objects to build])dnl ])# _LT_WITH_PIC LT_OPTION_DEFINE([LT_INIT], [pic-only], [_LT_WITH_PIC([yes])]) LT_OPTION_DEFINE([LT_INIT], [no-pic], [_LT_WITH_PIC([no])]) # Old name: AU_DEFUN([AC_LIBTOOL_PICMODE], [_LT_SET_OPTION([LT_INIT], [pic-only]) AC_DIAGNOSE([obsolete], [$0: Remove this warning and the call to _LT_SET_OPTION when you put the 'pic-only' option into LT_INIT's first parameter.]) ]) dnl aclocal-1.4 backwards compatibility: dnl AC_DEFUN([AC_LIBTOOL_PICMODE], []) ## ----------------- ## ## LTDL_INIT Options ## ## ----------------- ## m4_define([_LTDL_MODE], []) LT_OPTION_DEFINE([LTDL_INIT], [nonrecursive], [m4_define([_LTDL_MODE], [nonrecursive])]) LT_OPTION_DEFINE([LTDL_INIT], [recursive], [m4_define([_LTDL_MODE], [recursive])]) LT_OPTION_DEFINE([LTDL_INIT], [subproject], [m4_define([_LTDL_MODE], [subproject])]) m4_define([_LTDL_TYPE], []) LT_OPTION_DEFINE([LTDL_INIT], [installable], [m4_define([_LTDL_TYPE], [installable])]) LT_OPTION_DEFINE([LTDL_INIT], [convenience], [m4_define([_LTDL_TYPE], [convenience])])
0
coqui_public_repos/TTS/TTS/vocoder
coqui_public_repos/TTS/TTS/vocoder/layers/wavegrad.py
import torch import torch.nn.functional as F from torch import nn from torch.nn.utils.parametrizations import weight_norm from torch.nn.utils.parametrize import remove_parametrizations class Conv1d(nn.Conv1d): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) nn.init.orthogonal_(self.weight) nn.init.zeros_(self.bias) class PositionalEncoding(nn.Module): """Positional encoding with noise level conditioning""" def __init__(self, n_channels, max_len=10000): super().__init__() self.n_channels = n_channels self.max_len = max_len self.C = 5000 self.pe = torch.zeros(0, 0) def forward(self, x, noise_level): if x.shape[2] > self.pe.shape[1]: self.init_pe_matrix(x.shape[1], x.shape[2], x) return x + noise_level[..., None, None] + self.pe[:, : x.size(2)].repeat(x.shape[0], 1, 1) / self.C def init_pe_matrix(self, n_channels, max_len, x): pe = torch.zeros(max_len, n_channels) position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) div_term = torch.pow(10000, torch.arange(0, n_channels, 2).float() / n_channels) pe[:, 0::2] = torch.sin(position / div_term) pe[:, 1::2] = torch.cos(position / div_term) self.pe = pe.transpose(0, 1).to(x) class FiLM(nn.Module): def __init__(self, input_size, output_size): super().__init__() self.encoding = PositionalEncoding(input_size) self.input_conv = nn.Conv1d(input_size, input_size, 3, padding=1) self.output_conv = nn.Conv1d(input_size, output_size * 2, 3, padding=1) nn.init.xavier_uniform_(self.input_conv.weight) nn.init.xavier_uniform_(self.output_conv.weight) nn.init.zeros_(self.input_conv.bias) nn.init.zeros_(self.output_conv.bias) def forward(self, x, noise_scale): o = self.input_conv(x) o = F.leaky_relu(o, 0.2) o = self.encoding(o, noise_scale) shift, scale = torch.chunk(self.output_conv(o), 2, dim=1) return shift, scale def remove_weight_norm(self): remove_parametrizations(self.input_conv, "weight") remove_parametrizations(self.output_conv, "weight") def apply_weight_norm(self): self.input_conv = weight_norm(self.input_conv) self.output_conv = weight_norm(self.output_conv) @torch.jit.script def shif_and_scale(x, scale, shift): o = shift + scale * x return o class UBlock(nn.Module): def __init__(self, input_size, hidden_size, factor, dilation): super().__init__() assert isinstance(dilation, (list, tuple)) assert len(dilation) == 4 self.factor = factor self.res_block = Conv1d(input_size, hidden_size, 1) self.main_block = nn.ModuleList( [ Conv1d(input_size, hidden_size, 3, dilation=dilation[0], padding=dilation[0]), Conv1d(hidden_size, hidden_size, 3, dilation=dilation[1], padding=dilation[1]), ] ) self.out_block = nn.ModuleList( [ Conv1d(hidden_size, hidden_size, 3, dilation=dilation[2], padding=dilation[2]), Conv1d(hidden_size, hidden_size, 3, dilation=dilation[3], padding=dilation[3]), ] ) def forward(self, x, shift, scale): x_inter = F.interpolate(x, size=x.shape[-1] * self.factor) res = self.res_block(x_inter) o = F.leaky_relu(x_inter, 0.2) o = F.interpolate(o, size=x.shape[-1] * self.factor) o = self.main_block[0](o) o = shif_and_scale(o, scale, shift) o = F.leaky_relu(o, 0.2) o = self.main_block[1](o) res2 = res + o o = shif_and_scale(res2, scale, shift) o = F.leaky_relu(o, 0.2) o = self.out_block[0](o) o = shif_and_scale(o, scale, shift) o = F.leaky_relu(o, 0.2) o = self.out_block[1](o) o = o + res2 return o def remove_weight_norm(self): remove_parametrizations(self.res_block, "weight") for _, layer in enumerate(self.main_block): if len(layer.state_dict()) != 0: remove_parametrizations(layer, "weight") for _, layer in enumerate(self.out_block): if len(layer.state_dict()) != 0: remove_parametrizations(layer, "weight") def apply_weight_norm(self): self.res_block = weight_norm(self.res_block) for idx, layer in enumerate(self.main_block): if len(layer.state_dict()) != 0: self.main_block[idx] = weight_norm(layer) for idx, layer in enumerate(self.out_block): if len(layer.state_dict()) != 0: self.out_block[idx] = weight_norm(layer) class DBlock(nn.Module): def __init__(self, input_size, hidden_size, factor): super().__init__() self.factor = factor self.res_block = Conv1d(input_size, hidden_size, 1) self.main_block = nn.ModuleList( [ Conv1d(input_size, hidden_size, 3, dilation=1, padding=1), Conv1d(hidden_size, hidden_size, 3, dilation=2, padding=2), Conv1d(hidden_size, hidden_size, 3, dilation=4, padding=4), ] ) def forward(self, x): size = x.shape[-1] // self.factor res = self.res_block(x) res = F.interpolate(res, size=size) o = F.interpolate(x, size=size) for layer in self.main_block: o = F.leaky_relu(o, 0.2) o = layer(o) return o + res def remove_weight_norm(self): remove_parametrizations(self.res_block, "weight") for _, layer in enumerate(self.main_block): if len(layer.state_dict()) != 0: remove_parametrizations(layer, "weight") def apply_weight_norm(self): self.res_block = weight_norm(self.res_block) for idx, layer in enumerate(self.main_block): if len(layer.state_dict()) != 0: self.main_block[idx] = weight_norm(layer)
0
coqui_public_repos/STT/native_client/java/.idea
coqui_public_repos/STT/native_client/java/.idea/codeStyles/Project.xml
<component name="ProjectCodeStyleConfiguration"> <code_scheme name="Project" version="173"> <Objective-C-extensions> <file> <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Import" /> <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Macro" /> <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Typedef" /> <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Enum" /> <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Constant" /> <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Global" /> <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Struct" /> <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="FunctionPredecl" /> <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Function" /> </file> <class> <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Property" /> <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Synthesize" /> <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="InitMethod" /> <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="StaticMethod" /> <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="InstanceMethod" /> <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="DeallocMethod" /> </class> <extensions> <pair source="cpp" header="h" fileNamingConvention="NONE" /> <pair source="c" header="h" fileNamingConvention="NONE" /> </extensions> </Objective-C-extensions> </code_scheme> </component>
0
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/bin/fstequal.cc
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. #include <fst/flags.h> #include <fst/weight.h> DEFINE_double(delta, fst::kDelta, "Comparison/quantization delta"); int fstequal_main(int argc, char **argv); int main(int argc, char **argv) { return fstequal_main(argc, argv); }
0
coqui_public_repos/STT-examples
coqui_public_repos/STT-examples/batch_processing/requirements.txt
absl-py==0.9.0 addignore==1.2.7 appdirs==1.4.4 astor==0.8.1 astunparse==1.6.3 attrs==19.3.0 black==19.10b0 bokeh==1.4.0 cachetools==4.1.0 certifi==2020.4.5.2 chardet==3.0.4 click==7.1.2 STT==1.0.0 delegator.py @ git+https://github.com/amitt001/delegator.py.git@194aa92543fbdbfbae0bcc24ca217819a7805da2 flask==1.1.2 gast==0.2.2 google-auth==1.16.1 google-auth-oauthlib==0.4.1 google-pasta==0.2.0 grpcio==1.29.0 h5py==2.10.0 idna==2.9 isort==4.3.21 Jinja2==2.11.2 Keras-Applications==1.0.8 Keras-Preprocessing==1.1.2 Markdown==3.2.2 MarkupSafe==1.1.1 numpy==1.17.3 oauthlib==3.1.0 opt-einsum==3.2.1 packaging==20.4 pathspec==0.8.0 pexpect==4.8.0 phonemizer==2.2 protobuf==3.12.2 ptyprocess==0.6.0 pyasn1==0.4.8 pyasn1-modules==0.2.8 pyparsing==2.4.7 PyYAML==5.3.1 regex==2020.6.7 requests==2.23.0 requests-oauthlib==1.3.0 rsa==4.0 scipy==1.4.1 six==1.15.0 soundfile==0.10.3.post1 tensorboard==2.1.1 tensorboard-plugin-wit==1.6.0.post3 tensorflow-estimator==2.1.0 tensorflow-gpu==2.2.0 tensorflow-gpu-estimator==2.2.0 termcolor==1.1.0 toml==0.10.1 tqdm==4.46.1 tts==0.0.2+f320992 typed-ast==1.4.1 urllib3==1.25.9 Werkzeug==1.0.1 wrapt==1.12.1
0
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/include/fst/extensions
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/include/fst/extensions/far/isomorphic.h
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. #ifndef FST_EXTENSIONS_FAR_ISOMORPHIC_H_ #define FST_EXTENSIONS_FAR_ISOMORPHIC_H_ #include <memory> #include <string> #include <fst/extensions/far/far.h> #include <fst/isomorphic.h> namespace fst { template <class Arc> bool FarIsomorphic(const string &filename1, const string &filename2, float delta = kDelta, const string &begin_key = string(), const string &end_key = string()) { std::unique_ptr<FarReader<Arc>> reader1(FarReader<Arc>::Open(filename1)); if (!reader1) { LOG(ERROR) << "FarIsomorphic: Cannot open FAR file " << filename1; return false; } std::unique_ptr<FarReader<Arc>> reader2(FarReader<Arc>::Open(filename2)); if (!reader2) { LOG(ERROR) << "FarIsomorphic: Cannot open FAR file " << filename2; return false; } if (!begin_key.empty()) { bool find_begin1 = reader1->Find(begin_key); bool find_begin2 = reader2->Find(begin_key); if (!find_begin1 || !find_begin2) { bool ret = !find_begin1 && !find_begin2; if (!ret) { VLOG(1) << "FarIsomorphic: Key " << begin_key << " missing from " << (find_begin1 ? "second" : "first") << " archive."; } return ret; } } for (; !reader1->Done() && !reader2->Done(); reader1->Next(), reader2->Next()) { const auto &key1 = reader1->GetKey(); const auto &key2 = reader2->GetKey(); if (!end_key.empty() && end_key < key1 && end_key < key2) return true; if (key1 != key2) { LOG(ERROR) << "FarIsomorphic: Mismatched keys " << key1 << " and " << key2; return false; } if (!Isomorphic(*(reader1->GetFst()), *(reader2->GetFst()), delta)) { LOG(ERROR) << "FarIsomorphic: FSTs for key " << key1 << " are not isomorphic"; return false; } } if (!reader1->Done() || !reader2->Done()) { LOG(ERROR) << "FarIsomorphic: Key " << (reader1->Done() ? reader2->GetKey() : reader1->GetKey()) << " missing form " << (reader2->Done() ? "first" : "second") << " archive"; return false; } return true; } } // namespace fst #endif // FST_EXTENSIONS_FAR_ISOMORPHIC_H_
0
coqui_public_repos/inference-engine/third_party/cereal/include/cereal
coqui_public_repos/inference-engine/third_party/cereal/include/cereal/types/queue.hpp
/*! \file queue.hpp \brief Support for types found in \<queue\> \ingroup STLSupport */ /* Copyright (c) 2014, Randolph Voorhies, Shane Grant All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of cereal nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL RANDOLPH VOORHIES OR SHANE GRANT BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ #ifndef CEREAL_TYPES_QUEUE_HPP_ #define CEREAL_TYPES_QUEUE_HPP_ #include "cereal/details/helpers.hpp" #include <queue> // The default container for queue is deque, so let's include that too #include "cereal/types/deque.hpp" // The default comparator for queue is less #include "cereal/types/functional.hpp" namespace cereal { namespace queue_detail { //! Allows access to the protected container in queue /*! @internal */ template <class T, class C> inline C const & container( std::queue<T, C> const & queue ) { struct H : public std::queue<T, C> { static C const & get( std::queue<T, C> const & q ) { return q.*(&H::c); } }; return H::get( queue ); } //! Allows access to the protected container in priority queue /*! @internal */ template <class T, class C, class Comp> inline C const & container( std::priority_queue<T, C, Comp> const & priority_queue ) { struct H : public std::priority_queue<T, C, Comp> { static C const & get( std::priority_queue<T, C, Comp> const & pq ) { return pq.*(&H::c); } }; return H::get( priority_queue ); } //! Allows access to the protected comparator in priority queue /*! @internal */ template <class T, class C, class Comp> inline Comp const & comparator( std::priority_queue<T, C, Comp> const & priority_queue ) { struct H : public std::priority_queue<T, C, Comp> { static Comp const & get( std::priority_queue<T, C, Comp> const & pq ) { return pq.*(&H::comp); } }; return H::get( priority_queue ); } } //! Saving for std::queue template <class Archive, class T, class C> inline void CEREAL_SAVE_FUNCTION_NAME( Archive & ar, std::queue<T, C> const & queue ) { ar( CEREAL_NVP_("container", queue_detail::container( queue )) ); } //! Loading for std::queue template <class Archive, class T, class C> inline void CEREAL_LOAD_FUNCTION_NAME( Archive & ar, std::queue<T, C> & queue ) { C container; ar( CEREAL_NVP_("container", container) ); queue = std::queue<T, C>( std::move( container ) ); } //! Saving for std::priority_queue template <class Archive, class T, class C, class Comp> inline void CEREAL_SAVE_FUNCTION_NAME( Archive & ar, std::priority_queue<T, C, Comp> const & priority_queue ) { ar( CEREAL_NVP_("comparator", queue_detail::comparator( priority_queue )) ); ar( CEREAL_NVP_("container", queue_detail::container( priority_queue )) ); } //! Loading for std::priority_queue template <class Archive, class T, class C, class Comp> inline void CEREAL_LOAD_FUNCTION_NAME( Archive & ar, std::priority_queue<T, C, Comp> & priority_queue ) { Comp comparator; ar( CEREAL_NVP_("comparator", comparator) ); C container; ar( CEREAL_NVP_("container", container) ); priority_queue = std::priority_queue<T, C, Comp>( comparator, std::move( container ) ); } } // namespace cereal #endif // CEREAL_TYPES_QUEUE_HPP_
0
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/lib/Makefile.in
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0
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/bin/fstdeterminize-main.cc
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. // // Determinizes an FST. #include <cstring> #include <memory> #include <string> #include <fst/flags.h> #include <fst/script/determinize.h> #include <fst/script/getters.h> DECLARE_double(delta); DECLARE_string(weight); DECLARE_int64(nstate); DECLARE_int64(subsequential_label); DECLARE_string(det_type); DECLARE_bool(increment_subsequential_label); int fstdeterminize_main(int argc, char **argv) { namespace s = fst::script; using fst::DeterminizeType; using fst::script::FstClass; using fst::script::VectorFstClass; using fst::script::WeightClass; string usage = "Determinizes an FST.\n\n Usage: "; usage += argv[0]; usage += " [in.fst [out.fst]]\n"; std::set_new_handler(FailedNewHandler); SET_FLAGS(usage.c_str(), &argc, &argv, true); if (argc > 3) { ShowUsage(); return 1; } DeterminizeType det_type; if (!s::GetDeterminizeType(FLAGS_det_type, &det_type)) { LOG(ERROR) << argv[0] << ": Unknown or unsupported determinization type: " << FLAGS_det_type; return 1; } const string in_name = (argc > 1 && strcmp(argv[1], "-") != 0) ? argv[1] : ""; const string out_name = argc > 2 ? argv[2] : ""; std::unique_ptr<FstClass> ifst(FstClass::Read(in_name)); if (!ifst) return 1; VectorFstClass ofst(ifst->ArcType()); const auto weight_threshold = FLAGS_weight.empty() ? WeightClass::Zero(ifst->WeightType()) : WeightClass(ifst->WeightType(), FLAGS_weight); const s::DeterminizeOptions opts(FLAGS_delta, weight_threshold, FLAGS_nstate, FLAGS_subsequential_label, det_type, FLAGS_increment_subsequential_label); s::Determinize(*ifst, &ofst, opts); return !ofst.Write(out_name); }
0
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/include
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/include/fst/compose (1).h
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. // // Compose an MPDT and an FST. #ifndef FST_EXTENSIONS_MPDT_COMPOSE_H_ #define FST_EXTENSIONS_MPDT_COMPOSE_H_ #include <list> #include <fst/extensions/mpdt/mpdt.h> #include <fst/extensions/pdt/compose.h> #include <fst/compose.h> namespace fst { template <class Filter> class MPdtParenFilter { public: using FST1 = typename Filter::FST1; using FST2 = typename Filter::FST2; using Arc = typename Filter::Arc; using Label = typename Arc::Label; using StateId = typename Arc::StateId; using Weight = typename Arc::Weight; using Matcher1 = typename Filter::Matcher1; using Matcher2 = typename Filter::Matcher2; using StackId = StateId; using ParenStack = internal::MPdtStack<StackId, Label>; using FilterState1 = typename Filter::FilterState; using FilterState2 = IntegerFilterState<StackId>; using FilterState = PairFilterState<FilterState1, FilterState2>; MPdtParenFilter(const FST1 &fst1, const FST2 &fst2, Matcher1 *matcher1 = nullptr, Matcher2 *matcher2 = nullptr, const std::vector<std::pair<Label, Label>> *parens = nullptr, const std::vector<Label> *assignments = nullptr, bool expand = false, bool keep_parens = true) : filter_(fst1, fst2, matcher1, matcher2), parens_(parens ? *parens : std::vector<std::pair<Label, Label>>()), assignments_(assignments ? *assignments : std::vector<Label>()), expand_(expand), keep_parens_(keep_parens), fs_(FilterState::NoState()), stack_(parens_, assignments_), paren_id_(-1) { if (parens) { for (const auto &pair : *parens) { parens_.push_back(pair); GetMatcher1()->AddOpenParen(pair.first); GetMatcher2()->AddOpenParen(pair.first); if (!expand_) { GetMatcher1()->AddCloseParen(pair.second); GetMatcher2()->AddCloseParen(pair.second); } } } } MPdtParenFilter(const MPdtParenFilter &filter, bool safe = false) : filter_(filter.filter_, safe), parens_(filter.parens_), expand_(filter.expand_), keep_parens_(filter.keep_parens_), fs_(FilterState::NoState()), stack_(filter.parens_, filter.assignments_), paren_id_(-1) {} FilterState Start() const { return FilterState(filter_.Start(), FilterState2(0)); } void SetState(StateId s1, StateId s2, const FilterState &fs) { fs_ = fs; filter_.SetState(s1, s2, fs_.GetState1()); if (!expand_) return; const auto paren_id = stack_.Top(fs.GetState2().GetState()); if (paren_id != paren_id_) { if (paren_id_ != -1) { GetMatcher1()->RemoveCloseParen(parens_[paren_id_].second); GetMatcher2()->RemoveCloseParen(parens_[paren_id_].second); } paren_id_ = paren_id; if (paren_id_ != -1) { GetMatcher1()->AddCloseParen(parens_[paren_id_].second); GetMatcher2()->AddCloseParen(parens_[paren_id_].second); } } } FilterState FilterArc(Arc *arc1, Arc *arc2) const { const auto fs1 = filter_.FilterArc(arc1, arc2); const auto &fs2 = fs_.GetState2(); if (fs1 == FilterState1::NoState()) return FilterState::NoState(); if (arc1->olabel == kNoLabel && arc2->ilabel) { // arc2 parentheses. if (keep_parens_) { arc1->ilabel = arc2->ilabel; } else if (arc2->ilabel) { arc2->olabel = arc1->ilabel; } return FilterParen(arc2->ilabel, fs1, fs2); } else if (arc2->ilabel == kNoLabel && arc1->olabel) { // arc1 parentheses if (keep_parens_) { arc2->olabel = arc1->olabel; } else { arc1->ilabel = arc2->olabel; } return FilterParen(arc1->olabel, fs1, fs2); } else { return FilterState(fs1, fs2); } } void FilterFinal(Weight *w1, Weight *w2) const { if (fs_.GetState2().GetState() != 0) *w1 = Weight::Zero(); filter_.FilterFinal(w1, w2); } // Returns respective matchers; ownership stays with filter. Matcher1 *GetMatcher1() { return filter_.GetMatcher1(); } Matcher2 *GetMatcher2() { return filter_.GetMatcher2(); } uint64 Properties(uint64 iprops) const { const auto oprops = filter_.Properties(iprops); return oprops & kILabelInvariantProperties & kOLabelInvariantProperties; } private: const FilterState FilterParen(Label label, const FilterState1 &fs1, const FilterState2 &fs2) const { if (!expand_) return FilterState(fs1, fs2); const auto stack_id = stack_.Find(fs2.GetState(), label); if (stack_id < 0) { return FilterState::NoState(); } else { return FilterState(fs1, FilterState2(stack_id)); } } Filter filter_; std::vector<std::pair<Label, Label>> parens_; std::vector<Label> assignments_; bool expand_; // Expands to FST? bool keep_parens_; // Retains parentheses in output? FilterState fs_; // Current filter state. mutable ParenStack stack_; std::ptrdiff_t paren_id_; }; // Class to setup composition options for MPDT composition. Default is to take // the MPDT as the first composition argument. template <class Arc, bool left_pdt = true> class MPdtComposeFstOptions : public ComposeFstOptions<Arc, ParenMatcher<Fst<Arc>>, MPdtParenFilter<AltSequenceComposeFilter< ParenMatcher<Fst<Arc>> >> > { public: using Label = typename Arc::Label; using MPdtMatcher = ParenMatcher<Fst<Arc>>; using MPdtFilter = MPdtParenFilter<AltSequenceComposeFilter<MPdtMatcher>>; using ComposeFstOptions<Arc, MPdtMatcher, MPdtFilter>::matcher1; using ComposeFstOptions<Arc, MPdtMatcher, MPdtFilter>::matcher2; using ComposeFstOptions<Arc, MPdtMatcher, MPdtFilter>::filter; MPdtComposeFstOptions(const Fst<Arc> &ifst1, const std::vector<std::pair<Label, Label>> &parens, const std::vector<typename Arc::Label> &assignments, const Fst<Arc> &ifst2, bool expand = false, bool keep_parens = true) { matcher1 = new MPdtMatcher(ifst1, MATCH_OUTPUT, kParenList); matcher2 = new MPdtMatcher(ifst2, MATCH_INPUT, kParenLoop); filter = new MPdtFilter(ifst1, ifst2, matcher1, matcher2, &parens, &assignments, expand, keep_parens); } }; // Class to setup composition options for PDT with FST composition. // Specialization is for the FST as the first composition argument. template <class Arc> class MPdtComposeFstOptions<Arc, false> : public ComposeFstOptions< Arc, ParenMatcher<Fst<Arc>>, MPdtParenFilter<SequenceComposeFilter<ParenMatcher<Fst<Arc>> >> > { public: using Label = typename Arc::Label; using MPdtMatcher = ParenMatcher<Fst<Arc>>; using MPdtFilter = MPdtParenFilter<SequenceComposeFilter<MPdtMatcher>>; using ComposeFstOptions<Arc, MPdtMatcher, MPdtFilter>::matcher1; using ComposeFstOptions<Arc, MPdtMatcher, MPdtFilter>::matcher2; using ComposeFstOptions<Arc, MPdtMatcher, MPdtFilter>::filter; MPdtComposeFstOptions(const Fst<Arc> &ifst1, const Fst<Arc> &ifst2, const std::vector<std::pair<Label, Label>> &parens, const std::vector<typename Arc::Label> &assignments, bool expand = false, bool keep_parens = true) { matcher1 = new MPdtMatcher(ifst1, MATCH_OUTPUT, kParenLoop); matcher2 = new MPdtMatcher(ifst2, MATCH_INPUT, kParenList); filter = new MPdtFilter(ifst1, ifst2, matcher1, matcher2, &parens, &assignments, expand, keep_parens); } }; struct MPdtComposeOptions { bool connect; // Connect output? PdtComposeFilter filter_type; // Which pre-defined filter to use. explicit MPdtComposeOptions(bool connect = true, PdtComposeFilter filter_type = PAREN_FILTER) : connect(connect), filter_type(filter_type) {} }; // Composes multi-pushdown transducer (MPDT) encoded as an FST (1st arg) and an // FST (2nd arg) with the result also an MPDT encoded as an FST (3rd arg). In // theMPDTs, some transitions are labeled with open or close parentheses (and // associated with a stack). To be interpreted as an MPDT, the parents on each // stack must balance on a path (see MPdtExpand()). The open-close parenthesis // label pairs are passed using the parens arguments, and the stack assignments // are passed using the assignments argument. template <class Arc> void Compose( const Fst<Arc> &ifst1, const std::vector<std::pair<typename Arc::Label, typename Arc::Label>> &parens, const std::vector<typename Arc::Label> &assignments, const Fst<Arc> &ifst2, MutableFst<Arc> *ofst, const MPdtComposeOptions &opts = MPdtComposeOptions()) { bool expand = opts.filter_type != PAREN_FILTER; bool keep_parens = opts.filter_type != EXPAND_FILTER; MPdtComposeFstOptions<Arc, true> copts(ifst1, parens, assignments, ifst2, expand, keep_parens); copts.gc_limit = 0; *ofst = ComposeFst<Arc>(ifst1, ifst2, copts); if (opts.connect) Connect(ofst); } // Composes an FST (1st arg) and a multi-pushdown transducer (MPDT) encoded as // an FST (2nd arg) with the result also an MPDT encoded as an FST (3rd arg). // In the MPDTs, some transitions are labeled with open or close parentheses // (and associated with a stack). To be interpreted as an MPDT, the parents on // each stack must balance on a path (see MPdtExpand()). The open-close // parenthesis label pairs are passed using the parens arguments, and the stack // assignments are passed using the assignments argument. template <class Arc> void Compose( const Fst<Arc> &ifst1, const Fst<Arc> &ifst2, const std::vector<std::pair<typename Arc::Label, typename Arc::Label>> &parens, const std::vector<typename Arc::Label> &assignments, MutableFst<Arc> *ofst, const MPdtComposeOptions &opts = MPdtComposeOptions()) { bool expand = opts.filter_type != PAREN_FILTER; bool keep_parens = opts.filter_type != EXPAND_FILTER; MPdtComposeFstOptions<Arc, false> copts(ifst1, ifst2, parens, assignments, expand, keep_parens); copts.gc_limit = 0; *ofst = ComposeFst<Arc>(ifst1, ifst2, copts); if (opts.connect) Connect(ofst); } } // namespace fst #endif // FST_EXTENSIONS_MPDT_COMPOSE_H_
0
coqui_public_repos/STT-examples
coqui_public_repos/STT-examples/electron/package-lock.json
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0
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/extensions
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/extensions/pdt/pdtinfo.cc
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. // // Prints out various information about a PDT such as number of states, arcs, // and parentheses. #include <cstring> #include <memory> #include <string> #include <vector> #include <fst/flags.h> #include <fst/log.h> #include <fst/extensions/pdt/pdtscript.h> #include <fst/util.h> DEFINE_string(pdt_parentheses, "", "PDT parenthesis label pairs"); int main(int argc, char **argv) { namespace s = fst::script; using fst::ReadLabelPairs; using fst::script::FstClass; string usage = "Prints out information about a PDT.\n\n Usage: "; usage += argv[0]; usage += " in.pdt\n"; std::set_new_handler(FailedNewHandler); SET_FLAGS(usage.c_str(), &argc, &argv, true); if (argc > 2) { ShowUsage(); return 1; } const string in_name = (argc > 1 && (strcmp(argv[1], "-") != 0)) ? argv[1] : ""; std::unique_ptr<FstClass> ifst(FstClass::Read(in_name)); if (!ifst) return 1; if (FLAGS_pdt_parentheses.empty()) { LOG(ERROR) << argv[0] << ": No PDT parenthesis label pairs provided"; return 1; } std::vector<s::LabelPair> parens; if (!ReadLabelPairs(FLAGS_pdt_parentheses, &parens, false)) return 1; s::PrintPdtInfo(*ifst, parens); return 0; }
0
coqui_public_repos/TTS/TTS/tts
coqui_public_repos/TTS/TTS/tts/models/base_tacotron.py
import copy from abc import abstractmethod from typing import Dict, Tuple import torch from coqpit import Coqpit from torch import nn from TTS.tts.layers.losses import TacotronLoss from TTS.tts.models.base_tts import BaseTTS from TTS.tts.utils.helpers import sequence_mask from TTS.tts.utils.speakers import SpeakerManager from TTS.tts.utils.synthesis import synthesis from TTS.tts.utils.text.tokenizer import TTSTokenizer from TTS.tts.utils.visual import plot_alignment, plot_spectrogram from TTS.utils.generic_utils import format_aux_input from TTS.utils.io import load_fsspec from TTS.utils.training import gradual_training_scheduler class BaseTacotron(BaseTTS): """Base class shared by Tacotron and Tacotron2""" def __init__( self, config: "TacotronConfig", ap: "AudioProcessor", tokenizer: "TTSTokenizer", speaker_manager: SpeakerManager = None, ): super().__init__(config, ap, tokenizer, speaker_manager) # pass all config fields as class attributes for key in config: setattr(self, key, config[key]) # layers self.embedding = None self.encoder = None self.decoder = None self.postnet = None # init tensors self.embedded_speakers = None self.embedded_speakers_projected = None # global style token if self.gst and self.use_gst: self.decoder_in_features += self.gst.gst_embedding_dim # add gst embedding dim self.gst_layer = None # Capacitron if self.capacitron_vae and self.use_capacitron_vae: self.decoder_in_features += self.capacitron_vae.capacitron_VAE_embedding_dim # add capacitron embedding dim self.capacitron_vae_layer = None # additional layers self.decoder_backward = None self.coarse_decoder = None @staticmethod def _format_aux_input(aux_input: Dict) -> Dict: """Set missing fields to their default values""" if aux_input: return format_aux_input({"d_vectors": None, "speaker_ids": None}, aux_input) return None ############################# # INIT FUNCTIONS ############################# def _init_backward_decoder(self): """Init the backward decoder for Forward-Backward decoding.""" self.decoder_backward = copy.deepcopy(self.decoder) def _init_coarse_decoder(self): """Init the coarse decoder for Double-Decoder Consistency.""" self.coarse_decoder = copy.deepcopy(self.decoder) self.coarse_decoder.r_init = self.ddc_r self.coarse_decoder.set_r(self.ddc_r) ############################# # CORE FUNCTIONS ############################# @abstractmethod def forward(self): pass @abstractmethod def inference(self): pass def load_checkpoint( self, config, checkpoint_path, eval=False, cache=False ): # pylint: disable=unused-argument, redefined-builtin """Load model checkpoint and set up internals. Args: config (Coqpi): model configuration. checkpoint_path (str): path to checkpoint file. eval (bool, optional): whether to load model for evaluation. cache (bool, optional): If True, cache the file locally for subsequent calls. It is cached under `get_user_data_dir()/tts_cache`. Defaults to False. """ state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"), cache=cache) self.load_state_dict(state["model"]) # TODO: set r in run-time by taking it from the new config if "r" in state: # set r from the state (for compatibility with older checkpoints) self.decoder.set_r(state["r"]) elif "config" in state: # set r from config used at training time (for inference) self.decoder.set_r(state["config"]["r"]) else: # set r from the new config (for new-models) self.decoder.set_r(config.r) if eval: self.eval() print(f" > Model's reduction rate `r` is set to: {self.decoder.r}") assert not self.training def get_criterion(self) -> nn.Module: """Get the model criterion used in training.""" return TacotronLoss(self.config) @staticmethod def init_from_config(config: Coqpit): """Initialize model from config.""" from TTS.utils.audio import AudioProcessor ap = AudioProcessor.init_from_config(config) tokenizer = TTSTokenizer.init_from_config(config) speaker_manager = SpeakerManager.init_from_config(config) return BaseTacotron(config, ap, tokenizer, speaker_manager) ########################## # TEST AND LOG FUNCTIONS # ########################## def test_run(self, assets: Dict) -> Tuple[Dict, Dict]: """Generic test run for `tts` models used by `Trainer`. You can override this for a different behaviour. Args: assets (dict): A dict of training assets. For `tts` models, it must include `{'audio_processor': ap}`. Returns: Tuple[Dict, Dict]: Test figures and audios to be projected to Tensorboard. """ print(" | > Synthesizing test sentences.") test_audios = {} test_figures = {} test_sentences = self.config.test_sentences aux_inputs = self._get_test_aux_input() for idx, sen in enumerate(test_sentences): outputs_dict = synthesis( self, sen, self.config, "cuda" in str(next(self.parameters()).device), speaker_id=aux_inputs["speaker_id"], d_vector=aux_inputs["d_vector"], style_wav=aux_inputs["style_wav"], use_griffin_lim=True, do_trim_silence=False, ) test_audios["{}-audio".format(idx)] = outputs_dict["wav"] test_figures["{}-prediction".format(idx)] = plot_spectrogram( outputs_dict["outputs"]["model_outputs"], self.ap, output_fig=False ) test_figures["{}-alignment".format(idx)] = plot_alignment( outputs_dict["outputs"]["alignments"], output_fig=False ) return {"figures": test_figures, "audios": test_audios} def test_log( self, outputs: dict, logger: "Logger", assets: dict, steps: int # pylint: disable=unused-argument ) -> None: logger.test_audios(steps, outputs["audios"], self.ap.sample_rate) logger.test_figures(steps, outputs["figures"]) ############################# # COMMON COMPUTE FUNCTIONS ############################# def compute_masks(self, text_lengths, mel_lengths): """Compute masks against sequence paddings.""" # B x T_in_max (boolean) input_mask = sequence_mask(text_lengths) output_mask = None if mel_lengths is not None: max_len = mel_lengths.max() r = self.decoder.r max_len = max_len + (r - (max_len % r)) if max_len % r > 0 else max_len output_mask = sequence_mask(mel_lengths, max_len=max_len) return input_mask, output_mask def _backward_pass(self, mel_specs, encoder_outputs, mask): """Run backwards decoder""" decoder_outputs_b, alignments_b, _ = self.decoder_backward( encoder_outputs, torch.flip(mel_specs, dims=(1,)), mask ) decoder_outputs_b = decoder_outputs_b.transpose(1, 2).contiguous() return decoder_outputs_b, alignments_b def _coarse_decoder_pass(self, mel_specs, encoder_outputs, alignments, input_mask): """Double Decoder Consistency""" T = mel_specs.shape[1] if T % self.coarse_decoder.r > 0: padding_size = self.coarse_decoder.r - (T % self.coarse_decoder.r) mel_specs = torch.nn.functional.pad(mel_specs, (0, 0, 0, padding_size, 0, 0)) decoder_outputs_backward, alignments_backward, _ = self.coarse_decoder( encoder_outputs.detach(), mel_specs, input_mask ) # scale_factor = self.decoder.r_init / self.decoder.r alignments_backward = torch.nn.functional.interpolate( alignments_backward.transpose(1, 2), size=alignments.shape[1], mode="nearest", ).transpose(1, 2) decoder_outputs_backward = decoder_outputs_backward.transpose(1, 2) decoder_outputs_backward = decoder_outputs_backward[:, :T, :] return decoder_outputs_backward, alignments_backward ############################# # EMBEDDING FUNCTIONS ############################# def compute_gst(self, inputs, style_input, speaker_embedding=None): """Compute global style token""" if isinstance(style_input, dict): # multiply each style token with a weight query = torch.zeros(1, 1, self.gst.gst_embedding_dim // 2).type_as(inputs) if speaker_embedding is not None: query = torch.cat([query, speaker_embedding.reshape(1, 1, -1)], dim=-1) _GST = torch.tanh(self.gst_layer.style_token_layer.style_tokens) gst_outputs = torch.zeros(1, 1, self.gst.gst_embedding_dim).type_as(inputs) for k_token, v_amplifier in style_input.items(): key = _GST[int(k_token)].unsqueeze(0).expand(1, -1, -1) gst_outputs_att = self.gst_layer.style_token_layer.attention(query, key) gst_outputs = gst_outputs + gst_outputs_att * v_amplifier elif style_input is None: # ignore style token and return zero tensor gst_outputs = torch.zeros(1, 1, self.gst.gst_embedding_dim).type_as(inputs) else: # compute style tokens gst_outputs = self.gst_layer(style_input, speaker_embedding) # pylint: disable=not-callable inputs = self._concat_speaker_embedding(inputs, gst_outputs) return inputs def compute_capacitron_VAE_embedding(self, inputs, reference_mel_info, text_info=None, speaker_embedding=None): """Capacitron Variational Autoencoder""" ( VAE_outputs, posterior_distribution, prior_distribution, capacitron_beta, ) = self.capacitron_vae_layer( reference_mel_info, text_info, speaker_embedding, # pylint: disable=not-callable ) VAE_outputs = VAE_outputs.to(inputs.device) encoder_output = self._concat_speaker_embedding( inputs, VAE_outputs ) # concatenate to the output of the basic tacotron encoder return ( encoder_output, posterior_distribution, prior_distribution, capacitron_beta, ) @staticmethod def _add_speaker_embedding(outputs, embedded_speakers): embedded_speakers_ = embedded_speakers.expand(outputs.size(0), outputs.size(1), -1) outputs = outputs + embedded_speakers_ return outputs @staticmethod def _concat_speaker_embedding(outputs, embedded_speakers): embedded_speakers_ = embedded_speakers.expand(outputs.size(0), outputs.size(1), -1) outputs = torch.cat([outputs, embedded_speakers_], dim=-1) return outputs ############################# # CALLBACKS ############################# def on_epoch_start(self, trainer): """Callback for setting values wrt gradual training schedule. Args: trainer (TrainerTTS): TTS trainer object that is used to train this model. """ if self.gradual_training: r, trainer.config.batch_size = gradual_training_scheduler(trainer.total_steps_done, trainer.config) trainer.config.r = r self.decoder.set_r(r) if trainer.config.bidirectional_decoder: trainer.model.decoder_backward.set_r(r) print(f"\n > Number of output frames: {self.decoder.r}")
0
coqui_public_repos/inference-engine/third_party/kenlm
coqui_public_repos/inference-engine/third_party/kenlm/lm/value_build.hh
#ifndef LM_VALUE_BUILD_H #define LM_VALUE_BUILD_H #include "lm/weights.hh" #include "lm/word_index.hh" #include "util/bit_packing.hh" #include <vector> namespace lm { namespace ngram { struct Config; struct BackoffValue; struct RestValue; class NoRestBuild { public: typedef BackoffValue Value; NoRestBuild() {} void SetRest(const WordIndex *, unsigned int, const Prob &/*prob*/) const {} void SetRest(const WordIndex *, unsigned int, const ProbBackoff &) const {} template <class Second> bool MarkExtends(ProbBackoff &weights, const Second &) const { util::UnsetSign(weights.prob); return false; } // Probing doesn't need to go back to unigram. const static bool kMarkEvenLower = false; }; class MaxRestBuild { public: typedef RestValue Value; MaxRestBuild() {} void SetRest(const WordIndex *, unsigned int, const Prob &/*prob*/) const {} void SetRest(const WordIndex *, unsigned int, RestWeights &weights) const { weights.rest = weights.prob; util::SetSign(weights.rest); } bool MarkExtends(RestWeights &weights, const RestWeights &to) const { util::UnsetSign(weights.prob); if (weights.rest >= to.rest) return false; weights.rest = to.rest; return true; } bool MarkExtends(RestWeights &weights, const Prob &to) const { util::UnsetSign(weights.prob); if (weights.rest >= to.prob) return false; weights.rest = to.prob; return true; } // Probing does need to go back to unigram. const static bool kMarkEvenLower = true; }; template <class Model> class LowerRestBuild { public: typedef RestValue Value; LowerRestBuild(const Config &config, unsigned int order, const typename Model::Vocabulary &vocab); ~LowerRestBuild(); void SetRest(const WordIndex *, unsigned int, const Prob &/*prob*/) const {} void SetRest(const WordIndex *vocab_ids, unsigned int n, RestWeights &weights) const { typename Model::State ignored; if (n == 1) { weights.rest = unigrams_[*vocab_ids]; } else { weights.rest = models_[n-2]->FullScoreForgotState(vocab_ids + 1, vocab_ids + n, *vocab_ids, ignored).prob; } } template <class Second> bool MarkExtends(RestWeights &weights, const Second &) const { util::UnsetSign(weights.prob); return false; } const static bool kMarkEvenLower = false; std::vector<float> unigrams_; std::vector<const Model*> models_; }; } // namespace ngram } // namespace lm #endif // LM_VALUE_BUILD_H
0
coqui_public_repos/TTS/docs/source
coqui_public_repos/TTS/docs/source/models/tacotron1-2.md
# 🌮 Tacotron 1 and 2 Tacotron is one of the first successful DL-based text-to-mel models and opened up the whole TTS field for more DL research. Tacotron mainly is an encoder-decoder model with attention. The encoder takes input tokens (characters or phonemes) and the decoder outputs mel-spectrogram* frames. Attention module in-between learns to align the input tokens with the output mel-spectrgorams. Tacotron1 and 2 are both built on the same encoder-decoder architecture but they use different layers. Additionally, Tacotron1 uses a Postnet module to convert mel-spectrograms to linear spectrograms with a higher resolution before the vocoder. Vanilla Tacotron models are slow at inference due to the auto-regressive* nature that prevents the model to process all the inputs in parallel. One trick is to use a higher “reduction rate” that helps the model to predict multiple frames at once. That is, reduction rate 2 reduces the number of decoder iterations by half. Tacotron also uses a Prenet module with Dropout that projects the model’s previous output before feeding it to the decoder again. The paper and most of the implementations use the Dropout layer even in inference and they report the attention fails or the voice quality degrades otherwise. But the issue with that, you get a slightly different output speech every time you run the model. Training the attention is notoriously problematic in Tacoron models. Especially, in inference, for some input sequences, the alignment fails and causes the model to produce unexpected results. There are many different methods proposed to improve the attention. After hundreds of experiments, @ 🐸TTS we suggest Double Decoder Consistency that leads to the most robust model performance. If you have a limited VRAM, then you can try using the Guided Attention Loss or the Dynamic Convolutional Attention. You can also combine the two. ## Important resources & papers - Tacotron: https://arxiv.org/abs/2006.06873 - Tacotron2: https://arxiv.org/abs/2008.03802 - Double Decoder Consistency: https://coqui.ai/blog/tts/solving-attention-problems-of-tts-models-with-double-decoder-consistency - Guided Attention Loss: https://arxiv.org/abs/1710.08969 - Forward & Backward Decoder: https://arxiv.org/abs/1907.09006 - Forward Attention: https://arxiv.org/abs/1807.06736 - Gaussian Attention: https://arxiv.org/abs/1910.10288 - Dynamic Convolutional Attention: https://arxiv.org/pdf/1910.10288.pdf ## BaseTacotron ```{eval-rst} .. autoclass:: TTS.tts.models.base_tacotron.BaseTacotron :members: ``` ## Tacotron ```{eval-rst} .. autoclass:: TTS.tts.models.tacotron.Tacotron :members: ``` ## Tacotron2 ```{eval-rst} .. autoclass:: TTS.tts.models.tacotron2.Tacotron2 :members: ``` ## TacotronConfig ```{eval-rst} .. autoclass:: TTS.tts.configs.tacotron_config.TacotronConfig :members: ``` ## Tacotron2Config ```{eval-rst} .. autoclass:: TTS.tts.configs.tacotron2_config.Tacotron2Config :members: ```
0
coqui_public_repos/STT-examples/django_api_streaming/stt_app/templates
coqui_public_repos/STT-examples/django_api_streaming/stt_app/templates/stt/base.html
<!DOCTYPE html> <html lang="en"> <head> <meta charset="utf-8"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <meta name="viewport" content="width=device-width, initial-scale=1"> <title>Coqui STT</title> <script src="https://code.jquery.com/jquery-3.2.1.min.js" integrity="sha256-hwg4gsxgFZhOsEEamdOYGBf13FyQuiTwlAQgxVSNgt4=" crossorigin="anonymous"> </script> <script src="/static/stt/script.js" type="text/javascript"></script> <script src="/static/stt/audioRecorder.js" type="text/javascript"></script> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-wvfXpqpZZVQGK6TAh5PVlGOfQNHSoD2xbE+QkPxCAFlNEevoEH3Sl0sibVcOQVnN" crossorigin="anonymous"> <link rel="stylesheet" href="/static/css/theme.css"> <link rel="stylesheet" href="/static/css/main.css"> {% load bootstrap3 %} {% bootstrap_css %} {% bootstrap_javascript %} {# Display django.contrib.messages as Bootstrap alerts #} {% bootstrap_messages %} </head> <body> <nav class="navbar navbar-inverse navbar-fixed-top"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false" aria-controls="navbar"> <span class="sr-only">Toggle navigation</span> <span class="icon-bar"></span> <span class="icon-bar"></span> <span class="icon-bar"></span> </button> <a class="navbar-brand" href="#">Coqui STT</a> </div> <div id="navbar" class="collapse navbar-collapse"> <ul class="nav navbar-nav"> <li class="active"><a href="#">Home</a></li> <li><a href="#about">About</a></li> </ul> </div><!--/.nav-collapse --> </div> </nav> <div class="container"> {% block content %} {% endblock %} </div> </body> </html>
0
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/bin/fstdifference-main.cc
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. // // Subtracts an unweighted DFA from an FSA. #include <cstring> #include <memory> #include <string> #include <fst/flags.h> #include <fst/log.h> #include <fst/script/getters.h> #include <fst/script/difference.h> DECLARE_string(compose_filter); DECLARE_bool(connect); int fstdifference_main(int argc, char **argv) { namespace s = fst::script; using fst::ComposeFilter; using fst::DifferenceOptions; using fst::script::FstClass; using fst::script::VectorFstClass; string usage = "Subtracts an unweighted DFA from an FSA.\n\n Usage: "; usage += argv[0]; usage += " in1.fst in2.fst [out.fst]\n"; std::set_new_handler(FailedNewHandler); SET_FLAGS(usage.c_str(), &argc, &argv, true); if (argc < 3 || argc > 4) { ShowUsage(); return 1; } const string in1_name = strcmp(argv[1], "-") == 0 ? "" : argv[1]; const string in2_name = strcmp(argv[2], "-") == 0 ? "" : argv[2]; const string out_name = argc > 3 ? argv[3] : ""; if (in1_name.empty() && in2_name.empty()) { LOG(ERROR) << argv[0] << ": Can't take both inputs from standard input"; return 1; } std::unique_ptr<FstClass> ifst1(FstClass::Read(in1_name)); if (!ifst1) return 1; std::unique_ptr<FstClass> ifst2(FstClass::Read(in2_name)); if (!ifst2) return 1; VectorFstClass ofst(ifst1->ArcType()); ComposeFilter compose_filter; if (!s::GetComposeFilter(FLAGS_compose_filter, &compose_filter)) { LOG(ERROR) << argv[0] << ": Unknown or unsupported compose filter type: " << FLAGS_compose_filter; return 1; } const DifferenceOptions opts(FLAGS_connect, compose_filter); s::Difference(*ifst1, *ifst2, &ofst, opts); return !ofst.Write(out_name); }
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coqui_public_repos/TTS/TTS/tts/layers
coqui_public_repos/TTS/TTS/tts/layers/delightful_tts/conv_layers.py
from typing import Tuple import torch import torch.nn as nn # pylint: disable=consider-using-from-import import torch.nn.functional as F from torch.nn.utils import parametrize from TTS.tts.layers.delightful_tts.kernel_predictor import KernelPredictor def calc_same_padding(kernel_size: int) -> Tuple[int, int]: pad = kernel_size // 2 return (pad, pad - (kernel_size + 1) % 2) class ConvNorm(nn.Module): """A 1-dimensional convolutional layer with optional weight normalization. This layer wraps a 1D convolutional layer from PyTorch and applies optional weight normalization. The layer can be used in a similar way to the convolutional layers in PyTorch's `torch.nn` module. Args: in_channels (int): The number of channels in the input signal. out_channels (int): The number of channels in the output signal. kernel_size (int, optional): The size of the convolving kernel. Defaults to 1. stride (int, optional): The stride of the convolution. Defaults to 1. padding (int, optional): Zero-padding added to both sides of the input. If `None`, the padding will be calculated so that the output has the same length as the input. Defaults to `None`. dilation (int, optional): Spacing between kernel elements. Defaults to 1. bias (bool, optional): If `True`, add bias after convolution. Defaults to `True`. w_init_gain (str, optional): The weight initialization function to use. Can be either 'linear' or 'relu'. Defaults to 'linear'. use_weight_norm (bool, optional): If `True`, apply weight normalization to the convolutional weights. Defaults to `False`. Shapes: - Input: :math:`[N, D, T]` - Output: :math:`[N, out_dim, T]` where `out_dim` is the number of output dimensions. """ def __init__( self, in_channels, out_channels, kernel_size=1, stride=1, padding=None, dilation=1, bias=True, w_init_gain="linear", use_weight_norm=False, ): super(ConvNorm, self).__init__() # pylint: disable=super-with-arguments if padding is None: assert kernel_size % 2 == 1 padding = int(dilation * (kernel_size - 1) / 2) self.kernel_size = kernel_size self.dilation = dilation self.use_weight_norm = use_weight_norm conv_fn = nn.Conv1d self.conv = conv_fn( in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias, ) nn.init.xavier_uniform_(self.conv.weight, gain=nn.init.calculate_gain(w_init_gain)) if self.use_weight_norm: self.conv = nn.utils.parametrizations.weight_norm(self.conv) def forward(self, signal, mask=None): conv_signal = self.conv(signal) if mask is not None: # always re-zero output if mask is # available to match zero-padding conv_signal = conv_signal * mask return conv_signal class ConvLSTMLinear(nn.Module): def __init__( self, in_dim, out_dim, n_layers=2, n_channels=256, kernel_size=3, p_dropout=0.1, lstm_type="bilstm", use_linear=True, ): super(ConvLSTMLinear, self).__init__() # pylint: disable=super-with-arguments self.out_dim = out_dim self.lstm_type = lstm_type self.use_linear = use_linear self.dropout = nn.Dropout(p=p_dropout) convolutions = [] for i in range(n_layers): conv_layer = ConvNorm( in_dim if i == 0 else n_channels, n_channels, kernel_size=kernel_size, stride=1, padding=int((kernel_size - 1) / 2), dilation=1, w_init_gain="relu", ) conv_layer = nn.utils.parametrizations.weight_norm(conv_layer.conv, name="weight") convolutions.append(conv_layer) self.convolutions = nn.ModuleList(convolutions) if not self.use_linear: n_channels = out_dim if self.lstm_type != "": use_bilstm = False lstm_channels = n_channels if self.lstm_type == "bilstm": use_bilstm = True lstm_channels = int(n_channels // 2) self.bilstm = nn.LSTM(n_channels, lstm_channels, 1, batch_first=True, bidirectional=use_bilstm) lstm_norm_fn_pntr = nn.utils.spectral_norm self.bilstm = lstm_norm_fn_pntr(self.bilstm, "weight_hh_l0") if self.lstm_type == "bilstm": self.bilstm = lstm_norm_fn_pntr(self.bilstm, "weight_hh_l0_reverse") if self.use_linear: self.dense = nn.Linear(n_channels, out_dim) def run_padded_sequence(self, context, lens): context_embedded = [] for b_ind in range(context.size()[0]): # TODO: speed up curr_context = context[b_ind : b_ind + 1, :, : lens[b_ind]].clone() for conv in self.convolutions: curr_context = self.dropout(F.relu(conv(curr_context))) context_embedded.append(curr_context[0].transpose(0, 1)) context = nn.utils.rnn.pad_sequence(context_embedded, batch_first=True) return context def run_unsorted_inputs(self, fn, context, lens): # pylint: disable=no-self-use lens_sorted, ids_sorted = torch.sort(lens, descending=True) unsort_ids = [0] * lens.size(0) for i in range(len(ids_sorted)): # pylint: disable=consider-using-enumerate unsort_ids[ids_sorted[i]] = i lens_sorted = lens_sorted.long().cpu() context = context[ids_sorted] context = nn.utils.rnn.pack_padded_sequence(context, lens_sorted, batch_first=True) context = fn(context)[0] context = nn.utils.rnn.pad_packed_sequence(context, batch_first=True)[0] # map back to original indices context = context[unsort_ids] return context def forward(self, context, lens): if context.size()[0] > 1: context = self.run_padded_sequence(context, lens) # to B, D, T context = context.transpose(1, 2) else: for conv in self.convolutions: context = self.dropout(F.relu(conv(context))) if self.lstm_type != "": context = context.transpose(1, 2) self.bilstm.flatten_parameters() if lens is not None: context = self.run_unsorted_inputs(self.bilstm, context, lens) else: context = self.bilstm(context)[0] context = context.transpose(1, 2) x_hat = context if self.use_linear: x_hat = self.dense(context.transpose(1, 2)).transpose(1, 2) return x_hat class DepthWiseConv1d(nn.Module): def __init__(self, in_channels: int, out_channels: int, kernel_size: int, padding: int): super().__init__() self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, padding=padding, groups=in_channels) def forward(self, x: torch.Tensor) -> torch.Tensor: return self.conv(x) class PointwiseConv1d(nn.Module): def __init__( self, in_channels: int, out_channels: int, stride: int = 1, padding: int = 0, bias: bool = True, ): super().__init__() self.conv = nn.Conv1d( in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=stride, padding=padding, bias=bias, ) def forward(self, x: torch.Tensor) -> torch.Tensor: return self.conv(x) class BSConv1d(nn.Module): """https://arxiv.org/pdf/2003.13549.pdf""" def __init__(self, channels_in: int, channels_out: int, kernel_size: int, padding: int): super().__init__() self.pointwise = nn.Conv1d(channels_in, channels_out, kernel_size=1) self.depthwise = nn.Conv1d( channels_out, channels_out, kernel_size=kernel_size, padding=padding, groups=channels_out, ) def forward(self, x: torch.Tensor) -> torch.Tensor: x1 = self.pointwise(x) x2 = self.depthwise(x1) return x2 class BSConv2d(nn.Module): """https://arxiv.org/pdf/2003.13549.pdf""" def __init__(self, channels_in: int, channels_out: int, kernel_size: int, padding: int): super().__init__() self.pointwise = nn.Conv2d(channels_in, channels_out, kernel_size=1) self.depthwise = nn.Conv2d( channels_out, channels_out, kernel_size=kernel_size, padding=padding, groups=channels_out, ) def forward(self, x: torch.Tensor) -> torch.Tensor: x1 = self.pointwise(x) x2 = self.depthwise(x1) return x2 class Conv1dGLU(nn.Module): """From DeepVoice 3""" def __init__(self, d_model: int, kernel_size: int, padding: int, embedding_dim: int): super().__init__() self.conv = BSConv1d(d_model, 2 * d_model, kernel_size=kernel_size, padding=padding) self.embedding_proj = nn.Linear(embedding_dim, d_model) self.register_buffer("sqrt", torch.sqrt(torch.FloatTensor([0.5])).squeeze(0)) self.softsign = torch.nn.Softsign() def forward(self, x: torch.Tensor, embeddings: torch.Tensor) -> torch.Tensor: x = x.permute((0, 2, 1)) residual = x x = self.conv(x) splitdim = 1 a, b = x.split(x.size(splitdim) // 2, dim=splitdim) embeddings = self.embedding_proj(embeddings).unsqueeze(2) softsign = self.softsign(embeddings) softsign = softsign.expand_as(a) a = a + softsign x = a * torch.sigmoid(b) x = x + residual x = x * self.sqrt x = x.permute((0, 2, 1)) return x class ConvTransposed(nn.Module): """ A 1D convolutional transposed layer for PyTorch. This layer applies a 1D convolutional transpose operation to its input tensor, where the number of channels of the input tensor is the same as the number of channels of the output tensor. Attributes: in_channels (int): The number of channels in the input tensor. out_channels (int): The number of channels in the output tensor. kernel_size (int): The size of the convolutional kernel. Default: 1. padding (int): The number of padding elements to add to the input tensor. Default: 0. conv (BSConv1d): The 1D convolutional transpose layer. """ def __init__( self, in_channels: int, out_channels: int, kernel_size: int = 1, padding: int = 0, ): super().__init__() self.conv = BSConv1d( in_channels, out_channels, kernel_size=kernel_size, padding=padding, ) def forward(self, x: torch.Tensor) -> torch.Tensor: x = x.contiguous().transpose(1, 2) x = self.conv(x) x = x.contiguous().transpose(1, 2) return x class DepthwiseConvModule(nn.Module): def __init__(self, dim: int, kernel_size: int = 7, expansion: int = 4, lrelu_slope: float = 0.3): super().__init__() padding = calc_same_padding(kernel_size) self.depthwise = nn.Conv1d( dim, dim * expansion, kernel_size=kernel_size, padding=padding[0], groups=dim, ) self.act = nn.LeakyReLU(lrelu_slope) self.out = nn.Conv1d(dim * expansion, dim, 1, 1, 0) self.ln = nn.LayerNorm(dim) def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.ln(x) x = x.permute((0, 2, 1)) x = self.depthwise(x) x = self.act(x) x = self.out(x) x = x.permute((0, 2, 1)) return x class AddCoords(nn.Module): def __init__(self, rank: int, with_r: bool = False): super().__init__() self.rank = rank self.with_r = with_r def forward(self, x: torch.Tensor) -> torch.Tensor: if self.rank == 1: batch_size_shape, channel_in_shape, dim_x = x.shape # pylint: disable=unused-variable xx_range = torch.arange(dim_x, dtype=torch.int32) xx_channel = xx_range[None, None, :] xx_channel = xx_channel.float() / (dim_x - 1) xx_channel = xx_channel * 2 - 1 xx_channel = xx_channel.repeat(batch_size_shape, 1, 1) xx_channel = xx_channel.to(x.device) out = torch.cat([x, xx_channel], dim=1) if self.with_r: rr = torch.sqrt(torch.pow(xx_channel - 0.5, 2)) out = torch.cat([out, rr], dim=1) elif self.rank == 2: batch_size_shape, channel_in_shape, dim_y, dim_x = x.shape xx_ones = torch.ones([1, 1, 1, dim_x], dtype=torch.int32) yy_ones = torch.ones([1, 1, 1, dim_y], dtype=torch.int32) xx_range = torch.arange(dim_y, dtype=torch.int32) yy_range = torch.arange(dim_x, dtype=torch.int32) xx_range = xx_range[None, None, :, None] yy_range = yy_range[None, None, :, None] xx_channel = torch.matmul(xx_range, xx_ones) yy_channel = torch.matmul(yy_range, yy_ones) # transpose y yy_channel = yy_channel.permute(0, 1, 3, 2) xx_channel = xx_channel.float() / (dim_y - 1) yy_channel = yy_channel.float() / (dim_x - 1) xx_channel = xx_channel * 2 - 1 yy_channel = yy_channel * 2 - 1 xx_channel = xx_channel.repeat(batch_size_shape, 1, 1, 1) yy_channel = yy_channel.repeat(batch_size_shape, 1, 1, 1) xx_channel = xx_channel.to(x.device) yy_channel = yy_channel.to(x.device) out = torch.cat([x, xx_channel, yy_channel], dim=1) if self.with_r: rr = torch.sqrt(torch.pow(xx_channel - 0.5, 2) + torch.pow(yy_channel - 0.5, 2)) out = torch.cat([out, rr], dim=1) elif self.rank == 3: batch_size_shape, channel_in_shape, dim_z, dim_y, dim_x = x.shape xx_ones = torch.ones([1, 1, 1, 1, dim_x], dtype=torch.int32) yy_ones = torch.ones([1, 1, 1, 1, dim_y], dtype=torch.int32) zz_ones = torch.ones([1, 1, 1, 1, dim_z], dtype=torch.int32) xy_range = torch.arange(dim_y, dtype=torch.int32) xy_range = xy_range[None, None, None, :, None] yz_range = torch.arange(dim_z, dtype=torch.int32) yz_range = yz_range[None, None, None, :, None] zx_range = torch.arange(dim_x, dtype=torch.int32) zx_range = zx_range[None, None, None, :, None] xy_channel = torch.matmul(xy_range, xx_ones) xx_channel = torch.cat([xy_channel + i for i in range(dim_z)], dim=2) yz_channel = torch.matmul(yz_range, yy_ones) yz_channel = yz_channel.permute(0, 1, 3, 4, 2) yy_channel = torch.cat([yz_channel + i for i in range(dim_x)], dim=4) zx_channel = torch.matmul(zx_range, zz_ones) zx_channel = zx_channel.permute(0, 1, 4, 2, 3) zz_channel = torch.cat([zx_channel + i for i in range(dim_y)], dim=3) xx_channel = xx_channel.to(x.device) yy_channel = yy_channel.to(x.device) zz_channel = zz_channel.to(x.device) out = torch.cat([x, xx_channel, yy_channel, zz_channel], dim=1) if self.with_r: rr = torch.sqrt( torch.pow(xx_channel - 0.5, 2) + torch.pow(yy_channel - 0.5, 2) + torch.pow(zz_channel - 0.5, 2) ) out = torch.cat([out, rr], dim=1) else: raise NotImplementedError return out class CoordConv1d(nn.modules.conv.Conv1d): def __init__( self, in_channels: int, out_channels: int, kernel_size: int, stride: int = 1, padding: int = 0, dilation: int = 1, groups: int = 1, bias: bool = True, with_r: bool = False, ): super().__init__( in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias, ) self.rank = 1 self.addcoords = AddCoords(self.rank, with_r) self.conv = nn.Conv1d( in_channels + self.rank + int(with_r), out_channels, kernel_size, stride, padding, dilation, groups, bias, ) def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.addcoords(x) x = self.conv(x) return x class CoordConv2d(nn.modules.conv.Conv2d): def __init__( self, in_channels: int, out_channels: int, kernel_size: int, stride: int = 1, padding: int = 0, dilation: int = 1, groups: int = 1, bias: bool = True, with_r: bool = False, ): super().__init__( in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias, ) self.rank = 2 self.addcoords = AddCoords(self.rank, with_r) self.conv = nn.Conv2d( in_channels + self.rank + int(with_r), out_channels, kernel_size, stride, padding, dilation, groups, bias, ) def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.addcoords(x) x = self.conv(x) return x class LVCBlock(torch.nn.Module): """the location-variable convolutions""" def __init__( # pylint: disable=dangerous-default-value self, in_channels, cond_channels, stride, dilations=[1, 3, 9, 27], lReLU_slope=0.2, conv_kernel_size=3, cond_hop_length=256, kpnet_hidden_channels=64, kpnet_conv_size=3, kpnet_dropout=0.0, ): super().__init__() self.cond_hop_length = cond_hop_length self.conv_layers = len(dilations) self.conv_kernel_size = conv_kernel_size self.kernel_predictor = KernelPredictor( cond_channels=cond_channels, conv_in_channels=in_channels, conv_out_channels=2 * in_channels, conv_layers=len(dilations), conv_kernel_size=conv_kernel_size, kpnet_hidden_channels=kpnet_hidden_channels, kpnet_conv_size=kpnet_conv_size, kpnet_dropout=kpnet_dropout, kpnet_nonlinear_activation_params={"negative_slope": lReLU_slope}, ) self.convt_pre = nn.Sequential( nn.LeakyReLU(lReLU_slope), nn.utils.parametrizations.weight_norm( nn.ConvTranspose1d( in_channels, in_channels, 2 * stride, stride=stride, padding=stride // 2 + stride % 2, output_padding=stride % 2, ) ), ) self.conv_blocks = nn.ModuleList() for dilation in dilations: self.conv_blocks.append( nn.Sequential( nn.LeakyReLU(lReLU_slope), nn.utils.parametrizations.weight_norm( nn.Conv1d( in_channels, in_channels, conv_kernel_size, padding=dilation * (conv_kernel_size - 1) // 2, dilation=dilation, ) ), nn.LeakyReLU(lReLU_slope), ) ) def forward(self, x, c): """forward propagation of the location-variable convolutions. Args: x (Tensor): the input sequence (batch, in_channels, in_length) c (Tensor): the conditioning sequence (batch, cond_channels, cond_length) Returns: Tensor: the output sequence (batch, in_channels, in_length) """ _, in_channels, _ = x.shape # (B, c_g, L') x = self.convt_pre(x) # (B, c_g, stride * L') kernels, bias = self.kernel_predictor(c) for i, conv in enumerate(self.conv_blocks): output = conv(x) # (B, c_g, stride * L') k = kernels[:, i, :, :, :, :] # (B, 2 * c_g, c_g, kernel_size, cond_length) b = bias[:, i, :, :] # (B, 2 * c_g, cond_length) output = self.location_variable_convolution( output, k, b, hop_size=self.cond_hop_length ) # (B, 2 * c_g, stride * L'): LVC x = x + torch.sigmoid(output[:, :in_channels, :]) * torch.tanh( output[:, in_channels:, :] ) # (B, c_g, stride * L'): GAU return x def location_variable_convolution(self, x, kernel, bias, dilation=1, hop_size=256): # pylint: disable=no-self-use """perform location-variable convolution operation on the input sequence (x) using the local convolution kernl. Time: 414 μs ± 309 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each), test on NVIDIA V100. Args: x (Tensor): the input sequence (batch, in_channels, in_length). kernel (Tensor): the local convolution kernel (batch, in_channel, out_channels, kernel_size, kernel_length) bias (Tensor): the bias for the local convolution (batch, out_channels, kernel_length) dilation (int): the dilation of convolution. hop_size (int): the hop_size of the conditioning sequence. Returns: (Tensor): the output sequence after performing local convolution. (batch, out_channels, in_length). """ batch, _, in_length = x.shape batch, _, out_channels, kernel_size, kernel_length = kernel.shape assert in_length == (kernel_length * hop_size), "length of (x, kernel) is not matched" padding = dilation * int((kernel_size - 1) / 2) x = F.pad(x, (padding, padding), "constant", 0) # (batch, in_channels, in_length + 2*padding) x = x.unfold(2, hop_size + 2 * padding, hop_size) # (batch, in_channels, kernel_length, hop_size + 2*padding) if hop_size < dilation: x = F.pad(x, (0, dilation), "constant", 0) x = x.unfold( 3, dilation, dilation ) # (batch, in_channels, kernel_length, (hop_size + 2*padding)/dilation, dilation) x = x[:, :, :, :, :hop_size] x = x.transpose(3, 4) # (batch, in_channels, kernel_length, dilation, (hop_size + 2*padding)/dilation) x = x.unfold(4, kernel_size, 1) # (batch, in_channels, kernel_length, dilation, _, kernel_size) o = torch.einsum("bildsk,biokl->bolsd", x, kernel) o = o.to(memory_format=torch.channels_last_3d) bias = bias.unsqueeze(-1).unsqueeze(-1).to(memory_format=torch.channels_last_3d) o = o + bias o = o.contiguous().view(batch, out_channels, -1) return o def remove_weight_norm(self): self.kernel_predictor.remove_weight_norm() parametrize.remove_parametrizations(self.convt_pre[1], "weight") for block in self.conv_blocks: parametrize.remove_parametrizations(block[1], "weight")
0
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/script/minimize.cc
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. #include <fst/script/fst-class.h> #include <fst/script/minimize.h> #include <fst/script/script-impl.h> namespace fst { namespace script { void Minimize(MutableFstClass *ofst1, MutableFstClass *ofst2, float delta, bool allow_nondet) { if (ofst2 && !internal::ArcTypesMatch(*ofst1, *ofst2, "Minimize")) { ofst1->SetProperties(kError, kError); ofst2->SetProperties(kError, kError); return; } MinimizeArgs args(ofst1, ofst2, delta, allow_nondet); Apply<Operation<MinimizeArgs>>("Minimize", ofst1->ArcType(), &args); } REGISTER_FST_OPERATION(Minimize, StdArc, MinimizeArgs); REGISTER_FST_OPERATION(Minimize, LogArc, MinimizeArgs); REGISTER_FST_OPERATION(Minimize, Log64Arc, MinimizeArgs); } // namespace script } // namespace fst
0
coqui_public_repos/STT-models/german/aashishag
coqui_public_repos/STT-models/german/aashishag/v0.9.0/LICENSE
Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. 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0
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/script/print.cc
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. #include <ostream> #include <string> #include <fst/script/fst-class.h> #include <fst/script/print.h> #include <fst/script/script-impl.h> namespace fst { namespace script { void PrintFst(const FstClass &fst, std::ostream &ostrm, const string &dest, const SymbolTable *isyms, const SymbolTable *osyms, const SymbolTable *ssyms, bool accept, bool show_weight_one, const string &missing_sym) { const auto sep = FLAGS_fst_field_separator.substr(0, 1); FstPrinterArgs args(fst, isyms, osyms, ssyms, accept, show_weight_one, &ostrm, dest, sep, missing_sym); Apply<Operation<FstPrinterArgs>>("PrintFst", fst.ArcType(), &args); } REGISTER_FST_OPERATION(PrintFst, StdArc, FstPrinterArgs); REGISTER_FST_OPERATION(PrintFst, LogArc, FstPrinterArgs); REGISTER_FST_OPERATION(PrintFst, Log64Arc, FstPrinterArgs); } // namespace script } // namespace fst
0
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/bin/fstpush-main.cc
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. // // Pushes weights and/or output labels in an FST toward the initial or final // states. #include <cstring> #include <memory> #include <string> #include <fst/flags.h> #include <fst/script/getters.h> #include <fst/script/push.h> DECLARE_double(delta); DECLARE_bool(push_weights); DECLARE_bool(push_labels); DECLARE_bool(remove_total_weight); DECLARE_bool(remove_common_affix); DECLARE_bool(to_final); int fstpush_main(int argc, char **argv) { namespace s = fst::script; using fst::script::FstClass; using fst::script::VectorFstClass; string usage = "Pushes weights and/or olabels in an FST.\n\n Usage: "; usage += argv[0]; usage += " [in.fst [out.fst]]\n"; std::set_new_handler(FailedNewHandler); SET_FLAGS(usage.c_str(), &argc, &argv, true); if (argc > 3) { ShowUsage(); return 1; } string in_name = (argc > 1 && strcmp(argv[1], "-") != 0) ? argv[1] : ""; string out_name = argc > 2 ? argv[2] : ""; std::unique_ptr<FstClass> ifst(FstClass::Read(in_name)); if (!ifst) return 1; const auto flags = s::GetPushFlags(FLAGS_push_weights, FLAGS_push_labels, FLAGS_remove_total_weight, FLAGS_remove_common_affix); VectorFstClass ofst(ifst->ArcType()); s::Push(*ifst, &ofst, flags, s::GetReweightType(FLAGS_to_final), FLAGS_delta); return !ofst.Write(out_name); }
0
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/bin/fstequivalent-main.cc
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. // // Two DFAs are equivalent iff their exit status is zero. #include <cstring> #include <memory> #include <string> #include <fst/flags.h> #include <fst/log.h> #include <fst/script/equivalent.h> #include <fst/script/getters.h> #include <fst/script/randequivalent.h> DECLARE_double(delta); DECLARE_bool(random); DECLARE_int32(max_length); DECLARE_int32(npath); DECLARE_int32(seed); DECLARE_string(select); int fstequivalent_main(int argc, char **argv) { namespace s = fst::script; using fst::RandGenOptions; using fst::script::FstClass; string usage = "Two DFAs are equivalent iff the exit status is zero.\n\n" " Usage: "; usage += argv[0]; usage += " in1.fst in2.fst\n"; std::set_new_handler(FailedNewHandler); SET_FLAGS(usage.c_str(), &argc, &argv, true); if (argc != 3) { ShowUsage(); return 1; } const string in1_name = strcmp(argv[1], "-") == 0 ? "" : argv[1]; const string in2_name = strcmp(argv[2], "-") == 0 ? "" : argv[2]; if (in1_name.empty() && in2_name.empty()) { LOG(ERROR) << argv[0] << ": Can't take both inputs from standard input"; return 1; } std::unique_ptr<FstClass> ifst1(FstClass::Read(in1_name)); if (!ifst1) return 1; std::unique_ptr<FstClass> ifst2(FstClass::Read(in2_name)); if (!ifst2) return 1; if (!FLAGS_random) { bool result = s::Equivalent(*ifst1, *ifst2, FLAGS_delta); if (!result) VLOG(1) << "FSTs are not equivalent"; return result ? 0 : 2; } else { s::RandArcSelection ras; if (!s::GetRandArcSelection(FLAGS_select, &ras)) { LOG(ERROR) << argv[0] << ": Unknown or unsupported select type " << FLAGS_select; return 1; } const RandGenOptions<s::RandArcSelection> opts(ras, FLAGS_max_length); bool result = s::RandEquivalent(*ifst1, *ifst2, FLAGS_npath, FLAGS_delta, FLAGS_seed, opts); if (!result) VLOG(1) << "FSTs are not equivalent"; return result ? 0 : 2; } }
0
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/include/fst
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/include/fst/script/topsort.h
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. #ifndef FST_SCRIPT_TOPSORT_H_ #define FST_SCRIPT_TOPSORT_H_ #include <fst/topsort.h> #include <fst/script/arg-packs.h> #include <fst/script/fst-class.h> namespace fst { namespace script { using TopSortArgs = WithReturnValue<bool, MutableFstClass *>; template <class Arc> void TopSort(TopSortArgs *args) { args->retval = TopSort(args->args->GetMutableFst<Arc>()); } bool TopSort(MutableFstClass *fst); } // namespace script } // namespace fst #endif // FST_SCRIPT_TOPSORT_H_
0
coqui_public_repos/STT
coqui_public_repos/STT/native_client/xldd
#!/bin/bash # ldd drop-in replacement for cross-compilation toolchains. # This file is a slightly modified version of xldd.in from # crosstool-ng 1.22.0 # In order to use it, copy it in same directory than other # toolchain binaries and rename it with same tuple. # (i.e. /opt/arm-sysmic-linux-gnueabihf/bin/arm-sysmic-linux-gnueabihf-ldd) # Thus, this will automaticaly detect necessary information # about your toolchain. export LC_ALL=C version="forked from crosstool-ng 1.22.0" # Change it to 64 if necessary bits="32" sed="${SED:-sed}" grep="${GREP:-grep}" my_name="$( basename "${0}" )" prefix="${0%-ldd}" gcc="${prefix}-gcc" readelf="${prefix}-readelf" fake_load_addr_root="$((0xdeadbeef))" fake_load_addr_rpath="$((0xdeadc0de))" fake_load_addr_sysroot="$((0x8badf00d))" ld_library_path="/lib:/usr/lib" do_error() { printf "%s: %s\n" "${my_name}" "$*" >&2 } do_opt_error() { do_error "$@" printf "Try \`%s --help' for more information\n" "${my_name}" >&2 } do_trace() { local depth=0 [ -z "${CT_XLDD_VERBOSE}" ] && return 0 for((depth=0; "${#FUNCNAME[$((depth+1))]}" != 0; depth++)); do :; done printf "%*s" $((4*(depth-1))) "" >&2 printf -- "$@" >&2 } show_version() { # Fake a real ldd, just in case some dumb script would check cat <<_EOF_ ldd (crosstool-NG) ${version} Copyright (C) 2010 "Yann E. MORIN" <yann.morin.1998@free.fr> This is free software; see the source for copying conditions. There is NO warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. Licensed under the GPLv2, see the file LICENSES in the top-directory of the sources for this package. _EOF_ } show_help() { cat <<_EOF_ Usage: ${my_name} [OPTION]... --root DIR FILE... --help print this help and exit --version print version information and exit --root dir treat dir as being the root of the target -s, --show-system mark libs from the sysroot with a trailing '[*]' and libs found via RPATH with a trailing '[+]' _EOF_ cat <<_EOF_ |fmt ${my_name} tries to mimick the behavior of a real native ldd, but can be used in a cross-development environment. Here is how it differs from a real native ldd: If the CT_XLDD_VERBOSE variable is set and non-empty, then ${my_name} will print a lot of debug messages, explaining how it builds the library search path, and how each library was found and why. The LD_LIBRARY_PATH variable is not used, as it can not reliably be guessed except at runtime, and we can't run. ${my_name} does not scan /etc/ld.so.cache, but instead uses /etc/ld.so.conf (it understands the include directives therein for libces that have that). ${my_name} also interprets (tries to!) the RPATH/RUNPATH records found in the dynamic ELF section. Such paths are searched for only relative to the specified root, not from the sysroot (see below). Also, those paths are searched for not only for the file they appear in, but also for its dependencies. ${my_name} will search the directory specified with --root for libraries to resolve the NEEDED tags. If --root is not set, then ${my_name} will use the value in the environment variable \${CT_XLDD_ROOT}. If neither is set, then this is an error. If NEEDED libraries can't be found in the specified root directory, then ${my_name} will also look in the sysroot of the toolchain to see if it can find them. For NEEDED libraries that were found, the output will look like: libneeded.so => /path/to/libneeded.so (0xloadaddr) and for those that were not found, the output will look like: libneeded.so not found The paths are relative to the specified root directory, or to the sysroot (eg. /lib/libneeded.so, /usr/lib/libneeded.so, and so on...). The expected load address 'loadaddr' is a faked address to match the output of the real ldd, but has no actual meaning (set to some constants for now, 0x8badf00d for libraries from the sysroot, 0xdeadc0de for those found via the RPATH/RUNPATH records, and 0xdeadbeef for others). _EOF_ # Unimplemeted yet: # -d, --data-relocs process data relocations # -r, --function-relocs process data and function relocations # -u, --unused print unused direct dependencies # -v, --verbose print all information # See also this thread: # http://sourceware.org/ml/crossgcc/2008-09/msg00057.html } # Parse command line options root="${CT_XLDD_ROOT}" show_system= while true; do case "${1}" in --help) show_help exit 0 ;; --version) show_version exit 0 ;; --root) root="$2" shift ;; --root=*) root="${1#--root=}" ;; --show-system|-s) show_system=1 ;; -*) do_opt_error "unrecognized option \`${1}'" exit 1 ;; *) break ;; esac shift done # Sanity checks sysroot="$( "${gcc}" -print-sysroot 2>/dev/null )" if [ -z "${sysroot}" ]; then sysroot="$( "${gcc}" -print-file-name=libc.so 2>/dev/null \ |${sed} -r -e 's:/usr/lib/libc.so$::;' \ )" fi if [ -z "${sysroot}" ]; then do_error "unable to find sysroot for \`${gcc}'" fi if [ -z "${root}" ]; then root=${sysroot} fi if [ ! -d "${root}" ]; then do_error "\`${root}': no such file or directory" exit 1 fi do_report_needed_found() { local needed="${1}" local path="${2}" local origin="${3}" local loadaddr local sys case "${origin}" in root) loadaddr="${fake_load_addr_root}" ;; rpath) loadaddr="${fake_load_addr_rpath}" if [ -n "${show_system}" ]; then sys=" [+]" fi ;; sysroot) loadaddr="${fake_load_addr_sysroot}" if [ -n "${show_system}" ]; then sys=" [*]" fi ;; esac printf "%8s%s => %s (0x%0*x)%s\n" \ "" \ "${needed}" \ "${path}" \ "$((bits/4))" \ "${loadaddr}" \ "${sys}" } # Search a needed file, scanning ${lib_dir} in the root directory do_find_needed() { local needed="${1}" local -a list local -a dirs local found local where local base local d i do_trace "Searching for '%s'\n" "${needed}" # rpath shall come first! list=( \ "rpath:${root}" \ "root:${root}" \ "sysroot:${sysroot}" \ ) for i in "${list[@]}"; do where="${i%%:*}" base="${i#*:}" if [ "${where}" = "rpath" ]; then dirs=( "${search_rpath[@]}" ) else dirs=( "${needed_search_path[@]}" ) fi for d in "${dirs[@]}"; do do_trace "-> looking in '%s' (%s)\n" "${d}" "${where}" if [ -f "${base}${d}/${needed}" ]; then found="${d}/${needed}" do_trace "---> found\n" break 2 fi done done if [ -n "${found}" ]; then do_report_needed_found "${needed}" "${found}" "${where}" do_process_file "${base}${found}" else printf "%8s%s not found\n" "" "${needed}" fi do_trace "Done searching for '%s'\n" "${needed}" } # Scan a file for all NEEDED tags do_process_file() { local file="${1}" local -a save_search_rpath local n m local found do_trace "Parsing file '%s'\n" "${file}" save_search_rpath=( "${search_rpath[@]}" ) for n in $( "${readelf}" -d "${file}" \ |"${grep}" -E '\((RPATH|RUNPATH)\)' \ |"${sed}" -r -e 's/^.*Library r(|un)path:[[:space:]]+\[(.*)\]$/\2/;'\ ); do do_trace "-> adding rpath '%s'\n" "${n}" search_rpath+=( "${n}" ) done do_trace ": search path:\n" for n in "${search_rpath[@]}" "${needed_search_path[@]}"; do do_trace ": - '%s'\n" "${n}" done do_trace ": end search path\n" for n in $( "${readelf}" -d "${file}" \ |"${grep}" -E '\(NEEDED\)' \ |"${sed}" -r -e 's/^.*Shared library:[[:space:]]+\[([^]]+)\].*/\1/;' \ ); do found=0 for m in "${needed_list[@]}"; do [ "${n}" = "${m}" ] && found=1 && break done if [ ${found} -ne 0 ]; then do_trace "-> skipping already known dependency '%s'\n" "${n}" continue fi do_trace "-> handling new dependency '%s'\n" "${n}" needed_list+=( "${n}" ) do_find_needed "${n}" do_trace "-> done handling dependency '%s'\n" "${n}" done search_rpath=( "${save_search_rpath[@]}" ) do_trace "Finished parsing file '%s'\n" "${file}" } # Recursively scan a /etc/ld.so.conf file do_scan_etc_ldsoconf() { local ldsoconf="${1}" local g local f [ -f "${ldsoconf}" ] || return 0 do_trace "Parsing ld.so.conf: '%s'\n" "${ldsoconf}" while read line; do case "${line}" in include\ *) g="${root}${line#include }" do_trace "-> handling include directive '%s'\n" "${g}" for f in ${g}; do do_scan_etc_ldsoconf "${f}" done do_trace "-> finished handling include directive '%s'\n" "${g}" ;; \#*|"") ;; *) do_trace "-> adding search dir '%s'\n" "${line}" needed_search_path+=( "${line}" ) ;; esac done <"${ldsoconf}" do_trace "Finished parsing ld.so.conf: '%s'\n" "${ldsoconf}" } # Build up the full list of search directories declare -a needed_search_path do_trace "Adding basic lib dirs\n" ld_library_path="${ld_library_path}:" while [ -n "${ld_library_path}" ]; do d="${ld_library_path%%:*}" if [ -n "${d}" ]; then do_trace "-> adding search dir '%s'\n" "${d}" needed_search_path+=( "${d}" ) fi ld_library_path="${ld_library_path#*:}" done do_trace "Done adding basic lib dirs\n" do_trace "Scanning '/etc/ld.so.conf'\n" do_scan_etc_ldsoconf "${root}/etc/ld.so.conf" do_trace "Done scanning '/etc/ld.so.conf'\n" do_trace "Search path:\n" for p in "${needed_search_path[@]}"; do do_trace "-> '%s'\n" "${p}" done declare -a needed_list declare -a search_rpath do_trace "Scanning file '%s'\n" "${1}" do_process_file "${1}" do_trace "Done scanning file '%s'\n" "${1}"
0
coqui_public_repos/STT/native_client/kenlm
coqui_public_repos/STT/native_client/kenlm/util/ersatz_progress.cc
#include "ersatz_progress.hh" #include <algorithm> #include <ostream> #include <limits> #include <string> namespace util { namespace { const unsigned char kWidth = 100; } const char kProgressBanner[] = "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n"; ErsatzProgress::ErsatzProgress() : current_(0), next_(std::numeric_limits<uint64_t>::max()), complete_(next_), out_(NULL) {} ErsatzProgress::~ErsatzProgress() { if (out_) Finished(); } ErsatzProgress::ErsatzProgress(uint64_t complete, std::ostream *to, const std::string &message) : current_(0), next_(complete / kWidth), complete_(complete), stones_written_(0), out_(to) { if (!out_) { next_ = std::numeric_limits<uint64_t>::max(); return; } if (!message.empty()) *out_ << message << '\n'; *out_ << kProgressBanner; } void ErsatzProgress::Milestone() { if (!out_) { current_ = 0; return; } if (!complete_) return; unsigned char stone = std::min(static_cast<uint64_t>(kWidth), (current_ * kWidth) / complete_); for (; stones_written_ < stone; ++stones_written_) { (*out_) << '*'; } if (stone == kWidth) { (*out_) << std::endl; next_ = std::numeric_limits<uint64_t>::max(); out_ = NULL; } else { next_ = std::max(next_, ((stone + 1) * complete_ + kWidth - 1) / kWidth); } } } // namespace util
0
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/include
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/include/fst/memory.h
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. // // FST memory utilities. #ifndef FST_MEMORY_H_ #define FST_MEMORY_H_ #include <list> #include <memory> #include <utility> #include <vector> #include <fst/types.h> #include <fst/log.h> #include <fstream> namespace fst { // Default block allocation size. constexpr int kAllocSize = 64; // Minimum number of allocations per block. constexpr int kAllocFit = 4; // Base class for MemoryArena that allows (e.g.) MemoryArenaCollection to // easily manipulate collections of variously sized arenas. class MemoryArenaBase { public: virtual ~MemoryArenaBase() {} virtual size_t Size() const = 0; }; namespace internal { // Allocates 'size' unintialized memory chunks of size object_size from // underlying blocks of (at least) size 'block_size * object_size'. // All blocks are freed when this class is deleted. Result of allocate() will // be aligned to object_size. template <size_t object_size> class MemoryArenaImpl : public MemoryArenaBase { public: enum { kObjectSize = object_size }; explicit MemoryArenaImpl(size_t block_size = kAllocSize) : block_size_(block_size * kObjectSize), block_pos_(0) { blocks_.emplace_front(new char[block_size_]); } void *Allocate(size_t size) { const auto byte_size = size * kObjectSize; if (byte_size * kAllocFit > block_size_) { // Large block; adds new large block. auto *ptr = new char[byte_size]; blocks_.emplace_back(ptr); return ptr; } if (block_pos_ + byte_size > block_size_) { // Doesn't fit; adds new standard block. auto *ptr = new char[block_size_]; block_pos_ = 0; blocks_.emplace_front(ptr); } // Fits; uses current block. auto *ptr = blocks_.front().get() + block_pos_; block_pos_ += byte_size; return ptr; } size_t Size() const override { return kObjectSize; } private: const size_t block_size_; // Default block size in bytes. size_t block_pos_; // Current position in block in bytes. std::list<std::unique_ptr<char[]>> blocks_; // List of allocated blocks. }; } // namespace internal template <typename T> using MemoryArena = internal::MemoryArenaImpl<sizeof(T)>; // Base class for MemoryPool that allows (e.g.) MemoryPoolCollection to easily // manipulate collections of variously sized pools. class MemoryPoolBase { public: virtual ~MemoryPoolBase() {} virtual size_t Size() const = 0; }; namespace internal { // Allocates and frees initially uninitialized memory chunks of size // object_size. Keeps an internal list of freed chunks that are reused (as is) // on the next allocation if available. Chunks are constructed in blocks of size // 'pool_size'. template <size_t object_size> class MemoryPoolImpl : public MemoryPoolBase { public: enum { kObjectSize = object_size }; struct Link { char buf[kObjectSize]; Link *next; }; explicit MemoryPoolImpl(size_t pool_size) : mem_arena_(pool_size), free_list_(nullptr) {} void *Allocate() { if (free_list_ == nullptr) { auto *link = static_cast<Link *>(mem_arena_.Allocate(1)); link->next = nullptr; return link; } else { auto *link = free_list_; free_list_ = link->next; return link; } } void Free(void *ptr) { if (ptr) { auto *link = static_cast<Link *>(ptr); link->next = free_list_; free_list_ = link; } } size_t Size() const override { return kObjectSize; } private: MemoryArena<Link> mem_arena_; Link *free_list_; MemoryPoolImpl(const MemoryPoolImpl &) = delete; MemoryPoolImpl &operator=(const MemoryPoolImpl &) = delete; }; } // namespace internal // Allocates and frees initially uninitialized memory chunks of size sizeof(T). // All memory is freed when the class is deleted. The result of Allocate() will // be suitably memory-aligned. Combined with placement operator new and destroy // functions for the T class, this can be used to improve allocation efficiency. // See nlp/fst/lib/visit.h (global new) and nlp/fst/lib/dfs-visit.h (class new) // for examples. template <typename T> class MemoryPool : public internal::MemoryPoolImpl<sizeof(T)> { public: // 'pool_size' specifies the size of the initial pool and how it is extended. MemoryPool(size_t pool_size = kAllocSize) : internal::MemoryPoolImpl<sizeof(T)>(pool_size) {} }; // Stores a collection of memory arenas. class MemoryArenaCollection { public: // 'block_size' specifies the block size of the arenas. explicit MemoryArenaCollection(size_t block_size = kAllocSize) : block_size_(block_size), ref_count_(1) {} template <typename T> MemoryArena<T> *Arena() { if (sizeof(T) >= arenas_.size()) arenas_.resize(sizeof(T) + 1); MemoryArenaBase *arena = arenas_[sizeof(T)].get(); if (arena == nullptr) { arena = new MemoryArena<T>(block_size_); arenas_[sizeof(T)].reset(arena); } return static_cast<MemoryArena<T> *>(arena); } size_t BlockSize() const { return block_size_; } size_t RefCount() const { return ref_count_; } size_t IncrRefCount() { return ++ref_count_; } size_t DecrRefCount() { return --ref_count_; } private: size_t block_size_; size_t ref_count_; std::vector<std::unique_ptr<MemoryArenaBase>> arenas_; }; // Stores a collection of memory pools class MemoryPoolCollection { public: // 'pool_size' specifies the size of initial pool and how it is extended. explicit MemoryPoolCollection(size_t pool_size = kAllocSize) : pool_size_(pool_size), ref_count_(1) {} template <typename T> MemoryPool<T> *Pool() { if (sizeof(T) >= pools_.size()) pools_.resize(sizeof(T) + 1); MemoryPoolBase *pool = pools_[sizeof(T)].get(); if (pool == nullptr) { pool = new MemoryPool<T>(pool_size_); pools_[sizeof(T)].reset(pool); } return static_cast<MemoryPool<T> *>(pool); } size_t PoolSize() const { return pool_size_; } size_t RefCount() const { return ref_count_; } size_t IncrRefCount() { return ++ref_count_; } size_t DecrRefCount() { return --ref_count_; } private: size_t pool_size_; size_t ref_count_; std::vector<std::unique_ptr<MemoryPoolBase>> pools_; }; // STL allocator using memory arenas. Memory is allocated from underlying // blocks of size 'block_size * sizeof(T)'. Memory is freed only when all // objects using this allocator are destroyed and there is otherwise no reuse // (unlike PoolAllocator). // // This allocator has object-local state so it should not be used with splicing // or swapping operations between objects created with different allocators nor // should it be used if copies must be thread-safe. The result of allocate() // will be suitably memory-aligned. template <typename T> class BlockAllocator { public: using Allocator = std::allocator<T>; using size_type = typename Allocator::size_type; using difference_type = typename Allocator::difference_type; using pointer = typename Allocator::pointer; using const_pointer = typename Allocator::const_pointer; using reference = typename Allocator::reference; using const_reference = typename Allocator::const_reference; using value_type = typename Allocator::value_type; template <typename U> struct rebind { using other = BlockAllocator<U>; }; explicit BlockAllocator(size_t block_size = kAllocSize) : arenas_(new MemoryArenaCollection(block_size)) {} BlockAllocator(const BlockAllocator<T> &arena_alloc) : arenas_(arena_alloc.Arenas()) { Arenas()->IncrRefCount(); } template <typename U> explicit BlockAllocator(const BlockAllocator<U> &arena_alloc) : arenas_(arena_alloc.Arenas()) { Arenas()->IncrRefCount(); } ~BlockAllocator() { if (Arenas()->DecrRefCount() == 0) delete Arenas(); } pointer address(reference ref) const { return Allocator().address(ref); } const_pointer address(const_reference ref) const { return Allocator().address(ref); } size_type max_size() const { return Allocator().max_size(); } template <class U, class... Args> void construct(U *p, Args &&... args) { Allocator().construct(p, std::forward<Args>(args)...); } void destroy(pointer p) { Allocator().destroy(p); } pointer allocate(size_type n, const void *hint = nullptr) { if (n * kAllocFit <= kAllocSize) { return static_cast<pointer>(Arena()->Allocate(n)); } else { return Allocator().allocate(n, hint); } } void deallocate(pointer p, size_type n) { if (n * kAllocFit > kAllocSize) Allocator().deallocate(p, n); } MemoryArenaCollection *Arenas() const { return arenas_; } private: MemoryArena<T> *Arena() { return arenas_->Arena<T>(); } MemoryArenaCollection *arenas_; BlockAllocator<T> operator=(const BlockAllocator<T> &); }; template <typename T, typename U> bool operator==(const BlockAllocator<T> &alloc1, const BlockAllocator<U> &alloc2) { return false; } template <typename T, typename U> bool operator!=(const BlockAllocator<T> &alloc1, const BlockAllocator<U> &alloc2) { return true; } // STL allocator using memory pools. Memory is allocated from underlying // blocks of size 'block_size * sizeof(T)'. Keeps an internal list of freed // chunks thare are reused on the next allocation. // // This allocator has object-local state so it should not be used with splicing // or swapping operations between objects created with different allocators nor // should it be used if copies must be thread-safe. The result of allocate() // will be suitably memory-aligned. template <typename T> class PoolAllocator { public: using Allocator = std::allocator<T>; using size_type = typename Allocator::size_type; using difference_type = typename Allocator::difference_type; using pointer = typename Allocator::pointer; using const_pointer = typename Allocator::const_pointer; using reference = typename Allocator::reference; using const_reference = typename Allocator::const_reference; using value_type = typename Allocator::value_type; template <typename U> struct rebind { using other = PoolAllocator<U>; }; explicit PoolAllocator(size_t pool_size = kAllocSize) : pools_(new MemoryPoolCollection(pool_size)) {} PoolAllocator(const PoolAllocator<T> &pool_alloc) : pools_(pool_alloc.Pools()) { Pools()->IncrRefCount(); } template <typename U> explicit PoolAllocator(const PoolAllocator<U> &pool_alloc) : pools_(pool_alloc.Pools()) { Pools()->IncrRefCount(); } ~PoolAllocator() { if (Pools()->DecrRefCount() == 0) delete Pools(); } pointer address(reference ref) const { return Allocator().address(ref); } const_pointer address(const_reference ref) const { return Allocator().address(ref); } size_type max_size() const { return Allocator().max_size(); } template <class U, class... Args> void construct(U *p, Args &&... args) { Allocator().construct(p, std::forward<Args>(args)...); } void destroy(pointer p) { Allocator().destroy(p); } pointer allocate(size_type n, const void *hint = nullptr) { if (n == 1) { return static_cast<pointer>(Pool<1>()->Allocate()); } else if (n == 2) { return static_cast<pointer>(Pool<2>()->Allocate()); } else if (n <= 4) { return static_cast<pointer>(Pool<4>()->Allocate()); } else if (n <= 8) { return static_cast<pointer>(Pool<8>()->Allocate()); } else if (n <= 16) { return static_cast<pointer>(Pool<16>()->Allocate()); } else if (n <= 32) { return static_cast<pointer>(Pool<32>()->Allocate()); } else if (n <= 64) { return static_cast<pointer>(Pool<64>()->Allocate()); } else { return Allocator().allocate(n, hint); } } void deallocate(pointer p, size_type n) { if (n == 1) { Pool<1>()->Free(p); } else if (n == 2) { Pool<2>()->Free(p); } else if (n <= 4) { Pool<4>()->Free(p); } else if (n <= 8) { Pool<8>()->Free(p); } else if (n <= 16) { Pool<16>()->Free(p); } else if (n <= 32) { Pool<32>()->Free(p); } else if (n <= 64) { Pool<64>()->Free(p); } else { Allocator().deallocate(p, n); } } MemoryPoolCollection *Pools() const { return pools_; } private: template <int n> struct TN { T buf[n]; }; template <int n> MemoryPool<TN<n>> *Pool() { return pools_->Pool<TN<n>>(); } MemoryPoolCollection *pools_; PoolAllocator<T> operator=(const PoolAllocator<T> &); }; template <typename T, typename U> bool operator==(const PoolAllocator<T> &alloc1, const PoolAllocator<U> &alloc2) { return false; } template <typename T, typename U> bool operator!=(const PoolAllocator<T> &alloc1, const PoolAllocator<U> &alloc2) { return true; } } // namespace fst #endif // FST_MEMORY_H_
0
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/extensions
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/extensions/compact/compact16_string-fst.cc
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. #include <fst/fst.h> #include <fst/compact-fst.h> namespace fst { static FstRegisterer<CompactStringFst<StdArc, uint16>> CompactStringFst_StdArc_uint16_registerer; static FstRegisterer<CompactStringFst<LogArc, uint16>> CompactStringFst_LogArc_uint16_registerer; } // namespace fst
0
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/include/fst/extensions
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/include/fst/extensions/mpdt/mpdt.h
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. // // Common classes for Multi Pushdown Transducer (MPDT) expansion/traversal. #ifndef FST_EXTENSIONS_MPDT_MPDT_H_ #define FST_EXTENSIONS_MPDT_MPDT_H_ #include <array> #include <functional> #include <map> #include <vector> #include <fst/compat.h> #include <fst/extensions/pdt/pdt.h> namespace fst { enum MPdtType { MPDT_READ_RESTRICT, // Can only read from first empty stack MPDT_WRITE_RESTRICT, // Can only write to first empty stack MPDT_NO_RESTRICT, // No read-write restrictions }; namespace internal { // PLEASE READ THIS CAREFULLY: // // When USEVECTOR is set, the stack configurations --- the statewise // representation of the StackId's for each substack --- is stored in a vector. // I would like to do this using an array for efficiency reasons, thus the // definition of StackConfig below. However, while this *works* in that tests // pass, etc. It causes a memory leak in the compose and expand tests, evidently // in the map[] that is being used to store the mapping between these // StackConfigs and the external StackId that the caller sees. There are no // memory leaks when I use a vector, only with this StackId. Why there should be // memory leaks given that I am not mallocing anything is a mystery. In case you // were wondering, clearing the map at the end does not help. template <typename StackId, typename Level, Level nlevels> struct StackConfig { StackConfig() : array_() {} StackConfig(const StackConfig<StackId, Level, nlevels> &config) { array_ = config.array_; } StackId &operator[](const int index) { return array_[index]; } const StackId &operator[](const int index) const { return array_[index]; } StackConfig &operator=(const StackConfig<StackId, Level, nlevels> &config) { if (this == &config) return *this; array_ = config.array_; return *this; } std::array<StackId, nlevels> array_; }; template <typename StackId, typename Level, Level nlevels> class CompConfig { public: using Config = StackConfig<StackId, Level, nlevels>; bool operator()(const Config &x, const Config &y) const { for (Level level = 0; level < nlevels; ++level) { if (x.array_[level] < y.array_[level]) { return true; } else if (x.array_[level] > y.array_[level]) { return false; } } return false; } }; // Defines the KeyPair type used as the key to MPdtStack.paren_id_map_. The hash // function is provided as a separate struct to match templating syntax. template <typename Level> struct KeyPair { Level level; size_t underlying_id; KeyPair(Level level, size_t id) : level(level), underlying_id(id) {} inline bool operator==(const KeyPair<Level> &rhs) const { return level == rhs.level && underlying_id == rhs.underlying_id; } }; template <typename Level> struct KeyPairHasher { inline size_t operator()(const KeyPair<Level> &keypair) const { return std::hash<Level>()(keypair.level) ^ (std::hash<size_t>()(keypair.underlying_id) << 1); } }; template <typename StackId, typename Level, Level nlevels = 2, MPdtType restrict = MPDT_READ_RESTRICT> class MPdtStack { public: using Label = Level; using Config = StackConfig<StackId, Level, nlevels>; using ConfigToStackId = std::map<Config, StackId, CompConfig<StackId, Level, nlevels>>; MPdtStack(const std::vector<std::pair<Label, Label>> &parens, const std::vector<Level> &assignments); MPdtStack(const MPdtStack &mstack); ~MPdtStack() { for (Level level = 0; level < nlevels; ++level) delete stacks_[level]; } StackId Find(StackId stack_id, Label label); // For now we do not implement Pop since this is needed only for // ShortestPath(). // For Top we find the first non-empty config, and find the paren ID of that // (or -1) if there is none, then map that to the external stack_id to return. std::ptrdiff_t Top(StackId stack_id) const { if (stack_id == -1) return -1; const auto config = InternalStackIds(stack_id); Level level = 0; StackId underlying_id = -1; for (; level < nlevels; ++level) { if (!Empty(config, level)) { underlying_id = stacks_[level]->Top(config[level]); break; } } if (underlying_id == -1) return -1; const auto it = paren_id_map_.find(KeyPair<Level>(level, underlying_id)); if (it == paren_id_map_.end()) return -1; // NB: shouldn't happen. return it->second; } std::ptrdiff_t ParenId(Label label) const { const auto it = paren_map_.find(label); return it != paren_map_.end() ? it->second : -1; } // TODO(rws): For debugging purposes only: remove later. string PrintConfig(const Config &config) const { string result = "["; for (Level i = 0; i < nlevels; ++i) { char s[128]; snprintf(s, sizeof(s), "%d", config[i]); result += string(s); if (i < nlevels - 1) result += ", "; } result += "]"; return result; } bool Error() { return error_; } // Each component stack has an internal stack ID for a given configuration and // label. // This function maps a configuration of those to the stack ID the caller // sees. inline StackId ExternalStackId(const Config &config) { const auto it = config_to_stack_id_map_.find(config); StackId result; if (it == config_to_stack_id_map_.end()) { result = next_stack_id_++; config_to_stack_id_map_.insert( std::pair<Config, StackId>(config, result)); stack_id_to_config_map_[result] = config; } else { result = it->second; } return result; } // Retrieves the internal stack ID for a corresponding external stack ID. inline const Config InternalStackIds(StackId stack_id) const { auto it = stack_id_to_config_map_.find(stack_id); if (it == stack_id_to_config_map_.end()) { it = stack_id_to_config_map_.find(-1); } return it->second; } inline bool Empty(const Config &config, Level level) const { return config[level] <= 0; } inline bool AllEmpty(const Config &config) { for (Level level = 0; level < nlevels; ++level) { if (!Empty(config, level)) return false; } return true; } bool error_; Label min_paren_; Label max_paren_; // Stores level of each paren. std::unordered_map<Label, Label> paren_levels_; std::vector<std::pair<Label, Label>> parens_; // As in pdt.h. std::unordered_map<Label, size_t> paren_map_; // As in pdt.h. // Maps between internal paren_id and external paren_id. std::unordered_map<KeyPair<Level>, size_t, KeyPairHasher<Level>> paren_id_map_; // Maps between internal stack ids and external stack id. ConfigToStackId config_to_stack_id_map_; std::unordered_map<StackId, Config> stack_id_to_config_map_; StackId next_stack_id_; // Array of stacks. PdtStack<StackId, Label> *stacks_[nlevels]; }; template <typename StackId, typename Level, Level nlevels, MPdtType restrict> MPdtStack<StackId, Level, nlevels, restrict>::MPdtStack( const std::vector<std::pair<Level, Level>> &parens, // NB: Label = Level. const std::vector<Level> &assignments) : error_(false), min_paren_(kNoLabel), max_paren_(kNoLabel), parens_(parens), next_stack_id_(1) { using Label = Level; if (parens.size() != assignments.size()) { FSTERROR() << "MPdtStack: Parens of different size from assignments"; error_ = true; return; } std::vector<std::pair<Label, Label>> vectors[nlevels]; for (Level i = 0; i < assignments.size(); ++i) { // Assignments here start at 0, so assuming the human-readable version has // them starting at 1, we should subtract 1 here const auto level = assignments[i] - 1; if (level < 0 || level >= nlevels) { FSTERROR() << "MPdtStack: Specified level " << level << " out of bounds"; error_ = true; return; } const auto &pair = parens[i]; vectors[level].push_back(pair); paren_levels_[pair.first] = level; paren_levels_[pair.second] = level; paren_map_[pair.first] = i; paren_map_[pair.second] = i; const KeyPair<Level> key(level, vectors[level].size() - 1); paren_id_map_[key] = i; if (min_paren_ == kNoLabel || pair.first < min_paren_) { min_paren_ = pair.first; } if (pair.second < min_paren_) min_paren_ = pair.second; if (max_paren_ == kNoLabel || pair.first > max_paren_) { max_paren_ = pair.first; } if (pair.second > max_paren_) max_paren_ = pair.second; } using Config = StackConfig<StackId, Level, nlevels>; Config neg_one; Config zero; for (Level level = 0; level < nlevels; ++level) { stacks_[level] = new PdtStack<StackId, Label>(vectors[level]); neg_one[level] = -1; zero[level] = 0; } config_to_stack_id_map_[neg_one] = -1; config_to_stack_id_map_[zero] = 0; stack_id_to_config_map_[-1] = neg_one; stack_id_to_config_map_[0] = zero; } template <typename StackId, typename Level, Level nlevels, MPdtType restrict> MPdtStack<StackId, Level, nlevels, restrict>::MPdtStack( const MPdtStack<StackId, Level, nlevels, restrict> &mstack) : error_(mstack.error_), min_paren_(mstack.min_paren_), max_paren_(mstack.max_paren_), parens_(mstack.parens_), next_stack_id_(mstack.next_stack_id_) { for (const auto &kv : mstack.paren_levels_) { paren_levels_[kv.first] = kv.second; } for (const auto &paren : mstack.parens_) parens_.push_back(paren); for (const auto &kv : mstack.paren_map_) { paren_map_[kv.first] = kv.second; } for (const auto &kv : mstack.paren_id_map_) { paren_id_map_[kv.first] = kv.second; } for (auto it = mstack.config_to_stack_id_map_.begin(); it != mstack.config_to_stack_id_map_.end(); ++it) { config_to_stack_id_map_[it->first] = it->second; } for (const auto &kv : mstack.stack_id_to_config_map_) { using Config = StackConfig<StackId, Level, nlevels>; const Config config(kv.second); stack_id_to_config_map_[kv.first] = config; } for (Level level = 0; level < nlevels; ++level) stacks_[level] = mstack.stacks_[level]; } template <typename StackId, typename Level, Level nlevels, MPdtType restrict> StackId MPdtStack<StackId, Level, nlevels, restrict>::Find(StackId stack_id, Level label) { // Non-paren. if (min_paren_ == kNoLabel || label < min_paren_ || label > max_paren_) { return stack_id; } const auto it = paren_map_.find(label); // Non-paren. if (it == paren_map_.end()) return stack_id; std::ptrdiff_t paren_id = it->second; // Gets the configuration associated with this stack_id. const auto config = InternalStackIds(stack_id); // Gets the level. const auto level = paren_levels_.find(label)->second; // If the label is an open paren we push: // // 1) if the restrict type is not MPDT_WRITE_RESTRICT, or // 2) the restrict type is MPDT_WRITE_RESTRICT, and all the stacks above the // level are empty. if (label == parens_[paren_id].first) { // Open paren. if (restrict == MPDT_WRITE_RESTRICT) { for (Level upper_level = 0; upper_level < level; ++upper_level) { if (!Empty(config, upper_level)) return -1; // Non-empty stack blocks. } } // If the label is an close paren we pop: // // 1) if the restrict type is not MPDT_READ_RESTRICT, or // 2) the restrict type is MPDT_READ_RESTRICT, and all the stacks above the // level are empty. } else if (restrict == MPDT_READ_RESTRICT) { for (Level lower_level = 0; lower_level < level; ++lower_level) { if (!Empty(config, lower_level)) return -1; // Non-empty stack blocks. } } const auto nid = stacks_[level]->Find(config[level], label); // If the new ID is -1, that means that there is no valid transition at the // level we want. if (nid == -1) { return -1; } else { using Config = StackConfig<StackId, Level, nlevels>; Config nconfig(config); nconfig[level] = nid; return ExternalStackId(nconfig); } } } // namespace internal } // namespace fst #endif // FST_EXTENSIONS_MPDT_MPDT_H_
0
coqui_public_repos/inference-engine/third_party/onnxruntime/include/onnxruntime/core
coqui_public_repos/inference-engine/third_party/onnxruntime/include/onnxruntime/core/eager/ort_kernel_invoker.h
// Copyright (c) Microsoft Corporation. All rights reserved. // Licensed under the MIT License. #pragma once #include <string> #include <vector> #include "core/common/common.h" #include "core/framework/allocator.h" #include "core/framework/tensor.h" #include "core/framework/execution_provider.h" #include "core/graph/constants.h" #include "core/session/environment.h" #include "core/graph/basic_types.h" #include "core/graph/model.h" namespace onnxruntime { #ifdef __GNUC__ #pragma GCC diagnostic push #endif class ORTInvoker { public: ORTInvoker(std::unique_ptr<IExecutionProvider> execution_provider, const logging::Logger& logger, const IOnnxRuntimeOpSchemaRegistryList& custom_op_registries) : execution_provider_(std::move(execution_provider)), logger_(logger), custom_op_registries_(custom_op_registries) { if (!execution_provider_) { ORT_THROW("Execution provider is nullptr"); } } IExecutionProvider& GetCurrentExecutionProvider() { return *execution_provider_; } common::Status Invoke(const std::string& op_name, //optional inputs / outputs? const std::vector<OrtValue>& inputs, std::vector<OrtValue>& outputs, const NodeAttributes* attributes, const std::string& domain = kOnnxDomain, const int version = -1); private: std::unique_ptr<IExecutionProvider> execution_provider_; const logging::Logger& logger_; // custom ops for current execution provider // we need the op schema to resolve the output type during invoke const IOnnxRuntimeOpSchemaRegistryList& custom_op_registries_; }; #ifdef __GNUC__ #pragma GCC diagnostic pop #endif } // namespace onnxruntime
0
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/script/prune.cc
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. #include <fst/script/fst-class.h> #include <fst/script/prune.h> #include <fst/script/script-impl.h> namespace fst { namespace script { void Prune(const FstClass &ifst, MutableFstClass *ofst, const WeightClass &weight_threshold, int64 state_threshold, float delta) { if (!internal::ArcTypesMatch(ifst, *ofst, "Prune") || !ofst->WeightTypesMatch(weight_threshold, "Prune")) { ofst->SetProperties(kError, kError); return; } PruneArgs1 args(ifst, ofst, weight_threshold, state_threshold, delta); Apply<Operation<PruneArgs1>>("Prune", ifst.ArcType(), &args); } void Prune(MutableFstClass *fst, const WeightClass &weight_threshold, int64 state_threshold, float delta) { if (!fst->WeightTypesMatch(weight_threshold, "Prune")) { fst->SetProperties(kError, kError); return; } PruneArgs2 args(fst, weight_threshold, state_threshold, delta); Apply<Operation<PruneArgs2>>("Prune", fst->ArcType(), &args); } REGISTER_FST_OPERATION(Prune, StdArc, PruneArgs1); REGISTER_FST_OPERATION(Prune, LogArc, PruneArgs1); REGISTER_FST_OPERATION(Prune, Log64Arc, PruneArgs1); REGISTER_FST_OPERATION(Prune, StdArc, PruneArgs2); REGISTER_FST_OPERATION(Prune, LogArc, PruneArgs2); REGISTER_FST_OPERATION(Prune, Log64Arc, PruneArgs2); } // namespace script } // namespace fst
0
coqui_public_repos/STT
coqui_public_repos/STT/taskcluster/build-python-wheel.tyml
$if: '(event.event != "push") && (event.event != "tag")' then: taskId: ${taskcluster.taskId} provisionerId: ${taskcluster.dockerarm64.provisionerId} workerType: ${taskcluster.dockerarm64.workerType} taskGroupId: ${taskcluster.taskGroupId} schedulerId: ${taskcluster.schedulerId} dependencies: $map: { $eval: build.dependencies } each(b): $eval: as_slugid(b) created: { $fromNow: '0 sec' } deadline: { $fromNow: '1 day' } expires: { $fromNow: '7 days' } payload: maxRunTime: 14400 image: "arm64v8/debian:buster-20190812" command: - "/bin/bash" - "--login" - "-cxe" - > apt-get -qq update && apt-get -qq -y install libopenblas-dev liblapack-dev gnupg pixz sudo wget python3.7-minimal python3-pip python3-wheel python3-setuptools && mkdir -p /opt/wheels /tmp/artifacts && cd /opt/wheels && pip3 wheel --verbose --wheel-dir /tmp/artifacts/ --build $(pwd)/ --no-binary :all: numpy && pip3 install /tmp/artifacts/numpy*.whl && pip3 wheel --verbose --wheel-dir /tmp/artifacts/ --build $(pwd)/ --no-binary :all: scipy && ls -hal artifacts: "public": type: "directory" path: "/tmp/artifacts/" expires: { $fromNow: '7 days' } metadata: name: ${build.metadata.name} description: ${build.metadata.description} owner: ${event.head.user.email} source: ${event.head.repo.url}
0
coqui_public_repos/TTS/tests
coqui_public_repos/TTS/tests/inputs/test_glow_tts.json
{ "model": "glow_tts", "run_name": "glow-tts-gatedconv", "run_description": "glow-tts model training with gated conv.", // AUDIO PARAMETERS "audio":{ "fft_size": 1024, // number of stft frequency levels. Size of the linear spectogram frame. "win_length": 1024, // stft window length in ms. "hop_length": 256, // stft window hop-lengh in ms. "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used. "frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used. // Audio processing parameters "sample_rate": 22050, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled. "preemphasis": 0.0, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis. "ref_level_db": 0, // reference level db, theoretically 20db is the sound of air. // Griffin-Lim "power": 1.1, // value to sharpen wav signals after GL algorithm. "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation. // Silence trimming "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true) "trim_db": 60, // threshold for timming silence. Set this according to your dataset. // MelSpectrogram parameters "num_mels": 80, // size of the mel spec frame. "mel_fmin": 50.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!! "mel_fmax": 7600.0, // maximum freq level for mel-spec. Tune for dataset!! "spec_gain": 1.0, // scaler value appplied after log transform of spectrogram. // Normalization parameters "signal_norm": true, // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params. "min_level_db": -100, // lower bound for normalization "symmetric_norm": true, // move normalization to range [-1, 1] "max_norm": 1.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm] "clip_norm": true, // clip normalized values into the range. "stats_path": null // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored }, // VOCABULARY PARAMETERS // if custom character set is not defined, // default set in symbols.py is used // "characters":{ // "pad": "_", // "eos": "~", // "bos": "^", // "characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!'(),-.:;? ", // "punctuations":"!'(),-.:;? ", // "phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫ" // }, "add_blank": false, // if true add a new token after each token of the sentence. This increases the size of the input sequence, but has considerably improved the prosody of the GlowTTS model. // DISTRIBUTED TRAINING "mixed_precision": false, "distributed":{ "backend": "nccl", "url": "tcp:\/\/localhost:54323" }, "reinit_layers": [], // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers. // MODEL PARAMETERS "use_mas": false, // use Monotonic Alignment Search if true. Otherwise use pre-computed attention alignments. // TRAINING "batch_size": 8, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'. "eval_batch_size": 8, "r": 1, // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled. "loss_masking": true, // enable / disable loss masking against the sequence padding. "data_dep_init_iter": 1, // VALIDATION "run_eval": true, "test_delay_epochs": 0, //Until attention is aligned, testing only wastes computation time. "test_sentences_file": null, // set a file to load sentences to be used for testing. If it is null then we use default english sentences. // OPTIMIZER "noam_schedule": true, // use noam warmup and lr schedule. "grad_clip": 5.0, // upper limit for gradients for clipping. "epochs": 1, // total number of epochs to train. "lr": 1e-3, // Initial learning rate. If Noam decay is active, maximum learning rate. "wd": 0.000001, // Weight decay weight. "warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr" "seq_len_norm": false, // Normalize eash sample loss with its length to alleviate imbalanced datasets. Use it if your dataset is small or has skewed distribution of sequence lengths. "hidden_channels_encoder": 192, "hidden_channels_decoder": 192, "hidden_channels_duration_predictor": 256, "use_encoder_prenet": true, "encoder_type": "rel_pos_transformer", "encoder_params": { "kernel_size":3, "dropout_p": 0.1, "num_layers": 6, "num_heads": 2, "hidden_channels_ffn": 768, "input_length": null }, // TENSORBOARD and LOGGING "print_step": 25, // Number of steps to log training on console. "tb_plot_step": 100, // Number of steps to plot TB training figures. "print_eval": false, // If True, it prints intermediate loss values in evalulation. "save_step": 5000, // Number of training steps expected to save traninpg stats and checkpoints. "checkpoint": true, // If true, it saves checkpoints per "save_step" "keep_all_best": true, // If true, keeps all best_models after keep_after steps "keep_after": 10000, // Global step after which to keep best models if keep_all_best is true "tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging. "apex_amp_level": null, // DATA LOADING "text_cleaner": "phoneme_cleaners", "enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars. "num_loader_workers": 0, // number of training data loader processes. Don't set it too big. 4-8 are good values. "num_eval_loader_workers": 0, // number of evaluation data loader processes. "batch_group_size": 0, //Number of batches to shuffle after bucketing. "min_seq_len": 3, // DATASET-RELATED: minimum text length to use in training "max_seq_len": 500, // DATASET-RELATED: maximum text length "compute_f0": false, // compute f0 values in data-loader "compute_input_seq_cache": true, "use_noise_augment": true, // PATHS "output_path": "tests/train_outputs/", // PHONEMES "phoneme_cache_path": "tests/outputs/phoneme_cache/", // phoneme computation is slow, therefore, it caches results in the given folder. "use_phonemes": false, // use phonemes instead of raw characters. It is suggested for better pronounciation. "phoneme_language": "en-us", // depending on your target language, pick one from https://github.com/bootphon/phonemizer#languages // MULTI-SPEAKER and GST "use_d_vector_file": false, "d_vector_file": null, "use_speaker_embedding": false, // use speaker embedding to enable multi-speaker learning. // DATASETS "datasets": // List of datasets. They all merged and they get different speaker_ids. [ { "formatter": "ljspeech", "path": "tests/data/ljspeech/", "meta_file_train": "metadata.csv", "meta_file_val": "metadata.csv" } ] }
0
coqui_public_repos
coqui_public_repos/xtts-streaming-server/demo.py
import gradio as gr import requests import base64 import tempfile import json import os SERVER_URL = 'http://localhost:8000' OUTPUT = "./demo_outputs" cloned_speakers = {} print("Preparing file structure...") if not os.path.exists(OUTPUT): os.mkdir(OUTPUT) os.mkdir(os.path.join(OUTPUT, "cloned_speakers")) os.mkdir(os.path.join(OUTPUT, "generated_audios")) elif os.path.exists(os.path.join(OUTPUT, "cloned_speakers")): print("Loading existing cloned speakers...") for file in os.listdir(os.path.join(OUTPUT, "cloned_speakers")): if file.endswith(".json"): with open(os.path.join(OUTPUT, "cloned_speakers", file), "r") as fp: cloned_speakers[file[:-5]] = json.load(fp) print("Available cloned speakers:", ", ".join(cloned_speakers.keys())) try: print("Getting metadata from server ...") LANUGAGES = requests.get(SERVER_URL + "/languages").json() print("Available languages:", ", ".join(LANUGAGES)) STUDIO_SPEAKERS = requests.get(SERVER_URL + "/studio_speakers").json() print("Available studio speakers:", ", ".join(STUDIO_SPEAKERS.keys())) except: raise Exception("Please make sure the server is running first.") def clone_speaker(upload_file, clone_speaker_name, cloned_speaker_names): files = {"wav_file": ("reference.wav", open(upload_file, "rb"))} embeddings = requests.post(SERVER_URL + "/clone_speaker", files=files).json() with open(os.path.join(OUTPUT, "cloned_speakers", clone_speaker_name + ".json"), "w") as fp: json.dump(embeddings, fp) cloned_speakers[clone_speaker_name] = embeddings cloned_speaker_names.append(clone_speaker_name) return upload_file, clone_speaker_name, cloned_speaker_names, gr.Dropdown.update(choices=cloned_speaker_names) def tts(text, speaker_type, speaker_name_studio, speaker_name_custom, lang): embeddings = STUDIO_SPEAKERS[speaker_name_studio] if speaker_type == 'Studio' else cloned_speakers[speaker_name_custom] generated_audio = requests.post( SERVER_URL + "/tts", json={ "text": text, "language": lang, "speaker_embedding": embeddings["speaker_embedding"], "gpt_cond_latent": embeddings["gpt_cond_latent"] } ).content generated_audio_path = os.path.join("demo_outputs", "generated_audios", next(tempfile._get_candidate_names()) + ".wav") with open(generated_audio_path, "wb") as fp: fp.write(base64.b64decode(generated_audio)) return fp.name with gr.Blocks() as demo: cloned_speaker_names = gr.State(list(cloned_speakers.keys())) with gr.Tab("TTS"): with gr.Column() as row4: with gr.Row() as col4: speaker_name_studio = gr.Dropdown( label="Studio speaker", choices=STUDIO_SPEAKERS.keys(), value="Asya Anara" if "Asya Anara" in STUDIO_SPEAKERS.keys() else None, ) speaker_name_custom = gr.Dropdown( label="Cloned speaker", choices=cloned_speaker_names.value, value=cloned_speaker_names.value[0] if len(cloned_speaker_names.value) != 0 else None, ) speaker_type = gr.Dropdown(label="Speaker type", choices=["Studio", "Cloned"], value="Studio") with gr.Column() as col2: lang = gr.Dropdown(label="Language", choices=LANUGAGES, value="en") text = gr.Textbox(label="text", value="A quick brown fox jumps over the lazy dog.") tts_button = gr.Button(value="TTS") with gr.Column() as col3: generated_audio = gr.Audio(label="Generated audio", autoplay=True) with gr.Tab("Clone a new speaker"): with gr.Column() as col1: upload_file = gr.Audio(label="Upload reference audio", type="filepath") clone_speaker_name = gr.Textbox(label="Speaker name", value="default_speaker") clone_button = gr.Button(value="Clone speaker") clone_button.click( fn=clone_speaker, inputs=[upload_file, clone_speaker_name, cloned_speaker_names], outputs=[upload_file, clone_speaker_name, cloned_speaker_names, speaker_name_custom], ) tts_button.click( fn=tts, inputs=[text, speaker_type, speaker_name_studio, speaker_name_custom, lang], outputs=[generated_audio], ) if __name__ == "__main__": print("Warming up server...") with open("test/default_speaker.json", "r") as fp: warmup_speaker = json.load(fp) resp = requests.post( SERVER_URL + "/tts", json={ "text": "This is a warmup request.", "language": "en", "speaker_embedding": warmup_speaker["speaker_embedding"], "gpt_cond_latent": warmup_speaker["gpt_cond_latent"], } ) resp.raise_for_status() print("Starting the demo...") demo.launch( share=False, debug=False, server_port=3009, server_name="0.0.0.0", )
0
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/include/fst
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/include/fst/script/arcsort.h
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. #ifndef FST_SCRIPT_ARCSORT_H_ #define FST_SCRIPT_ARCSORT_H_ #include <utility> #include <fst/arcsort.h> #include <fst/script/fst-class.h> namespace fst { namespace script { enum ArcSortType { ILABEL_SORT, OLABEL_SORT }; using ArcSortArgs = std::pair<MutableFstClass *, ArcSortType>; template <class Arc> void ArcSort(ArcSortArgs *args) { MutableFst<Arc> *fst = std::get<0>(*args)->GetMutableFst<Arc>(); switch (std::get<1>(*args)) { case ILABEL_SORT: { const ILabelCompare<Arc> icomp; ArcSort(fst, icomp); return; } case OLABEL_SORT: { const OLabelCompare<Arc> ocomp; ArcSort(fst, ocomp); return; } } } void ArcSort(MutableFstClass *ofst, ArcSortType); } // namespace script } // namespace fst #endif // FST_SCRIPT_ARCSORT_H_
0
coqui_public_repos/STT-models/swahili/coqui
coqui_public_repos/STT-models/swahili/coqui/v8.0-large-vocab/MODEL_CARD.md
# Model card for Swahili STT v8.0 Jump to section: - [Model details](#model-details) - [Intended use](#intended-use) - [Performance Factors](#performance-factors) - [Metrics](#metrics) - [Training data](#training-data) - [Evaluation data](#evaluation-data) - [Ethical considerations](#ethical-considerations) - [Caveats and recommendations](#caveats-and-recommendations) ## Model details - Person or organization developing model: Maintained by [Coqui](https://coqui.ai/). - Model language: Swahili / kiswahili / `sw` - Model date: March 8, 2022 - Model type: `Speech-to-Text` - Model version: `v8.0` - Compatible with 🐸 STT version: `v1.3.0` - License: Apache 2.0 - Citation details: `@techreport{swahili-stt, author = {Coqui}, title = {Swahili STT v8.0}, institution = {Coqui}, address = {\url{https://coqui.ai/models}} year = {2022}, month = {March}, number = {STT-SW-8.0} }` - Where to send questions or comments about the model: You can leave an issue on [`STT` issues](https://github.com/coqui-ai/STT/issues), open a new discussion on [`STT` discussions](https://github.com/coqui-ai/STT/discussions), or chat with us on [Gitter](https://gitter.im/coqui-ai/). ## Intended use Speech-to-Text for the [Swahili Language](https://en.wikipedia.org/wiki/Swahili_language) on 16kHz, mono-channel audio. ## Performance Factors Factors relevant to Speech-to-Text performance include but are not limited to speaker demographics, recording quality, and background noise. Read more about STT performance factors [here](https://stt.readthedocs.io/en/latest/DEPLOYMENT.html#how-will-a-model-perform-on-my-data). ## Metrics STT models are usually evaluated in terms of their transcription accuracy, deployment Real-Time Factor, and model size on disk. #### Transcription Accuracy Using the language model with settings `lm_alpha=0.898202045251655` and `lm_beta=2.2684674938753755` (found via `lm_optimizer.py`): - Swahili Common Voice 8.0 Test: WER: 15.8\%, CER: 6.6\% #### Model Size For STT, you always must deploy an acoustic model, and it is often the case you also will want to deploy an application-specific language model. |Model type|Vocabulary|Filename|Size| ----------------|-----|----------------|-----| |Acoustic model | open | `model.tflite` | 45M| |Language model | large | `large-vocabulary.scorer` |321M| ### Approaches to uncertainty and variability Confidence scores and multiple paths from the decoding beam can be used to measure model uncertainty and provide multiple, variable transcripts for any processed audio. ## Training data This model was trained on the following corpora: Common Voice 8.0 Swahili. ## Evaluation data The validation ("dev") sets came from Common Voice 8.0. ## Ethical considerations Deploying a Speech-to-Text model into any production setting has ethical implications. You should consider these implications before use. ### Demographic Bias You should assume every machine learning model has demographic bias unless proven otherwise. For STT models, it is often the case that transcription accuracy is better for men than it is for women. If you are using this model in production, you should acknowledge this as a potential issue. ### Surveillance Speech-to-Text may be mis-used to invade the privacy of others by recording and mining information from private conversations. This kind of individual privacy is protected by law in may countries. You should not assume consent to record and analyze private speech. ## Caveats and recommendations Machine learning models (like this STT model) perform best on data that is similar to the data on which they were trained. Read about what to expect from an STT model with regard to your data [here](https://stt.readthedocs.io/en/latest/DEPLOYMENT.html#how-will-a-model-perform-on-my-data). In most applications, it is recommended that you [train your own language model](https://stt.readthedocs.io/en/latest/LANGUAGE_MODEL.html) to improve transcription accuracy on your speech data.
0
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/include/fst
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/include/fst/script/relabel.h
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. #ifndef FST_SCRIPT_RELABEL_H_ #define FST_SCRIPT_RELABEL_H_ #include <algorithm> #include <tuple> #include <utility> #include <vector> #include <fst/relabel.h> #include <fst/script/fst-class.h> namespace fst { namespace script { using RelabelArgs1 = std::tuple<MutableFstClass *, const SymbolTable *, const SymbolTable *, const string &, bool, const SymbolTable *, const SymbolTable *, const string &, bool>; template <class Arc> void Relabel(RelabelArgs1 *args) { MutableFst<Arc> *ofst = std::get<0>(*args)->GetMutableFst<Arc>(); Relabel(ofst, std::get<1>(*args), std::get<2>(*args), std::get<3>(*args), std::get<4>(*args), std::get<5>(*args), std::get<6>(*args), std::get<7>(*args), std::get<8>(*args)); } using LabelPair = std::pair<int64, int64>; using RelabelArgs2 = std::tuple<MutableFstClass *, const std::vector<LabelPair> &, const std::vector<LabelPair> &>; template <class Arc> void Relabel(RelabelArgs2 *args) { MutableFst<Arc> *ofst = std::get<0>(*args)->GetMutableFst<Arc>(); using LabelPair = std::pair<typename Arc::Label, typename Arc::Label>; // In case the MutableFstClass::Label is not the same as Arc::Label, // make a copy. std::vector<LabelPair> typed_ipairs(std::get<1>(*args).size()); std::copy(std::get<1>(*args).begin(), std::get<1>(*args).end(), typed_ipairs.begin()); std::vector<LabelPair> typed_opairs(std::get<2>(*args).size()); std::copy(std::get<2>(*args).begin(), std::get<2>(*args).end(), typed_opairs.begin()); Relabel(ofst, typed_ipairs, typed_opairs); } void Relabel(MutableFstClass *ofst, const SymbolTable *old_isymbols, const SymbolTable *new_isymbols, const string &unknown_isymbol, bool attach_new_isymbols, const SymbolTable *old_osymbols, const SymbolTable *new_osymbols, const string &unknown_osymbol, bool attach_new_osymbols); void Relabel(MutableFstClass *ofst, const std::vector<LabelPair> &ipairs, const std::vector<LabelPair> &opairs); } // namespace script } // namespace fst #endif // FST_SCRIPT_RELABEL_H_
0
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/include/fst/extensions
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/include/fst/extensions/far/getters.h
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. // // Classes and functions for registering and invoking FAR main // functions that support multiple and extensible arc types. #ifndef FST_EXTENSIONS_FAR_GETTERS_H_ #define FST_EXTENSIONS_FAR_GETTERS_H_ #include <fst/flags.h> #include <fst/extensions/far/far.h> namespace fst { namespace script { FarType GetFarType(const string &str); bool GetFarEntryType(const string &str, FarEntryType *entry_type); bool GetFarTokenType(const string &str, FarTokenType *token_type); void ExpandArgs(int argc, char **argv, int *argcp, char ***argvp); } // namespace script string GetFarTypeString(FarType type); } // namespace fst #endif // FST_EXTENSIONS_FAR_GETTERS_H_
0
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/include
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/include/fst/push.h
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. // // Class to reweight/push an FST, and utility functions to weigh and reweight // an FST. #ifndef FST_PUSH_H_ #define FST_PUSH_H_ #include <vector> #include <fst/log.h> #include <fst/arc-map.h> #include <fst/factor-weight.h> #include <fst/fst.h> #include <fst/reweight.h> #include <fst/shortest-distance.h> namespace fst { // Computes the total weight (sum of the weights of all accepting paths) from // the output of ShortestDistance, using the shortest distance from the final // state when reverse is true and from the initial state otherwise. template <class Arc> typename Arc::Weight ComputeTotalWeight( const Fst<Arc> &fst, const std::vector<typename Arc::Weight> &distance, bool reverse) { if (reverse) { return fst.Start() < distance.size() ? distance[fst.Start()] : Arc::Weight::Zero(); } auto sum = Arc::Weight::Zero(); for (typename Arc::StateId s = 0; s < distance.size(); ++s) { sum = Plus(sum, Times(distance[s], fst.Final(s))); } return sum; } // Divides the weight of every accepting path by a fixed weight. This weight // is also divided at the final state if at_final is true and at the initial // state otherwise. template <class Arc> void RemoveWeight(MutableFst<Arc> *fst, const typename Arc::Weight &weight, bool at_final) { using Weight = typename Arc::Weight; if ((weight == Weight::One()) || (weight == Weight::Zero())) return; if (at_final) { for (StateIterator<MutableFst<Arc>> siter(*fst); !siter.Done(); siter.Next()) { fst->SetFinal(siter.Value(), Divide(fst->Final(siter.Value()), weight, DIVIDE_RIGHT)); } } else { const auto start = fst->Start(); for (MutableArcIterator<MutableFst<Arc>> aiter(fst, start); !aiter.Done(); aiter.Next()) { auto arc = aiter.Value(); arc.weight = Divide(arc.weight, weight, DIVIDE_LEFT); aiter.SetValue(arc); } fst->SetFinal(start, Divide(fst->Final(start), weight, DIVIDE_LEFT)); } } // Pushes the weights in FST in the direction defined by TYPE. If // pushing towards the initial state, the sum of the weight of the // outgoing transitions and final weight at a non-initial state is // equal to One() in the resulting machine. If pushing towards the // final state, the same property holds on the reverse machine. // // Weight needs to be left distributive when pushing towards the // initial state and right distributive when pushing towards the final // states. template <class Arc> void Push(MutableFst<Arc> *fst, ReweightType type, float delta = kDelta, bool remove_total_weight = false) { using Weight = typename Arc::Weight; std::vector<Weight> distance; ShortestDistance(*fst, &distance, type == REWEIGHT_TO_INITIAL, delta); auto total_weight = Weight::One(); if (remove_total_weight) { total_weight = ComputeTotalWeight(*fst, distance, type == REWEIGHT_TO_INITIAL); } Reweight(fst, distance, type); if (remove_total_weight) { RemoveWeight(fst, total_weight, type == REWEIGHT_TO_FINAL); } } constexpr uint32_t kPushWeights = 0x0001; constexpr uint32_t kPushLabels = 0x0002; constexpr uint32_t kPushRemoveTotalWeight = 0x0004; constexpr uint32_t kPushRemoveCommonAffix = 0x0008; // Pushes the weights and/or labels of the input FST into the output // mutable FST by pushing weights and/or labels (as determined by the // ptype argument) towards the initial state or final states (as // determined by the rtype template parameter). The weight type must // be left distributive when pushing weights towards the initial state, and // right distribution when pushing weights towards the final states. template <class Arc, ReweightType rtype> void Push(const Fst<Arc> &ifst, MutableFst<Arc> *ofst, uint32_t ptype, float delta = kDelta) { using Label = typename Arc::Label; using Weight = typename Arc::Weight; if ((ptype & (kPushWeights | kPushLabels)) == kPushWeights) { *ofst = ifst; Push(ofst, rtype, delta, ptype & kPushRemoveTotalWeight); } else if (ptype & kPushLabels) { const auto gtype = rtype == REWEIGHT_TO_INITIAL ? GALLIC_LEFT : GALLIC_RIGHT; using GallicWeight = typename GallicArc<Arc, gtype>::Weight; std::vector<GallicWeight> gdistance; VectorFst<GallicArc<Arc, gtype>> gfst; ArcMap(ifst, &gfst, ToGallicMapper<Arc, gtype>()); if (ptype & kPushWeights) { ShortestDistance(gfst, &gdistance, rtype == REWEIGHT_TO_INITIAL, delta); } else { ArcMapFst<Arc, Arc, RmWeightMapper<Arc>> uwfst(ifst, RmWeightMapper<Arc>()); ArcMapFst<Arc, GallicArc<Arc, gtype>, ToGallicMapper<Arc, gtype>> guwfst( uwfst, ToGallicMapper<Arc, gtype>()); ShortestDistance(guwfst, &gdistance, rtype == REWEIGHT_TO_INITIAL, delta); } auto total_weight = GallicWeight::One(); if (ptype & (kPushRemoveTotalWeight | kPushRemoveCommonAffix)) { total_weight = ComputeTotalWeight(gfst, gdistance, rtype == REWEIGHT_TO_INITIAL); total_weight = GallicWeight( ptype & kPushRemoveCommonAffix ? total_weight.Value1() : StringWeight<Label, GallicStringType(gtype)>::One(), ptype & kPushRemoveTotalWeight ? total_weight.Value2() : Weight::One()); } Reweight(&gfst, gdistance, rtype); if (ptype & (kPushRemoveTotalWeight | kPushRemoveCommonAffix)) { RemoveWeight(&gfst, total_weight, rtype == REWEIGHT_TO_FINAL); } FactorWeightFst<GallicArc<Arc, gtype>, GallicFactor<Label, Weight, gtype>> fwfst(gfst); ArcMap(fwfst, ofst, FromGallicMapper<Arc, gtype>()); ofst->SetOutputSymbols(ifst.OutputSymbols()); } else { LOG(WARNING) << "Push: pushing type is set to 0, so not pushing"; *ofst = ifst; } } } // namespace fst #endif // FST_PUSH_H_
0
coqui_public_repos/STT-examples/uwp/STTUWP
coqui_public_repos/STT-examples/uwp/STTUWP/Properties/Default.rd.xml
<!-- This file contains Runtime Directives used by .NET Native. The defaults here are suitable for most developers. However, you can modify these parameters to modify the behavior of the .NET Native optimizer. Runtime Directives are documented at https://go.microsoft.com/fwlink/?LinkID=391919 To fully enable reflection for App1.MyClass and all of its public/private members <Type Name="App1.MyClass" Dynamic="Required All"/> To enable dynamic creation of the specific instantiation of AppClass<T> over System.Int32 <TypeInstantiation Name="App1.AppClass" Arguments="System.Int32" Activate="Required Public" /> Using the Namespace directive to apply reflection policy to all the types in a particular namespace <Namespace Name="DataClasses.ViewModels" Serialize="All" /> --> <Directives xmlns="http://schemas.microsoft.com/netfx/2013/01/metadata"> <Application> <!-- An Assembly element with Name="*Application*" applies to all assemblies in the application package. The asterisks are not wildcards. --> <Assembly Name="*Application*" Dynamic="Required All" /> <!-- Add your application specific runtime directives here. --> </Application> </Directives>
0
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/bin/fstunion-main.cc
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. // // Creates the union of two FSTs. #include <cstring> #include <memory> #include <string> #include <fst/flags.h> #include <fst/log.h> #include <fst/script/union.h> int fstunion_main(int argc, char **argv) { namespace s = fst::script; using fst::script::FstClass; using fst::script::MutableFstClass; string usage = "Creates the union of two FSTs.\n\n Usage: "; usage += argv[0]; usage += " in1.fst in2.fst [out.fst]\n"; std::set_new_handler(FailedNewHandler); SET_FLAGS(usage.c_str(), &argc, &argv, true); if (argc < 3 || argc > 4) { ShowUsage(); return 1; } const string in1_name = strcmp(argv[1], "-") != 0 ? argv[1] : ""; const string in2_name = strcmp(argv[2], "-") != 0 ? argv[2] : ""; const string out_name = argc > 3 ? argv[3] : ""; if (in1_name == "" && in2_name == "") { LOG(ERROR) << argv[0] << ": Can't take both inputs from standard input"; return 1; } std::unique_ptr<MutableFstClass> fst1(MutableFstClass::Read(in1_name, true)); if (!fst1) return 1; std::unique_ptr<FstClass> fst2(FstClass::Read(in2_name)); if (!fst2) return 1; s::Union(fst1.get(), *fst2); return !fst1->Write(out_name); }
0
coqui_public_repos
coqui_public_repos/TTS/requirements.notebooks.txt
bokeh==1.4.0
0
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/include/fst/extensions
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/include/fst/extensions/far/equal.h
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. #ifndef FST_EXTENSIONS_FAR_EQUAL_H_ #define FST_EXTENSIONS_FAR_EQUAL_H_ #include <memory> #include <string> #include <fst/extensions/far/far.h> #include <fst/equal.h> namespace fst { template <class Arc> bool FarEqual(const string &filename1, const string &filename2, float delta = kDelta, const string &begin_key = string(), const string &end_key = string()) { std::unique_ptr<FarReader<Arc>> reader1(FarReader<Arc>::Open(filename1)); if (!reader1) { LOG(ERROR) << "FarEqual: Could not open FAR file " << filename1; return false; } std::unique_ptr<FarReader<Arc>> reader2(FarReader<Arc>::Open(filename2)); if (!reader2) { LOG(ERROR) << "FarEqual: Could not open FAR file " << filename2; return false; } if (!begin_key.empty()) { bool find_begin1 = reader1->Find(begin_key); bool find_begin2 = reader2->Find(begin_key); if (!find_begin1 || !find_begin2) { bool ret = !find_begin1 && !find_begin2; if (!ret) { LOG(ERROR) << "FarEqual: Key " << begin_key << " missing from " << (find_begin1 ? "second" : "first") << " archive"; } return ret; } } for (; !reader1->Done() && !reader2->Done(); reader1->Next(), reader2->Next()) { const auto &key1 = reader1->GetKey(); const auto &key2 = reader2->GetKey(); if (!end_key.empty() && end_key < key1 && end_key < key2) { return true; } if (key1 != key2) { LOG(ERROR) << "FarEqual: Mismatched keys " << key1 << " and " << key2; return false; } if (!Equal(*(reader1->GetFst()), *(reader2->GetFst()), delta)) { LOG(ERROR) << "FarEqual: FSTs for key " << key1 << " are not equal"; return false; } } if (!reader1->Done() || !reader2->Done()) { LOG(ERROR) << "FarEqual: Key " << (reader1->Done() ? reader2->GetKey() : reader1->GetKey()) << " missing from " << (reader2->Done() ? "first" : "second") << " archive"; return false; } return true; } } // namespace fst #endif // FST_EXTENSIONS_FAR_EQUAL_H_
0
coqui_public_repos/inference-engine/third_party/kenlm/lm
coqui_public_repos/inference-engine/third_party/kenlm/lm/common/ngram_stream.hh
#ifndef LM_BUILDER_NGRAM_STREAM_H #define LM_BUILDER_NGRAM_STREAM_H #include "lm/common/ngram.hh" #include "util/stream/chain.hh" #include "util/stream/multi_stream.hh" #include "util/stream/stream.hh" #include <cstddef> namespace lm { template <class Proxy> class ProxyStream { public: // Make an invalid stream. ProxyStream() {} explicit ProxyStream(const util::stream::ChainPosition &position, const Proxy &proxy = Proxy()) : proxy_(proxy), stream_(position) { proxy_.ReBase(stream_.Get()); } Proxy &operator*() { return proxy_; } const Proxy &operator*() const { return proxy_; } Proxy *operator->() { return &proxy_; } const Proxy *operator->() const { return &proxy_; } void *Get() { return stream_.Get(); } const void *Get() const { return stream_.Get(); } operator bool() const { return stream_; } bool operator!() const { return !stream_; } void Poison() { stream_.Poison(); } ProxyStream<Proxy> &operator++() { ++stream_; proxy_.ReBase(stream_.Get()); return *this; } private: Proxy proxy_; util::stream::Stream stream_; }; template <class Payload> class NGramStream : public ProxyStream<NGram<Payload> > { public: // Make an invalid stream. NGramStream() {} explicit NGramStream(const util::stream::ChainPosition &position) : ProxyStream<NGram<Payload> >(position, NGram<Payload>(NULL, NGram<Payload>::OrderFromSize(position.GetChain().EntrySize()))) {} }; template <class Payload> class NGramStreams : public util::stream::GenericStreams<NGramStream<Payload> > { private: typedef util::stream::GenericStreams<NGramStream<Payload> > P; public: NGramStreams() : P() {} NGramStreams(const util::stream::ChainPositions &positions) : P(positions) {} }; } // namespace #endif // LM_BUILDER_NGRAM_STREAM_H
0
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/include
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/include/fst/bi-table.h
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. // // Classes for representing a bijective mapping between an arbitrary entry // of type T and a signed integral ID. #ifndef FST_BI_TABLE_H_ #define FST_BI_TABLE_H_ #include <deque> #include <memory> #include <functional> #include <unordered_map> #include <unordered_set> #include <vector> #include <fst/log.h> #include <fst/memory.h> namespace fst { // Bitables model bijective mappings between entries of an arbitrary type T and // an signed integral ID of type I. The IDs are allocated starting from 0 in // order. // // template <class I, class T> // class BiTable { // public: // // // Required constructors. // BiTable(); // // // Looks up integer ID from entry. If it doesn't exist and insert // / is true, adds it; otherwise, returns -1. // I FindId(const T &entry, bool insert = true); // // // Looks up entry from integer ID. // const T &FindEntry(I) const; // // // Returns number of stored entries. // I Size() const; // }; // An implementation using a hash map for the entry to ID mapping. H is the // hash function and E is the equality function. If passed to the constructor, // ownership is given to this class. template <class I, class T, class H, class E = std::equal_to<T>> class HashBiTable { public: // Reserves space for table_size elements. If passing H and E to the // constructor, this class owns them. explicit HashBiTable(size_t table_size = 0, H *h = nullptr, E *e = nullptr) : hash_func_(h ? h : new H()), hash_equal_(e ? e : new E()), entry2id_(table_size, *hash_func_, *hash_equal_) { if (table_size) id2entry_.reserve(table_size); } HashBiTable(const HashBiTable<I, T, H, E> &table) : hash_func_(new H(*table.hash_func_)), hash_equal_(new E(*table.hash_equal_)), entry2id_(table.entry2id_.begin(), table.entry2id_.end(), table.entry2id_.size(), *hash_func_, *hash_equal_), id2entry_(table.id2entry_) {} I FindId(const T &entry, bool insert = true) { if (!insert) { const auto it = entry2id_.find(entry); return it == entry2id_.end() ? -1 : it->second - 1; } I &id_ref = entry2id_[entry]; if (id_ref == 0) { // T not found; stores and assigns a new ID. id2entry_.push_back(entry); id_ref = id2entry_.size(); } return id_ref - 1; // NB: id_ref = ID + 1. } const T &FindEntry(I s) const { return id2entry_[s]; } I Size() const { return id2entry_.size(); } // TODO(riley): Add fancy clear-to-size, as in CompactHashBiTable. void Clear() { entry2id_.clear(); id2entry_.clear(); } private: std::unique_ptr<H> hash_func_; std::unique_ptr<E> hash_equal_; std::unordered_map<T, I, H, E> entry2id_; std::vector<T> id2entry_; }; // Enables alternative hash set representations below. enum HSType { HS_STL = 0, HS_DENSE = 1, HS_SPARSE = 2, HS_FLAT = 3 }; // Default hash set is STL hash_set. template <class K, class H, class E, HSType HS> struct HashSet : public std::unordered_set<K, H, E, PoolAllocator<K>> { explicit HashSet(size_t n = 0, const H &h = H(), const E &e = E()) : std::unordered_set<K, H, E, PoolAllocator<K>>(n, h, e) {} void rehash(size_t n) {} }; // An implementation using a hash set for the entry to ID mapping. The hash set // holds keys which are either the ID or kCurrentKey. These keys can be mapped // to entries either by looking up in the entry vector or, if kCurrentKey, in // current_entry_. The hash and key equality functions map to entries first. H // is the hash function and E is the equality function. If passed to the // constructor, ownership is given to this class. template <class I, class T, class H, class E = std::equal_to<T>, HSType HS = HS_FLAT> class CompactHashBiTable { public: friend class HashFunc; friend class HashEqual; // Reserves space for table_size elements. If passing H and E to the // constructor, this class owns them. explicit CompactHashBiTable(size_t table_size = 0, H *h = nullptr, E *e = nullptr) : hash_func_(h ? h : new H()), hash_equal_(e ? e : new E()), compact_hash_func_(*this), compact_hash_equal_(*this), keys_(table_size, compact_hash_func_, compact_hash_equal_) { if (table_size) id2entry_.reserve(table_size); } CompactHashBiTable(const CompactHashBiTable<I, T, H, E, HS> &table) : hash_func_(new H(*table.hash_func_)), hash_equal_(new E(*table.hash_equal_)), compact_hash_func_(*this), compact_hash_equal_(*this), keys_(table.keys_.size(), compact_hash_func_, compact_hash_equal_), id2entry_(table.id2entry_) { keys_.insert(table.keys_.begin(), table.keys_.end()); } I FindId(const T &entry, bool insert = true) { current_entry_ = &entry; if (insert) { auto result = keys_.insert(kCurrentKey); if (!result.second) return *result.first; // Already exists. // Overwrites kCurrentKey with a new key value; this is safe because it // doesn't affect hashing or equality testing. I key = id2entry_.size(); const_cast<I &>(*result.first) = key; id2entry_.push_back(entry); return key; } const auto it = keys_.find(kCurrentKey); return it == keys_.end() ? -1 : *it; } const T &FindEntry(I s) const { return id2entry_[s]; } I Size() const { return id2entry_.size(); } // Clears content; with argument, erases last n IDs. void Clear(ssize_t n = -1) { if (n < 0 || n >= id2entry_.size()) { // Clears completely. keys_.clear(); id2entry_.clear(); } else if (n == id2entry_.size() - 1) { // Leaves only key 0. const T entry = FindEntry(0); keys_.clear(); id2entry_.clear(); FindId(entry, true); } else { while (n-- > 0) { I key = id2entry_.size() - 1; keys_.erase(key); id2entry_.pop_back(); } keys_.rehash(0); } } private: static constexpr I kCurrentKey = -1; static constexpr I kEmptyKey = -2; static constexpr I kDeletedKey = -3; class HashFunc { public: explicit HashFunc(const CompactHashBiTable &ht) : ht_(&ht) {} size_t operator()(I k) const { if (k >= kCurrentKey) { return (*ht_->hash_func_)(ht_->Key2Entry(k)); } else { return 0; } } private: const CompactHashBiTable *ht_; }; class HashEqual { public: explicit HashEqual(const CompactHashBiTable &ht) : ht_(&ht) {} bool operator()(I k1, I k2) const { if (k1 == k2) { return true; } else if (k1 >= kCurrentKey && k2 >= kCurrentKey) { return (*ht_->hash_equal_)(ht_->Key2Entry(k1), ht_->Key2Entry(k2)); } else { return false; } } private: const CompactHashBiTable *ht_; }; using KeyHashSet = HashSet<I, HashFunc, HashEqual, HS>; const T &Key2Entry(I k) const { if (k == kCurrentKey) { return *current_entry_; } else { return id2entry_[k]; } } std::unique_ptr<H> hash_func_; std::unique_ptr<E> hash_equal_; HashFunc compact_hash_func_; HashEqual compact_hash_equal_; KeyHashSet keys_; std::vector<T> id2entry_; const T *current_entry_; }; template <class I, class T, class H, class E, HSType HS> constexpr I CompactHashBiTable<I, T, H, E, HS>::kCurrentKey; template <class I, class T, class H, class E, HSType HS> constexpr I CompactHashBiTable<I, T, H, E, HS>::kEmptyKey; template <class I, class T, class H, class E, HSType HS> constexpr I CompactHashBiTable<I, T, H, E, HS>::kDeletedKey; // An implementation using a vector for the entry to ID mapping. It is passed a // function object FP that should fingerprint entries uniquely to an integer // that can used as a vector index. Normally, VectorBiTable constructs the FP // object. The user can instead pass in this object; in that case, VectorBiTable // takes its ownership. template <class I, class T, class FP> class VectorBiTable { public: // Reserves table_size cells of space. If passing FP argument to the // constructor, this class owns it. explicit VectorBiTable(FP *fp = nullptr, size_t table_size = 0) : fp_(fp ? fp : new FP()) { if (table_size) id2entry_.reserve(table_size); } VectorBiTable(const VectorBiTable<I, T, FP> &table) : fp_(new FP(*table.fp_)), fp2id_(table.fp2id_), id2entry_(table.id2entry_) {} I FindId(const T &entry, bool insert = true) { ssize_t fp = (*fp_)(entry); if (fp >= fp2id_.size()) fp2id_.resize(fp + 1); I &id_ref = fp2id_[fp]; if (id_ref == 0) { // T not found. if (insert) { // Stores and assigns a new ID. id2entry_.push_back(entry); id_ref = id2entry_.size(); } else { return -1; } } return id_ref - 1; // NB: id_ref = ID + 1. } const T &FindEntry(I s) const { return id2entry_[s]; } I Size() const { return id2entry_.size(); } const FP &Fingerprint() const { return *fp_; } private: std::unique_ptr<FP> fp_; std::vector<I> fp2id_; std::vector<T> id2entry_; }; // An implementation using a vector and a compact hash table. The selecting // functor S returns true for entries to be hashed in the vector. The // fingerprinting functor FP returns a unique fingerprint for each entry to be // hashed in the vector (these need to be suitable for indexing in a vector). // The hash functor H is used when hashing entry into the compact hash table. // If passed to the constructor, ownership is given to this class. template <class I, class T, class S, class FP, class H, HSType HS = HS_DENSE> class VectorHashBiTable { public: friend class HashFunc; friend class HashEqual; explicit VectorHashBiTable(S *s, FP *fp, H *h, size_t vector_size = 0, size_t entry_size = 0) : selector_(s), fp_(fp), h_(h), hash_func_(*this), hash_equal_(*this), keys_(0, hash_func_, hash_equal_) { if (vector_size) fp2id_.reserve(vector_size); if (entry_size) id2entry_.reserve(entry_size); } VectorHashBiTable(const VectorHashBiTable<I, T, S, FP, H, HS> &table) : selector_(new S(table.s_)), fp_(new FP(*table.fp_)), h_(new H(*table.h_)), id2entry_(table.id2entry_), fp2id_(table.fp2id_), hash_func_(*this), hash_equal_(*this), keys_(table.keys_.size(), hash_func_, hash_equal_) { keys_.insert(table.keys_.begin(), table.keys_.end()); } I FindId(const T &entry, bool insert = true) { if ((*selector_)(entry)) { // Uses the vector if selector_(entry) == true. uint64 fp = (*fp_)(entry); if (fp2id_.size() <= fp) fp2id_.resize(fp + 1, 0); if (fp2id_[fp] == 0) { // T not found. if (insert) { // Stores and assigns a new ID. id2entry_.push_back(entry); fp2id_[fp] = id2entry_.size(); } else { return -1; } } return fp2id_[fp] - 1; // NB: assoc_value = ID + 1. } else { // Uses the hash table otherwise. current_entry_ = &entry; const auto it = keys_.find(kCurrentKey); if (it == keys_.end()) { if (insert) { I key = id2entry_.size(); id2entry_.push_back(entry); keys_.insert(key); return key; } else { return -1; } } else { return *it; } } } const T &FindEntry(I s) const { return id2entry_[s]; } I Size() const { return id2entry_.size(); } const S &Selector() const { return *selector_; } const FP &Fingerprint() const { return *fp_; } const H &Hash() const { return *h_; } private: static constexpr I kCurrentKey = -1; static constexpr I kEmptyKey = -2; class HashFunc { public: explicit HashFunc(const VectorHashBiTable &ht) : ht_(&ht) {} size_t operator()(I k) const { if (k >= kCurrentKey) { return (*(ht_->h_))(ht_->Key2Entry(k)); } else { return 0; } } private: const VectorHashBiTable *ht_; }; class HashEqual { public: explicit HashEqual(const VectorHashBiTable &ht) : ht_(&ht) {} bool operator()(I k1, I k2) const { if (k1 >= kCurrentKey && k2 >= kCurrentKey) { return ht_->Key2Entry(k1) == ht_->Key2Entry(k2); } else { return k1 == k2; } } private: const VectorHashBiTable *ht_; }; using KeyHashSet = HashSet<I, HashFunc, HashEqual, HS>; const T &Key2Entry(I k) const { if (k == kCurrentKey) { return *current_entry_; } else { return id2entry_[k]; } } std::unique_ptr<S> selector_; // True if entry hashed into vector. std::unique_ptr<FP> fp_; // Fingerprint used for hashing into vector. std::unique_ptr<H> h_; // Hash funcion used for hashing into hash_set. std::vector<T> id2entry_; // Maps state IDs to entry. std::vector<I> fp2id_; // Maps entry fingerprints to IDs. // Compact implementation of the hash table mapping entries to state IDs // using the hash function h_. HashFunc hash_func_; HashEqual hash_equal_; KeyHashSet keys_; const T *current_entry_; }; template <class I, class T, class S, class FP, class H, HSType HS> constexpr I VectorHashBiTable<I, T, S, FP, H, HS>::kCurrentKey; template <class I, class T, class S, class FP, class H, HSType HS> constexpr I VectorHashBiTable<I, T, S, FP, H, HS>::kEmptyKey; // An implementation using a hash map for the entry to ID mapping. This version // permits erasing of arbitrary states. The entry T must have == defined and // its default constructor must produce a entry that will never be seen. F is // the hash function. template <class I, class T, class F> class ErasableBiTable { public: ErasableBiTable() : first_(0) {} I FindId(const T &entry, bool insert = true) { I &id_ref = entry2id_[entry]; if (id_ref == 0) { // T not found. if (insert) { // Stores and assigns a new ID. id2entry_.push_back(entry); id_ref = id2entry_.size() + first_; } else { return -1; } } return id_ref - 1; // NB: id_ref = ID + 1. } const T &FindEntry(I s) const { return id2entry_[s - first_]; } I Size() const { return id2entry_.size(); } void Erase(I s) { auto &ref = id2entry_[s - first_]; entry2id_.erase(ref); ref = empty_entry_; while (!id2entry_.empty() && id2entry_.front() == empty_entry_) { id2entry_.pop_front(); ++first_; } } private: std::unordered_map<T, I, F> entry2id_; std::deque<T> id2entry_; const T empty_entry_; I first_; // I of first element in the deque. }; } // namespace fst #endif // FST_BI_TABLE_H_
0
coqui_public_repos/STT-models/kinyarwanda/digital-umuganda
coqui_public_repos/STT-models/kinyarwanda/digital-umuganda/v0.0.1/LICENSE
Mozilla Public License Version 2.0 ================================== 1. Definitions -------------- 1.1. "Contributor" means each individual or legal entity that creates, contributes to the creation of, or owns Covered Software. 1.2. "Contributor Version" means the combination of the Contributions of others (if any) used by a Contributor and that particular Contributor's Contribution. 1.3. "Contribution" means Covered Software of a particular Contributor. 1.4. "Covered Software" means Source Code Form to which the initial Contributor has attached the notice in Exhibit A, the Executable Form of such Source Code Form, and Modifications of such Source Code Form, in each case including portions thereof. 1.5. "Incompatible With Secondary Licenses" means (a) that the initial Contributor has attached the notice described in Exhibit B to the Covered Software; or (b) that the Covered Software was made available under the terms of version 1.1 or earlier of the License, but not also under the terms of a Secondary License. 1.6. "Executable Form" means any form of the work other than Source Code Form. 1.7. "Larger Work" means a work that combines Covered Software with other material, in a separate file or files, that is not Covered Software. 1.8. "License" means this document. 1.9. "Licensable" means having the right to grant, to the maximum extent possible, whether at the time of the initial grant or subsequently, any and all of the rights conveyed by this License. 1.10. "Modifications" means any of the following: (a) any file in Source Code Form that results from an addition to, deletion from, or modification of the contents of Covered Software; or (b) any new file in Source Code Form that contains any Covered Software. 1.11. "Patent Claims" of a Contributor means any patent claim(s), including without limitation, method, process, and apparatus claims, in any patent Licensable by such Contributor that would be infringed, but for the grant of the License, by the making, using, selling, offering for sale, having made, import, or transfer of either its Contributions or its Contributor Version. 1.12. "Secondary License" means either the GNU General Public License, Version 2.0, the GNU Lesser General Public License, Version 2.1, the GNU Affero General Public License, Version 3.0, or any later versions of those licenses. 1.13. "Source Code Form" means the form of the work preferred for making modifications. 1.14. "You" (or "Your") means an individual or a legal entity exercising rights under this License. For legal entities, "You" includes any entity that controls, is controlled by, or is under common control with You. For purposes of this definition, "control" means (a) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (b) ownership of more than fifty percent (50%) of the outstanding shares or beneficial ownership of such entity. 2. License Grants and Conditions -------------------------------- 2.1. Grants Each Contributor hereby grants You a world-wide, royalty-free, non-exclusive license: (a) under intellectual property rights (other than patent or trademark) Licensable by such Contributor to use, reproduce, make available, modify, display, perform, distribute, and otherwise exploit its Contributions, either on an unmodified basis, with Modifications, or as part of a Larger Work; and (b) under Patent Claims of such Contributor to make, use, sell, offer for sale, have made, import, and otherwise transfer either its Contributions or its Contributor Version. 2.2. Effective Date The licenses granted in Section 2.1 with respect to any Contribution become effective for each Contribution on the date the Contributor first distributes such Contribution. 2.3. Limitations on Grant Scope The licenses granted in this Section 2 are the only rights granted under this License. No additional rights or licenses will be implied from the distribution or licensing of Covered Software under this License. Notwithstanding Section 2.1(b) above, no patent license is granted by a Contributor: (a) for any code that a Contributor has removed from Covered Software; or (b) for infringements caused by: (i) Your and any other third party's modifications of Covered Software, or (ii) the combination of its Contributions with other software (except as part of its Contributor Version); or (c) under Patent Claims infringed by Covered Software in the absence of its Contributions. This License does not grant any rights in the trademarks, service marks, or logos of any Contributor (except as may be necessary to comply with the notice requirements in Section 3.4). 2.4. Subsequent Licenses No Contributor makes additional grants as a result of Your choice to distribute the Covered Software under a subsequent version of this License (see Section 10.2) or under the terms of a Secondary License (if permitted under the terms of Section 3.3). 2.5. Representation Each Contributor represents that the Contributor believes its Contributions are its original creation(s) or it has sufficient rights to grant the rights to its Contributions conveyed by this License. 2.6. Fair Use This License is not intended to limit any rights You have under applicable copyright doctrines of fair use, fair dealing, or other equivalents. 2.7. Conditions Sections 3.1, 3.2, 3.3, and 3.4 are conditions of the licenses granted in Section 2.1. 3. Responsibilities ------------------- 3.1. Distribution of Source Form All distribution of Covered Software in Source Code Form, including any Modifications that You create or to which You contribute, must be under the terms of this License. You must inform recipients that the Source Code Form of the Covered Software is governed by the terms of this License, and how they can obtain a copy of this License. You may not attempt to alter or restrict the recipients' rights in the Source Code Form. 3.2. Distribution of Executable Form If You distribute Covered Software in Executable Form then: (a) such Covered Software must also be made available in Source Code Form, as described in Section 3.1, and You must inform recipients of the Executable Form how they can obtain a copy of such Source Code Form by reasonable means in a timely manner, at a charge no more than the cost of distribution to the recipient; and (b) You may distribute such Executable Form under the terms of this License, or sublicense it under different terms, provided that the license for the Executable Form does not attempt to limit or alter the recipients' rights in the Source Code Form under this License. 3.3. Distribution of a Larger Work You may create and distribute a Larger Work under terms of Your choice, provided that You also comply with the requirements of this License for the Covered Software. If the Larger Work is a combination of Covered Software with a work governed by one or more Secondary Licenses, and the Covered Software is not Incompatible With Secondary Licenses, this License permits You to additionally distribute such Covered Software under the terms of such Secondary License(s), so that the recipient of the Larger Work may, at their option, further distribute the Covered Software under the terms of either this License or such Secondary License(s). 3.4. Notices You may not remove or alter the substance of any license notices (including copyright notices, patent notices, disclaimers of warranty, or limitations of liability) contained within the Source Code Form of the Covered Software, except that You may alter any license notices to the extent required to remedy known factual inaccuracies. 3.5. Application of Additional Terms You may choose to offer, and to charge a fee for, warranty, support, indemnity or liability obligations to one or more recipients of Covered Software. However, You may do so only on Your own behalf, and not on behalf of any Contributor. You must make it absolutely clear that any such warranty, support, indemnity, or liability obligation is offered by You alone, and You hereby agree to indemnify every Contributor for any liability incurred by such Contributor as a result of warranty, support, indemnity or liability terms You offer. You may include additional disclaimers of warranty and limitations of liability specific to any jurisdiction. 4. Inability to Comply Due to Statute or Regulation --------------------------------------------------- If it is impossible for You to comply with any of the terms of this License with respect to some or all of the Covered Software due to statute, judicial order, or regulation then You must: (a) comply with the terms of this License to the maximum extent possible; and (b) describe the limitations and the code they affect. Such description must be placed in a text file included with all distributions of the Covered Software under this License. Except to the extent prohibited by statute or regulation, such description must be sufficiently detailed for a recipient of ordinary skill to be able to understand it. 5. Termination -------------- 5.1. The rights granted under this License will terminate automatically if You fail to comply with any of its terms. However, if You become compliant, then the rights granted under this License from a particular Contributor are reinstated (a) provisionally, unless and until such Contributor explicitly and finally terminates Your grants, and (b) on an ongoing basis, if such Contributor fails to notify You of the non-compliance by some reasonable means prior to 60 days after You have come back into compliance. Moreover, Your grants from a particular Contributor are reinstated on an ongoing basis if such Contributor notifies You of the non-compliance by some reasonable means, this is the first time You have received notice of non-compliance with this License from such Contributor, and You become compliant prior to 30 days after Your receipt of the notice. 5.2. If You initiate litigation against any entity by asserting a patent infringement claim (excluding declaratory judgment actions, counter-claims, and cross-claims) alleging that a Contributor Version directly or indirectly infringes any patent, then the rights granted to You by any and all Contributors for the Covered Software under Section 2.1 of this License shall terminate. 5.3. In the event of termination under Sections 5.1 or 5.2 above, all end user license agreements (excluding distributors and resellers) which have been validly granted by You or Your distributors under this License prior to termination shall survive termination. ************************************************************************ * * * 6. Disclaimer of Warranty * * ------------------------- * * * * Covered Software is provided under this License on an "as is" * * basis, without warranty of any kind, either expressed, implied, or * * statutory, including, without limitation, warranties that the * * Covered Software is free of defects, merchantable, fit for a * * particular purpose or non-infringing. The entire risk as to the * * quality and performance of the Covered Software is with You. * * Should any Covered Software prove defective in any respect, You * * (not any Contributor) assume the cost of any necessary servicing, * * repair, or correction. This disclaimer of warranty constitutes an * * essential part of this License. No use of any Covered Software is * * authorized under this License except under this disclaimer. * * * ************************************************************************ ************************************************************************ * * * 7. Limitation of Liability * * -------------------------- * * * * Under no circumstances and under no legal theory, whether tort * * (including negligence), contract, or otherwise, shall any * * Contributor, or anyone who distributes Covered Software as * * permitted above, be liable to You for any direct, indirect, * * special, incidental, or consequential damages of any character * * including, without limitation, damages for lost profits, loss of * * goodwill, work stoppage, computer failure or malfunction, or any * * and all other commercial damages or losses, even if such party * * shall have been informed of the possibility of such damages. This * * limitation of liability shall not apply to liability for death or * * personal injury resulting from such party's negligence to the * * extent applicable law prohibits such limitation. Some * * jurisdictions do not allow the exclusion or limitation of * * incidental or consequential damages, so this exclusion and * * limitation may not apply to You. * * * ************************************************************************ 8. Litigation ------------- Any litigation relating to this License may be brought only in the courts of a jurisdiction where the defendant maintains its principal place of business and such litigation shall be governed by laws of that jurisdiction, without reference to its conflict-of-law provisions. Nothing in this Section shall prevent a party's ability to bring cross-claims or counter-claims. 9. Miscellaneous ---------------- This License represents the complete agreement concerning the subject matter hereof. If any provision of this License is held to be unenforceable, such provision shall be reformed only to the extent necessary to make it enforceable. Any law or regulation which provides that the language of a contract shall be construed against the drafter shall not be used to construe this License against a Contributor. 10. Versions of the License --------------------------- 10.1. New Versions Mozilla Foundation is the license steward. Except as provided in Section 10.3, no one other than the license steward has the right to modify or publish new versions of this License. Each version will be given a distinguishing version number. 10.2. Effect of New Versions You may distribute the Covered Software under the terms of the version of the License under which You originally received the Covered Software, or under the terms of any subsequent version published by the license steward. 10.3. Modified Versions If you create software not governed by this License, and you want to create a new license for such software, you may create and use a modified version of this License if you rename the license and remove any references to the name of the license steward (except to note that such modified license differs from this License). 10.4. Distributing Source Code Form that is Incompatible With Secondary Licenses If You choose to distribute Source Code Form that is Incompatible With Secondary Licenses under the terms of this version of the License, the notice described in Exhibit B of this License must be attached. Exhibit A - Source Code Form License Notice ------------------------------------------- This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. If it is not possible or desirable to put the notice in a particular file, then You may include the notice in a location (such as a LICENSE file in a relevant directory) where a recipient would be likely to look for such a notice. You may add additional accurate notices of copyright ownership. Exhibit B - "Incompatible With Secondary Licenses" Notice --------------------------------------------------------- This Source Code Form is "Incompatible With Secondary Licenses", as defined by the Mozilla Public License, v. 2.0.
0
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/include/fst
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/include/fst/script/replace.h
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. #ifndef FST_SCRIPT_REPLACE_H_ #define FST_SCRIPT_REPLACE_H_ #include <tuple> #include <utility> #include <vector> #include <fst/replace.h> #include <fst/script/fst-class.h> namespace fst { namespace script { struct ReplaceOptions { const int64 root; // Root rule for expansion. const ReplaceLabelType call_label_type; // How to label call arc. const ReplaceLabelType return_label_type; // How to label return arc. const int64 return_label; // Specifies return arc label. explicit ReplaceOptions(int64 root, ReplaceLabelType call_label_type = REPLACE_LABEL_INPUT, ReplaceLabelType return_label_type = REPLACE_LABEL_NEITHER, int64 return_label = 0) : root(root), call_label_type(call_label_type), return_label_type(return_label_type), return_label(return_label) {} }; using LabelFstClassPair = std::pair<int64, const FstClass *>; using ReplaceArgs = std::tuple<const std::vector<LabelFstClassPair> &, MutableFstClass *, const ReplaceOptions &>; template <class Arc> void Replace(ReplaceArgs *args) { using LabelFstPair = std::pair<typename Arc::Label, const Fst<Arc> *>; // Now that we know the arc type, we construct a vector of // std::pair<real label, real fst> that the real Replace will use. const auto &untyped_pairs = std::get<0>(*args); std::vector<LabelFstPair> typed_pairs; typed_pairs.reserve(untyped_pairs.size()); for (const auto &untyped_pair : untyped_pairs) { typed_pairs.emplace_back(untyped_pair.first, // Converts label. untyped_pair.second->GetFst<Arc>()); } MutableFst<Arc> *ofst = std::get<1>(*args)->GetMutableFst<Arc>(); const auto &opts = std::get<2>(*args); ReplaceFstOptions<Arc> typed_opts(opts.root, opts.call_label_type, opts.return_label_type, opts.return_label); ReplaceFst<Arc> rfst(typed_pairs, typed_opts); // Checks for cyclic dependencies before attempting expansion. if (rfst.CyclicDependencies()) { FSTERROR() << "Replace: Cyclic dependencies detected; cannot expand"; ofst->SetProperties(kError, kError); return; } typed_opts.gc = true; // Caching options to speed up batch copy. typed_opts.gc_limit = 0; *ofst = rfst; } void Replace(const std::vector<LabelFstClassPair> &pairs, MutableFstClass *ofst, const ReplaceOptions &opts); } // namespace script } // namespace fst #endif // FST_SCRIPT_REPLACE_H_
0
coqui_public_repos/Trainer
coqui_public_repos/Trainer/trainer/distribute.py
#!/usr/bin/env python3 # -*- coding: utf-8 -*- import os import pathlib import subprocess import time from trainer import TrainerArgs, logger def distribute(): """ Call 👟Trainer training script in DDP mode. """ parser = TrainerArgs().init_argparse(arg_prefix="") parser.add_argument("--script", type=str, help="Target training script to distibute.") parser.add_argument( "--gpus", type=str, help='GPU IDs to be used for distributed training in the format ```"0,1"```. Used if ```CUDA_VISIBLE_DEVICES``` is not set.', ) args, unargs = parser.parse_known_args() gpus = get_gpus(args) group_id = time.strftime("%Y_%m_%d-%H%M%S") # set arguments for train.py folder_path = pathlib.Path(__file__).parent.absolute() if os.path.exists(os.path.join(folder_path, args.script)): command = [os.path.join(folder_path, args.script)] else: command = [args.script] # Pass all the TrainerArgs fields command.append(f"--continue_path={args.continue_path}") command.append(f"--restore_path={args.restore_path}") command.append(f"--group_id=group_{group_id}") command.append("--use_ddp=true") command += unargs command.append("") # run processes processes = [] for rank, local_gpu_id in enumerate(gpus): my_env = os.environ.copy() my_env["PYTHON_EGG_CACHE"] = f"/tmp/tmp{local_gpu_id}" my_env["RANK"] = f"{rank}" my_env["CUDA_VISIBLE_DEVICES"] = f"{','.join(gpus)}" command[-1] = f"--rank={rank}" # prevent stdout for processes with rank != 0 stdout = None p = subprocess.Popen(["python3"] + command, stdout=stdout, env=my_env) # pylint: disable=consider-using-with processes.append(p) logger.info(command) for p in processes: p.wait() def get_gpus(args): # set active gpus from CUDA_VISIBLE_DEVICES or --gpus if "CUDA_VISIBLE_DEVICES" in os.environ and os.environ["CUDA_VISIBLE_DEVICES"] != "": gpus = os.environ["CUDA_VISIBLE_DEVICES"] else: gpus = args.gpus gpus = list(map(str.strip, gpus.split(","))) return gpus if __name__ == "__main__": distribute()
0
coqui_public_repos/inference-engine/third_party/onnxruntime/include/onnxruntime/core
coqui_public_repos/inference-engine/third_party/onnxruntime/include/onnxruntime/core/framework/float16.h
// Copyright (c) Microsoft Corporation. All rights reserved. // Licensed under the MIT License. #pragma once #include "endian.h" namespace onnxruntime { // MLFloat16 struct MLFloat16 { uint16_t val; MLFloat16() : val(0) {} explicit MLFloat16(uint16_t x) : val(x) {} explicit MLFloat16(float f); float ToFloat() const; operator float() const { return ToFloat(); } }; inline bool operator==(const MLFloat16& left, const MLFloat16& right) { return left.val == right.val; } inline bool operator!=(const MLFloat16& left, const MLFloat16& right) { return left.val != right.val; } inline bool operator<(const MLFloat16& left, const MLFloat16& right) { return left.val < right.val; } //BFloat16 struct BFloat16 { uint16_t val{0}; explicit BFloat16() = default; explicit BFloat16(uint16_t v) : val(v) {} explicit BFloat16(float v) { ORT_IF_CONSTEXPR(endian::native == endian::little) { std::memcpy(&val, reinterpret_cast<char*>(&v) + sizeof(uint16_t), sizeof(uint16_t)); } else { std::memcpy(&val, &v, sizeof(uint16_t)); } } float ToFloat() const { float result; char* const first = reinterpret_cast<char*>(&result); char* const second = first + sizeof(uint16_t); ORT_IF_CONSTEXPR(endian::native == endian::little) { std::memset(first, 0, sizeof(uint16_t)); std::memcpy(second, &val, sizeof(uint16_t)); } else { std::memcpy(first, &val, sizeof(uint16_t)); std::memset(second, 0, sizeof(uint16_t)); } return result; } operator float() const { return ToFloat(); } }; inline void BFloat16ToFloat(const BFloat16* blf, float* flt, size_t size) { auto src = blf; auto d = flt; for (; size != 0; ++src, ++d, --size) { *d = src->ToFloat(); } } inline void FloatToBFloat16(const float* flt, BFloat16* blf, size_t size) { auto src = flt; auto d = blf; for (; size != 0; ++src, ++d, --size) { new (d) BFloat16(*src); } } inline bool operator==(const BFloat16& left, const BFloat16& right) { return left.val == right.val; } inline bool operator!=(const BFloat16& left, const BFloat16& right) { return left.val != right.val; } inline bool operator<(const BFloat16& left, const BFloat16& right) { return left.val < right.val; } }
0
coqui_public_repos/STT
coqui_public_repos/STT/taskcluster/test-cpp_16k-win-cuda-opt.yml
build: template_file: test-win-cuda-opt-base.tyml dependencies: - "win-amd64-gpu-opt" - "test-training_16k-linux-amd64-py36m-opt" test_model_task: "test-training_16k-linux-amd64-py36m-opt" args: tests_cmdline: "$TASKCLUSTER_TASK_DIR/DeepSpeech/ds/taskcluster/tc-cppwin-ds-tests.sh 16k cuda" metadata: name: "DeepSpeech Windows AMD64 CUDA C++ tests (16kHz)" description: "Testing DeepSpeech C++ for Windows/AMD64, CUDA, optimized version (16kHz)"
0
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/include/fst/extensions
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/include/fst/extensions/linear/linear-fst-data.h
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. // // Data structures for storing and looking up the actual feature weights. #ifndef FST_EXTENSIONS_LINEAR_LINEAR_FST_DATA_H_ #define FST_EXTENSIONS_LINEAR_LINEAR_FST_DATA_H_ #include <memory> #include <numeric> #include <string> #include <utility> #include <vector> #include <fst/compat.h> #include <fst/fst.h> #include <fst/extensions/linear/trie.h> namespace fst { // Forward declarations template <class A> class LinearFstDataBuilder; template <class A> class FeatureGroup; // Immutable data storage of the feature weights in a linear // model. Produces state tuples that represent internal states of a // LinearTaggerFst. Object of this class can only be constructed via // either `LinearFstDataBuilder::Dump()` or `LinearFstData::Read()` // and usually used as refcount'd object shared across mutiple // `LinearTaggerFst` copies. // // TODO(wuke): more efficient trie implementation template <class A> class LinearFstData { public: friend class LinearFstDataBuilder<A>; // For builder access typedef typename A::Label Label; typedef typename A::Weight Weight; // Sentence boundary labels. Both of them are negative labels other // than `kNoLabel`. static const Label kStartOfSentence; static const Label kEndOfSentence; // Constructs empty data; for non-trivial ways of construction see // `Read()` and `LinearFstDataBuilder`. LinearFstData() : max_future_size_(0), max_input_label_(1), input_attribs_(1) {} // Appends the state tuple of the start state to `output`, where // each tuple holds the node ids of a trie for each feature group. void EncodeStartState(std::vector<Label> *output) const { for (int i = 0; i < NumGroups(); ++i) output->push_back(GroupStartState(i)); } // Takes a transition from the trie states stored in // `(trie_state_begin, trie_state_end)` with input label `ilabel` // and output label `olabel`; appends the destination state tuple to // `next` and multiplies the weight of the transition onto // `weight`. `next` should be the shifted input buffer of the caller // in `LinearTaggerFstImpl` (i.e. of size `LinearTaggerFstImpl::delay_`; // the last element is `ilabel`). template <class Iterator> void TakeTransition(Iterator buffer_end, Iterator trie_state_begin, Iterator trie_state_end, Label ilabel, Label olabel, std::vector<Label> *next, Weight *weight) const; // Returns the final weight of the given trie state sequence. template <class Iterator> Weight FinalWeight(Iterator trie_state_begin, Iterator trie_state_end) const; // Returns the start trie state of the given group. Label GroupStartState(int group_id) const { return groups_[group_id]->Start(); } // Takes a transition only within the given group. Returns the // destination trie state and multiplies the weight onto `weight`. Label GroupTransition(int group_id, int trie_state, Label ilabel, Label olabel, Weight *weight) const; // Returns the final weight of the given trie state in the given group. Weight GroupFinalWeight(int group_id, int trie_state) const { return groups_[group_id]->FinalWeight(trie_state); } Label MinInputLabel() const { return 1; } Label MaxInputLabel() const { return max_input_label_; } // Returns the maximum future size of all feature groups. Future is // the look-ahead window of a feature, e.g. if a feature looks at // the next 2 words after the current input, then the future size is // 2. There is no look-ahead for output. Features inside a single // `FeatureGroup` must have equal future size. size_t MaxFutureSize() const { return max_future_size_; } // Returns the number of feature groups size_t NumGroups() const { return groups_.size(); } // Returns the range of possible output labels for an input label. std::pair<typename std::vector<Label>::const_iterator, typename std::vector<Label>::const_iterator> PossibleOutputLabels(Label word) const; static LinearFstData<A> *Read(std::istream &strm); // NOLINT std::ostream &Write(std::ostream &strm) const; // NOLINT private: // Offsets in `output_pool_` struct InputAttribute { size_t output_begin, output_length; std::istream &Read(std::istream &strm); // NOLINT std::ostream &Write(std::ostream &strm) const; // NOLINT }; // Mapping from input label to per-group feature label class GroupFeatureMap; // Translates the input label into input feature label of group // `group`; returns `kNoLabel` when there is no feature for that // group. Label FindFeature(size_t group, Label word) const; size_t max_future_size_; Label max_input_label_; std::vector<std::unique_ptr<const FeatureGroup<A>>> groups_; std::vector<InputAttribute> input_attribs_; std::vector<Label> output_pool_, output_set_; GroupFeatureMap group_feat_map_; LinearFstData(const LinearFstData &) = delete; LinearFstData &operator=(const LinearFstData &) = delete; }; template <class A> const typename A::Label LinearFstData<A>::kStartOfSentence = -3; template <class A> const typename A::Label LinearFstData<A>::kEndOfSentence = -2; template <class A> template <class Iterator> void LinearFstData<A>::TakeTransition(Iterator buffer_end, Iterator trie_state_begin, Iterator trie_state_end, Label ilabel, Label olabel, std::vector<Label> *next, Weight *weight) const { DCHECK_EQ(trie_state_end - trie_state_begin, groups_.size()); DCHECK(ilabel > 0 || ilabel == kEndOfSentence); DCHECK(olabel > 0 || olabel == kStartOfSentence); size_t group_id = 0; for (Iterator it = trie_state_begin; it != trie_state_end; ++it, ++group_id) { size_t delay = groups_[group_id]->Delay(); // On the buffer, there may also be `kStartOfSentence` from the // initial empty buffer. Label real_ilabel = delay == 0 ? ilabel : *(buffer_end - delay); next->push_back( GroupTransition(group_id, *it, real_ilabel, olabel, weight)); } } template <class A> typename A::Label LinearFstData<A>::GroupTransition(int group_id, int trie_state, Label ilabel, Label olabel, Weight *weight) const { Label group_ilabel = FindFeature(group_id, ilabel); return groups_[group_id]->Walk(trie_state, group_ilabel, olabel, weight); } template <class A> template <class Iterator> inline typename A::Weight LinearFstData<A>::FinalWeight( Iterator trie_state_begin, Iterator trie_state_end) const { DCHECK_EQ(trie_state_end - trie_state_begin, groups_.size()); size_t group_id = 0; Weight accum = Weight::One(); for (Iterator it = trie_state_begin; it != trie_state_end; ++it, ++group_id) accum = Times(accum, GroupFinalWeight(group_id, *it)); return accum; } template <class A> inline std::pair<typename std::vector<typename A::Label>::const_iterator, typename std::vector<typename A::Label>::const_iterator> LinearFstData<A>::PossibleOutputLabels(Label word) const { const InputAttribute &attrib = input_attribs_[word]; if (attrib.output_length == 0) return std::make_pair(output_set_.begin(), output_set_.end()); else return std::make_pair( output_pool_.begin() + attrib.output_begin, output_pool_.begin() + attrib.output_begin + attrib.output_length); } template <class A> inline LinearFstData<A> *LinearFstData<A>::Read(std::istream &strm) { // NOLINT std::unique_ptr<LinearFstData<A>> data(new LinearFstData<A>()); ReadType(strm, &(data->max_future_size_)); ReadType(strm, &(data->max_input_label_)); // Feature groups size_t num_groups = 0; ReadType(strm, &num_groups); data->groups_.resize(num_groups); for (size_t i = 0; i < num_groups; ++i) data->groups_[i].reset(FeatureGroup<A>::Read(strm)); // Other data ReadType(strm, &(data->input_attribs_)); ReadType(strm, &(data->output_pool_)); ReadType(strm, &(data->output_set_)); ReadType(strm, &(data->group_feat_map_)); if (strm) { return data.release(); } else { return nullptr; } } template <class A> inline std::ostream &LinearFstData<A>::Write( std::ostream &strm) const { // NOLINT WriteType(strm, max_future_size_); WriteType(strm, max_input_label_); // Feature groups WriteType(strm, groups_.size()); for (size_t i = 0; i < groups_.size(); ++i) { groups_[i]->Write(strm); } // Other data WriteType(strm, input_attribs_); WriteType(strm, output_pool_); WriteType(strm, output_set_); WriteType(strm, group_feat_map_); return strm; } template <class A> typename A::Label LinearFstData<A>::FindFeature(size_t group, Label word) const { DCHECK(word > 0 || word == kStartOfSentence || word == kEndOfSentence); if (word == kStartOfSentence || word == kEndOfSentence) return word; else return group_feat_map_.Find(group, word); } template <class A> inline std::istream &LinearFstData<A>::InputAttribute::Read( std::istream &strm) { // NOLINT ReadType(strm, &output_begin); ReadType(strm, &output_length); return strm; } template <class A> inline std::ostream &LinearFstData<A>::InputAttribute::Write( std::ostream &strm) const { // NOLINT WriteType(strm, output_begin); WriteType(strm, output_length); return strm; } // Forward declaration template <class A> class FeatureGroupBuilder; // An immutable grouping of features with similar context shape. Like // `LinearFstData`, this can only be constructed via `Read()` or // via its builder. // // Internally it uses a trie to store all feature n-grams and their // weights. The label of a trie edge is a pair (feat, olabel) of // labels. They can be either positive (ordinary label), `kNoLabel`, // `kStartOfSentence`, or `kEndOfSentence`. `kNoLabel` usually means // matching anything, with one exception: from the root of the trie, // there is a special (kNoLabel, kNoLabel) that leads to the implicit // start-of-sentence state. This edge is never actually matched // (`FindFirstMatch()` ensures this). template <class A> class FeatureGroup { public: friend class FeatureGroupBuilder<A>; // for builder access typedef typename A::Label Label; typedef typename A::Weight Weight; int Start() const { return start_; } // Finds destination node from `cur` by consuming `ilabel` and // `olabel`. The transition weight is multiplied onto `weight`. int Walk(int cur, Label ilabel, Label olabel, Weight *weight) const; // Returns the final weight of the current trie state. Only valid if // the state is already known to be part of a final state (see // `LinearFstData<>::CanBeFinal()`). Weight FinalWeight(int trie_state) const { return trie_[trie_state].final_weight; } static FeatureGroup<A> *Read(std::istream &strm) { // NOLINT size_t delay; ReadType(strm, &delay); int start; ReadType(strm, &start); Trie trie; ReadType(strm, &trie); std::unique_ptr<FeatureGroup<A>> ret(new FeatureGroup<A>(delay, start)); ret->trie_.swap(trie); ReadType(strm, &ret->next_state_); if (strm) { return ret.release(); } else { return nullptr; } } std::ostream &Write(std::ostream &strm) const { // NOLINT WriteType(strm, delay_); WriteType(strm, start_); WriteType(strm, trie_); WriteType(strm, next_state_); return strm; } size_t Delay() const { return delay_; } string Stats() const; private: // Label along the arcs on the trie. `kNoLabel` means anything // (non-negative label) can match; both sides holding `kNoLabel` // is not allow; otherwise the label is > 0 (enforced by // `LinearFstDataBuilder::AddWeight()`). struct InputOutputLabel; struct InputOutputLabelHash; // Data to be stored on the trie struct WeightBackLink { int back_link; Weight weight, final_weight; WeightBackLink() : back_link(kNoTrieNodeId), weight(Weight::One()), final_weight(Weight::One()) {} std::istream &Read(std::istream &strm) { // NOLINT ReadType(strm, &back_link); ReadType(strm, &weight); ReadType(strm, &final_weight); return strm; } std::ostream &Write(std::ostream &strm) const { // NOLINT WriteType(strm, back_link); WriteType(strm, weight); WriteType(strm, final_weight); return strm; } }; typedef FlatTrieTopology<InputOutputLabel, InputOutputLabelHash> Topology; typedef MutableTrie<InputOutputLabel, WeightBackLink, Topology> Trie; explicit FeatureGroup(size_t delay, int start) : delay_(delay), start_(start) {} // Finds the first node with an arc with `label` following the // back-off chain of `parent`. Returns the node index or // `kNoTrieNodeId` when not found. int FindFirstMatch(InputOutputLabel label, int parent) const; size_t delay_; int start_; Trie trie_; // Where to go after hitting this state. When we reach a state with // no child and with no additional final weight (i.e. its final // weight is the same as its back-off), we can immediately go to its // back-off state. std::vector<int> next_state_; FeatureGroup(const FeatureGroup &) = delete; FeatureGroup &operator=(const FeatureGroup &) = delete; }; template <class A> struct FeatureGroup<A>::InputOutputLabel { Label input, output; InputOutputLabel(Label i = kNoLabel, Label o = kNoLabel) : input(i), output(o) {} bool operator==(InputOutputLabel that) const { return input == that.input && output == that.output; } std::istream &Read(std::istream &strm) { // NOLINT ReadType(strm, &input); ReadType(strm, &output); return strm; } std::ostream &Write(std::ostream &strm) const { // NOLINT WriteType(strm, input); WriteType(strm, output); return strm; } }; template <class A> struct FeatureGroup<A>::InputOutputLabelHash { size_t operator()(InputOutputLabel label) const { return static_cast<size_t>(label.input * 7853 + label.output); } }; template <class A> int FeatureGroup<A>::Walk(int cur, Label ilabel, Label olabel, Weight *weight) const { // Note: user of this method need to ensure `ilabel` and `olabel` // are valid (e.g. see DCHECKs in // `LinearFstData<>::TakeTransition()` and // `LinearFstData<>::FindFeature()`). int next; if (ilabel == LinearFstData<A>::kStartOfSentence) { // An observed start-of-sentence only occurs in the beginning of // the input, when this feature group is delayed (i.e. there is // another feature group with a larger future size). The actual // input hasn't arrived so stay at the start state. DCHECK_EQ(cur, start_); next = start_; } else { // First, try exact match next = FindFirstMatch(InputOutputLabel(ilabel, olabel), cur); // Then try with don't cares if (next == kNoTrieNodeId) next = FindFirstMatch(InputOutputLabel(ilabel, kNoLabel), cur); if (next == kNoTrieNodeId) next = FindFirstMatch(InputOutputLabel(kNoLabel, olabel), cur); // All failed, go to empty context if (next == kNoTrieNodeId) next = trie_.Root(); *weight = Times(*weight, trie_[next].weight); next = next_state_[next]; } return next; } template <class A> inline int FeatureGroup<A>::FindFirstMatch(InputOutputLabel label, int parent) const { if (label.input == kNoLabel && label.output == kNoLabel) return kNoTrieNodeId; // very important; see class doc. for (; parent != kNoTrieNodeId; parent = trie_[parent].back_link) { int next = trie_.Find(parent, label); if (next != kNoTrieNodeId) return next; } return kNoTrieNodeId; } template <class A> inline string FeatureGroup<A>::Stats() const { std::ostringstream strm; int num_states = 2; for (int i = 2; i < next_state_.size(); ++i) num_states += i == next_state_[i]; strm << trie_.NumNodes() << " node(s); " << num_states << " state(s)"; return strm.str(); } template <class A> class LinearFstData<A>::GroupFeatureMap { public: GroupFeatureMap() {} void Init(size_t num_groups, size_t num_words) { num_groups_ = num_groups; pool_.clear(); pool_.resize(num_groups * num_words, kNoLabel); } Label Find(size_t group_id, Label ilabel) const { return pool_[IndexOf(group_id, ilabel)]; } bool Set(size_t group_id, Label ilabel, Label feat) { size_t i = IndexOf(group_id, ilabel); if (pool_[i] != kNoLabel && pool_[i] != feat) { FSTERROR() << "Feature group " << group_id << " already has feature for word " << ilabel; return false; } pool_[i] = feat; return true; } std::istream &Read(std::istream &strm) { // NOLINT ReadType(strm, &num_groups_); ReadType(strm, &pool_); return strm; } std::ostream &Write(std::ostream &strm) const { // NOLINT WriteType(strm, num_groups_); WriteType(strm, pool_); return strm; } private: size_t IndexOf(size_t group_id, Label ilabel) const { return ilabel * num_groups_ + group_id; } size_t num_groups_; // `pool_[ilabel * num_groups_ + group_id]` is the feature active // for group `group_id` with input `ilabel` std::vector<Label> pool_; }; } // namespace fst #endif // FST_EXTENSIONS_LINEAR_LINEAR_FST_DATA_H_
0
coqui_public_repos/TTS/recipes/thorsten_DE
coqui_public_repos/TTS/recipes/thorsten_DE/speedy_speech/train_speedy_speech.py
import os from trainer import Trainer, TrainerArgs from TTS.config import BaseAudioConfig, BaseDatasetConfig from TTS.tts.configs.speedy_speech_config import SpeedySpeechConfig from TTS.tts.datasets import load_tts_samples from TTS.tts.models.forward_tts import ForwardTTS from TTS.tts.utils.text.tokenizer import TTSTokenizer from TTS.utils.audio import AudioProcessor from TTS.utils.downloaders import download_thorsten_de output_path = os.path.dirname(os.path.abspath(__file__)) dataset_config = BaseDatasetConfig( formatter="thorsten", meta_file_train="metadata.csv", path=os.path.join(output_path, "../thorsten-de/") ) # download dataset if not already present if not os.path.exists(dataset_config.path): print("Downloading dataset") download_thorsten_de(os.path.split(os.path.abspath(dataset_config.path))[0]) audio_config = BaseAudioConfig( sample_rate=22050, do_trim_silence=True, trim_db=60.0, signal_norm=False, mel_fmin=0.0, mel_fmax=8000, spec_gain=1.0, log_func="np.log", ref_level_db=20, preemphasis=0.0, ) config = SpeedySpeechConfig( run_name="speedy_speech_thorsten-de", audio=audio_config, batch_size=32, eval_batch_size=16, num_loader_workers=4, num_eval_loader_workers=4, compute_input_seq_cache=True, run_eval=True, test_delay_epochs=-1, epochs=1000, min_audio_len=11050, # need to up min_audio_len to avois speedy speech error text_cleaner="phoneme_cleaners", use_phonemes=True, phoneme_language="de", phoneme_cache_path=os.path.join(output_path, "phoneme_cache"), precompute_num_workers=4, print_step=50, print_eval=False, mixed_precision=False, test_sentences=[ "Es hat mich viel Zeit gekostet ein Stimme zu entwickeln, jetzt wo ich sie habe werde ich nicht mehr schweigen.", "Sei eine Stimme, kein Echo.", "Es tut mir Leid David. Das kann ich leider nicht machen.", "Dieser Kuchen ist großartig. Er ist so lecker und feucht.", "Vor dem 22. November 1963.", ], max_seq_len=500000, output_path=output_path, datasets=[dataset_config], ) # INITIALIZE THE AUDIO PROCESSOR # Audio processor is used for feature extraction and audio I/O. # It mainly serves to the dataloader and the training loggers. ap = AudioProcessor.init_from_config(config) # INITIALIZE THE TOKENIZER # Tokenizer is used to convert text to sequences of token IDs. # If characters are not defined in the config, default characters are passed to the config tokenizer, config = TTSTokenizer.init_from_config(config) # LOAD DATA SAMPLES # Each sample is a list of ```[text, audio_file_path, speaker_name]``` # You can define your custom sample loader returning the list of samples. # Or define your custom formatter and pass it to the `load_tts_samples`. # Check `TTS.tts.datasets.load_tts_samples` for more details. train_samples, eval_samples = load_tts_samples( dataset_config, eval_split=True, eval_split_max_size=config.eval_split_max_size, eval_split_size=config.eval_split_size, ) # init model model = ForwardTTS(config, ap, tokenizer) # INITIALIZE THE TRAINER # Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training, # distributed training, etc. trainer = Trainer( TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples ) # AND... 3,2,1... 🚀 trainer.fit()
0
coqui_public_repos/STT-models/bengali/twb
coqui_public_repos/STT-models/bengali/twb/v0.1.0/alphabet.txt
ঁ ং ঃ অ আ ই ঈ উ ঊ ঋ এ ঐ ও ঔ ক খ গ ঘ ঙ চ ছ জ ঝ ঞ ট ঠ ড ঢ ণ ত থ দ ধ ন প ফ ব ভ ম য র ল শ ষ স হ ় া ি ী ু ূ ৃ ে ৈ ো ৌ ্ ৎ ৗ ড় ঢ় য় ০ ১ ২ ৩ ৪ ৫ ৬ ৭ ৮ ৯ ৰ
0
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/m4/libtool.m4
# libtool.m4 - Configure libtool for the host system. -*-Autoconf-*- # # Copyright (C) 1996, 1997, 1998, 1999, 2000, 2001, 2003, 2004, 2005, # 2006, 2007, 2008, 2009, 2010, 2011 Free Software # Foundation, Inc. # Written by Gordon Matzigkeit, 1996 # # This file is free software; the Free Software Foundation gives # unlimited permission to copy and/or distribute it, with or without # modifications, as long as this notice is preserved. m4_define([_LT_COPYING], [dnl # Copyright (C) 1996, 1997, 1998, 1999, 2000, 2001, 2003, 2004, 2005, # 2006, 2007, 2008, 2009, 2010, 2011 Free Software # Foundation, Inc. # Written by Gordon Matzigkeit, 1996 # # This file is part of GNU Libtool. # # GNU Libtool is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License as # published by the Free Software Foundation; either version 2 of # the License, or (at your option) any later version. # # As a special exception to the GNU General Public License, # if you distribute this file as part of a program or library that # is built using GNU Libtool, you may include this file under the # same distribution terms that you use for the rest of that program. # # GNU Libtool is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. 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#endif int fnord () { return 42; } int main () { void *self = dlopen (0, LT_DLGLOBAL|LT_DLLAZY_OR_NOW); int status = $lt_dlunknown; if (self) { if (dlsym (self,"fnord")) status = $lt_dlno_uscore; else { if (dlsym( self,"_fnord")) status = $lt_dlneed_uscore; else puts (dlerror ()); } /* dlclose (self); */ } else puts (dlerror ()); return status; }] _LT_EOF if AC_TRY_EVAL(ac_link) && test -s conftest${ac_exeext} 2>/dev/null; then (./conftest; exit; ) >&AS_MESSAGE_LOG_FD 2>/dev/null lt_status=$? case x$lt_status in x$lt_dlno_uscore) $1 ;; x$lt_dlneed_uscore) $2 ;; x$lt_dlunknown|x*) $3 ;; esac else : # compilation failed $3 fi fi rm -fr conftest* ])# _LT_TRY_DLOPEN_SELF # LT_SYS_DLOPEN_SELF # ------------------ AC_DEFUN([LT_SYS_DLOPEN_SELF], [m4_require([_LT_HEADER_DLFCN])dnl if test "x$enable_dlopen" != xyes; then enable_dlopen=unknown enable_dlopen_self=unknown enable_dlopen_self_static=unknown else lt_cv_dlopen=no lt_cv_dlopen_libs= case $host_os in beos*) lt_cv_dlopen="load_add_on" lt_cv_dlopen_libs= lt_cv_dlopen_self=yes ;; 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*) lt_cv_deplibs_check_method='file_magic (s[[0-9]][[0-9]][[0-9]]|PA-RISC[[0-9]]\.[[0-9]]) shared library' lt_cv_file_magic_test_file=/usr/lib/libc.sl ;; esac ;; interix[[3-9]]*) # PIC code is broken on Interix 3.x, that's why |\.a not |_pic\.a here lt_cv_deplibs_check_method='match_pattern /lib[[^/]]+(\.so|\.a)$' ;; irix5* | irix6* | nonstopux*) case $LD in *-32|*"-32 ") libmagic=32-bit;; *-n32|*"-n32 ") libmagic=N32;; *-64|*"-64 ") libmagic=64-bit;; *) libmagic=never-match;; esac lt_cv_deplibs_check_method=pass_all ;; # This must be glibc/ELF. linux* | k*bsd*-gnu | kopensolaris*-gnu | gnu*) lt_cv_deplibs_check_method=pass_all ;; netbsd* | netbsdelf*-gnu) if echo __ELF__ | $CC -E - | $GREP __ELF__ > /dev/null; then lt_cv_deplibs_check_method='match_pattern /lib[[^/]]+(\.so\.[[0-9]]+\.[[0-9]]+|_pic\.a)$' else lt_cv_deplibs_check_method='match_pattern /lib[[^/]]+(\.so|_pic\.a)$' fi ;; newos6*) lt_cv_deplibs_check_method='file_magic ELF [[0-9]][[0-9]]*-bit [[ML]]SB (executable|dynamic lib)' lt_cv_file_magic_cmd=/usr/bin/file lt_cv_file_magic_test_file=/usr/lib/libnls.so ;; 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then lt_cv_nm_interface="MS dumpbin" fi rm -f conftest*]) ])# LT_PATH_NM # Old names: AU_ALIAS([AM_PROG_NM], [LT_PATH_NM]) AU_ALIAS([AC_PROG_NM], [LT_PATH_NM]) dnl aclocal-1.4 backwards compatibility: dnl AC_DEFUN([AM_PROG_NM], []) dnl AC_DEFUN([AC_PROG_NM], []) # _LT_CHECK_SHAREDLIB_FROM_LINKLIB # -------------------------------- # how to determine the name of the shared library # associated with a specific link library. # -- PORTME fill in with the dynamic library characteristics m4_defun([_LT_CHECK_SHAREDLIB_FROM_LINKLIB], [m4_require([_LT_DECL_EGREP]) m4_require([_LT_DECL_OBJDUMP]) m4_require([_LT_DECL_DLLTOOL]) AC_CACHE_CHECK([how to associate runtime and link libraries], lt_cv_sharedlib_from_linklib_cmd, [lt_cv_sharedlib_from_linklib_cmd='unknown' case $host_os in cygwin* | mingw* | pw32* | cegcc*) # two different shell functions defined in ltmain.sh # decide which to use based on capabilities of $DLLTOOL case `$DLLTOOL --help 2>&1` in *--identify-strict*) lt_cv_sharedlib_from_linklib_cmd=func_cygming_dll_for_implib ;; *) lt_cv_sharedlib_from_linklib_cmd=func_cygming_dll_for_implib_fallback ;; esac ;; *) # fallback: assume linklib IS sharedlib lt_cv_sharedlib_from_linklib_cmd="$ECHO" ;; esac ]) sharedlib_from_linklib_cmd=$lt_cv_sharedlib_from_linklib_cmd test -z "$sharedlib_from_linklib_cmd" && sharedlib_from_linklib_cmd=$ECHO _LT_DECL([], [sharedlib_from_linklib_cmd], [1], [Command to associate shared and link libraries]) ])# _LT_CHECK_SHAREDLIB_FROM_LINKLIB # _LT_PATH_MANIFEST_TOOL # ---------------------- # locate the manifest tool m4_defun([_LT_PATH_MANIFEST_TOOL], [AC_CHECK_TOOL(MANIFEST_TOOL, mt, :) test -z "$MANIFEST_TOOL" && MANIFEST_TOOL=mt AC_CACHE_CHECK([if $MANIFEST_TOOL is a manifest tool], [lt_cv_path_mainfest_tool], [lt_cv_path_mainfest_tool=no echo "$as_me:$LINENO: $MANIFEST_TOOL '-?'" >&AS_MESSAGE_LOG_FD $MANIFEST_TOOL '-?' 2>conftest.err > conftest.out cat conftest.err >&AS_MESSAGE_LOG_FD if $GREP 'Manifest Tool' conftest.out > /dev/null; then lt_cv_path_mainfest_tool=yes fi rm -f conftest*]) if test "x$lt_cv_path_mainfest_tool" != xyes; then MANIFEST_TOOL=: fi _LT_DECL([], [MANIFEST_TOOL], [1], [Manifest tool])dnl ])# _LT_PATH_MANIFEST_TOOL # LT_LIB_M # -------- # check for math library AC_DEFUN([LT_LIB_M], [AC_REQUIRE([AC_CANONICAL_HOST])dnl LIBM= case $host in *-*-beos* | *-*-cegcc* | *-*-cygwin* | *-*-haiku* | *-*-pw32* | *-*-darwin*) # These system don't have libm, or don't need it ;; *-ncr-sysv4.3*) AC_CHECK_LIB(mw, _mwvalidcheckl, LIBM="-lmw") AC_CHECK_LIB(m, cos, LIBM="$LIBM -lm") ;; *) AC_CHECK_LIB(m, cos, LIBM="-lm") ;; esac AC_SUBST([LIBM]) ])# LT_LIB_M # Old name: AU_ALIAS([AC_CHECK_LIBM], [LT_LIB_M]) dnl aclocal-1.4 backwards compatibility: dnl AC_DEFUN([AC_CHECK_LIBM], []) # _LT_COMPILER_NO_RTTI([TAGNAME]) # ------------------------------- m4_defun([_LT_COMPILER_NO_RTTI], [m4_require([_LT_TAG_COMPILER])dnl _LT_TAGVAR(lt_prog_compiler_no_builtin_flag, $1)= if test "$GCC" = yes; then case $cc_basename in nvcc*) _LT_TAGVAR(lt_prog_compiler_no_builtin_flag, $1)=' -Xcompiler -fno-builtin' ;; *) _LT_TAGVAR(lt_prog_compiler_no_builtin_flag, $1)=' -fno-builtin' ;; 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What could be older than Ultrix?!! ;)] # Character class describing NM global symbol codes. symcode='[[BCDEGRST]]' # Regexp to match symbols that can be accessed directly from C. sympat='\([[_A-Za-z]][[_A-Za-z0-9]]*\)' # Define system-specific variables. case $host_os in aix*) symcode='[[BCDT]]' ;; cygwin* | mingw* | pw32* | cegcc*) symcode='[[ABCDGISTW]]' ;; hpux*) if test "$host_cpu" = ia64; then symcode='[[ABCDEGRST]]' fi ;; irix* | nonstopux*) symcode='[[BCDEGRST]]' ;; osf*) symcode='[[BCDEGQRST]]' ;; solaris*) symcode='[[BDRT]]' ;; sco3.2v5*) symcode='[[DT]]' ;; sysv4.2uw2*) symcode='[[DT]]' ;; sysv5* | sco5v6* | unixware* | OpenUNIX*) symcode='[[ABDT]]' ;; sysv4) symcode='[[DFNSTU]]' ;; esac # If we're using GNU nm, then use its standard symbol codes. case `$NM -V 2>&1` in *GNU* | *'with BFD'*) symcode='[[ABCDGIRSTW]]' ;; esac # Transform an extracted symbol line into a proper C declaration. # Some systems (esp. on ia64) link data and code symbols differently, # so use this general approach. lt_cv_sys_global_symbol_to_cdecl="sed -n -e 's/^T .* \(.*\)$/extern int \1();/p' -e 's/^$symcode* .* \(.*\)$/extern char \1;/p'" # Transform an extracted symbol line into symbol name and symbol address lt_cv_sys_global_symbol_to_c_name_address="sed -n -e 's/^: \([[^ ]]*\)[[ ]]*$/ {\\\"\1\\\", (void *) 0},/p' -e 's/^$symcode* \([[^ ]]*\) \([[^ ]]*\)$/ {\"\2\", (void *) \&\2},/p'" lt_cv_sys_global_symbol_to_c_name_address_lib_prefix="sed -n -e 's/^: \([[^ ]]*\)[[ ]]*$/ {\\\"\1\\\", (void *) 0},/p' -e 's/^$symcode* \([[^ ]]*\) \(lib[[^ ]]*\)$/ {\"\2\", (void *) \&\2},/p' -e 's/^$symcode* \([[^ ]]*\) \([[^ ]]*\)$/ {\"lib\2\", (void *) \&\2},/p'" # Handle CRLF in mingw tool chain opt_cr= case $build_os in mingw*) opt_cr=`$ECHO 'x\{0,1\}' | tr x '\015'` # option cr in regexp ;; esac # Try without a prefix underscore, then with it. for ac_symprfx in "" "_"; do # Transform symcode, sympat, and symprfx into a raw symbol and a C symbol. symxfrm="\\1 $ac_symprfx\\2 \\2" # Write the raw and C identifiers. if test "$lt_cv_nm_interface" = "MS dumpbin"; then # Fake it for dumpbin and say T for any non-static function # and D for any global variable. # Also find C++ and __fastcall symbols from MSVC++, # which start with @ or ?. lt_cv_sys_global_symbol_pipe="$AWK ['"\ " {last_section=section; section=\$ 3};"\ " /^COFF SYMBOL TABLE/{for(i in hide) delete hide[i]};"\ " /Section length .*#relocs.*(pick any)/{hide[last_section]=1};"\ " \$ 0!~/External *\|/{next};"\ " / 0+ UNDEF /{next}; / UNDEF \([^|]\)*()/{next};"\ " {if(hide[section]) next};"\ " {f=0}; \$ 0~/\(\).*\|/{f=1}; {printf f ? \"T \" : \"D \"};"\ " {split(\$ 0, a, /\||\r/); split(a[2], s)};"\ " s[1]~/^[@?]/{print s[1], s[1]; next};"\ " s[1]~prfx {split(s[1],t,\"@\"); print t[1], substr(t[1],length(prfx))}"\ " ' prfx=^$ac_symprfx]" else lt_cv_sys_global_symbol_pipe="sed -n -e 's/^.*[[ ]]\($symcode$symcode*\)[[ ]][[ ]]*$ac_symprfx$sympat$opt_cr$/$symxfrm/p'" fi lt_cv_sys_global_symbol_pipe="$lt_cv_sys_global_symbol_pipe | sed '/ __gnu_lto/d'" # Check to see that the pipe works correctly. pipe_works=no rm -f conftest* cat > conftest.$ac_ext <<_LT_EOF #ifdef __cplusplus extern "C" { #endif char nm_test_var; void nm_test_func(void); void nm_test_func(void){} #ifdef __cplusplus } #endif int main(){nm_test_var='a';nm_test_func();return(0);} _LT_EOF if AC_TRY_EVAL(ac_compile); then # Now try to grab the symbols. nlist=conftest.nm if AC_TRY_EVAL(NM conftest.$ac_objext \| "$lt_cv_sys_global_symbol_pipe" \> $nlist) && test -s "$nlist"; then # Try sorting and uniquifying the output. if sort "$nlist" | uniq > "$nlist"T; then mv -f "$nlist"T "$nlist" else rm -f "$nlist"T fi # Make sure that we snagged all the symbols we need. if $GREP ' nm_test_var$' "$nlist" >/dev/null; then if $GREP ' nm_test_func$' "$nlist" >/dev/null; then cat <<_LT_EOF > conftest.$ac_ext /* Keep this code in sync between libtool.m4, ltmain, lt_system.h, and tests. */ #if defined(_WIN32) || defined(__CYGWIN__) || defined(_WIN32_WCE) /* DATA imports from DLLs on WIN32 con't be const, because runtime relocations are performed -- see ld's documentation on pseudo-relocs. */ # define LT@&t@_DLSYM_CONST #elif defined(__osf__) /* This system does not cope well with relocations in const data. */ # define LT@&t@_DLSYM_CONST #else # define LT@&t@_DLSYM_CONST const #endif #ifdef __cplusplus extern "C" { #endif _LT_EOF # Now generate the symbol file. eval "$lt_cv_sys_global_symbol_to_cdecl"' < "$nlist" | $GREP -v main >> conftest.$ac_ext' cat <<_LT_EOF >> conftest.$ac_ext /* The mapping between symbol names and symbols. */ LT@&t@_DLSYM_CONST struct { const char *name; void *address; } lt__PROGRAM__LTX_preloaded_symbols[[]] = { { "@PROGRAM@", (void *) 0 }, _LT_EOF $SED "s/^$symcode$symcode* \(.*\) \(.*\)$/ {\"\2\", (void *) \&\2},/" < "$nlist" | $GREP -v main >> conftest.$ac_ext cat <<\_LT_EOF >> conftest.$ac_ext {0, (void *) 0} }; /* This works around a problem in FreeBSD linker */ #ifdef FREEBSD_WORKAROUND static const void *lt_preloaded_setup() { return lt__PROGRAM__LTX_preloaded_symbols; } #endif #ifdef __cplusplus } #endif _LT_EOF # Now try linking the two files. mv conftest.$ac_objext conftstm.$ac_objext lt_globsym_save_LIBS=$LIBS lt_globsym_save_CFLAGS=$CFLAGS LIBS="conftstm.$ac_objext" CFLAGS="$CFLAGS$_LT_TAGVAR(lt_prog_compiler_no_builtin_flag, $1)" if AC_TRY_EVAL(ac_link) && test -s conftest${ac_exeext}; then pipe_works=yes fi LIBS=$lt_globsym_save_LIBS CFLAGS=$lt_globsym_save_CFLAGS else echo "cannot find nm_test_func in $nlist" >&AS_MESSAGE_LOG_FD fi else echo "cannot find nm_test_var in $nlist" >&AS_MESSAGE_LOG_FD fi else echo "cannot run $lt_cv_sys_global_symbol_pipe" >&AS_MESSAGE_LOG_FD fi else echo "$progname: failed program was:" >&AS_MESSAGE_LOG_FD cat conftest.$ac_ext >&5 fi rm -rf conftest* conftst* # Do not use the global_symbol_pipe unless it works. if test "$pipe_works" = yes; then break else lt_cv_sys_global_symbol_pipe= fi done ]) if test -z "$lt_cv_sys_global_symbol_pipe"; then lt_cv_sys_global_symbol_to_cdecl= fi if test -z "$lt_cv_sys_global_symbol_pipe$lt_cv_sys_global_symbol_to_cdecl"; then AC_MSG_RESULT(failed) else AC_MSG_RESULT(ok) fi # Response file support. if test "$lt_cv_nm_interface" = "MS dumpbin"; then nm_file_list_spec='@' elif $NM --help 2>/dev/null | grep '[[@]]FILE' >/dev/null; then nm_file_list_spec='@' fi _LT_DECL([global_symbol_pipe], [lt_cv_sys_global_symbol_pipe], [1], [Take the output of nm and produce a listing of raw symbols and C names]) _LT_DECL([global_symbol_to_cdecl], [lt_cv_sys_global_symbol_to_cdecl], [1], [Transform the output of nm in a proper C declaration]) _LT_DECL([global_symbol_to_c_name_address], [lt_cv_sys_global_symbol_to_c_name_address], [1], [Transform the output of nm in a C name address pair]) _LT_DECL([global_symbol_to_c_name_address_lib_prefix], [lt_cv_sys_global_symbol_to_c_name_address_lib_prefix], [1], [Transform the output of nm in a C name address pair when lib prefix is needed]) _LT_DECL([], [nm_file_list_spec], [1], [Specify filename containing input files for $NM]) ]) # _LT_CMD_GLOBAL_SYMBOLS # _LT_COMPILER_PIC([TAGNAME]) # --------------------------- m4_defun([_LT_COMPILER_PIC], [m4_require([_LT_TAG_COMPILER])dnl _LT_TAGVAR(lt_prog_compiler_wl, $1)= _LT_TAGVAR(lt_prog_compiler_pic, $1)= _LT_TAGVAR(lt_prog_compiler_static, $1)= m4_if([$1], [CXX], [ # C++ specific cases for pic, static, wl, etc. if test "$GXX" = yes; then _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_static, $1)='-static' case $host_os in aix*) # All AIX code is PIC. if test "$host_cpu" = ia64; then # AIX 5 now supports IA64 processor _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' fi ;; amigaos*) case $host_cpu in powerpc) # see comment about AmigaOS4 .so support _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' ;; m68k) # FIXME: we need at least 68020 code to build shared libraries, but # adding the `-m68020' flag to GCC prevents building anything better, # like `-m68040'. _LT_TAGVAR(lt_prog_compiler_pic, $1)='-m68020 -resident32 -malways-restore-a4' ;; esac ;; beos* | irix5* | irix6* | nonstopux* | osf3* | osf4* | osf5*) # PIC is the default for these OSes. ;; mingw* | cygwin* | os2* | pw32* | cegcc*) # This hack is so that the source file can tell whether it is being # built for inclusion in a dll (and should export symbols for example). # Although the cygwin gcc ignores -fPIC, still need this for old-style # (--disable-auto-import) libraries m4_if([$1], [GCJ], [], [_LT_TAGVAR(lt_prog_compiler_pic, $1)='-DDLL_EXPORT']) ;; darwin* | rhapsody*) # PIC is the default on this platform # Common symbols not allowed in MH_DYLIB files _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fno-common' ;; *djgpp*) # DJGPP does not support shared libraries at all _LT_TAGVAR(lt_prog_compiler_pic, $1)= ;; haiku*) # PIC is the default for Haiku. # The "-static" flag exists, but is broken. _LT_TAGVAR(lt_prog_compiler_static, $1)= ;; interix[[3-9]]*) # Interix 3.x gcc -fpic/-fPIC options generate broken code. # Instead, we relocate shared libraries at runtime. ;; sysv4*MP*) if test -d /usr/nec; then _LT_TAGVAR(lt_prog_compiler_pic, $1)=-Kconform_pic fi ;; hpux*) # PIC is the default for 64-bit PA HP-UX, but not for 32-bit # PA HP-UX. On IA64 HP-UX, PIC is the default but the pic flag # sets the default TLS model and affects inlining. case $host_cpu in hppa*64*) ;; *) _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' ;; esac ;; *qnx* | *nto*) # QNX uses GNU C++, but need to define -shared option too, otherwise # it will coredump. _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC -shared' ;; *) _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' ;; esac else case $host_os in aix[[4-9]]*) # All AIX code is PIC. if test "$host_cpu" = ia64; then # AIX 5 now supports IA64 processor _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' else _LT_TAGVAR(lt_prog_compiler_static, $1)='-bnso -bI:/lib/syscalls.exp' fi ;; chorus*) case $cc_basename in cxch68*) # Green Hills C++ Compiler # _LT_TAGVAR(lt_prog_compiler_static, $1)="--no_auto_instantiation -u __main -u __premain -u _abort -r $COOL_DIR/lib/libOrb.a $MVME_DIR/lib/CC/libC.a $MVME_DIR/lib/classix/libcx.s.a" ;; esac ;; mingw* | cygwin* | os2* | pw32* | cegcc*) # This hack is so that the source file can tell whether it is being # built for inclusion in a dll (and should export symbols for example). m4_if([$1], [GCJ], [], [_LT_TAGVAR(lt_prog_compiler_pic, $1)='-DDLL_EXPORT']) ;; dgux*) case $cc_basename in ec++*) _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' ;; ghcx*) # Green Hills C++ Compiler _LT_TAGVAR(lt_prog_compiler_pic, $1)='-pic' ;; *) ;; esac ;; freebsd* | dragonfly*) # FreeBSD uses GNU C++ ;; hpux9* | hpux10* | hpux11*) case $cc_basename in CC*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_static, $1)='${wl}-a ${wl}archive' if test "$host_cpu" != ia64; then _LT_TAGVAR(lt_prog_compiler_pic, $1)='+Z' fi ;; aCC*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_static, $1)='${wl}-a ${wl}archive' case $host_cpu in hppa*64*|ia64*) # +Z the default ;; *) _LT_TAGVAR(lt_prog_compiler_pic, $1)='+Z' ;; esac ;; *) ;; esac ;; interix*) # This is c89, which is MS Visual C++ (no shared libs) # Anyone wants to do a port? ;; irix5* | irix6* | nonstopux*) case $cc_basename in CC*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_static, $1)='-non_shared' # CC pic flag -KPIC is the default. ;; *) ;; esac ;; linux* | k*bsd*-gnu | kopensolaris*-gnu | gnu*) case $cc_basename in KCC*) # KAI C++ Compiler _LT_TAGVAR(lt_prog_compiler_wl, $1)='--backend -Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' ;; ecpc* ) # old Intel C++ for x86_64 which still supported -KPIC. _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-static' ;; icpc* ) # Intel C++, used to be incompatible with GCC. # ICC 10 doesn't accept -KPIC any more. _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-static' ;; pgCC* | pgcpp*) # Portland Group C++ compiler _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fpic' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; cxx*) # Compaq C++ # Make sure the PIC flag is empty. It appears that all Alpha # Linux and Compaq Tru64 Unix objects are PIC. _LT_TAGVAR(lt_prog_compiler_pic, $1)= _LT_TAGVAR(lt_prog_compiler_static, $1)='-non_shared' ;; xlc* | xlC* | bgxl[[cC]]* | mpixl[[cC]]*) # IBM XL 8.0, 9.0 on PPC and BlueGene _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-qpic' _LT_TAGVAR(lt_prog_compiler_static, $1)='-qstaticlink' ;; *) case `$CC -V 2>&1 | sed 5q` in *Sun\ C*) # Sun C++ 5.9 _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Qoption ld ' ;; esac ;; esac ;; lynxos*) ;; m88k*) ;; mvs*) case $cc_basename in cxx*) _LT_TAGVAR(lt_prog_compiler_pic, $1)='-W c,exportall' ;; *) ;; esac ;; netbsd* | netbsdelf*-gnu) ;; *qnx* | *nto*) # QNX uses GNU C++, but need to define -shared option too, otherwise # it will coredump. _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC -shared' ;; osf3* | osf4* | osf5*) case $cc_basename in KCC*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='--backend -Wl,' ;; RCC*) # Rational C++ 2.4.1 _LT_TAGVAR(lt_prog_compiler_pic, $1)='-pic' ;; cxx*) # Digital/Compaq C++ _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' # Make sure the PIC flag is empty. It appears that all Alpha # Linux and Compaq Tru64 Unix objects are PIC. _LT_TAGVAR(lt_prog_compiler_pic, $1)= _LT_TAGVAR(lt_prog_compiler_static, $1)='-non_shared' ;; *) ;; esac ;; psos*) ;; solaris*) case $cc_basename in CC* | sunCC*) # Sun C++ 4.2, 5.x and Centerline C++ _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Qoption ld ' ;; gcx*) # Green Hills C++ Compiler _LT_TAGVAR(lt_prog_compiler_pic, $1)='-PIC' ;; *) ;; esac ;; sunos4*) case $cc_basename in CC*) # Sun C++ 4.x _LT_TAGVAR(lt_prog_compiler_pic, $1)='-pic' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; lcc*) # Lucid _LT_TAGVAR(lt_prog_compiler_pic, $1)='-pic' ;; *) ;; esac ;; sysv5* | unixware* | sco3.2v5* | sco5v6* | OpenUNIX*) case $cc_basename in CC*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; esac ;; tandem*) case $cc_basename in NCC*) # NonStop-UX NCC 3.20 _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' ;; *) ;; esac ;; vxworks*) ;; *) _LT_TAGVAR(lt_prog_compiler_can_build_shared, $1)=no ;; esac fi ], [ if test "$GCC" = yes; then _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_static, $1)='-static' case $host_os in aix*) # All AIX code is PIC. if test "$host_cpu" = ia64; then # AIX 5 now supports IA64 processor _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' fi ;; amigaos*) case $host_cpu in powerpc) # see comment about AmigaOS4 .so support _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' ;; m68k) # FIXME: we need at least 68020 code to build shared libraries, but # adding the `-m68020' flag to GCC prevents building anything better, # like `-m68040'. _LT_TAGVAR(lt_prog_compiler_pic, $1)='-m68020 -resident32 -malways-restore-a4' ;; esac ;; beos* | irix5* | irix6* | nonstopux* | osf3* | osf4* | osf5*) # PIC is the default for these OSes. ;; mingw* | cygwin* | pw32* | os2* | cegcc*) # This hack is so that the source file can tell whether it is being # built for inclusion in a dll (and should export symbols for example). # Although the cygwin gcc ignores -fPIC, still need this for old-style # (--disable-auto-import) libraries m4_if([$1], [GCJ], [], [_LT_TAGVAR(lt_prog_compiler_pic, $1)='-DDLL_EXPORT']) ;; darwin* | rhapsody*) # PIC is the default on this platform # Common symbols not allowed in MH_DYLIB files _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fno-common' ;; haiku*) # PIC is the default for Haiku. # The "-static" flag exists, but is broken. _LT_TAGVAR(lt_prog_compiler_static, $1)= ;; hpux*) # PIC is the default for 64-bit PA HP-UX, but not for 32-bit # PA HP-UX. On IA64 HP-UX, PIC is the default but the pic flag # sets the default TLS model and affects inlining. case $host_cpu in hppa*64*) # +Z the default ;; *) _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' ;; esac ;; interix[[3-9]]*) # Interix 3.x gcc -fpic/-fPIC options generate broken code. # Instead, we relocate shared libraries at runtime. ;; msdosdjgpp*) # Just because we use GCC doesn't mean we suddenly get shared libraries # on systems that don't support them. _LT_TAGVAR(lt_prog_compiler_can_build_shared, $1)=no enable_shared=no ;; *nto* | *qnx*) # QNX uses GNU C++, but need to define -shared option too, otherwise # it will coredump. _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC -shared' ;; sysv4*MP*) if test -d /usr/nec; then _LT_TAGVAR(lt_prog_compiler_pic, $1)=-Kconform_pic fi ;; *) _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' ;; esac case $cc_basename in nvcc*) # Cuda Compiler Driver 2.2 _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Xlinker ' if test -n "$_LT_TAGVAR(lt_prog_compiler_pic, $1)"; then _LT_TAGVAR(lt_prog_compiler_pic, $1)="-Xcompiler $_LT_TAGVAR(lt_prog_compiler_pic, $1)" fi ;; esac else # PORTME Check for flag to pass linker flags through the system compiler. case $host_os in aix*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' if test "$host_cpu" = ia64; then # AIX 5 now supports IA64 processor _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' else _LT_TAGVAR(lt_prog_compiler_static, $1)='-bnso -bI:/lib/syscalls.exp' fi ;; mingw* | cygwin* | pw32* | os2* | cegcc*) # This hack is so that the source file can tell whether it is being # built for inclusion in a dll (and should export symbols for example). m4_if([$1], [GCJ], [], [_LT_TAGVAR(lt_prog_compiler_pic, $1)='-DDLL_EXPORT']) ;; hpux9* | hpux10* | hpux11*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' # PIC is the default for IA64 HP-UX and 64-bit HP-UX, but # not for PA HP-UX. case $host_cpu in hppa*64*|ia64*) # +Z the default ;; *) _LT_TAGVAR(lt_prog_compiler_pic, $1)='+Z' ;; esac # Is there a better lt_prog_compiler_static that works with the bundled CC? _LT_TAGVAR(lt_prog_compiler_static, $1)='${wl}-a ${wl}archive' ;; irix5* | irix6* | nonstopux*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' # PIC (with -KPIC) is the default. _LT_TAGVAR(lt_prog_compiler_static, $1)='-non_shared' ;; linux* | k*bsd*-gnu | kopensolaris*-gnu | gnu*) case $cc_basename in # old Intel for x86_64 which still supported -KPIC. ecc*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-static' ;; # icc used to be incompatible with GCC. # ICC 10 doesn't accept -KPIC any more. icc* | ifort*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-static' ;; # Lahey Fortran 8.1. lf95*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='--shared' _LT_TAGVAR(lt_prog_compiler_static, $1)='--static' ;; nagfor*) # NAG Fortran compiler _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,-Wl,,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-PIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; pgcc* | pgf77* | pgf90* | pgf95* | pgfortran*) # Portland Group compilers (*not* the Pentium gcc compiler, # which looks to be a dead project) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fpic' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; ccc*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' # All Alpha code is PIC. _LT_TAGVAR(lt_prog_compiler_static, $1)='-non_shared' ;; xl* | bgxl* | bgf* | mpixl*) # IBM XL C 8.0/Fortran 10.1, 11.1 on PPC and BlueGene _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-qpic' _LT_TAGVAR(lt_prog_compiler_static, $1)='-qstaticlink' ;; *) case `$CC -V 2>&1 | sed 5q` in *Sun\ Ceres\ Fortran* | *Sun*Fortran*\ [[1-7]].* | *Sun*Fortran*\ 8.[[0-3]]*) # Sun Fortran 8.3 passes all unrecognized flags to the linker _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' _LT_TAGVAR(lt_prog_compiler_wl, $1)='' ;; *Sun\ F* | *Sun*Fortran*) _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Qoption ld ' ;; 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*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,';; esac ;; sunos4*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Qoption ld ' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-PIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; sysv4 | sysv4.2uw2* | sysv4.3*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; sysv4*MP*) if test -d /usr/nec ;then _LT_TAGVAR(lt_prog_compiler_pic, $1)='-Kconform_pic' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' fi ;; sysv5* | unixware* | sco3.2v5* | sco5v6* | OpenUNIX*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; unicos*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_can_build_shared, $1)=no ;; uts4*) _LT_TAGVAR(lt_prog_compiler_pic, $1)='-pic' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; *) _LT_TAGVAR(lt_prog_compiler_can_build_shared, $1)=no ;; 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FIXME _LT_TAGVAR(archive_cmds, $1)='$CC -nostart $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' else _LT_TAGVAR(ld_shlibs, $1)=no fi ;; cygwin* | mingw* | pw32* | cegcc*) # _LT_TAGVAR(hardcode_libdir_flag_spec, $1) is actually meaningless, # as there is no search path for DLLs. _LT_TAGVAR(hardcode_libdir_flag_spec, $1)='-L$libdir' _LT_TAGVAR(export_dynamic_flag_spec, $1)='${wl}--export-all-symbols' _LT_TAGVAR(allow_undefined_flag, $1)=unsupported _LT_TAGVAR(always_export_symbols, $1)=no _LT_TAGVAR(enable_shared_with_static_runtimes, $1)=yes _LT_TAGVAR(export_symbols_cmds, $1)='$NM $libobjs $convenience | $global_symbol_pipe | $SED -e '\''/^[[BCDGRS]][[ ]]/s/.*[[ ]]\([[^ ]]*\)/\1 DATA/;s/^.*[[ ]]__nm__\([[^ ]]*\)[[ ]][[^ ]]*/\1 DATA/;/^I[[ ]]/d;/^[[AITW]][[ ]]/s/.* //'\'' | sort | uniq > $export_symbols' _LT_TAGVAR(exclude_expsyms, $1)=['[_]+GLOBAL_OFFSET_TABLE_|[_]+GLOBAL__[FID]_.*|[_]+head_[A-Za-z0-9_]+_dll|[A-Za-z0-9_]+_dll_iname'] if $LD --help 2>&1 | $GREP 'auto-import' > /dev/null; then _LT_TAGVAR(archive_cmds, $1)='$CC -shared $libobjs $deplibs $compiler_flags -o $output_objdir/$soname ${wl}--enable-auto-image-base -Xlinker --out-implib -Xlinker $lib' # If the export-symbols file already is a .def file (1st line # is EXPORTS), use it as is; otherwise, prepend... _LT_TAGVAR(archive_expsym_cmds, $1)='if test "x`$SED 1q $export_symbols`" = xEXPORTS; then cp $export_symbols $output_objdir/$soname.def; else echo EXPORTS > $output_objdir/$soname.def; cat $export_symbols >> $output_objdir/$soname.def; fi~ $CC -shared $output_objdir/$soname.def $libobjs $deplibs $compiler_flags -o $output_objdir/$soname ${wl}--enable-auto-image-base -Xlinker --out-implib -Xlinker $lib' else _LT_TAGVAR(ld_shlibs, $1)=no fi ;; haiku*) _LT_TAGVAR(archive_cmds, $1)='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' _LT_TAGVAR(link_all_deplibs, $1)=yes ;; interix[[3-9]]*) _LT_TAGVAR(hardcode_direct, $1)=no _LT_TAGVAR(hardcode_shlibpath_var, $1)=no _LT_TAGVAR(hardcode_libdir_flag_spec, $1)='${wl}-rpath,$libdir' _LT_TAGVAR(export_dynamic_flag_spec, $1)='${wl}-E' # Hack: On Interix 3.x, we cannot compile PIC because of a broken gcc. # Instead, shared libraries are loaded at an image base (0x10000000 by # default) and relocated if they conflict, which is a slow very memory # consuming and fragmenting process. 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Moving up from 0x10000000 also allows more sbrk(2) space. _LT_TAGVAR(archive_cmds, $1)='$CC -shared $pic_flag $libobjs $deplibs $compiler_flags ${wl}-h,$soname ${wl}--image-base,`expr ${RANDOM-$$} % 4096 / 2 \* 262144 + 1342177280` -o $lib' _LT_TAGVAR(archive_expsym_cmds, $1)='sed "s,^,_," $export_symbols >$output_objdir/$soname.expsym~$CC -shared $pic_flag $libobjs $deplibs $compiler_flags ${wl}-h,$soname ${wl}--retain-symbols-file,$output_objdir/$soname.expsym ${wl}--image-base,`expr ${RANDOM-$$} % 4096 / 2 \* 262144 + 1342177280` -o $lib' ;; gnu* | linux* | tpf* | k*bsd*-gnu | kopensolaris*-gnu) tmp_diet=no if test "$host_os" = linux-dietlibc; then case $cc_basename in diet\ *) tmp_diet=yes;; # linux-dietlibc with static linking (!diet-dyn) esac fi if $LD --help 2>&1 | $EGREP ': supported targets:.* elf' > /dev/null \ && test "$tmp_diet" = no then tmp_addflag=' $pic_flag' tmp_sharedflag='-shared' case $cc_basename,$host_cpu in pgcc*) # Portland Group C compiler _LT_TAGVAR(whole_archive_flag_spec, $1)='${wl}--whole-archive`for conv in $convenience\"\"; do test -n \"$conv\" && new_convenience=\"$new_convenience,$conv\"; done; func_echo_all \"$new_convenience\"` ${wl}--no-whole-archive' tmp_addflag=' $pic_flag' ;; pgf77* | pgf90* | pgf95* | pgfortran*) # Portland Group f77 and f90 compilers _LT_TAGVAR(whole_archive_flag_spec, $1)='${wl}--whole-archive`for conv in $convenience\"\"; do test -n \"$conv\" && new_convenience=\"$new_convenience,$conv\"; done; func_echo_all \"$new_convenience\"` ${wl}--no-whole-archive' tmp_addflag=' $pic_flag -Mnomain' ;; ecc*,ia64* | icc*,ia64*) # Intel C compiler on ia64 tmp_addflag=' -i_dynamic' ;; efc*,ia64* | ifort*,ia64*) # Intel Fortran compiler on ia64 tmp_addflag=' -i_dynamic -nofor_main' ;; ifc* | ifort*) # Intel Fortran compiler tmp_addflag=' -nofor_main' ;; lf95*) # Lahey Fortran 8.1 _LT_TAGVAR(whole_archive_flag_spec, $1)= tmp_sharedflag='--shared' ;; xl[[cC]]* | bgxl[[cC]]* | mpixl[[cC]]*) # IBM XL C 8.0 on PPC (deal with xlf below) tmp_sharedflag='-qmkshrobj' tmp_addflag= ;; nvcc*) # Cuda Compiler Driver 2.2 _LT_TAGVAR(whole_archive_flag_spec, $1)='${wl}--whole-archive`for conv in $convenience\"\"; do test -n \"$conv\" && new_convenience=\"$new_convenience,$conv\"; done; func_echo_all \"$new_convenience\"` ${wl}--no-whole-archive' _LT_TAGVAR(compiler_needs_object, $1)=yes ;; esac case `$CC -V 2>&1 | sed 5q` in *Sun\ C*) # Sun C 5.9 _LT_TAGVAR(whole_archive_flag_spec, $1)='${wl}--whole-archive`new_convenience=; for conv in $convenience\"\"; do test -z \"$conv\" || new_convenience=\"$new_convenience,$conv\"; done; func_echo_all \"$new_convenience\"` ${wl}--no-whole-archive' _LT_TAGVAR(compiler_needs_object, $1)=yes tmp_sharedflag='-G' ;; *Sun\ F*) # Sun Fortran 8.3 tmp_sharedflag='-G' ;; esac _LT_TAGVAR(archive_cmds, $1)='$CC '"$tmp_sharedflag""$tmp_addflag"' $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' if test "x$supports_anon_versioning" = xyes; then _LT_TAGVAR(archive_expsym_cmds, $1)='echo "{ global:" > $output_objdir/$libname.ver~ cat $export_symbols | sed -e "s/\(.*\)/\1;/" >> $output_objdir/$libname.ver~ echo "local: *; };" >> $output_objdir/$libname.ver~ $CC '"$tmp_sharedflag""$tmp_addflag"' $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname ${wl}-version-script ${wl}$output_objdir/$libname.ver -o $lib' fi case $cc_basename in xlf* | bgf* | bgxlf* | mpixlf*) # IBM XL Fortran 10.1 on PPC cannot create shared libs itself _LT_TAGVAR(whole_archive_flag_spec, $1)='--whole-archive$convenience --no-whole-archive' _LT_TAGVAR(hardcode_libdir_flag_spec, $1)='${wl}-rpath ${wl}$libdir' _LT_TAGVAR(archive_cmds, $1)='$LD -shared $libobjs $deplibs $linker_flags -soname $soname -o $lib' if test "x$supports_anon_versioning" = xyes; then _LT_TAGVAR(archive_expsym_cmds, $1)='echo "{ global:" > $output_objdir/$libname.ver~ cat $export_symbols | sed -e "s/\(.*\)/\1;/" >> $output_objdir/$libname.ver~ echo "local: *; };" >> $output_objdir/$libname.ver~ $LD -shared $libobjs $deplibs $linker_flags -soname $soname -version-script $output_objdir/$libname.ver -o $lib' fi ;; esac else _LT_TAGVAR(ld_shlibs, $1)=no fi ;; netbsd* | netbsdelf*-gnu) if echo __ELF__ | $CC -E - | $GREP __ELF__ >/dev/null; then _LT_TAGVAR(archive_cmds, $1)='$LD -Bshareable $libobjs $deplibs $linker_flags -o $lib' wlarc= else _LT_TAGVAR(archive_cmds, $1)='$CC -shared $pic_flag $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' _LT_TAGVAR(archive_expsym_cmds, $1)='$CC -shared $pic_flag $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname ${wl}-retain-symbols-file $wl$export_symbols -o $lib' fi ;; solaris*) if $LD -v 2>&1 | $GREP 'BFD 2\.8' > /dev/null; then _LT_TAGVAR(ld_shlibs, $1)=no cat <<_LT_EOF 1>&2 *** Warning: The releases 2.8.* of the GNU linker cannot reliably *** create shared libraries on Solaris systems. 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esac ;; darwin* | rhapsody*) _LT_DARWIN_LINKER_FEATURES($1) ;; dgux*) _LT_TAGVAR(archive_cmds, $1)='$LD -G -h $soname -o $lib $libobjs $deplibs $linker_flags' _LT_TAGVAR(hardcode_libdir_flag_spec, $1)='-L$libdir' _LT_TAGVAR(hardcode_shlibpath_var, $1)=no ;; # FreeBSD 2.2.[012] allows us to include c++rt0.o to get C++ constructor # support. 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|| _lt_function_replace_fail=: else # Save a `func_append' function call even when '+=' is not available sed -e 's%func_append \([[a-zA-Z_]]\{1,\}\) "%\1="$\1%g' $cfgfile > $cfgfile.tmp \ && mv -f "$cfgfile.tmp" "$cfgfile" \ || (rm -f "$cfgfile" && cp "$cfgfile.tmp" "$cfgfile" && rm -f "$cfgfile.tmp") test 0 -eq $? || _lt_function_replace_fail=: fi if test x"$_lt_function_replace_fail" = x":"; then AC_MSG_WARN([Unable to substitute extended shell functions in $ofile]) fi ]) # _LT_PATH_CONVERSION_FUNCTIONS # ----------------------------- # Determine which file name conversion functions should be used by # func_to_host_file (and, implicitly, by func_to_host_path). These are needed # for certain cross-compile configurations and native mingw. m4_defun([_LT_PATH_CONVERSION_FUNCTIONS], [AC_REQUIRE([AC_CANONICAL_HOST])dnl AC_REQUIRE([AC_CANONICAL_BUILD])dnl AC_MSG_CHECKING([how to convert $build file names to $host format]) AC_CACHE_VAL(lt_cv_to_host_file_cmd, [case $host in *-*-mingw* ) case $build in *-*-mingw* ) # actually msys lt_cv_to_host_file_cmd=func_convert_file_msys_to_w32 ;; *-*-cygwin* ) lt_cv_to_host_file_cmd=func_convert_file_cygwin_to_w32 ;; * ) # otherwise, assume *nix lt_cv_to_host_file_cmd=func_convert_file_nix_to_w32 ;; esac ;; *-*-cygwin* ) case $build in *-*-mingw* ) # actually msys lt_cv_to_host_file_cmd=func_convert_file_msys_to_cygwin ;; *-*-cygwin* ) lt_cv_to_host_file_cmd=func_convert_file_noop ;; * ) # otherwise, assume *nix lt_cv_to_host_file_cmd=func_convert_file_nix_to_cygwin ;; esac ;; * ) # unhandled hosts (and "normal" native builds) lt_cv_to_host_file_cmd=func_convert_file_noop ;; esac ]) to_host_file_cmd=$lt_cv_to_host_file_cmd AC_MSG_RESULT([$lt_cv_to_host_file_cmd]) _LT_DECL([to_host_file_cmd], [lt_cv_to_host_file_cmd], [0], [convert $build file names to $host format])dnl AC_MSG_CHECKING([how to convert $build file names to toolchain format]) AC_CACHE_VAL(lt_cv_to_tool_file_cmd, [#assume ordinary cross tools, or native build. lt_cv_to_tool_file_cmd=func_convert_file_noop case $host in *-*-mingw* ) case $build in *-*-mingw* ) # actually msys lt_cv_to_tool_file_cmd=func_convert_file_msys_to_w32 ;; esac ;; esac ]) to_tool_file_cmd=$lt_cv_to_tool_file_cmd AC_MSG_RESULT([$lt_cv_to_tool_file_cmd]) _LT_DECL([to_tool_file_cmd], [lt_cv_to_tool_file_cmd], [0], [convert $build files to toolchain format])dnl ])# _LT_PATH_CONVERSION_FUNCTIONS
0
coqui_public_repos/inference-engine/third_party/cereal/include/cereal
coqui_public_repos/inference-engine/third_party/cereal/include/cereal/external/base64.hpp
/* Copyright (C) 2004-2008 René Nyffenegger This source code is provided 'as-is', without any express or implied warranty. In no event will the author be held liable for any damages arising from the use of this software. Permission is granted to anyone to use this software for any purpose, including commercial applications, and to alter it and redistribute it freely, subject to the following restrictions: 1. The origin of this source code must not be misrepresented; you must not claim that you wrote the original source code. If you use this source code in a product, an acknowledgment in the product documentation would be appreciated but is not required. 2. Altered source versions must be plainly marked as such, and must not be misrepresented as being the original source code. 3. This notice may not be removed or altered from any source distribution. René Nyffenegger rene.nyffenegger@adp-gmbh.ch */ #ifndef CEREAL_EXTERNAL_BASE64_HPP_ #define CEREAL_EXTERNAL_BASE64_HPP_ #ifdef __GNUC__ #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wconversion" #endif #include <string> namespace cereal { namespace base64 { static const std::string chars = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" "abcdefghijklmnopqrstuvwxyz" "0123456789+/"; static inline bool is_base64(unsigned char c) { return (isalnum(c) || (c == '+') || (c == '/')); } inline std::string encode(unsigned char const* bytes_to_encode, size_t in_len) { std::string ret; int i = 0; int j = 0; unsigned char char_array_3[3]; unsigned char char_array_4[4]; while (in_len--) { char_array_3[i++] = *(bytes_to_encode++); if (i == 3) { char_array_4[0] = static_cast<unsigned char>((char_array_3[0] & 0xfc) >> 2); char_array_4[1] = static_cast<unsigned char>( ( ( char_array_3[0] & 0x03 ) << 4 ) + ( ( char_array_3[1] & 0xf0 ) >> 4 ) ); char_array_4[2] = static_cast<unsigned char>( ( ( char_array_3[1] & 0x0f ) << 2 ) + ( ( char_array_3[2] & 0xc0 ) >> 6 ) ); char_array_4[3] = static_cast<unsigned char>( char_array_3[2] & 0x3f ); for(i = 0; (i <4) ; i++) ret += chars[char_array_4[i]]; i = 0; } } if (i) { for(j = i; j < 3; j++) char_array_3[j] = '\0'; char_array_4[0] = (char_array_3[0] & 0xfc) >> 2; char_array_4[1] = ((char_array_3[0] & 0x03) << 4) + ((char_array_3[1] & 0xf0) >> 4); char_array_4[2] = ((char_array_3[1] & 0x0f) << 2) + ((char_array_3[2] & 0xc0) >> 6); char_array_4[3] = char_array_3[2] & 0x3f; for (j = 0; (j < i + 1); j++) ret += chars[char_array_4[j]]; while((i++ < 3)) ret += '='; } return ret; } inline std::string decode(std::string const& encoded_string) { size_t in_len = encoded_string.size(); size_t i = 0; size_t j = 0; int in_ = 0; unsigned char char_array_4[4], char_array_3[3]; std::string ret; while (in_len-- && ( encoded_string[in_] != '=') && is_base64(encoded_string[in_])) { char_array_4[i++] = encoded_string[in_]; in_++; if (i ==4) { for (i = 0; i <4; i++) char_array_4[i] = static_cast<unsigned char>(chars.find( char_array_4[i] )); char_array_3[0] = (char_array_4[0] << 2) + ((char_array_4[1] & 0x30) >> 4); char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; for (i = 0; (i < 3); i++) ret += char_array_3[i]; i = 0; } } if (i) { for (j = i; j <4; j++) char_array_4[j] = 0; for (j = 0; j <4; j++) char_array_4[j] = static_cast<unsigned char>(chars.find( char_array_4[j] )); char_array_3[0] = (char_array_4[0] << 2) + ((char_array_4[1] & 0x30) >> 4); char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; for (j = 0; (j < i - 1); j++) ret += char_array_3[j]; } return ret; } } // namespace base64 } // namespace cereal #ifdef __GNUC__ #pragma GCC diagnostic pop #endif #endif // CEREAL_EXTERNAL_BASE64_HPP_
0
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/include
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/include/fst/shortest-distance.h
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. // // Functions and classes to find shortest distance in an FST. #ifndef FST_SHORTEST_DISTANCE_H_ #define FST_SHORTEST_DISTANCE_H_ #include <deque> #include <vector> #include <fst/log.h> #include <fst/arcfilter.h> #include <fst/cache.h> #include <fst/queue.h> #include <fst/reverse.h> #include <fst/test-properties.h> namespace fst { // A representable float for shortest distance and shortest path algorithms. constexpr float kShortestDelta = 1e-6; template <class Arc, class Queue, class ArcFilter> struct ShortestDistanceOptions { using StateId = typename Arc::StateId; Queue *state_queue; // Queue discipline used; owned by caller. ArcFilter arc_filter; // Arc filter (e.g., limit to only epsilon graph). StateId source; // If kNoStateId, use the FST's initial state. float delta; // Determines the degree of convergence required bool first_path; // For a semiring with the path property (o.w. // undefined), compute the shortest-distances along // along the first path to a final state found // by the algorithm. That path is the shortest-path // only if the FST has a unique final state (or all // the final states have the same final weight), the // queue discipline is shortest-first and all the // weights in the FST are between One() and Zero() // according to NaturalLess. ShortestDistanceOptions(Queue *state_queue, ArcFilter arc_filter, StateId source = kNoStateId, float delta = kShortestDelta, bool first_path = false) : state_queue(state_queue), arc_filter(arc_filter), source(source), delta(delta), first_path(first_path) {} }; namespace internal { // Computation state of the shortest-distance algorithm. Reusable information // is maintained across calls to member function ShortestDistance(source) when // retain is true for improved efficiency when calling multiple times from // different source states (e.g., in epsilon removal). Contrary to the usual // conventions, fst may not be freed before this class. Vector distance // should not be modified by the user between these calls. The Error() method // returns true iff an error was encountered. template <class Arc, class Queue, class ArcFilter> class ShortestDistanceState { public: using StateId = typename Arc::StateId; using Weight = typename Arc::Weight; ShortestDistanceState( const Fst<Arc> &fst, std::vector<Weight> *distance, const ShortestDistanceOptions<Arc, Queue, ArcFilter> &opts, bool retain) : fst_(fst), distance_(distance), state_queue_(opts.state_queue), arc_filter_(opts.arc_filter), delta_(opts.delta), first_path_(opts.first_path), retain_(retain), source_id_(0), error_(false) { distance_->clear(); } void ShortestDistance(StateId source); bool Error() const { return error_; } private: const Fst<Arc> &fst_; std::vector<Weight> *distance_; Queue *state_queue_; ArcFilter arc_filter_; const float delta_; const bool first_path_; const bool retain_; // Retain and reuse information across calls. std::vector<Adder<Weight>> adder_; // Sums distance_ accurately. std::vector<Adder<Weight>> radder_; // Relaxation distance. std::vector<bool> enqueued_; // Is state enqueued? std::vector<StateId> sources_; // Source ID for ith state in distance_, // (r)adder_, and enqueued_ if retained. StateId source_id_; // Unique ID characterizing each call. bool error_; }; // Compute the shortest distance; if source is kNoStateId, uses the initial // state of the FST. template <class Arc, class Queue, class ArcFilter> void ShortestDistanceState<Arc, Queue, ArcFilter>::ShortestDistance( StateId source) { if (fst_.Start() == kNoStateId) { if (fst_.Properties(kError, false)) error_ = true; return; } if (!(Weight::Properties() & kRightSemiring)) { FSTERROR() << "ShortestDistance: Weight needs to be right distributive: " << Weight::Type(); error_ = true; return; } if (first_path_ && !(Weight::Properties() & kPath)) { FSTERROR() << "ShortestDistance: The first_path option is disallowed when " << "Weight does not have the path property: " << Weight::Type(); error_ = true; return; } state_queue_->Clear(); if (!retain_) { distance_->clear(); adder_.clear(); radder_.clear(); enqueued_.clear(); } if (source == kNoStateId) source = fst_.Start(); while (distance_->size() <= source) { distance_->push_back(Weight::Zero()); adder_.push_back(Adder<Weight>()); radder_.push_back(Adder<Weight>()); enqueued_.push_back(false); } if (retain_) { while (sources_.size() <= source) sources_.push_back(kNoStateId); sources_[source] = source_id_; } (*distance_)[source] = Weight::One(); adder_[source].Reset(Weight::One()); radder_[source].Reset(Weight::One()); enqueued_[source] = true; state_queue_->Enqueue(source); while (!state_queue_->Empty()) { const auto state = state_queue_->Head(); state_queue_->Dequeue(); while (distance_->size() <= state) { distance_->push_back(Weight::Zero()); adder_.push_back(Adder<Weight>()); radder_.push_back(Adder<Weight>()); enqueued_.push_back(false); } if (first_path_ && (fst_.Final(state) != Weight::Zero())) break; enqueued_[state] = false; const auto r = radder_[state].Sum(); radder_[state].Reset(); for (ArcIterator<Fst<Arc>> aiter(fst_, state); !aiter.Done(); aiter.Next()) { const auto &arc = aiter.Value(); if (!arc_filter_(arc)) continue; while (distance_->size() <= arc.nextstate) { distance_->push_back(Weight::Zero()); adder_.push_back(Adder<Weight>()); radder_.push_back(Adder<Weight>()); enqueued_.push_back(false); } if (retain_) { while (sources_.size() <= arc.nextstate) sources_.push_back(kNoStateId); if (sources_[arc.nextstate] != source_id_) { (*distance_)[arc.nextstate] = Weight::Zero(); adder_[arc.nextstate].Reset(); radder_[arc.nextstate].Reset(); enqueued_[arc.nextstate] = false; sources_[arc.nextstate] = source_id_; } } auto &nd = (*distance_)[arc.nextstate]; auto &na = adder_[arc.nextstate]; auto &nr = radder_[arc.nextstate]; auto weight = Times(r, arc.weight); if (!ApproxEqual(nd, Plus(nd, weight), delta_)) { nd = na.Add(weight); nr.Add(weight); if (!nd.Member() || !nr.Sum().Member()) { error_ = true; return; } if (!enqueued_[arc.nextstate]) { state_queue_->Enqueue(arc.nextstate); enqueued_[arc.nextstate] = true; } else { state_queue_->Update(arc.nextstate); } } } } ++source_id_; if (fst_.Properties(kError, false)) error_ = true; } } // namespace internal // Shortest-distance algorithm: this version allows fine control // via the options argument. See below for a simpler interface. // // This computes the shortest distance from the opts.source state to each // visited state S and stores the value in the distance vector. An // unvisited state S has distance Zero(), which will be stored in the // distance vector if S is less than the maximum visited state. The state // queue discipline, arc filter, and convergence delta are taken in the // options argument. The distance vector will contain a unique element for // which Member() is false if an error was encountered. // // The weights must must be right distributive and k-closed (i.e., 1 + // x + x^2 + ... + x^(k +1) = 1 + x + x^2 + ... + x^k). // // Complexity: // // Depends on properties of the semiring and the queue discipline. // // For more information, see: // // Mohri, M. 2002. Semiring framework and algorithms for shortest-distance // problems, Journal of Automata, Languages and // Combinatorics 7(3): 321-350, 2002. template <class Arc, class Queue, class ArcFilter> void ShortestDistance( const Fst<Arc> &fst, std::vector<typename Arc::Weight> *distance, const ShortestDistanceOptions<Arc, Queue, ArcFilter> &opts) { internal::ShortestDistanceState<Arc, Queue, ArcFilter> sd_state(fst, distance, opts, false); sd_state.ShortestDistance(opts.source); if (sd_state.Error()) { distance->clear(); distance->resize(1, Arc::Weight::NoWeight()); } } // Shortest-distance algorithm: simplified interface. See above for a version // that permits finer control. // // If reverse is false, this computes the shortest distance from the initial // state to each state S and stores the value in the distance vector. If // reverse is true, this computes the shortest distance from each state to the // final states. An unvisited state S has distance Zero(), which will be stored // in the distance vector if S is less than the maximum visited state. The // state queue discipline is automatically-selected. The distance vector will // contain a unique element for which Member() is false if an error was // encountered. // // The weights must must be right (left) distributive if reverse is false (true) // and k-closed (i.e., 1 + x + x^2 + ... + x^(k +1) = 1 + x + x^2 + ... + x^k). // // Arc weights must satisfy the property that the sum of the weights of one or // more paths from some state S to T is never Zero(). In particular, arc weights // are never Zero(). // // Complexity: // // Depends on properties of the semiring and the queue discipline. // // For more information, see: // // Mohri, M. 2002. Semiring framework and algorithms for // shortest-distance problems, Journal of Automata, Languages and // Combinatorics 7(3): 321-350, 2002. template <class Arc> void ShortestDistance(const Fst<Arc> &fst, std::vector<typename Arc::Weight> *distance, bool reverse = false, float delta = kShortestDelta) { using StateId = typename Arc::StateId; using Weight = typename Arc::Weight; if (!reverse) { AnyArcFilter<Arc> arc_filter; AutoQueue<StateId> state_queue(fst, distance, arc_filter); const ShortestDistanceOptions<Arc, AutoQueue<StateId>, AnyArcFilter<Arc>> opts(&state_queue, arc_filter, kNoStateId, delta); ShortestDistance(fst, distance, opts); } else { using ReverseArc = ReverseArc<Arc>; using ReverseWeight = typename ReverseArc::Weight; AnyArcFilter<ReverseArc> rarc_filter; VectorFst<ReverseArc> rfst; Reverse(fst, &rfst); std::vector<ReverseWeight> rdistance; AutoQueue<StateId> state_queue(rfst, &rdistance, rarc_filter); const ShortestDistanceOptions<ReverseArc, AutoQueue<StateId>, AnyArcFilter<ReverseArc>> ropts(&state_queue, rarc_filter, kNoStateId, delta); ShortestDistance(rfst, &rdistance, ropts); distance->clear(); if (rdistance.size() == 1 && !rdistance[0].Member()) { distance->resize(1, Arc::Weight::NoWeight()); return; } while (distance->size() < rdistance.size() - 1) { distance->push_back(rdistance[distance->size() + 1].Reverse()); } } } // Return the sum of the weight of all successful paths in an FST, i.e., the // shortest-distance from the initial state to the final states. Returns a // weight such that Member() is false if an error was encountered. template <class Arc> typename Arc::Weight ShortestDistance(const Fst<Arc> &fst, float delta = kShortestDelta) { using StateId = typename Arc::StateId; using Weight = typename Arc::Weight; std::vector<Weight> distance; if (Weight::Properties() & kRightSemiring) { ShortestDistance(fst, &distance, false, delta); if (distance.size() == 1 && !distance[0].Member()) { return Arc::Weight::NoWeight(); } Adder<Weight> adder; // maintains cumulative sum accurately for (StateId state = 0; state < distance.size(); ++state) { adder.Add(Times(distance[state], fst.Final(state))); } return adder.Sum(); } else { ShortestDistance(fst, &distance, true, delta); const auto state = fst.Start(); if (distance.size() == 1 && !distance[0].Member()) { return Arc::Weight::NoWeight(); } return state != kNoStateId && state < distance.size() ? distance[state] : Weight::Zero(); } } } // namespace fst #endif // FST_SHORTEST_DISTANCE_H_
0
coqui_public_repos/snakepit/scripts
coqui_public_repos/snakepit/scripts/worker/20auto-upgrades
APT::Periodic::Update-Package-Lists "0"; APT::Periodic::Download-Upgradeable-Packages "0"; APT::Periodic::AutocleanInterval "0"; APT::Periodic::Unattended-Upgrade "0";
0
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/include
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/include/fst/sparse-power-weight.h
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. // // Cartesian power weight semiring operation definitions, using // SparseTupleWeight as underlying representation. #ifndef FST_SPARSE_POWER_WEIGHT_H_ #define FST_SPARSE_POWER_WEIGHT_H_ #include <climits> #include <string> #include <fst/sparse-tuple-weight.h> #include <fst/weight.h> namespace fst { // Sparse cartesian power semiring: W ^ n // // Forms: // // - a left semimodule when W is a left semiring, // - a right semimodule when W is a right semiring, // - a bisemimodule when W is a semiring, // the free semimodule of rank n over W // // The Times operation is overloaded to provide the left and right scalar // products. // // K is the key value type. kNoKey (-1) is reserved for internal use template <class W, class K = int> class SparsePowerWeight : public SparseTupleWeight<W, K> { public: using ReverseWeight = SparsePowerWeight<typename W::ReverseWeight, K>; SparsePowerWeight() {} explicit SparsePowerWeight(const SparseTupleWeight<W, K> &weight) : SparseTupleWeight<W, K>(weight) {} template <class Iterator> SparsePowerWeight(Iterator begin, Iterator end) : SparseTupleWeight<W, K>(begin, end) {} // Initialize component `key` to `weight`, with `default_weight` for all // other components. SparsePowerWeight(const K &key, const W &weight, const W &default_weight = W::Zero()) : SparseTupleWeight<W, K>(key, weight, default_weight) {} static const SparsePowerWeight &Zero() { static const SparsePowerWeight zero(SparseTupleWeight<W, K>::Zero()); return zero; } static const SparsePowerWeight &One() { static const SparsePowerWeight one(SparseTupleWeight<W, K>::One()); return one; } static const SparsePowerWeight &NoWeight() { static const SparsePowerWeight no_weight( SparseTupleWeight<W, K>::NoWeight()); return no_weight; } // Overide this: Overwrite the Type method to reflect the key type if using // a non-default key type. static const string &Type() { static const string *const type = [] { string type = W::Type() + "_^n"; if (sizeof(K) != sizeof(uint32_t)) { type += "_" + std::to_string(CHAR_BIT * sizeof(K)); } return new string(type); }(); return *type; } static constexpr uint64_t Properties() { return W::Properties() & (kLeftSemiring | kRightSemiring | kCommutative | kIdempotent); } SparsePowerWeight Quantize(float delta = kDelta) const { return SparsePowerWeight(SparseTupleWeight<W, K>::Quantize(delta)); } ReverseWeight Reverse() const { return ReverseWeight(SparseTupleWeight<W, K>::Reverse()); } }; template <class W, class K, class M> inline SparsePowerWeight<W, K> SparsePowerWeightMap( const SparsePowerWeight<W, K> &w1, const SparsePowerWeight<W, K> &w2, const M &operator_mapper) { SparsePowerWeight<W, K> result; SparseTupleWeightMap(&result, w1, w2, operator_mapper); return result; } // Semimodule plus operation. template <class W, class K> inline SparsePowerWeight<W, K> Plus(const SparsePowerWeight<W, K> &w1, const SparsePowerWeight<W, K> &w2) { return SparsePowerWeightMap(w1, w2, [](const K &k, const W &v1, const W &v2) { return Plus(v1, v2); }); } // Semimodule times operation. template <class W, class K> inline SparsePowerWeight<W, K> Times(const SparsePowerWeight<W, K> &w1, const SparsePowerWeight<W, K> &w2) { return SparsePowerWeightMap(w1, w2, [](const K &k, const W &v1, const W &v2) { return Times(v1, v2); }); } // Semimodule divide operation. template <class W, class K> inline SparsePowerWeight<W, K> Divide(const SparsePowerWeight<W, K> &w1, const SparsePowerWeight<W, K> &w2, DivideType type = DIVIDE_ANY) { return SparsePowerWeightMap(w1, w2, [type](const K &k, const W &v1, const W &v2) { return Divide(v1, v2, type); }); } // Semimodule dot product operation. template <class W, class K> inline const W &DotProduct(const SparsePowerWeight<W, K> &w1, const SparsePowerWeight<W, K> &w2) { const SparsePowerWeight<W, K> product = Times(w1, w2); W result(W::Zero()); for (SparseTupleWeightIterator<W, K> it(product); !it.Done(); it.Next()) { result = Plus(result, it.Value().second); } return result; } template <class W, class K> inline bool ApproxEqual(const SparsePowerWeight<W, K> &w1, const SparsePowerWeight<W, K> &w2, float delta = kDelta) { auto result = SparsePowerWeightMap( w1, w2, [delta](const K &k, const W &v1, const W &v2) { return ApproxEqual(v1, v2, delta) ? W::One() : W::Zero(); }); return result == SparsePowerWeight<W, K>::One(); } template <class W, class K> inline SparsePowerWeight<W, K> Times(const W &k, const SparsePowerWeight<W, K> &w2) { const SparseTupleWeight<W, K> t2(k); const SparsePowerWeight<W, K> w1(t2); return Times(w1, w2); } template <class W, class K> inline SparsePowerWeight<W, K> Times(const SparsePowerWeight<W, K> &w1, const W &k) { const SparseTupleWeight<W, K> t2(k); const SparsePowerWeight<W, K> w2(t2); return Times(w1, w2); } template <class W, class K> inline SparsePowerWeight<W, K> Divide(const SparsePowerWeight<W, K> &w1, const W &k, DivideType divide_type = DIVIDE_ANY) { const SparseTupleWeight<W, K> t2(k); const SparsePowerWeight<W, K> w2(t2); return Divide(w1, w2, divide_type); } // This function object generates weights over the Cartesian power of rank // n over the underlying weight. This is intended primarily for testing. template <class W, class K> class WeightGenerate<SparsePowerWeight<W, K>> { public: using Weight = SparsePowerWeight<W, K>; using Generate = WeightGenerate<W>; explicit WeightGenerate(bool allow_zero = true, size_t sparse_power_rank = 3) : generate_(allow_zero), sparse_power_rank_(sparse_power_rank) {} Weight operator()() const { Weight weight; for (size_t i = 1; i <= sparse_power_rank_; ++i) { weight.PushBack(i, generate_(), true); } return weight; } private: const Generate generate_; const size_t sparse_power_rank_; }; } // namespace fst #endif // FST_SPARSE_POWER_WEIGHT_H_
0
coqui_public_repos/inference-engine/third_party/kenlm/util
coqui_public_repos/inference-engine/third_party/kenlm/util/double-conversion/cached-powers.h
// Copyright 2010 the V8 project authors. All rights reserved. // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are // met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // * Redistributions in binary form must reproduce the above // copyright notice, this list of conditions and the following // disclaimer in the documentation and/or other materials provided // with the distribution. // * Neither the name of Google Inc. nor the names of its // contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS // "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT // LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR // A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT // OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, // SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT // LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, // DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY // THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT // (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE // OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #ifndef DOUBLE_CONVERSION_CACHED_POWERS_H_ #define DOUBLE_CONVERSION_CACHED_POWERS_H_ #include "diy-fp.h" namespace kenlm_double_conversion { class PowersOfTenCache { public: // Not all powers of ten are cached. The decimal exponent of two neighboring // cached numbers will differ by kDecimalExponentDistance. static const int kDecimalExponentDistance; static const int kMinDecimalExponent; static const int kMaxDecimalExponent; // Returns a cached power-of-ten with a binary exponent in the range // [min_exponent; max_exponent] (boundaries included). static void GetCachedPowerForBinaryExponentRange(int min_exponent, int max_exponent, DiyFp* power, int* decimal_exponent); // Returns a cached power of ten x ~= 10^k such that // k <= decimal_exponent < k + kCachedPowersDecimalDistance. // The given decimal_exponent must satisfy // kMinDecimalExponent <= requested_exponent, and // requested_exponent < kMaxDecimalExponent + kDecimalExponentDistance. static void GetCachedPowerForDecimalExponent(int requested_exponent, DiyFp* power, int* found_exponent); }; } // namespace kenlm_double_conversion #endif // DOUBLE_CONVERSION_CACHED_POWERS_H_
0
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/script/reverse.cc
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. #include <fst/script/fst-class.h> #include <fst/script/reverse.h> #include <fst/script/script-impl.h> namespace fst { namespace script { void Reverse(const FstClass &ifst, MutableFstClass *ofst, bool require_superinitial) { if (!internal::ArcTypesMatch(ifst, *ofst, "Reverse")) { ofst->SetProperties(kError, kError); return; } ReverseArgs args(ifst, ofst, require_superinitial); Apply<Operation<ReverseArgs>>("Reverse", ifst.ArcType(), &args); } REGISTER_FST_OPERATION(Reverse, StdArc, ReverseArgs); REGISTER_FST_OPERATION(Reverse, LogArc, ReverseArgs); REGISTER_FST_OPERATION(Reverse, Log64Arc, ReverseArgs); } // namespace script } // namespace fst
0
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/include
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/include/fst/difference.h
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. // // Class to compute the difference between two FSAs. #ifndef FST_DIFFERENCE_H_ #define FST_DIFFERENCE_H_ #include <memory> #include <fst/cache.h> #include <fst/complement.h> #include <fst/compose.h> namespace fst { template <class Arc, class M = Matcher<Fst<Arc>>, class Filter = SequenceComposeFilter<M>, class StateTable = GenericComposeStateTable<Arc, typename Filter::FilterState>> struct DifferenceFstOptions : public ComposeFstOptions<Arc, M, Filter, StateTable> { explicit DifferenceFstOptions(const CacheOptions &opts = CacheOptions(), M *matcher1 = nullptr, M *matcher2 = nullptr, Filter *filter = nullptr, StateTable *state_table = nullptr) : ComposeFstOptions<Arc, M, Filter, StateTable>(opts, matcher1, matcher2, filter, state_table) {} }; // Computes the difference between two FSAs. This version is a delayed FST. // Only strings that are in the first automaton but not in second are retained // in the result. // // The first argument must be an acceptor; the second argument must be an // unweighted, epsilon-free, deterministic acceptor. One of the arguments must // be label-sorted. // // Complexity: same as ComposeFst. // // Caveats: same as ComposeFst. template <class A> class DifferenceFst : public ComposeFst<A> { public: using Arc = A; using Weight = typename Arc::Weight; using StateId = typename Arc::StateId; using ComposeFst<Arc>::CreateBase1; // A - B = A ^ B'. DifferenceFst(const Fst<Arc> &fst1, const Fst<Arc> &fst2, const CacheOptions &opts = CacheOptions()) : ComposeFst<Arc>(CreateDifferenceImplWithCacheOpts(fst1, fst2, opts)) { if (!fst1.Properties(kAcceptor, true)) { FSTERROR() << "DifferenceFst: 1st argument not an acceptor"; GetImpl()->SetProperties(kError, kError); } } template <class Matcher, class Filter, class StateTable> DifferenceFst( const Fst<Arc> &fst1, const Fst<Arc> &fst2, const DifferenceFstOptions<Arc, Matcher, Filter, StateTable> &opts) : ComposeFst<Arc>( CreateDifferenceImplWithDifferenceOpts(fst1, fst2, opts)) { if (!fst1.Properties(kAcceptor, true)) { FSTERROR() << "DifferenceFst: 1st argument not an acceptor"; GetImpl()->SetProperties(kError, kError); } } // See Fst<>::Copy() for doc. DifferenceFst(const DifferenceFst<Arc> &fst, bool safe = false) : ComposeFst<Arc>(fst, safe) {} // Get a copy of this DifferenceFst. See Fst<>::Copy() for further doc. DifferenceFst<Arc> *Copy(bool safe = false) const override { return new DifferenceFst<Arc>(*this, safe); } private: using Impl = internal::ComposeFstImplBase<Arc>; using ImplToFst<Impl>::GetImpl; static std::shared_ptr<Impl> CreateDifferenceImplWithCacheOpts( const Fst<Arc> &fst1, const Fst<Arc> &fst2, const CacheOptions &opts) { using RM = RhoMatcher<Matcher<Fst<A>>>; ComplementFst<Arc> cfst(fst2); ComposeFstOptions<A, RM> copts( CacheOptions(), new RM(fst1, MATCH_NONE), new RM(cfst, MATCH_INPUT, ComplementFst<Arc>::kRhoLabel)); return CreateBase1(fst1, cfst, copts); } template <class Matcher, class Filter, class StateTable> static std::shared_ptr<Impl> CreateDifferenceImplWithDifferenceOpts( const Fst<Arc> &fst1, const Fst<Arc> &fst2, const DifferenceFstOptions<Arc, Matcher, Filter, StateTable> &opts) { using RM = RhoMatcher<Matcher>; ComplementFst<Arc> cfst(fst2); ComposeFstOptions<Arc, RM> copts(opts); copts.matcher1 = new RM(fst1, MATCH_NONE, kNoLabel, MATCHER_REWRITE_ALWAYS, opts.matcher1); copts.matcher2 = new RM(cfst, MATCH_INPUT, ComplementFst<Arc>::kRhoLabel, MATCHER_REWRITE_ALWAYS, opts.matcher2); return CreateBase1(fst1, cfst, copts); } }; // Specialization for DifferenceFst. template <class Arc> class StateIterator<DifferenceFst<Arc>> : public StateIterator<ComposeFst<Arc>> { public: explicit StateIterator(const DifferenceFst<Arc> &fst) : StateIterator<ComposeFst<Arc>>(fst) {} }; // Specialization for DifferenceFst. template <class Arc> class ArcIterator<DifferenceFst<Arc>> : public ArcIterator<ComposeFst<Arc>> { public: using StateId = typename Arc::StateId; ArcIterator(const DifferenceFst<Arc> &fst, StateId s) : ArcIterator<ComposeFst<Arc>>(fst, s) {} }; using DifferenceOptions = ComposeOptions; // Useful alias when using StdArc. using StdDifferenceFst = DifferenceFst<StdArc>; using DifferenceOptions = ComposeOptions; // Computes the difference between two FSAs. This version writes the difference // to an output MutableFst. Only strings that are in the first automaton but not // in the second are retained in the result. // // The first argument must be an acceptor; the second argument must be an // unweighted, epsilon-free, deterministic acceptor. One of the arguments must // be label-sorted. // // Complexity: same as Compose. // // Caveats: same as Compose. template <class Arc> void Difference(const Fst<Arc> &ifst1, const Fst<Arc> &ifst2, MutableFst<Arc> *ofst, const DifferenceOptions &opts = DifferenceOptions()) { using M = Matcher<Fst<Arc>>; if (opts.filter_type == AUTO_FILTER) { CacheOptions nopts; nopts.gc_limit = 0; // Cache only the last state for fastest copy. *ofst = DifferenceFst<Arc>(ifst1, ifst2, nopts); } else if (opts.filter_type == SEQUENCE_FILTER) { DifferenceFstOptions<Arc> dopts; dopts.gc_limit = 0; // Cache only the last state for fastest copy. *ofst = DifferenceFst<Arc>(ifst1, ifst2, dopts); } else if (opts.filter_type == ALT_SEQUENCE_FILTER) { DifferenceFstOptions<Arc, M, AltSequenceComposeFilter<M>> dopts; dopts.gc_limit = 0; // Cache only the last state for fastest copy. *ofst = DifferenceFst<Arc>(ifst1, ifst2, dopts); } else if (opts.filter_type == MATCH_FILTER) { DifferenceFstOptions<Arc, M, MatchComposeFilter<M>> dopts; dopts.gc_limit = 0; // Cache only the last state for fastest copy. *ofst = DifferenceFst<Arc>(ifst1, ifst2, dopts); } if (opts.connect) Connect(ofst); } } // namespace fst #endif // FST_DIFFERENCE_H_
0
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/extensions
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/extensions/compact/compact8_string-fst.cc
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. #include <fst/fst.h> #include <fst/compact-fst.h> namespace fst { static FstRegisterer<CompactStringFst<StdArc, uint8>> CompactStringFst_StdArc_uint8_registerer; static FstRegisterer<CompactStringFst<LogArc, uint8>> CompactStringFst_LogArc_uint8_registerer; } // namespace fst
0
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/bin/fstdraw-main.cc
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. // // Draws a binary FSTs in the Graphviz dot text format. #include <cstring> #include <fstream> #include <memory> #include <ostream> #include <string> #include <fst/flags.h> #include <fst/log.h> #include <fst/script/draw.h> DECLARE_bool(acceptor); DECLARE_string(isymbols); DECLARE_string(osymbols); DECLARE_string(ssymbols); DECLARE_bool(numeric); DECLARE_int32(precision); DECLARE_string(float_format); DECLARE_bool(show_weight_one); DECLARE_string(title); DECLARE_bool(portrait); DECLARE_bool(vertical); DECLARE_int32(fontsize); DECLARE_double(height); DECLARE_double(width); DECLARE_double(nodesep); DECLARE_double(ranksep); DECLARE_bool(allow_negative_labels); int fstdraw_main(int argc, char **argv) { namespace s = fst::script; using fst::script::FstClass; using fst::SymbolTable; using fst::SymbolTableTextOptions; string usage = "Prints out binary FSTs in dot text format.\n\n Usage: "; usage += argv[0]; usage += " [binary.fst [text.dot]]\n"; std::set_new_handler(FailedNewHandler); SET_FLAGS(usage.c_str(), &argc, &argv, true); if (argc > 3) { ShowUsage(); return 1; } const string in_name = (argc > 1 && strcmp(argv[1], "-") != 0) ? argv[1] : ""; std::unique_ptr<FstClass> fst(FstClass::Read(in_name)); if (!fst) return 1; string dest = "stdout"; std::ofstream fstrm; if (argc == 3) { fstrm.open(argv[2]); if (!fstrm) { LOG(ERROR) << argv[0] << ": Open failed, file = " << argv[2]; return 1; } dest = argv[2]; } std::ostream &ostrm = fstrm.is_open() ? fstrm : std::cout; const SymbolTableTextOptions opts(FLAGS_allow_negative_labels); std::unique_ptr<const SymbolTable> isyms; if (!FLAGS_isymbols.empty() && !FLAGS_numeric) { isyms.reset(SymbolTable::ReadText(FLAGS_isymbols, opts)); if (!isyms) return 1; } std::unique_ptr<const SymbolTable> osyms; if (!FLAGS_osymbols.empty() && !FLAGS_numeric) { osyms.reset(SymbolTable::ReadText(FLAGS_osymbols, opts)); if (!osyms) return 1; } std::unique_ptr<const SymbolTable> ssyms; if (!FLAGS_ssymbols.empty() && !FLAGS_numeric) { ssyms.reset(SymbolTable::ReadText(FLAGS_ssymbols)); if (!ssyms) return 1; } if (!isyms && !FLAGS_numeric && fst->InputSymbols()) { isyms.reset(fst->InputSymbols()->Copy()); } if (!osyms && !FLAGS_numeric && fst->OutputSymbols()) { osyms.reset(fst->OutputSymbols()->Copy()); } s::DrawFst(*fst, isyms.get(), osyms.get(), ssyms.get(), FLAGS_acceptor, FLAGS_title, FLAGS_width, FLAGS_height, FLAGS_portrait, FLAGS_vertical, FLAGS_ranksep, FLAGS_nodesep, FLAGS_fontsize, FLAGS_precision, FLAGS_float_format, FLAGS_show_weight_one, &ostrm, dest); return 0; }
0
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/include/fst
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/include/fst/script/minimize.h
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. #ifndef FST_SCRIPT_MINIMIZE_H_ #define FST_SCRIPT_MINIMIZE_H_ #include <tuple> #include <fst/minimize.h> #include <fst/script/fst-class.h> namespace fst { namespace script { using MinimizeArgs = std::tuple<MutableFstClass *, MutableFstClass *, float, bool>; template <class Arc> void Minimize(MinimizeArgs *args) { MutableFst<Arc> *ofst1 = std::get<0>(*args)->GetMutableFst<Arc>(); MutableFst<Arc> *ofst2 = (std::get<1>(*args) ? std::get<1>(*args)->GetMutableFst<Arc>() : nullptr); Minimize(ofst1, ofst2, std::get<2>(*args), std::get<3>(*args)); } void Minimize(MutableFstClass *ofst1, MutableFstClass *ofst2 = nullptr, float delta = kShortestDelta, bool allow_nondet = false); } // namespace script } // namespace fst #endif // FST_SCRIPT_MINIMIZE_H_
0
coqui_public_repos/inference-engine/third_party/cereal/include/cereal/external
coqui_public_repos/inference-engine/third_party/cereal/include/cereal/external/rapidjson/schema.h
// Tencent is pleased to support the open source community by making RapidJSON available-> // // Copyright (C) 2015 THL A29 Limited, a Tencent company, and Milo Yip-> All rights reserved-> // // Licensed under the MIT License (the "License"); you may not use this file except // in compliance with the License-> You may obtain a copy of the License at // // http://opensource->org/licenses/MIT // // Unless required by applicable law or agreed to in writing, software distributed // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR // CONDITIONS OF ANY KIND, either express or implied-> See the License for the // specific language governing permissions and limitations under the License-> #ifndef CEREAL_RAPIDJSON_SCHEMA_H_ #define CEREAL_RAPIDJSON_SCHEMA_H_ #include "document.h" #include "pointer.h" #include "stringbuffer.h" #include <cmath> // abs, floor #if !defined(CEREAL_RAPIDJSON_SCHEMA_USE_INTERNALREGEX) #define CEREAL_RAPIDJSON_SCHEMA_USE_INTERNALREGEX 1 #else #define CEREAL_RAPIDJSON_SCHEMA_USE_INTERNALREGEX 0 #endif #if !CEREAL_RAPIDJSON_SCHEMA_USE_INTERNALREGEX && defined(CEREAL_RAPIDJSON_SCHEMA_USE_STDREGEX) && (__cplusplus >=201103L || (defined(_MSC_VER) && _MSC_VER >= 1800)) #define CEREAL_RAPIDJSON_SCHEMA_USE_STDREGEX 1 #else #define CEREAL_RAPIDJSON_SCHEMA_USE_STDREGEX 0 #endif #if CEREAL_RAPIDJSON_SCHEMA_USE_INTERNALREGEX #include "internal/regex.h" #elif CEREAL_RAPIDJSON_SCHEMA_USE_STDREGEX #include <regex> #endif #if CEREAL_RAPIDJSON_SCHEMA_USE_INTERNALREGEX || CEREAL_RAPIDJSON_SCHEMA_USE_STDREGEX #define CEREAL_RAPIDJSON_SCHEMA_HAS_REGEX 1 #else #define CEREAL_RAPIDJSON_SCHEMA_HAS_REGEX 0 #endif #ifndef CEREAL_RAPIDJSON_SCHEMA_VERBOSE #define CEREAL_RAPIDJSON_SCHEMA_VERBOSE 0 #endif #if CEREAL_RAPIDJSON_SCHEMA_VERBOSE #include "stringbuffer.h" #endif CEREAL_RAPIDJSON_DIAG_PUSH #if defined(__GNUC__) CEREAL_RAPIDJSON_DIAG_OFF(effc++) #endif #ifdef __clang__ CEREAL_RAPIDJSON_DIAG_OFF(weak-vtables) CEREAL_RAPIDJSON_DIAG_OFF(exit-time-destructors) CEREAL_RAPIDJSON_DIAG_OFF(c++98-compat-pedantic) CEREAL_RAPIDJSON_DIAG_OFF(variadic-macros) #elif defined(_MSC_VER) CEREAL_RAPIDJSON_DIAG_OFF(4512) // assignment operator could not be generated #endif CEREAL_RAPIDJSON_NAMESPACE_BEGIN /////////////////////////////////////////////////////////////////////////////// // Verbose Utilities #if CEREAL_RAPIDJSON_SCHEMA_VERBOSE namespace internal { inline void PrintInvalidKeyword(const char* keyword) { printf("Fail keyword: %s\n", keyword); } inline void PrintInvalidKeyword(const wchar_t* keyword) { wprintf(L"Fail keyword: %ls\n", keyword); } inline void PrintInvalidDocument(const char* document) { printf("Fail document: %s\n\n", document); } inline void PrintInvalidDocument(const wchar_t* document) { wprintf(L"Fail document: %ls\n\n", document); } inline void PrintValidatorPointers(unsigned depth, const char* s, const char* d) { printf("S: %*s%s\nD: %*s%s\n\n", depth * 4, " ", s, depth * 4, " ", d); } inline void PrintValidatorPointers(unsigned depth, const wchar_t* s, const wchar_t* d) { wprintf(L"S: %*ls%ls\nD: %*ls%ls\n\n", depth * 4, L" ", s, depth * 4, L" ", d); } } // namespace internal #endif // CEREAL_RAPIDJSON_SCHEMA_VERBOSE /////////////////////////////////////////////////////////////////////////////// // CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN #if CEREAL_RAPIDJSON_SCHEMA_VERBOSE #define CEREAL_RAPIDJSON_INVALID_KEYWORD_VERBOSE(keyword) internal::PrintInvalidKeyword(keyword) #else #define CEREAL_RAPIDJSON_INVALID_KEYWORD_VERBOSE(keyword) #endif #define CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(keyword)\ CEREAL_RAPIDJSON_MULTILINEMACRO_BEGIN\ context.invalidKeyword = keyword.GetString();\ CEREAL_RAPIDJSON_INVALID_KEYWORD_VERBOSE(keyword.GetString());\ return false;\ CEREAL_RAPIDJSON_MULTILINEMACRO_END /////////////////////////////////////////////////////////////////////////////// // Forward declarations template <typename ValueType, typename Allocator> class GenericSchemaDocument; namespace internal { template <typename SchemaDocumentType> class Schema; /////////////////////////////////////////////////////////////////////////////// // ISchemaValidator class ISchemaValidator { public: virtual ~ISchemaValidator() {} virtual bool IsValid() const = 0; }; /////////////////////////////////////////////////////////////////////////////// // ISchemaStateFactory template <typename SchemaType> class ISchemaStateFactory { public: virtual ~ISchemaStateFactory() {} virtual ISchemaValidator* CreateSchemaValidator(const SchemaType&) = 0; virtual void DestroySchemaValidator(ISchemaValidator* validator) = 0; virtual void* CreateHasher() = 0; virtual uint64_t GetHashCode(void* hasher) = 0; virtual void DestroryHasher(void* hasher) = 0; virtual void* MallocState(size_t size) = 0; virtual void FreeState(void* p) = 0; }; /////////////////////////////////////////////////////////////////////////////// // IValidationErrorHandler template <typename SchemaType> class IValidationErrorHandler { public: typedef typename SchemaType::Ch Ch; typedef typename SchemaType::SValue SValue; virtual ~IValidationErrorHandler() {} virtual void NotMultipleOf(int64_t actual, const SValue& expected) = 0; virtual void NotMultipleOf(uint64_t actual, const SValue& expected) = 0; virtual void NotMultipleOf(double actual, const SValue& expected) = 0; virtual void AboveMaximum(int64_t actual, const SValue& expected, bool exclusive) = 0; virtual void AboveMaximum(uint64_t actual, const SValue& expected, bool exclusive) = 0; virtual void AboveMaximum(double actual, const SValue& expected, bool exclusive) = 0; virtual void BelowMinimum(int64_t actual, const SValue& expected, bool exclusive) = 0; virtual void BelowMinimum(uint64_t actual, const SValue& expected, bool exclusive) = 0; virtual void BelowMinimum(double actual, const SValue& expected, bool exclusive) = 0; virtual void TooLong(const Ch* str, SizeType length, SizeType expected) = 0; virtual void TooShort(const Ch* str, SizeType length, SizeType expected) = 0; virtual void DoesNotMatch(const Ch* str, SizeType length) = 0; virtual void DisallowedItem(SizeType index) = 0; virtual void TooFewItems(SizeType actualCount, SizeType expectedCount) = 0; virtual void TooManyItems(SizeType actualCount, SizeType expectedCount) = 0; virtual void DuplicateItems(SizeType index1, SizeType index2) = 0; virtual void TooManyProperties(SizeType actualCount, SizeType expectedCount) = 0; virtual void TooFewProperties(SizeType actualCount, SizeType expectedCount) = 0; virtual void StartMissingProperties() = 0; virtual void AddMissingProperty(const SValue& name) = 0; virtual bool EndMissingProperties() = 0; virtual void PropertyViolations(ISchemaValidator** subvalidators, SizeType count) = 0; virtual void DisallowedProperty(const Ch* name, SizeType length) = 0; virtual void StartDependencyErrors() = 0; virtual void StartMissingDependentProperties() = 0; virtual void AddMissingDependentProperty(const SValue& targetName) = 0; virtual void EndMissingDependentProperties(const SValue& sourceName) = 0; virtual void AddDependencySchemaError(const SValue& souceName, ISchemaValidator* subvalidator) = 0; virtual bool EndDependencyErrors() = 0; virtual void DisallowedValue() = 0; virtual void StartDisallowedType() = 0; virtual void AddExpectedType(const typename SchemaType::ValueType& expectedType) = 0; virtual void EndDisallowedType(const typename SchemaType::ValueType& actualType) = 0; virtual void NotAllOf(ISchemaValidator** subvalidators, SizeType count) = 0; virtual void NoneOf(ISchemaValidator** subvalidators, SizeType count) = 0; virtual void NotOneOf(ISchemaValidator** subvalidators, SizeType count) = 0; virtual void Disallowed() = 0; }; /////////////////////////////////////////////////////////////////////////////// // Hasher // For comparison of compound value template<typename Encoding, typename Allocator> class Hasher { public: typedef typename Encoding::Ch Ch; Hasher(Allocator* allocator = 0, size_t stackCapacity = kDefaultSize) : stack_(allocator, stackCapacity) {} bool Null() { return WriteType(kNullType); } bool Bool(bool b) { return WriteType(b ? kTrueType : kFalseType); } bool Int(int i) { Number n; n.u.i = i; n.d = static_cast<double>(i); return WriteNumber(n); } bool Uint(unsigned u) { Number n; n.u.u = u; n.d = static_cast<double>(u); return WriteNumber(n); } bool Int64(int64_t i) { Number n; n.u.i = i; n.d = static_cast<double>(i); return WriteNumber(n); } bool Uint64(uint64_t u) { Number n; n.u.u = u; n.d = static_cast<double>(u); return WriteNumber(n); } bool Double(double d) { Number n; if (d < 0) n.u.i = static_cast<int64_t>(d); else n.u.u = static_cast<uint64_t>(d); n.d = d; return WriteNumber(n); } bool RawNumber(const Ch* str, SizeType len, bool) { WriteBuffer(kNumberType, str, len * sizeof(Ch)); return true; } bool String(const Ch* str, SizeType len, bool) { WriteBuffer(kStringType, str, len * sizeof(Ch)); return true; } bool StartObject() { return true; } bool Key(const Ch* str, SizeType len, bool copy) { return String(str, len, copy); } bool EndObject(SizeType memberCount) { uint64_t h = Hash(0, kObjectType); uint64_t* kv = stack_.template Pop<uint64_t>(memberCount * 2); for (SizeType i = 0; i < memberCount; i++) h ^= Hash(kv[i * 2], kv[i * 2 + 1]); // Use xor to achieve member order insensitive *stack_.template Push<uint64_t>() = h; return true; } bool StartArray() { return true; } bool EndArray(SizeType elementCount) { uint64_t h = Hash(0, kArrayType); uint64_t* e = stack_.template Pop<uint64_t>(elementCount); for (SizeType i = 0; i < elementCount; i++) h = Hash(h, e[i]); // Use hash to achieve element order sensitive *stack_.template Push<uint64_t>() = h; return true; } bool IsValid() const { return stack_.GetSize() == sizeof(uint64_t); } uint64_t GetHashCode() const { CEREAL_RAPIDJSON_ASSERT(IsValid()); return *stack_.template Top<uint64_t>(); } private: static const size_t kDefaultSize = 256; struct Number { union U { uint64_t u; int64_t i; }u; double d; }; bool WriteType(Type type) { return WriteBuffer(type, 0, 0); } bool WriteNumber(const Number& n) { return WriteBuffer(kNumberType, &n, sizeof(n)); } bool WriteBuffer(Type type, const void* data, size_t len) { // FNV-1a from http://isthe.com/chongo/tech/comp/fnv/ uint64_t h = Hash(CEREAL_RAPIDJSON_UINT64_C2(0x84222325, 0xcbf29ce4), type); const unsigned char* d = static_cast<const unsigned char*>(data); for (size_t i = 0; i < len; i++) h = Hash(h, d[i]); *stack_.template Push<uint64_t>() = h; return true; } static uint64_t Hash(uint64_t h, uint64_t d) { static const uint64_t kPrime = CEREAL_RAPIDJSON_UINT64_C2(0x00000100, 0x000001b3); h ^= d; h *= kPrime; return h; } Stack<Allocator> stack_; }; /////////////////////////////////////////////////////////////////////////////// // SchemaValidationContext template <typename SchemaDocumentType> struct SchemaValidationContext { typedef Schema<SchemaDocumentType> SchemaType; typedef ISchemaStateFactory<SchemaType> SchemaValidatorFactoryType; typedef IValidationErrorHandler<SchemaType> ErrorHandlerType; typedef typename SchemaType::ValueType ValueType; typedef typename ValueType::Ch Ch; enum PatternValidatorType { kPatternValidatorOnly, kPatternValidatorWithProperty, kPatternValidatorWithAdditionalProperty }; SchemaValidationContext(SchemaValidatorFactoryType& f, ErrorHandlerType& eh, const SchemaType* s) : factory(f), error_handler(eh), schema(s), valueSchema(), invalidKeyword(), hasher(), arrayElementHashCodes(), validators(), validatorCount(), patternPropertiesValidators(), patternPropertiesValidatorCount(), patternPropertiesSchemas(), patternPropertiesSchemaCount(), valuePatternValidatorType(kPatternValidatorOnly), propertyExist(), inArray(false), valueUniqueness(false), arrayUniqueness(false) { } ~SchemaValidationContext() { if (hasher) factory.DestroryHasher(hasher); if (validators) { for (SizeType i = 0; i < validatorCount; i++) factory.DestroySchemaValidator(validators[i]); factory.FreeState(validators); } if (patternPropertiesValidators) { for (SizeType i = 0; i < patternPropertiesValidatorCount; i++) factory.DestroySchemaValidator(patternPropertiesValidators[i]); factory.FreeState(patternPropertiesValidators); } if (patternPropertiesSchemas) factory.FreeState(patternPropertiesSchemas); if (propertyExist) factory.FreeState(propertyExist); } SchemaValidatorFactoryType& factory; ErrorHandlerType& error_handler; const SchemaType* schema; const SchemaType* valueSchema; const Ch* invalidKeyword; void* hasher; // Only validator access void* arrayElementHashCodes; // Only validator access this ISchemaValidator** validators; SizeType validatorCount; ISchemaValidator** patternPropertiesValidators; SizeType patternPropertiesValidatorCount; const SchemaType** patternPropertiesSchemas; SizeType patternPropertiesSchemaCount; PatternValidatorType valuePatternValidatorType; PatternValidatorType objectPatternValidatorType; SizeType arrayElementIndex; bool* propertyExist; bool inArray; bool valueUniqueness; bool arrayUniqueness; }; /////////////////////////////////////////////////////////////////////////////// // Schema template <typename SchemaDocumentType> class Schema { public: typedef typename SchemaDocumentType::ValueType ValueType; typedef typename SchemaDocumentType::AllocatorType AllocatorType; typedef typename SchemaDocumentType::PointerType PointerType; typedef typename ValueType::EncodingType EncodingType; typedef typename EncodingType::Ch Ch; typedef SchemaValidationContext<SchemaDocumentType> Context; typedef Schema<SchemaDocumentType> SchemaType; typedef GenericValue<EncodingType, AllocatorType> SValue; typedef IValidationErrorHandler<Schema> ErrorHandler; friend class GenericSchemaDocument<ValueType, AllocatorType>; Schema(SchemaDocumentType* schemaDocument, const PointerType& p, const ValueType& value, const ValueType& document, AllocatorType* allocator) : allocator_(allocator), uri_(schemaDocument->GetURI(), *allocator), pointer_(p, allocator), typeless_(schemaDocument->GetTypeless()), enum_(), enumCount_(), not_(), type_((1 << kTotalSchemaType) - 1), // typeless validatorCount_(), notValidatorIndex_(), properties_(), additionalPropertiesSchema_(), patternProperties_(), patternPropertyCount_(), propertyCount_(), minProperties_(), maxProperties_(SizeType(~0)), additionalProperties_(true), hasDependencies_(), hasRequired_(), hasSchemaDependencies_(), additionalItemsSchema_(), itemsList_(), itemsTuple_(), itemsTupleCount_(), minItems_(), maxItems_(SizeType(~0)), additionalItems_(true), uniqueItems_(false), pattern_(), minLength_(0), maxLength_(~SizeType(0)), exclusiveMinimum_(false), exclusiveMaximum_(false), defaultValueLength_(0) { typedef typename SchemaDocumentType::ValueType ValueType; typedef typename ValueType::ConstValueIterator ConstValueIterator; typedef typename ValueType::ConstMemberIterator ConstMemberIterator; if (!value.IsObject()) return; if (const ValueType* v = GetMember(value, GetTypeString())) { type_ = 0; if (v->IsString()) AddType(*v); else if (v->IsArray()) for (ConstValueIterator itr = v->Begin(); itr != v->End(); ++itr) AddType(*itr); } if (const ValueType* v = GetMember(value, GetEnumString())) if (v->IsArray() && v->Size() > 0) { enum_ = static_cast<uint64_t*>(allocator_->Malloc(sizeof(uint64_t) * v->Size())); for (ConstValueIterator itr = v->Begin(); itr != v->End(); ++itr) { typedef Hasher<EncodingType, MemoryPoolAllocator<> > EnumHasherType; char buffer[256u + 24]; MemoryPoolAllocator<> hasherAllocator(buffer, sizeof(buffer)); EnumHasherType h(&hasherAllocator, 256); itr->Accept(h); enum_[enumCount_++] = h.GetHashCode(); } } if (schemaDocument) { AssignIfExist(allOf_, *schemaDocument, p, value, GetAllOfString(), document); AssignIfExist(anyOf_, *schemaDocument, p, value, GetAnyOfString(), document); AssignIfExist(oneOf_, *schemaDocument, p, value, GetOneOfString(), document); } if (const ValueType* v = GetMember(value, GetNotString())) { schemaDocument->CreateSchema(&not_, p.Append(GetNotString(), allocator_), *v, document); notValidatorIndex_ = validatorCount_; validatorCount_++; } // Object const ValueType* properties = GetMember(value, GetPropertiesString()); const ValueType* required = GetMember(value, GetRequiredString()); const ValueType* dependencies = GetMember(value, GetDependenciesString()); { // Gather properties from properties/required/dependencies SValue allProperties(kArrayType); if (properties && properties->IsObject()) for (ConstMemberIterator itr = properties->MemberBegin(); itr != properties->MemberEnd(); ++itr) AddUniqueElement(allProperties, itr->name); if (required && required->IsArray()) for (ConstValueIterator itr = required->Begin(); itr != required->End(); ++itr) if (itr->IsString()) AddUniqueElement(allProperties, *itr); if (dependencies && dependencies->IsObject()) for (ConstMemberIterator itr = dependencies->MemberBegin(); itr != dependencies->MemberEnd(); ++itr) { AddUniqueElement(allProperties, itr->name); if (itr->value.IsArray()) for (ConstValueIterator i = itr->value.Begin(); i != itr->value.End(); ++i) if (i->IsString()) AddUniqueElement(allProperties, *i); } if (allProperties.Size() > 0) { propertyCount_ = allProperties.Size(); properties_ = static_cast<Property*>(allocator_->Malloc(sizeof(Property) * propertyCount_)); for (SizeType i = 0; i < propertyCount_; i++) { new (&properties_[i]) Property(); properties_[i].name = allProperties[i]; properties_[i].schema = typeless_; } } } if (properties && properties->IsObject()) { PointerType q = p.Append(GetPropertiesString(), allocator_); for (ConstMemberIterator itr = properties->MemberBegin(); itr != properties->MemberEnd(); ++itr) { SizeType index; if (FindPropertyIndex(itr->name, &index)) schemaDocument->CreateSchema(&properties_[index].schema, q.Append(itr->name, allocator_), itr->value, document); } } if (const ValueType* v = GetMember(value, GetPatternPropertiesString())) { PointerType q = p.Append(GetPatternPropertiesString(), allocator_); patternProperties_ = static_cast<PatternProperty*>(allocator_->Malloc(sizeof(PatternProperty) * v->MemberCount())); patternPropertyCount_ = 0; for (ConstMemberIterator itr = v->MemberBegin(); itr != v->MemberEnd(); ++itr) { new (&patternProperties_[patternPropertyCount_]) PatternProperty(); patternProperties_[patternPropertyCount_].pattern = CreatePattern(itr->name); schemaDocument->CreateSchema(&patternProperties_[patternPropertyCount_].schema, q.Append(itr->name, allocator_), itr->value, document); patternPropertyCount_++; } } if (required && required->IsArray()) for (ConstValueIterator itr = required->Begin(); itr != required->End(); ++itr) if (itr->IsString()) { SizeType index; if (FindPropertyIndex(*itr, &index)) { properties_[index].required = true; hasRequired_ = true; } } if (dependencies && dependencies->IsObject()) { PointerType q = p.Append(GetDependenciesString(), allocator_); hasDependencies_ = true; for (ConstMemberIterator itr = dependencies->MemberBegin(); itr != dependencies->MemberEnd(); ++itr) { SizeType sourceIndex; if (FindPropertyIndex(itr->name, &sourceIndex)) { if (itr->value.IsArray()) { properties_[sourceIndex].dependencies = static_cast<bool*>(allocator_->Malloc(sizeof(bool) * propertyCount_)); std::memset(properties_[sourceIndex].dependencies, 0, sizeof(bool)* propertyCount_); for (ConstValueIterator targetItr = itr->value.Begin(); targetItr != itr->value.End(); ++targetItr) { SizeType targetIndex; if (FindPropertyIndex(*targetItr, &targetIndex)) properties_[sourceIndex].dependencies[targetIndex] = true; } } else if (itr->value.IsObject()) { hasSchemaDependencies_ = true; schemaDocument->CreateSchema(&properties_[sourceIndex].dependenciesSchema, q.Append(itr->name, allocator_), itr->value, document); properties_[sourceIndex].dependenciesValidatorIndex = validatorCount_; validatorCount_++; } } } } if (const ValueType* v = GetMember(value, GetAdditionalPropertiesString())) { if (v->IsBool()) additionalProperties_ = v->GetBool(); else if (v->IsObject()) schemaDocument->CreateSchema(&additionalPropertiesSchema_, p.Append(GetAdditionalPropertiesString(), allocator_), *v, document); } AssignIfExist(minProperties_, value, GetMinPropertiesString()); AssignIfExist(maxProperties_, value, GetMaxPropertiesString()); // Array if (const ValueType* v = GetMember(value, GetItemsString())) { PointerType q = p.Append(GetItemsString(), allocator_); if (v->IsObject()) // List validation schemaDocument->CreateSchema(&itemsList_, q, *v, document); else if (v->IsArray()) { // Tuple validation itemsTuple_ = static_cast<const Schema**>(allocator_->Malloc(sizeof(const Schema*) * v->Size())); SizeType index = 0; for (ConstValueIterator itr = v->Begin(); itr != v->End(); ++itr, index++) schemaDocument->CreateSchema(&itemsTuple_[itemsTupleCount_++], q.Append(index, allocator_), *itr, document); } } AssignIfExist(minItems_, value, GetMinItemsString()); AssignIfExist(maxItems_, value, GetMaxItemsString()); if (const ValueType* v = GetMember(value, GetAdditionalItemsString())) { if (v->IsBool()) additionalItems_ = v->GetBool(); else if (v->IsObject()) schemaDocument->CreateSchema(&additionalItemsSchema_, p.Append(GetAdditionalItemsString(), allocator_), *v, document); } AssignIfExist(uniqueItems_, value, GetUniqueItemsString()); // String AssignIfExist(minLength_, value, GetMinLengthString()); AssignIfExist(maxLength_, value, GetMaxLengthString()); if (const ValueType* v = GetMember(value, GetPatternString())) pattern_ = CreatePattern(*v); // Number if (const ValueType* v = GetMember(value, GetMinimumString())) if (v->IsNumber()) minimum_.CopyFrom(*v, *allocator_); if (const ValueType* v = GetMember(value, GetMaximumString())) if (v->IsNumber()) maximum_.CopyFrom(*v, *allocator_); AssignIfExist(exclusiveMinimum_, value, GetExclusiveMinimumString()); AssignIfExist(exclusiveMaximum_, value, GetExclusiveMaximumString()); if (const ValueType* v = GetMember(value, GetMultipleOfString())) if (v->IsNumber() && v->GetDouble() > 0.0) multipleOf_.CopyFrom(*v, *allocator_); // Default if (const ValueType* v = GetMember(value, GetDefaultValueString())) if (v->IsString()) defaultValueLength_ = v->GetStringLength(); } ~Schema() { AllocatorType::Free(enum_); if (properties_) { for (SizeType i = 0; i < propertyCount_; i++) properties_[i].~Property(); AllocatorType::Free(properties_); } if (patternProperties_) { for (SizeType i = 0; i < patternPropertyCount_; i++) patternProperties_[i].~PatternProperty(); AllocatorType::Free(patternProperties_); } AllocatorType::Free(itemsTuple_); #if CEREAL_RAPIDJSON_SCHEMA_HAS_REGEX if (pattern_) { pattern_->~RegexType(); AllocatorType::Free(pattern_); } #endif } const SValue& GetURI() const { return uri_; } const PointerType& GetPointer() const { return pointer_; } bool BeginValue(Context& context) const { if (context.inArray) { if (uniqueItems_) context.valueUniqueness = true; if (itemsList_) context.valueSchema = itemsList_; else if (itemsTuple_) { if (context.arrayElementIndex < itemsTupleCount_) context.valueSchema = itemsTuple_[context.arrayElementIndex]; else if (additionalItemsSchema_) context.valueSchema = additionalItemsSchema_; else if (additionalItems_) context.valueSchema = typeless_; else { context.error_handler.DisallowedItem(context.arrayElementIndex); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetItemsString()); } } else context.valueSchema = typeless_; context.arrayElementIndex++; } return true; } CEREAL_RAPIDJSON_FORCEINLINE bool EndValue(Context& context) const { if (context.patternPropertiesValidatorCount > 0) { bool otherValid = false; SizeType count = context.patternPropertiesValidatorCount; if (context.objectPatternValidatorType != Context::kPatternValidatorOnly) otherValid = context.patternPropertiesValidators[--count]->IsValid(); bool patternValid = true; for (SizeType i = 0; i < count; i++) if (!context.patternPropertiesValidators[i]->IsValid()) { patternValid = false; break; } if (context.objectPatternValidatorType == Context::kPatternValidatorOnly) { if (!patternValid) { context.error_handler.PropertyViolations(context.patternPropertiesValidators, count); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetPatternPropertiesString()); } } else if (context.objectPatternValidatorType == Context::kPatternValidatorWithProperty) { if (!patternValid || !otherValid) { context.error_handler.PropertyViolations(context.patternPropertiesValidators, count + 1); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetPatternPropertiesString()); } } else if (!patternValid && !otherValid) { // kPatternValidatorWithAdditionalProperty) context.error_handler.PropertyViolations(context.patternPropertiesValidators, count + 1); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetPatternPropertiesString()); } } if (enum_) { const uint64_t h = context.factory.GetHashCode(context.hasher); for (SizeType i = 0; i < enumCount_; i++) if (enum_[i] == h) goto foundEnum; context.error_handler.DisallowedValue(); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetEnumString()); foundEnum:; } if (allOf_.schemas) for (SizeType i = allOf_.begin; i < allOf_.begin + allOf_.count; i++) if (!context.validators[i]->IsValid()) { context.error_handler.NotAllOf(&context.validators[allOf_.begin], allOf_.count); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetAllOfString()); } if (anyOf_.schemas) { for (SizeType i = anyOf_.begin; i < anyOf_.begin + anyOf_.count; i++) if (context.validators[i]->IsValid()) goto foundAny; context.error_handler.NoneOf(&context.validators[anyOf_.begin], anyOf_.count); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetAnyOfString()); foundAny:; } if (oneOf_.schemas) { bool oneValid = false; for (SizeType i = oneOf_.begin; i < oneOf_.begin + oneOf_.count; i++) if (context.validators[i]->IsValid()) { if (oneValid) { context.error_handler.NotOneOf(&context.validators[oneOf_.begin], oneOf_.count); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetOneOfString()); } else oneValid = true; } if (!oneValid) { context.error_handler.NotOneOf(&context.validators[oneOf_.begin], oneOf_.count); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetOneOfString()); } } if (not_ && context.validators[notValidatorIndex_]->IsValid()) { context.error_handler.Disallowed(); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetNotString()); } return true; } bool Null(Context& context) const { if (!(type_ & (1 << kNullSchemaType))) { DisallowedType(context, GetNullString()); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetTypeString()); } return CreateParallelValidator(context); } bool Bool(Context& context, bool) const { if (!(type_ & (1 << kBooleanSchemaType))) { DisallowedType(context, GetBooleanString()); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetTypeString()); } return CreateParallelValidator(context); } bool Int(Context& context, int i) const { if (!CheckInt(context, i)) return false; return CreateParallelValidator(context); } bool Uint(Context& context, unsigned u) const { if (!CheckUint(context, u)) return false; return CreateParallelValidator(context); } bool Int64(Context& context, int64_t i) const { if (!CheckInt(context, i)) return false; return CreateParallelValidator(context); } bool Uint64(Context& context, uint64_t u) const { if (!CheckUint(context, u)) return false; return CreateParallelValidator(context); } bool Double(Context& context, double d) const { if (!(type_ & (1 << kNumberSchemaType))) { DisallowedType(context, GetNumberString()); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetTypeString()); } if (!minimum_.IsNull() && !CheckDoubleMinimum(context, d)) return false; if (!maximum_.IsNull() && !CheckDoubleMaximum(context, d)) return false; if (!multipleOf_.IsNull() && !CheckDoubleMultipleOf(context, d)) return false; return CreateParallelValidator(context); } bool String(Context& context, const Ch* str, SizeType length, bool) const { if (!(type_ & (1 << kStringSchemaType))) { DisallowedType(context, GetStringString()); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetTypeString()); } if (minLength_ != 0 || maxLength_ != SizeType(~0)) { SizeType count; if (internal::CountStringCodePoint<EncodingType>(str, length, &count)) { if (count < minLength_) { context.error_handler.TooShort(str, length, minLength_); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetMinLengthString()); } if (count > maxLength_) { context.error_handler.TooLong(str, length, maxLength_); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetMaxLengthString()); } } } if (pattern_ && !IsPatternMatch(pattern_, str, length)) { context.error_handler.DoesNotMatch(str, length); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetPatternString()); } return CreateParallelValidator(context); } bool StartObject(Context& context) const { if (!(type_ & (1 << kObjectSchemaType))) { DisallowedType(context, GetObjectString()); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetTypeString()); } if (hasDependencies_ || hasRequired_) { context.propertyExist = static_cast<bool*>(context.factory.MallocState(sizeof(bool) * propertyCount_)); std::memset(context.propertyExist, 0, sizeof(bool) * propertyCount_); } if (patternProperties_) { // pre-allocate schema array SizeType count = patternPropertyCount_ + 1; // extra for valuePatternValidatorType context.patternPropertiesSchemas = static_cast<const SchemaType**>(context.factory.MallocState(sizeof(const SchemaType*) * count)); context.patternPropertiesSchemaCount = 0; std::memset(context.patternPropertiesSchemas, 0, sizeof(SchemaType*) * count); } return CreateParallelValidator(context); } bool Key(Context& context, const Ch* str, SizeType len, bool) const { if (patternProperties_) { context.patternPropertiesSchemaCount = 0; for (SizeType i = 0; i < patternPropertyCount_; i++) if (patternProperties_[i].pattern && IsPatternMatch(patternProperties_[i].pattern, str, len)) { context.patternPropertiesSchemas[context.patternPropertiesSchemaCount++] = patternProperties_[i].schema; context.valueSchema = typeless_; } } SizeType index; if (FindPropertyIndex(ValueType(str, len).Move(), &index)) { if (context.patternPropertiesSchemaCount > 0) { context.patternPropertiesSchemas[context.patternPropertiesSchemaCount++] = properties_[index].schema; context.valueSchema = typeless_; context.valuePatternValidatorType = Context::kPatternValidatorWithProperty; } else context.valueSchema = properties_[index].schema; if (context.propertyExist) context.propertyExist[index] = true; return true; } if (additionalPropertiesSchema_) { if (additionalPropertiesSchema_ && context.patternPropertiesSchemaCount > 0) { context.patternPropertiesSchemas[context.patternPropertiesSchemaCount++] = additionalPropertiesSchema_; context.valueSchema = typeless_; context.valuePatternValidatorType = Context::kPatternValidatorWithAdditionalProperty; } else context.valueSchema = additionalPropertiesSchema_; return true; } else if (additionalProperties_) { context.valueSchema = typeless_; return true; } if (context.patternPropertiesSchemaCount == 0) { // patternProperties are not additional properties context.error_handler.DisallowedProperty(str, len); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetAdditionalPropertiesString()); } return true; } bool EndObject(Context& context, SizeType memberCount) const { if (hasRequired_) { context.error_handler.StartMissingProperties(); for (SizeType index = 0; index < propertyCount_; index++) if (properties_[index].required && !context.propertyExist[index]) if (properties_[index].schema->defaultValueLength_ == 0 ) context.error_handler.AddMissingProperty(properties_[index].name); if (context.error_handler.EndMissingProperties()) CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetRequiredString()); } if (memberCount < minProperties_) { context.error_handler.TooFewProperties(memberCount, minProperties_); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetMinPropertiesString()); } if (memberCount > maxProperties_) { context.error_handler.TooManyProperties(memberCount, maxProperties_); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetMaxPropertiesString()); } if (hasDependencies_) { context.error_handler.StartDependencyErrors(); for (SizeType sourceIndex = 0; sourceIndex < propertyCount_; sourceIndex++) { const Property& source = properties_[sourceIndex]; if (context.propertyExist[sourceIndex]) { if (source.dependencies) { context.error_handler.StartMissingDependentProperties(); for (SizeType targetIndex = 0; targetIndex < propertyCount_; targetIndex++) if (source.dependencies[targetIndex] && !context.propertyExist[targetIndex]) context.error_handler.AddMissingDependentProperty(properties_[targetIndex].name); context.error_handler.EndMissingDependentProperties(source.name); } else if (source.dependenciesSchema) { ISchemaValidator* dependenciesValidator = context.validators[source.dependenciesValidatorIndex]; if (!dependenciesValidator->IsValid()) context.error_handler.AddDependencySchemaError(source.name, dependenciesValidator); } } } if (context.error_handler.EndDependencyErrors()) CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetDependenciesString()); } return true; } bool StartArray(Context& context) const { if (!(type_ & (1 << kArraySchemaType))) { DisallowedType(context, GetArrayString()); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetTypeString()); } context.arrayElementIndex = 0; context.inArray = true; return CreateParallelValidator(context); } bool EndArray(Context& context, SizeType elementCount) const { context.inArray = false; if (elementCount < minItems_) { context.error_handler.TooFewItems(elementCount, minItems_); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetMinItemsString()); } if (elementCount > maxItems_) { context.error_handler.TooManyItems(elementCount, maxItems_); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetMaxItemsString()); } return true; } // Generate functions for string literal according to Ch #define CEREAL_RAPIDJSON_STRING_(name, ...) \ static const ValueType& Get##name##String() {\ static const Ch s[] = { __VA_ARGS__, '\0' };\ static const ValueType v(s, static_cast<SizeType>(sizeof(s) / sizeof(Ch) - 1));\ return v;\ } CEREAL_RAPIDJSON_STRING_(Null, 'n', 'u', 'l', 'l') CEREAL_RAPIDJSON_STRING_(Boolean, 'b', 'o', 'o', 'l', 'e', 'a', 'n') CEREAL_RAPIDJSON_STRING_(Object, 'o', 'b', 'j', 'e', 'c', 't') CEREAL_RAPIDJSON_STRING_(Array, 'a', 'r', 'r', 'a', 'y') CEREAL_RAPIDJSON_STRING_(String, 's', 't', 'r', 'i', 'n', 'g') CEREAL_RAPIDJSON_STRING_(Number, 'n', 'u', 'm', 'b', 'e', 'r') CEREAL_RAPIDJSON_STRING_(Integer, 'i', 'n', 't', 'e', 'g', 'e', 'r') CEREAL_RAPIDJSON_STRING_(Type, 't', 'y', 'p', 'e') CEREAL_RAPIDJSON_STRING_(Enum, 'e', 'n', 'u', 'm') CEREAL_RAPIDJSON_STRING_(AllOf, 'a', 'l', 'l', 'O', 'f') CEREAL_RAPIDJSON_STRING_(AnyOf, 'a', 'n', 'y', 'O', 'f') CEREAL_RAPIDJSON_STRING_(OneOf, 'o', 'n', 'e', 'O', 'f') CEREAL_RAPIDJSON_STRING_(Not, 'n', 'o', 't') CEREAL_RAPIDJSON_STRING_(Properties, 'p', 'r', 'o', 'p', 'e', 'r', 't', 'i', 'e', 's') CEREAL_RAPIDJSON_STRING_(Required, 'r', 'e', 'q', 'u', 'i', 'r', 'e', 'd') CEREAL_RAPIDJSON_STRING_(Dependencies, 'd', 'e', 'p', 'e', 'n', 'd', 'e', 'n', 'c', 'i', 'e', 's') CEREAL_RAPIDJSON_STRING_(PatternProperties, 'p', 'a', 't', 't', 'e', 'r', 'n', 'P', 'r', 'o', 'p', 'e', 'r', 't', 'i', 'e', 's') CEREAL_RAPIDJSON_STRING_(AdditionalProperties, 'a', 'd', 'd', 'i', 't', 'i', 'o', 'n', 'a', 'l', 'P', 'r', 'o', 'p', 'e', 'r', 't', 'i', 'e', 's') CEREAL_RAPIDJSON_STRING_(MinProperties, 'm', 'i', 'n', 'P', 'r', 'o', 'p', 'e', 'r', 't', 'i', 'e', 's') CEREAL_RAPIDJSON_STRING_(MaxProperties, 'm', 'a', 'x', 'P', 'r', 'o', 'p', 'e', 'r', 't', 'i', 'e', 's') CEREAL_RAPIDJSON_STRING_(Items, 'i', 't', 'e', 'm', 's') CEREAL_RAPIDJSON_STRING_(MinItems, 'm', 'i', 'n', 'I', 't', 'e', 'm', 's') CEREAL_RAPIDJSON_STRING_(MaxItems, 'm', 'a', 'x', 'I', 't', 'e', 'm', 's') CEREAL_RAPIDJSON_STRING_(AdditionalItems, 'a', 'd', 'd', 'i', 't', 'i', 'o', 'n', 'a', 'l', 'I', 't', 'e', 'm', 's') CEREAL_RAPIDJSON_STRING_(UniqueItems, 'u', 'n', 'i', 'q', 'u', 'e', 'I', 't', 'e', 'm', 's') CEREAL_RAPIDJSON_STRING_(MinLength, 'm', 'i', 'n', 'L', 'e', 'n', 'g', 't', 'h') CEREAL_RAPIDJSON_STRING_(MaxLength, 'm', 'a', 'x', 'L', 'e', 'n', 'g', 't', 'h') CEREAL_RAPIDJSON_STRING_(Pattern, 'p', 'a', 't', 't', 'e', 'r', 'n') CEREAL_RAPIDJSON_STRING_(Minimum, 'm', 'i', 'n', 'i', 'm', 'u', 'm') CEREAL_RAPIDJSON_STRING_(Maximum, 'm', 'a', 'x', 'i', 'm', 'u', 'm') CEREAL_RAPIDJSON_STRING_(ExclusiveMinimum, 'e', 'x', 'c', 'l', 'u', 's', 'i', 'v', 'e', 'M', 'i', 'n', 'i', 'm', 'u', 'm') CEREAL_RAPIDJSON_STRING_(ExclusiveMaximum, 'e', 'x', 'c', 'l', 'u', 's', 'i', 'v', 'e', 'M', 'a', 'x', 'i', 'm', 'u', 'm') CEREAL_RAPIDJSON_STRING_(MultipleOf, 'm', 'u', 'l', 't', 'i', 'p', 'l', 'e', 'O', 'f') CEREAL_RAPIDJSON_STRING_(DefaultValue, 'd', 'e', 'f', 'a', 'u', 'l', 't') #undef CEREAL_RAPIDJSON_STRING_ private: enum SchemaValueType { kNullSchemaType, kBooleanSchemaType, kObjectSchemaType, kArraySchemaType, kStringSchemaType, kNumberSchemaType, kIntegerSchemaType, kTotalSchemaType }; #if CEREAL_RAPIDJSON_SCHEMA_USE_INTERNALREGEX typedef internal::GenericRegex<EncodingType, AllocatorType> RegexType; #elif CEREAL_RAPIDJSON_SCHEMA_USE_STDREGEX typedef std::basic_regex<Ch> RegexType; #else typedef char RegexType; #endif struct SchemaArray { SchemaArray() : schemas(), count() {} ~SchemaArray() { AllocatorType::Free(schemas); } const SchemaType** schemas; SizeType begin; // begin index of context.validators SizeType count; }; template <typename V1, typename V2> void AddUniqueElement(V1& a, const V2& v) { for (typename V1::ConstValueIterator itr = a.Begin(); itr != a.End(); ++itr) if (*itr == v) return; V1 c(v, *allocator_); a.PushBack(c, *allocator_); } static const ValueType* GetMember(const ValueType& value, const ValueType& name) { typename ValueType::ConstMemberIterator itr = value.FindMember(name); return itr != value.MemberEnd() ? &(itr->value) : 0; } static void AssignIfExist(bool& out, const ValueType& value, const ValueType& name) { if (const ValueType* v = GetMember(value, name)) if (v->IsBool()) out = v->GetBool(); } static void AssignIfExist(SizeType& out, const ValueType& value, const ValueType& name) { if (const ValueType* v = GetMember(value, name)) if (v->IsUint64() && v->GetUint64() <= SizeType(~0)) out = static_cast<SizeType>(v->GetUint64()); } void AssignIfExist(SchemaArray& out, SchemaDocumentType& schemaDocument, const PointerType& p, const ValueType& value, const ValueType& name, const ValueType& document) { if (const ValueType* v = GetMember(value, name)) { if (v->IsArray() && v->Size() > 0) { PointerType q = p.Append(name, allocator_); out.count = v->Size(); out.schemas = static_cast<const Schema**>(allocator_->Malloc(out.count * sizeof(const Schema*))); memset(out.schemas, 0, sizeof(Schema*)* out.count); for (SizeType i = 0; i < out.count; i++) schemaDocument.CreateSchema(&out.schemas[i], q.Append(i, allocator_), (*v)[i], document); out.begin = validatorCount_; validatorCount_ += out.count; } } } #if CEREAL_RAPIDJSON_SCHEMA_USE_INTERNALREGEX template <typename ValueType> RegexType* CreatePattern(const ValueType& value) { if (value.IsString()) { RegexType* r = new (allocator_->Malloc(sizeof(RegexType))) RegexType(value.GetString(), allocator_); if (!r->IsValid()) { r->~RegexType(); AllocatorType::Free(r); r = 0; } return r; } return 0; } static bool IsPatternMatch(const RegexType* pattern, const Ch *str, SizeType) { GenericRegexSearch<RegexType> rs(*pattern); return rs.Search(str); } #elif CEREAL_RAPIDJSON_SCHEMA_USE_STDREGEX template <typename ValueType> RegexType* CreatePattern(const ValueType& value) { if (value.IsString()) { RegexType *r = static_cast<RegexType*>(allocator_->Malloc(sizeof(RegexType))); try { return new (r) RegexType(value.GetString(), std::size_t(value.GetStringLength()), std::regex_constants::ECMAScript); } catch (const std::regex_error&) { AllocatorType::Free(r); } } return 0; } static bool IsPatternMatch(const RegexType* pattern, const Ch *str, SizeType length) { std::match_results<const Ch*> r; return std::regex_search(str, str + length, r, *pattern); } #else template <typename ValueType> RegexType* CreatePattern(const ValueType&) { return 0; } static bool IsPatternMatch(const RegexType*, const Ch *, SizeType) { return true; } #endif // CEREAL_RAPIDJSON_SCHEMA_USE_STDREGEX void AddType(const ValueType& type) { if (type == GetNullString() ) type_ |= 1 << kNullSchemaType; else if (type == GetBooleanString()) type_ |= 1 << kBooleanSchemaType; else if (type == GetObjectString() ) type_ |= 1 << kObjectSchemaType; else if (type == GetArrayString() ) type_ |= 1 << kArraySchemaType; else if (type == GetStringString() ) type_ |= 1 << kStringSchemaType; else if (type == GetIntegerString()) type_ |= 1 << kIntegerSchemaType; else if (type == GetNumberString() ) type_ |= (1 << kNumberSchemaType) | (1 << kIntegerSchemaType); } bool CreateParallelValidator(Context& context) const { if (enum_ || context.arrayUniqueness) context.hasher = context.factory.CreateHasher(); if (validatorCount_) { CEREAL_RAPIDJSON_ASSERT(context.validators == 0); context.validators = static_cast<ISchemaValidator**>(context.factory.MallocState(sizeof(ISchemaValidator*) * validatorCount_)); context.validatorCount = validatorCount_; if (allOf_.schemas) CreateSchemaValidators(context, allOf_); if (anyOf_.schemas) CreateSchemaValidators(context, anyOf_); if (oneOf_.schemas) CreateSchemaValidators(context, oneOf_); if (not_) context.validators[notValidatorIndex_] = context.factory.CreateSchemaValidator(*not_); if (hasSchemaDependencies_) { for (SizeType i = 0; i < propertyCount_; i++) if (properties_[i].dependenciesSchema) context.validators[properties_[i].dependenciesValidatorIndex] = context.factory.CreateSchemaValidator(*properties_[i].dependenciesSchema); } } return true; } void CreateSchemaValidators(Context& context, const SchemaArray& schemas) const { for (SizeType i = 0; i < schemas.count; i++) context.validators[schemas.begin + i] = context.factory.CreateSchemaValidator(*schemas.schemas[i]); } // O(n) bool FindPropertyIndex(const ValueType& name, SizeType* outIndex) const { SizeType len = name.GetStringLength(); const Ch* str = name.GetString(); for (SizeType index = 0; index < propertyCount_; index++) if (properties_[index].name.GetStringLength() == len && (std::memcmp(properties_[index].name.GetString(), str, sizeof(Ch) * len) == 0)) { *outIndex = index; return true; } return false; } bool CheckInt(Context& context, int64_t i) const { if (!(type_ & ((1 << kIntegerSchemaType) | (1 << kNumberSchemaType)))) { DisallowedType(context, GetIntegerString()); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetTypeString()); } if (!minimum_.IsNull()) { if (minimum_.IsInt64()) { if (exclusiveMinimum_ ? i <= minimum_.GetInt64() : i < minimum_.GetInt64()) { context.error_handler.BelowMinimum(i, minimum_, exclusiveMinimum_); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetMinimumString()); } } else if (minimum_.IsUint64()) { context.error_handler.BelowMinimum(i, minimum_, exclusiveMinimum_); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetMinimumString()); // i <= max(int64_t) < minimum.GetUint64() } else if (!CheckDoubleMinimum(context, static_cast<double>(i))) return false; } if (!maximum_.IsNull()) { if (maximum_.IsInt64()) { if (exclusiveMaximum_ ? i >= maximum_.GetInt64() : i > maximum_.GetInt64()) { context.error_handler.AboveMaximum(i, maximum_, exclusiveMaximum_); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetMaximumString()); } } else if (maximum_.IsUint64()) { } /* do nothing */ // i <= max(int64_t) < maximum_.GetUint64() else if (!CheckDoubleMaximum(context, static_cast<double>(i))) return false; } if (!multipleOf_.IsNull()) { if (multipleOf_.IsUint64()) { if (static_cast<uint64_t>(i >= 0 ? i : -i) % multipleOf_.GetUint64() != 0) { context.error_handler.NotMultipleOf(i, multipleOf_); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetMultipleOfString()); } } else if (!CheckDoubleMultipleOf(context, static_cast<double>(i))) return false; } return true; } bool CheckUint(Context& context, uint64_t i) const { if (!(type_ & ((1 << kIntegerSchemaType) | (1 << kNumberSchemaType)))) { DisallowedType(context, GetIntegerString()); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetTypeString()); } if (!minimum_.IsNull()) { if (minimum_.IsUint64()) { if (exclusiveMinimum_ ? i <= minimum_.GetUint64() : i < minimum_.GetUint64()) { context.error_handler.BelowMinimum(i, minimum_, exclusiveMinimum_); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetMinimumString()); } } else if (minimum_.IsInt64()) /* do nothing */; // i >= 0 > minimum.Getint64() else if (!CheckDoubleMinimum(context, static_cast<double>(i))) return false; } if (!maximum_.IsNull()) { if (maximum_.IsUint64()) { if (exclusiveMaximum_ ? i >= maximum_.GetUint64() : i > maximum_.GetUint64()) { context.error_handler.AboveMaximum(i, maximum_, exclusiveMaximum_); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetMaximumString()); } } else if (maximum_.IsInt64()) { context.error_handler.AboveMaximum(i, maximum_, exclusiveMaximum_); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetMaximumString()); // i >= 0 > maximum_ } else if (!CheckDoubleMaximum(context, static_cast<double>(i))) return false; } if (!multipleOf_.IsNull()) { if (multipleOf_.IsUint64()) { if (i % multipleOf_.GetUint64() != 0) { context.error_handler.NotMultipleOf(i, multipleOf_); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetMultipleOfString()); } } else if (!CheckDoubleMultipleOf(context, static_cast<double>(i))) return false; } return true; } bool CheckDoubleMinimum(Context& context, double d) const { if (exclusiveMinimum_ ? d <= minimum_.GetDouble() : d < minimum_.GetDouble()) { context.error_handler.BelowMinimum(d, minimum_, exclusiveMinimum_); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetMinimumString()); } return true; } bool CheckDoubleMaximum(Context& context, double d) const { if (exclusiveMaximum_ ? d >= maximum_.GetDouble() : d > maximum_.GetDouble()) { context.error_handler.AboveMaximum(d, maximum_, exclusiveMaximum_); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetMaximumString()); } return true; } bool CheckDoubleMultipleOf(Context& context, double d) const { double a = std::abs(d), b = std::abs(multipleOf_.GetDouble()); double q = std::floor(a / b); double r = a - q * b; if (r > 0.0) { context.error_handler.NotMultipleOf(d, multipleOf_); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(GetMultipleOfString()); } return true; } void DisallowedType(Context& context, const ValueType& actualType) const { ErrorHandler& eh = context.error_handler; eh.StartDisallowedType(); if (type_ & (1 << kNullSchemaType)) eh.AddExpectedType(GetNullString()); if (type_ & (1 << kBooleanSchemaType)) eh.AddExpectedType(GetBooleanString()); if (type_ & (1 << kObjectSchemaType)) eh.AddExpectedType(GetObjectString()); if (type_ & (1 << kArraySchemaType)) eh.AddExpectedType(GetArrayString()); if (type_ & (1 << kStringSchemaType)) eh.AddExpectedType(GetStringString()); if (type_ & (1 << kNumberSchemaType)) eh.AddExpectedType(GetNumberString()); else if (type_ & (1 << kIntegerSchemaType)) eh.AddExpectedType(GetIntegerString()); eh.EndDisallowedType(actualType); } struct Property { Property() : schema(), dependenciesSchema(), dependenciesValidatorIndex(), dependencies(), required(false) {} ~Property() { AllocatorType::Free(dependencies); } SValue name; const SchemaType* schema; const SchemaType* dependenciesSchema; SizeType dependenciesValidatorIndex; bool* dependencies; bool required; }; struct PatternProperty { PatternProperty() : schema(), pattern() {} ~PatternProperty() { if (pattern) { pattern->~RegexType(); AllocatorType::Free(pattern); } } const SchemaType* schema; RegexType* pattern; }; AllocatorType* allocator_; SValue uri_; PointerType pointer_; const SchemaType* typeless_; uint64_t* enum_; SizeType enumCount_; SchemaArray allOf_; SchemaArray anyOf_; SchemaArray oneOf_; const SchemaType* not_; unsigned type_; // bitmask of kSchemaType SizeType validatorCount_; SizeType notValidatorIndex_; Property* properties_; const SchemaType* additionalPropertiesSchema_; PatternProperty* patternProperties_; SizeType patternPropertyCount_; SizeType propertyCount_; SizeType minProperties_; SizeType maxProperties_; bool additionalProperties_; bool hasDependencies_; bool hasRequired_; bool hasSchemaDependencies_; const SchemaType* additionalItemsSchema_; const SchemaType* itemsList_; const SchemaType** itemsTuple_; SizeType itemsTupleCount_; SizeType minItems_; SizeType maxItems_; bool additionalItems_; bool uniqueItems_; RegexType* pattern_; SizeType minLength_; SizeType maxLength_; SValue minimum_; SValue maximum_; SValue multipleOf_; bool exclusiveMinimum_; bool exclusiveMaximum_; SizeType defaultValueLength_; }; template<typename Stack, typename Ch> struct TokenHelper { CEREAL_RAPIDJSON_FORCEINLINE static void AppendIndexToken(Stack& documentStack, SizeType index) { *documentStack.template Push<Ch>() = '/'; char buffer[21]; size_t length = static_cast<size_t>((sizeof(SizeType) == 4 ? u32toa(index, buffer) : u64toa(index, buffer)) - buffer); for (size_t i = 0; i < length; i++) *documentStack.template Push<Ch>() = static_cast<Ch>(buffer[i]); } }; // Partial specialized version for char to prevent buffer copying. template <typename Stack> struct TokenHelper<Stack, char> { CEREAL_RAPIDJSON_FORCEINLINE static void AppendIndexToken(Stack& documentStack, SizeType index) { if (sizeof(SizeType) == 4) { char *buffer = documentStack.template Push<char>(1 + 10); // '/' + uint *buffer++ = '/'; const char* end = internal::u32toa(index, buffer); documentStack.template Pop<char>(static_cast<size_t>(10 - (end - buffer))); } else { char *buffer = documentStack.template Push<char>(1 + 20); // '/' + uint64 *buffer++ = '/'; const char* end = internal::u64toa(index, buffer); documentStack.template Pop<char>(static_cast<size_t>(20 - (end - buffer))); } } }; } // namespace internal /////////////////////////////////////////////////////////////////////////////// // IGenericRemoteSchemaDocumentProvider template <typename SchemaDocumentType> class IGenericRemoteSchemaDocumentProvider { public: typedef typename SchemaDocumentType::Ch Ch; virtual ~IGenericRemoteSchemaDocumentProvider() {} virtual const SchemaDocumentType* GetRemoteDocument(const Ch* uri, SizeType length) = 0; }; /////////////////////////////////////////////////////////////////////////////// // GenericSchemaDocument //! JSON schema document. /*! A JSON schema document is a compiled version of a JSON schema. It is basically a tree of internal::Schema. \note This is an immutable class (i.e. its instance cannot be modified after construction). \tparam ValueT Type of JSON value (e.g. \c Value ), which also determine the encoding. \tparam Allocator Allocator type for allocating memory of this document. */ template <typename ValueT, typename Allocator = CrtAllocator> class GenericSchemaDocument { public: typedef ValueT ValueType; typedef IGenericRemoteSchemaDocumentProvider<GenericSchemaDocument> IRemoteSchemaDocumentProviderType; typedef Allocator AllocatorType; typedef typename ValueType::EncodingType EncodingType; typedef typename EncodingType::Ch Ch; typedef internal::Schema<GenericSchemaDocument> SchemaType; typedef GenericPointer<ValueType, Allocator> PointerType; typedef GenericValue<EncodingType, Allocator> URIType; friend class internal::Schema<GenericSchemaDocument>; template <typename, typename, typename> friend class GenericSchemaValidator; //! Constructor. /*! Compile a JSON document into schema document. \param document A JSON document as source. \param uri The base URI of this schema document for purposes of violation reporting. \param uriLength Length of \c name, in code points. \param remoteProvider An optional remote schema document provider for resolving remote reference. Can be null. \param allocator An optional allocator instance for allocating memory. Can be null. */ explicit GenericSchemaDocument(const ValueType& document, const Ch* uri = 0, SizeType uriLength = 0, IRemoteSchemaDocumentProviderType* remoteProvider = 0, Allocator* allocator = 0) : remoteProvider_(remoteProvider), allocator_(allocator), ownAllocator_(), root_(), typeless_(), schemaMap_(allocator, kInitialSchemaMapSize), schemaRef_(allocator, kInitialSchemaRefSize) { if (!allocator_) ownAllocator_ = allocator_ = CEREAL_RAPIDJSON_NEW(Allocator)(); Ch noUri[1] = {0}; uri_.SetString(uri ? uri : noUri, uriLength, *allocator_); typeless_ = static_cast<SchemaType*>(allocator_->Malloc(sizeof(SchemaType))); new (typeless_) SchemaType(this, PointerType(), ValueType(kObjectType).Move(), ValueType(kObjectType).Move(), allocator_); // Generate root schema, it will call CreateSchema() to create sub-schemas, // And call AddRefSchema() if there are $ref. CreateSchemaRecursive(&root_, PointerType(), document, document); // Resolve $ref while (!schemaRef_.Empty()) { SchemaRefEntry* refEntry = schemaRef_.template Pop<SchemaRefEntry>(1); if (const SchemaType* s = GetSchema(refEntry->target)) { if (refEntry->schema) *refEntry->schema = s; // Create entry in map if not exist if (!GetSchema(refEntry->source)) { new (schemaMap_.template Push<SchemaEntry>()) SchemaEntry(refEntry->source, const_cast<SchemaType*>(s), false, allocator_); } } else if (refEntry->schema) *refEntry->schema = typeless_; refEntry->~SchemaRefEntry(); } CEREAL_RAPIDJSON_ASSERT(root_ != 0); schemaRef_.ShrinkToFit(); // Deallocate all memory for ref } #if CEREAL_RAPIDJSON_HAS_CXX11_RVALUE_REFS //! Move constructor in C++11 GenericSchemaDocument(GenericSchemaDocument&& rhs) CEREAL_RAPIDJSON_NOEXCEPT : remoteProvider_(rhs.remoteProvider_), allocator_(rhs.allocator_), ownAllocator_(rhs.ownAllocator_), root_(rhs.root_), typeless_(rhs.typeless_), schemaMap_(std::move(rhs.schemaMap_)), schemaRef_(std::move(rhs.schemaRef_)), uri_(std::move(rhs.uri_)) { rhs.remoteProvider_ = 0; rhs.allocator_ = 0; rhs.ownAllocator_ = 0; rhs.typeless_ = 0; } #endif //! Destructor ~GenericSchemaDocument() { while (!schemaMap_.Empty()) schemaMap_.template Pop<SchemaEntry>(1)->~SchemaEntry(); if (typeless_) { typeless_->~SchemaType(); Allocator::Free(typeless_); } CEREAL_RAPIDJSON_DELETE(ownAllocator_); } const URIType& GetURI() const { return uri_; } //! Get the root schema. const SchemaType& GetRoot() const { return *root_; } private: //! Prohibit copying GenericSchemaDocument(const GenericSchemaDocument&); //! Prohibit assignment GenericSchemaDocument& operator=(const GenericSchemaDocument&); struct SchemaRefEntry { SchemaRefEntry(const PointerType& s, const PointerType& t, const SchemaType** outSchema, Allocator *allocator) : source(s, allocator), target(t, allocator), schema(outSchema) {} PointerType source; PointerType target; const SchemaType** schema; }; struct SchemaEntry { SchemaEntry(const PointerType& p, SchemaType* s, bool o, Allocator* allocator) : pointer(p, allocator), schema(s), owned(o) {} ~SchemaEntry() { if (owned) { schema->~SchemaType(); Allocator::Free(schema); } } PointerType pointer; SchemaType* schema; bool owned; }; void CreateSchemaRecursive(const SchemaType** schema, const PointerType& pointer, const ValueType& v, const ValueType& document) { if (schema) *schema = typeless_; if (v.GetType() == kObjectType) { const SchemaType* s = GetSchema(pointer); if (!s) CreateSchema(schema, pointer, v, document); for (typename ValueType::ConstMemberIterator itr = v.MemberBegin(); itr != v.MemberEnd(); ++itr) CreateSchemaRecursive(0, pointer.Append(itr->name, allocator_), itr->value, document); } else if (v.GetType() == kArrayType) for (SizeType i = 0; i < v.Size(); i++) CreateSchemaRecursive(0, pointer.Append(i, allocator_), v[i], document); } void CreateSchema(const SchemaType** schema, const PointerType& pointer, const ValueType& v, const ValueType& document) { CEREAL_RAPIDJSON_ASSERT(pointer.IsValid()); if (v.IsObject()) { if (!HandleRefSchema(pointer, schema, v, document)) { SchemaType* s = new (allocator_->Malloc(sizeof(SchemaType))) SchemaType(this, pointer, v, document, allocator_); new (schemaMap_.template Push<SchemaEntry>()) SchemaEntry(pointer, s, true, allocator_); if (schema) *schema = s; } } } bool HandleRefSchema(const PointerType& source, const SchemaType** schema, const ValueType& v, const ValueType& document) { static const Ch kRefString[] = { '$', 'r', 'e', 'f', '\0' }; static const ValueType kRefValue(kRefString, 4); typename ValueType::ConstMemberIterator itr = v.FindMember(kRefValue); if (itr == v.MemberEnd()) return false; if (itr->value.IsString()) { SizeType len = itr->value.GetStringLength(); if (len > 0) { const Ch* s = itr->value.GetString(); SizeType i = 0; while (i < len && s[i] != '#') // Find the first # i++; if (i > 0) { // Remote reference, resolve immediately if (remoteProvider_) { if (const GenericSchemaDocument* remoteDocument = remoteProvider_->GetRemoteDocument(s, i)) { PointerType pointer(&s[i], len - i, allocator_); if (pointer.IsValid()) { if (const SchemaType* sc = remoteDocument->GetSchema(pointer)) { if (schema) *schema = sc; new (schemaMap_.template Push<SchemaEntry>()) SchemaEntry(source, const_cast<SchemaType*>(sc), false, allocator_); return true; } } } } } else if (s[i] == '#') { // Local reference, defer resolution PointerType pointer(&s[i], len - i, allocator_); if (pointer.IsValid()) { if (const ValueType* nv = pointer.Get(document)) if (HandleRefSchema(source, schema, *nv, document)) return true; new (schemaRef_.template Push<SchemaRefEntry>()) SchemaRefEntry(source, pointer, schema, allocator_); return true; } } } } return false; } const SchemaType* GetSchema(const PointerType& pointer) const { for (const SchemaEntry* target = schemaMap_.template Bottom<SchemaEntry>(); target != schemaMap_.template End<SchemaEntry>(); ++target) if (pointer == target->pointer) return target->schema; return 0; } PointerType GetPointer(const SchemaType* schema) const { for (const SchemaEntry* target = schemaMap_.template Bottom<SchemaEntry>(); target != schemaMap_.template End<SchemaEntry>(); ++target) if (schema == target->schema) return target->pointer; return PointerType(); } const SchemaType* GetTypeless() const { return typeless_; } static const size_t kInitialSchemaMapSize = 64; static const size_t kInitialSchemaRefSize = 64; IRemoteSchemaDocumentProviderType* remoteProvider_; Allocator *allocator_; Allocator *ownAllocator_; const SchemaType* root_; //!< Root schema. SchemaType* typeless_; internal::Stack<Allocator> schemaMap_; // Stores created Pointer -> Schemas internal::Stack<Allocator> schemaRef_; // Stores Pointer from $ref and schema which holds the $ref URIType uri_; }; //! GenericSchemaDocument using Value type. typedef GenericSchemaDocument<Value> SchemaDocument; //! IGenericRemoteSchemaDocumentProvider using SchemaDocument. typedef IGenericRemoteSchemaDocumentProvider<SchemaDocument> IRemoteSchemaDocumentProvider; /////////////////////////////////////////////////////////////////////////////// // GenericSchemaValidator //! JSON Schema Validator. /*! A SAX style JSON schema validator. It uses a \c GenericSchemaDocument to validate SAX events. It delegates the incoming SAX events to an output handler. The default output handler does nothing. It can be reused multiple times by calling \c Reset(). \tparam SchemaDocumentType Type of schema document. \tparam OutputHandler Type of output handler. Default handler does nothing. \tparam StateAllocator Allocator for storing the internal validation states. */ template < typename SchemaDocumentType, typename OutputHandler = BaseReaderHandler<typename SchemaDocumentType::SchemaType::EncodingType>, typename StateAllocator = CrtAllocator> class GenericSchemaValidator : public internal::ISchemaStateFactory<typename SchemaDocumentType::SchemaType>, public internal::ISchemaValidator, public internal::IValidationErrorHandler<typename SchemaDocumentType::SchemaType> { public: typedef typename SchemaDocumentType::SchemaType SchemaType; typedef typename SchemaDocumentType::PointerType PointerType; typedef typename SchemaType::EncodingType EncodingType; typedef typename SchemaType::SValue SValue; typedef typename EncodingType::Ch Ch; typedef GenericStringRef<Ch> StringRefType; typedef GenericValue<EncodingType, StateAllocator> ValueType; //! Constructor without output handler. /*! \param schemaDocument The schema document to conform to. \param allocator Optional allocator for storing internal validation states. \param schemaStackCapacity Optional initial capacity of schema path stack. \param documentStackCapacity Optional initial capacity of document path stack. */ GenericSchemaValidator( const SchemaDocumentType& schemaDocument, StateAllocator* allocator = 0, size_t schemaStackCapacity = kDefaultSchemaStackCapacity, size_t documentStackCapacity = kDefaultDocumentStackCapacity) : schemaDocument_(&schemaDocument), root_(schemaDocument.GetRoot()), stateAllocator_(allocator), ownStateAllocator_(0), schemaStack_(allocator, schemaStackCapacity), documentStack_(allocator, documentStackCapacity), outputHandler_(0), error_(kObjectType), currentError_(), missingDependents_(), valid_(true) #if CEREAL_RAPIDJSON_SCHEMA_VERBOSE , depth_(0) #endif { } //! Constructor with output handler. /*! \param schemaDocument The schema document to conform to. \param allocator Optional allocator for storing internal validation states. \param schemaStackCapacity Optional initial capacity of schema path stack. \param documentStackCapacity Optional initial capacity of document path stack. */ GenericSchemaValidator( const SchemaDocumentType& schemaDocument, OutputHandler& outputHandler, StateAllocator* allocator = 0, size_t schemaStackCapacity = kDefaultSchemaStackCapacity, size_t documentStackCapacity = kDefaultDocumentStackCapacity) : schemaDocument_(&schemaDocument), root_(schemaDocument.GetRoot()), stateAllocator_(allocator), ownStateAllocator_(0), schemaStack_(allocator, schemaStackCapacity), documentStack_(allocator, documentStackCapacity), outputHandler_(&outputHandler), error_(kObjectType), currentError_(), missingDependents_(), valid_(true) #if CEREAL_RAPIDJSON_SCHEMA_VERBOSE , depth_(0) #endif { } //! Destructor. ~GenericSchemaValidator() { Reset(); CEREAL_RAPIDJSON_DELETE(ownStateAllocator_); } //! Reset the internal states. void Reset() { while (!schemaStack_.Empty()) PopSchema(); documentStack_.Clear(); error_.SetObject(); currentError_.SetNull(); missingDependents_.SetNull(); valid_ = true; } //! Checks whether the current state is valid. // Implementation of ISchemaValidator virtual bool IsValid() const { return valid_; } //! Gets the error object. ValueType& GetError() { return error_; } const ValueType& GetError() const { return error_; } //! Gets the JSON pointer pointed to the invalid schema. PointerType GetInvalidSchemaPointer() const { return schemaStack_.Empty() ? PointerType() : CurrentSchema().GetPointer(); } //! Gets the keyword of invalid schema. const Ch* GetInvalidSchemaKeyword() const { return schemaStack_.Empty() ? 0 : CurrentContext().invalidKeyword; } //! Gets the JSON pointer pointed to the invalid value. PointerType GetInvalidDocumentPointer() const { if (documentStack_.Empty()) { return PointerType(); } else { return PointerType(documentStack_.template Bottom<Ch>(), documentStack_.GetSize() / sizeof(Ch)); } } void NotMultipleOf(int64_t actual, const SValue& expected) { AddNumberError(SchemaType::GetMultipleOfString(), ValueType(actual).Move(), expected); } void NotMultipleOf(uint64_t actual, const SValue& expected) { AddNumberError(SchemaType::GetMultipleOfString(), ValueType(actual).Move(), expected); } void NotMultipleOf(double actual, const SValue& expected) { AddNumberError(SchemaType::GetMultipleOfString(), ValueType(actual).Move(), expected); } void AboveMaximum(int64_t actual, const SValue& expected, bool exclusive) { AddNumberError(SchemaType::GetMaximumString(), ValueType(actual).Move(), expected, exclusive ? &SchemaType::GetExclusiveMaximumString : 0); } void AboveMaximum(uint64_t actual, const SValue& expected, bool exclusive) { AddNumberError(SchemaType::GetMaximumString(), ValueType(actual).Move(), expected, exclusive ? &SchemaType::GetExclusiveMaximumString : 0); } void AboveMaximum(double actual, const SValue& expected, bool exclusive) { AddNumberError(SchemaType::GetMaximumString(), ValueType(actual).Move(), expected, exclusive ? &SchemaType::GetExclusiveMaximumString : 0); } void BelowMinimum(int64_t actual, const SValue& expected, bool exclusive) { AddNumberError(SchemaType::GetMinimumString(), ValueType(actual).Move(), expected, exclusive ? &SchemaType::GetExclusiveMinimumString : 0); } void BelowMinimum(uint64_t actual, const SValue& expected, bool exclusive) { AddNumberError(SchemaType::GetMinimumString(), ValueType(actual).Move(), expected, exclusive ? &SchemaType::GetExclusiveMinimumString : 0); } void BelowMinimum(double actual, const SValue& expected, bool exclusive) { AddNumberError(SchemaType::GetMinimumString(), ValueType(actual).Move(), expected, exclusive ? &SchemaType::GetExclusiveMinimumString : 0); } void TooLong(const Ch* str, SizeType length, SizeType expected) { AddNumberError(SchemaType::GetMaxLengthString(), ValueType(str, length, GetStateAllocator()).Move(), SValue(expected).Move()); } void TooShort(const Ch* str, SizeType length, SizeType expected) { AddNumberError(SchemaType::GetMinLengthString(), ValueType(str, length, GetStateAllocator()).Move(), SValue(expected).Move()); } void DoesNotMatch(const Ch* str, SizeType length) { currentError_.SetObject(); currentError_.AddMember(GetActualString(), ValueType(str, length, GetStateAllocator()).Move(), GetStateAllocator()); AddCurrentError(SchemaType::GetPatternString()); } void DisallowedItem(SizeType index) { currentError_.SetObject(); currentError_.AddMember(GetDisallowedString(), ValueType(index).Move(), GetStateAllocator()); AddCurrentError(SchemaType::GetAdditionalItemsString(), true); } void TooFewItems(SizeType actualCount, SizeType expectedCount) { AddNumberError(SchemaType::GetMinItemsString(), ValueType(actualCount).Move(), SValue(expectedCount).Move()); } void TooManyItems(SizeType actualCount, SizeType expectedCount) { AddNumberError(SchemaType::GetMaxItemsString(), ValueType(actualCount).Move(), SValue(expectedCount).Move()); } void DuplicateItems(SizeType index1, SizeType index2) { ValueType duplicates(kArrayType); duplicates.PushBack(index1, GetStateAllocator()); duplicates.PushBack(index2, GetStateAllocator()); currentError_.SetObject(); currentError_.AddMember(GetDuplicatesString(), duplicates, GetStateAllocator()); AddCurrentError(SchemaType::GetUniqueItemsString(), true); } void TooManyProperties(SizeType actualCount, SizeType expectedCount) { AddNumberError(SchemaType::GetMaxPropertiesString(), ValueType(actualCount).Move(), SValue(expectedCount).Move()); } void TooFewProperties(SizeType actualCount, SizeType expectedCount) { AddNumberError(SchemaType::GetMinPropertiesString(), ValueType(actualCount).Move(), SValue(expectedCount).Move()); } void StartMissingProperties() { currentError_.SetArray(); } void AddMissingProperty(const SValue& name) { currentError_.PushBack(ValueType(name, GetStateAllocator()).Move(), GetStateAllocator()); } bool EndMissingProperties() { if (currentError_.Empty()) return false; ValueType error(kObjectType); error.AddMember(GetMissingString(), currentError_, GetStateAllocator()); currentError_ = error; AddCurrentError(SchemaType::GetRequiredString()); return true; } void PropertyViolations(ISchemaValidator** subvalidators, SizeType count) { for (SizeType i = 0; i < count; ++i) MergeError(static_cast<GenericSchemaValidator*>(subvalidators[i])->GetError()); } void DisallowedProperty(const Ch* name, SizeType length) { currentError_.SetObject(); currentError_.AddMember(GetDisallowedString(), ValueType(name, length, GetStateAllocator()).Move(), GetStateAllocator()); AddCurrentError(SchemaType::GetAdditionalPropertiesString(), true); } void StartDependencyErrors() { currentError_.SetObject(); } void StartMissingDependentProperties() { missingDependents_.SetArray(); } void AddMissingDependentProperty(const SValue& targetName) { missingDependents_.PushBack(ValueType(targetName, GetStateAllocator()).Move(), GetStateAllocator()); } void EndMissingDependentProperties(const SValue& sourceName) { if (!missingDependents_.Empty()) currentError_.AddMember(ValueType(sourceName, GetStateAllocator()).Move(), missingDependents_, GetStateAllocator()); } void AddDependencySchemaError(const SValue& sourceName, ISchemaValidator* subvalidator) { currentError_.AddMember(ValueType(sourceName, GetStateAllocator()).Move(), static_cast<GenericSchemaValidator*>(subvalidator)->GetError(), GetStateAllocator()); } bool EndDependencyErrors() { if (currentError_.ObjectEmpty()) return false; ValueType error(kObjectType); error.AddMember(GetErrorsString(), currentError_, GetStateAllocator()); currentError_ = error; AddCurrentError(SchemaType::GetDependenciesString()); return true; } void DisallowedValue() { currentError_.SetObject(); AddCurrentError(SchemaType::GetEnumString()); } void StartDisallowedType() { currentError_.SetArray(); } void AddExpectedType(const typename SchemaType::ValueType& expectedType) { currentError_.PushBack(ValueType(expectedType, GetStateAllocator()).Move(), GetStateAllocator()); } void EndDisallowedType(const typename SchemaType::ValueType& actualType) { ValueType error(kObjectType); error.AddMember(GetExpectedString(), currentError_, GetStateAllocator()); error.AddMember(GetActualString(), ValueType(actualType, GetStateAllocator()).Move(), GetStateAllocator()); currentError_ = error; AddCurrentError(SchemaType::GetTypeString()); } void NotAllOf(ISchemaValidator** subvalidators, SizeType count) { for (SizeType i = 0; i < count; ++i) { MergeError(static_cast<GenericSchemaValidator*>(subvalidators[i])->GetError()); } } void NoneOf(ISchemaValidator** subvalidators, SizeType count) { AddErrorArray(SchemaType::GetAnyOfString(), subvalidators, count); } void NotOneOf(ISchemaValidator** subvalidators, SizeType count) { AddErrorArray(SchemaType::GetOneOfString(), subvalidators, count); } void Disallowed() { currentError_.SetObject(); AddCurrentError(SchemaType::GetNotString()); } #define CEREAL_RAPIDJSON_STRING_(name, ...) \ static const StringRefType& Get##name##String() {\ static const Ch s[] = { __VA_ARGS__, '\0' };\ static const StringRefType v(s, static_cast<SizeType>(sizeof(s) / sizeof(Ch) - 1)); \ return v;\ } CEREAL_RAPIDJSON_STRING_(InstanceRef, 'i', 'n', 's', 't', 'a', 'n', 'c', 'e', 'R', 'e', 'f') CEREAL_RAPIDJSON_STRING_(SchemaRef, 's', 'c', 'h', 'e', 'm', 'a', 'R', 'e', 'f') CEREAL_RAPIDJSON_STRING_(Expected, 'e', 'x', 'p', 'e', 'c', 't', 'e', 'd') CEREAL_RAPIDJSON_STRING_(Actual, 'a', 'c', 't', 'u', 'a', 'l') CEREAL_RAPIDJSON_STRING_(Disallowed, 'd', 'i', 's', 'a', 'l', 'l', 'o', 'w', 'e', 'd') CEREAL_RAPIDJSON_STRING_(Missing, 'm', 'i', 's', 's', 'i', 'n', 'g') CEREAL_RAPIDJSON_STRING_(Errors, 'e', 'r', 'r', 'o', 'r', 's') CEREAL_RAPIDJSON_STRING_(Duplicates, 'd', 'u', 'p', 'l', 'i', 'c', 'a', 't', 'e', 's') #undef CEREAL_RAPIDJSON_STRING_ #if CEREAL_RAPIDJSON_SCHEMA_VERBOSE #define CEREAL_RAPIDJSON_SCHEMA_HANDLE_BEGIN_VERBOSE_() \ CEREAL_RAPIDJSON_MULTILINEMACRO_BEGIN\ *documentStack_.template Push<Ch>() = '\0';\ documentStack_.template Pop<Ch>(1);\ internal::PrintInvalidDocument(documentStack_.template Bottom<Ch>());\ CEREAL_RAPIDJSON_MULTILINEMACRO_END #else #define CEREAL_RAPIDJSON_SCHEMA_HANDLE_BEGIN_VERBOSE_() #endif #define CEREAL_RAPIDJSON_SCHEMA_HANDLE_BEGIN_(method, arg1)\ if (!valid_) return false; \ if (!BeginValue() || !CurrentSchema().method arg1) {\ CEREAL_RAPIDJSON_SCHEMA_HANDLE_BEGIN_VERBOSE_();\ return valid_ = false;\ } #define CEREAL_RAPIDJSON_SCHEMA_HANDLE_PARALLEL_(method, arg2)\ for (Context* context = schemaStack_.template Bottom<Context>(); context != schemaStack_.template End<Context>(); context++) {\ if (context->hasher)\ static_cast<HasherType*>(context->hasher)->method arg2;\ if (context->validators)\ for (SizeType i_ = 0; i_ < context->validatorCount; i_++)\ static_cast<GenericSchemaValidator*>(context->validators[i_])->method arg2;\ if (context->patternPropertiesValidators)\ for (SizeType i_ = 0; i_ < context->patternPropertiesValidatorCount; i_++)\ static_cast<GenericSchemaValidator*>(context->patternPropertiesValidators[i_])->method arg2;\ } #define CEREAL_RAPIDJSON_SCHEMA_HANDLE_END_(method, arg2)\ return valid_ = EndValue() && (!outputHandler_ || outputHandler_->method arg2) #define CEREAL_RAPIDJSON_SCHEMA_HANDLE_VALUE_(method, arg1, arg2) \ CEREAL_RAPIDJSON_SCHEMA_HANDLE_BEGIN_ (method, arg1);\ CEREAL_RAPIDJSON_SCHEMA_HANDLE_PARALLEL_(method, arg2);\ CEREAL_RAPIDJSON_SCHEMA_HANDLE_END_ (method, arg2) bool Null() { CEREAL_RAPIDJSON_SCHEMA_HANDLE_VALUE_(Null, (CurrentContext()), ( )); } bool Bool(bool b) { CEREAL_RAPIDJSON_SCHEMA_HANDLE_VALUE_(Bool, (CurrentContext(), b), (b)); } bool Int(int i) { CEREAL_RAPIDJSON_SCHEMA_HANDLE_VALUE_(Int, (CurrentContext(), i), (i)); } bool Uint(unsigned u) { CEREAL_RAPIDJSON_SCHEMA_HANDLE_VALUE_(Uint, (CurrentContext(), u), (u)); } bool Int64(int64_t i) { CEREAL_RAPIDJSON_SCHEMA_HANDLE_VALUE_(Int64, (CurrentContext(), i), (i)); } bool Uint64(uint64_t u) { CEREAL_RAPIDJSON_SCHEMA_HANDLE_VALUE_(Uint64, (CurrentContext(), u), (u)); } bool Double(double d) { CEREAL_RAPIDJSON_SCHEMA_HANDLE_VALUE_(Double, (CurrentContext(), d), (d)); } bool RawNumber(const Ch* str, SizeType length, bool copy) { CEREAL_RAPIDJSON_SCHEMA_HANDLE_VALUE_(String, (CurrentContext(), str, length, copy), (str, length, copy)); } bool String(const Ch* str, SizeType length, bool copy) { CEREAL_RAPIDJSON_SCHEMA_HANDLE_VALUE_(String, (CurrentContext(), str, length, copy), (str, length, copy)); } bool StartObject() { CEREAL_RAPIDJSON_SCHEMA_HANDLE_BEGIN_(StartObject, (CurrentContext())); CEREAL_RAPIDJSON_SCHEMA_HANDLE_PARALLEL_(StartObject, ()); return valid_ = !outputHandler_ || outputHandler_->StartObject(); } bool Key(const Ch* str, SizeType len, bool copy) { if (!valid_) return false; AppendToken(str, len); if (!CurrentSchema().Key(CurrentContext(), str, len, copy)) return valid_ = false; CEREAL_RAPIDJSON_SCHEMA_HANDLE_PARALLEL_(Key, (str, len, copy)); return valid_ = !outputHandler_ || outputHandler_->Key(str, len, copy); } bool EndObject(SizeType memberCount) { if (!valid_) return false; CEREAL_RAPIDJSON_SCHEMA_HANDLE_PARALLEL_(EndObject, (memberCount)); if (!CurrentSchema().EndObject(CurrentContext(), memberCount)) return valid_ = false; CEREAL_RAPIDJSON_SCHEMA_HANDLE_END_(EndObject, (memberCount)); } bool StartArray() { CEREAL_RAPIDJSON_SCHEMA_HANDLE_BEGIN_(StartArray, (CurrentContext())); CEREAL_RAPIDJSON_SCHEMA_HANDLE_PARALLEL_(StartArray, ()); return valid_ = !outputHandler_ || outputHandler_->StartArray(); } bool EndArray(SizeType elementCount) { if (!valid_) return false; CEREAL_RAPIDJSON_SCHEMA_HANDLE_PARALLEL_(EndArray, (elementCount)); if (!CurrentSchema().EndArray(CurrentContext(), elementCount)) return valid_ = false; CEREAL_RAPIDJSON_SCHEMA_HANDLE_END_(EndArray, (elementCount)); } #undef CEREAL_RAPIDJSON_SCHEMA_HANDLE_BEGIN_VERBOSE_ #undef CEREAL_RAPIDJSON_SCHEMA_HANDLE_BEGIN_ #undef CEREAL_RAPIDJSON_SCHEMA_HANDLE_PARALLEL_ #undef CEREAL_RAPIDJSON_SCHEMA_HANDLE_VALUE_ // Implementation of ISchemaStateFactory<SchemaType> virtual ISchemaValidator* CreateSchemaValidator(const SchemaType& root) { return new (GetStateAllocator().Malloc(sizeof(GenericSchemaValidator))) GenericSchemaValidator(*schemaDocument_, root, documentStack_.template Bottom<char>(), documentStack_.GetSize(), #if CEREAL_RAPIDJSON_SCHEMA_VERBOSE depth_ + 1, #endif &GetStateAllocator()); } virtual void DestroySchemaValidator(ISchemaValidator* validator) { GenericSchemaValidator* v = static_cast<GenericSchemaValidator*>(validator); v->~GenericSchemaValidator(); StateAllocator::Free(v); } virtual void* CreateHasher() { return new (GetStateAllocator().Malloc(sizeof(HasherType))) HasherType(&GetStateAllocator()); } virtual uint64_t GetHashCode(void* hasher) { return static_cast<HasherType*>(hasher)->GetHashCode(); } virtual void DestroryHasher(void* hasher) { HasherType* h = static_cast<HasherType*>(hasher); h->~HasherType(); StateAllocator::Free(h); } virtual void* MallocState(size_t size) { return GetStateAllocator().Malloc(size); } virtual void FreeState(void* p) { StateAllocator::Free(p); } private: typedef typename SchemaType::Context Context; typedef GenericValue<UTF8<>, StateAllocator> HashCodeArray; typedef internal::Hasher<EncodingType, StateAllocator> HasherType; GenericSchemaValidator( const SchemaDocumentType& schemaDocument, const SchemaType& root, const char* basePath, size_t basePathSize, #if CEREAL_RAPIDJSON_SCHEMA_VERBOSE unsigned depth, #endif StateAllocator* allocator = 0, size_t schemaStackCapacity = kDefaultSchemaStackCapacity, size_t documentStackCapacity = kDefaultDocumentStackCapacity) : schemaDocument_(&schemaDocument), root_(root), stateAllocator_(allocator), ownStateAllocator_(0), schemaStack_(allocator, schemaStackCapacity), documentStack_(allocator, documentStackCapacity), outputHandler_(0), error_(kObjectType), currentError_(), missingDependents_(), valid_(true) #if CEREAL_RAPIDJSON_SCHEMA_VERBOSE , depth_(depth) #endif { if (basePath && basePathSize) memcpy(documentStack_.template Push<char>(basePathSize), basePath, basePathSize); } StateAllocator& GetStateAllocator() { if (!stateAllocator_) stateAllocator_ = ownStateAllocator_ = CEREAL_RAPIDJSON_NEW(StateAllocator)(); return *stateAllocator_; } bool BeginValue() { if (schemaStack_.Empty()) PushSchema(root_); else { if (CurrentContext().inArray) internal::TokenHelper<internal::Stack<StateAllocator>, Ch>::AppendIndexToken(documentStack_, CurrentContext().arrayElementIndex); if (!CurrentSchema().BeginValue(CurrentContext())) return false; SizeType count = CurrentContext().patternPropertiesSchemaCount; const SchemaType** sa = CurrentContext().patternPropertiesSchemas; typename Context::PatternValidatorType patternValidatorType = CurrentContext().valuePatternValidatorType; bool valueUniqueness = CurrentContext().valueUniqueness; CEREAL_RAPIDJSON_ASSERT(CurrentContext().valueSchema); PushSchema(*CurrentContext().valueSchema); if (count > 0) { CurrentContext().objectPatternValidatorType = patternValidatorType; ISchemaValidator**& va = CurrentContext().patternPropertiesValidators; SizeType& validatorCount = CurrentContext().patternPropertiesValidatorCount; va = static_cast<ISchemaValidator**>(MallocState(sizeof(ISchemaValidator*) * count)); for (SizeType i = 0; i < count; i++) va[validatorCount++] = CreateSchemaValidator(*sa[i]); } CurrentContext().arrayUniqueness = valueUniqueness; } return true; } bool EndValue() { if (!CurrentSchema().EndValue(CurrentContext())) return false; #if CEREAL_RAPIDJSON_SCHEMA_VERBOSE GenericStringBuffer<EncodingType> sb; schemaDocument_->GetPointer(&CurrentSchema()).Stringify(sb); *documentStack_.template Push<Ch>() = '\0'; documentStack_.template Pop<Ch>(1); internal::PrintValidatorPointers(depth_, sb.GetString(), documentStack_.template Bottom<Ch>()); #endif uint64_t h = CurrentContext().arrayUniqueness ? static_cast<HasherType*>(CurrentContext().hasher)->GetHashCode() : 0; PopSchema(); if (!schemaStack_.Empty()) { Context& context = CurrentContext(); if (context.valueUniqueness) { HashCodeArray* a = static_cast<HashCodeArray*>(context.arrayElementHashCodes); if (!a) CurrentContext().arrayElementHashCodes = a = new (GetStateAllocator().Malloc(sizeof(HashCodeArray))) HashCodeArray(kArrayType); for (typename HashCodeArray::ConstValueIterator itr = a->Begin(); itr != a->End(); ++itr) if (itr->GetUint64() == h) { DuplicateItems(static_cast<SizeType>(itr - a->Begin()), a->Size()); CEREAL_RAPIDJSON_INVALID_KEYWORD_RETURN(SchemaType::GetUniqueItemsString()); } a->PushBack(h, GetStateAllocator()); } } // Remove the last token of document pointer while (!documentStack_.Empty() && *documentStack_.template Pop<Ch>(1) != '/') ; return true; } void AppendToken(const Ch* str, SizeType len) { documentStack_.template Reserve<Ch>(1 + len * 2); // worst case all characters are escaped as two characters *documentStack_.template PushUnsafe<Ch>() = '/'; for (SizeType i = 0; i < len; i++) { if (str[i] == '~') { *documentStack_.template PushUnsafe<Ch>() = '~'; *documentStack_.template PushUnsafe<Ch>() = '0'; } else if (str[i] == '/') { *documentStack_.template PushUnsafe<Ch>() = '~'; *documentStack_.template PushUnsafe<Ch>() = '1'; } else *documentStack_.template PushUnsafe<Ch>() = str[i]; } } CEREAL_RAPIDJSON_FORCEINLINE void PushSchema(const SchemaType& schema) { new (schemaStack_.template Push<Context>()) Context(*this, *this, &schema); } CEREAL_RAPIDJSON_FORCEINLINE void PopSchema() { Context* c = schemaStack_.template Pop<Context>(1); if (HashCodeArray* a = static_cast<HashCodeArray*>(c->arrayElementHashCodes)) { a->~HashCodeArray(); StateAllocator::Free(a); } c->~Context(); } void AddErrorLocation(ValueType& result, bool parent) { GenericStringBuffer<EncodingType> sb; PointerType instancePointer = GetInvalidDocumentPointer(); ((parent && instancePointer.GetTokenCount() > 0) ? PointerType(instancePointer.GetTokens(), instancePointer.GetTokenCount() - 1) : instancePointer).StringifyUriFragment(sb); ValueType instanceRef(sb.GetString(), static_cast<SizeType>(sb.GetSize() / sizeof(Ch)), GetStateAllocator()); result.AddMember(GetInstanceRefString(), instanceRef, GetStateAllocator()); sb.Clear(); memcpy(sb.Push(CurrentSchema().GetURI().GetStringLength()), CurrentSchema().GetURI().GetString(), CurrentSchema().GetURI().GetStringLength() * sizeof(Ch)); GetInvalidSchemaPointer().StringifyUriFragment(sb); ValueType schemaRef(sb.GetString(), static_cast<SizeType>(sb.GetSize() / sizeof(Ch)), GetStateAllocator()); result.AddMember(GetSchemaRefString(), schemaRef, GetStateAllocator()); } void AddError(ValueType& keyword, ValueType& error) { typename ValueType::MemberIterator member = error_.FindMember(keyword); if (member == error_.MemberEnd()) error_.AddMember(keyword, error, GetStateAllocator()); else { if (member->value.IsObject()) { ValueType errors(kArrayType); errors.PushBack(member->value, GetStateAllocator()); member->value = errors; } member->value.PushBack(error, GetStateAllocator()); } } void AddCurrentError(const typename SchemaType::ValueType& keyword, bool parent = false) { AddErrorLocation(currentError_, parent); AddError(ValueType(keyword, GetStateAllocator(), false).Move(), currentError_); } void MergeError(ValueType& other) { for (typename ValueType::MemberIterator it = other.MemberBegin(), end = other.MemberEnd(); it != end; ++it) { AddError(it->name, it->value); } } void AddNumberError(const typename SchemaType::ValueType& keyword, ValueType& actual, const SValue& expected, const typename SchemaType::ValueType& (*exclusive)() = 0) { currentError_.SetObject(); currentError_.AddMember(GetActualString(), actual, GetStateAllocator()); currentError_.AddMember(GetExpectedString(), ValueType(expected, GetStateAllocator()).Move(), GetStateAllocator()); if (exclusive) currentError_.AddMember(ValueType(exclusive(), GetStateAllocator()).Move(), true, GetStateAllocator()); AddCurrentError(keyword); } void AddErrorArray(const typename SchemaType::ValueType& keyword, ISchemaValidator** subvalidators, SizeType count) { ValueType errors(kArrayType); for (SizeType i = 0; i < count; ++i) errors.PushBack(static_cast<GenericSchemaValidator*>(subvalidators[i])->GetError(), GetStateAllocator()); currentError_.SetObject(); currentError_.AddMember(GetErrorsString(), errors, GetStateAllocator()); AddCurrentError(keyword); } const SchemaType& CurrentSchema() const { return *schemaStack_.template Top<Context>()->schema; } Context& CurrentContext() { return *schemaStack_.template Top<Context>(); } const Context& CurrentContext() const { return *schemaStack_.template Top<Context>(); } static const size_t kDefaultSchemaStackCapacity = 1024; static const size_t kDefaultDocumentStackCapacity = 256; const SchemaDocumentType* schemaDocument_; const SchemaType& root_; StateAllocator* stateAllocator_; StateAllocator* ownStateAllocator_; internal::Stack<StateAllocator> schemaStack_; //!< stack to store the current path of schema (BaseSchemaType *) internal::Stack<StateAllocator> documentStack_; //!< stack to store the current path of validating document (Ch) OutputHandler* outputHandler_; ValueType error_; ValueType currentError_; ValueType missingDependents_; bool valid_; #if CEREAL_RAPIDJSON_SCHEMA_VERBOSE unsigned depth_; #endif }; typedef GenericSchemaValidator<SchemaDocument> SchemaValidator; /////////////////////////////////////////////////////////////////////////////// // SchemaValidatingReader //! A helper class for parsing with validation. /*! This helper class is a functor, designed as a parameter of \ref GenericDocument::Populate(). \tparam parseFlags Combination of \ref ParseFlag. \tparam InputStream Type of input stream, implementing Stream concept. \tparam SourceEncoding Encoding of the input stream. \tparam SchemaDocumentType Type of schema document. \tparam StackAllocator Allocator type for stack. */ template < unsigned parseFlags, typename InputStream, typename SourceEncoding, typename SchemaDocumentType = SchemaDocument, typename StackAllocator = CrtAllocator> class SchemaValidatingReader { public: typedef typename SchemaDocumentType::PointerType PointerType; typedef typename InputStream::Ch Ch; typedef GenericValue<SourceEncoding, StackAllocator> ValueType; //! Constructor /*! \param is Input stream. \param sd Schema document. */ SchemaValidatingReader(InputStream& is, const SchemaDocumentType& sd) : is_(is), sd_(sd), invalidSchemaKeyword_(), error_(kObjectType), isValid_(true) {} template <typename Handler> bool operator()(Handler& handler) { GenericReader<SourceEncoding, typename SchemaDocumentType::EncodingType, StackAllocator> reader; GenericSchemaValidator<SchemaDocumentType, Handler> validator(sd_, handler); parseResult_ = reader.template Parse<parseFlags>(is_, validator); isValid_ = validator.IsValid(); if (isValid_) { invalidSchemaPointer_ = PointerType(); invalidSchemaKeyword_ = 0; invalidDocumentPointer_ = PointerType(); error_.SetObject(); } else { invalidSchemaPointer_ = validator.GetInvalidSchemaPointer(); invalidSchemaKeyword_ = validator.GetInvalidSchemaKeyword(); invalidDocumentPointer_ = validator.GetInvalidDocumentPointer(); error_.CopyFrom(validator.GetError(), allocator_); } return parseResult_; } const ParseResult& GetParseResult() const { return parseResult_; } bool IsValid() const { return isValid_; } const PointerType& GetInvalidSchemaPointer() const { return invalidSchemaPointer_; } const Ch* GetInvalidSchemaKeyword() const { return invalidSchemaKeyword_; } const PointerType& GetInvalidDocumentPointer() const { return invalidDocumentPointer_; } const ValueType& GetError() const { return error_; } private: InputStream& is_; const SchemaDocumentType& sd_; ParseResult parseResult_; PointerType invalidSchemaPointer_; const Ch* invalidSchemaKeyword_; PointerType invalidDocumentPointer_; StackAllocator allocator_; ValueType error_; bool isValid_; }; CEREAL_RAPIDJSON_NAMESPACE_END CEREAL_RAPIDJSON_DIAG_POP #endif // CEREAL_RAPIDJSON_SCHEMA_H_
0
coqui_public_repos/STT/native_client/ctcdecode/third_party
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/README
OpenFst: Release 1.6.9. OpenFst is a library for constructing, combining, optimizing, and searching weighted finite-state transducers (FSTs). REQUIREMENTS: This version is known to work under Linux using g++ (>= 4.7) and OS X using XCode (>= 5). It is expected to work wherever adequate POSIX (dlopen, ssize_t, basename), C99 (snprintf, strtoll, <stdint.h>), and C++11 (<unordered_set>, <unordered_map>, <forward_list>) support is available. INSTALLATION: Follow the generic GNU build system instructions in ./INSTALL. We recommend configuring with --enable-static=no for faster compiles. Optional features: --enable-bin Enable fst::script and executables (def: yes) --enable-compact-fsts Enable CompactFst extensions (def: no) --enable-compress Enable compression extension (def: no) --enable-const-fsts Enable ConstFst extensions (def: no) --enable-far Enable FAR extensions (def: no) --enable-grm Enable all dependencies of OpenGrm (def: no) --enable-linear-fsts Enable LinearTagger/ClassifierFst extensions (def: no) --enable-lookahead-fsts Enable LookAheadFst extensions (def: no) --enable-mpdt Enable MPDT extensions (def: no) --enable-ngram-fsts Enable NGramFst extensions (def: no) --enable-pdt Enable PDT extensions (def: no) --enable-python Enable Python extension (def: no) --enable-special Enable special-matcher extensions (def: no) Configuring with --enable-bin=no gives very fast compiles, but excludes the command line utilities. Configuring with --enable-python will attempt to install the Python module to whichever site-packages (or dist-packages, on Debian or Ubuntu) is found during configuration. The flag --with-libfstdir specifies where FST extensions should be installed; it defaults to ${libdir}/fst. Compiling with -Wall -Wno-sign-compare under g++ should give no warnings from this library. If you encounter an error about loading shared objects when attempting to use the library immediately after installation, (e.g, `...cannot open shared object file...`) you may need to refresh your system's shared object cache. On Linux, this is accomplished by invoking ldconfig; the corresponding command on OS X is called update_dyld_shared_cache. Both of these require superuser privileges (and so should be executed with sudo). USAGE: Assuming you've installed under the default /usr/local, the FST binaries are found on /usr/local/bin. To use in your own program, include <fst/fstlib.h> and compile with -I/usr/local/include. The compiler must support C++11 (for g++ add the flag -std=c++11). Link against /usr/local/lib/libfst.so and -ldl. Set your LD_LIBRARY_PATH (or equivalent) to contain /usr/local/lib. The linking is, by default, dynamic so that the Fst and Arc type DSO extensions can be used correctly if desired. Any extensions will be found under /usr/local/lib/fst or /usr/local/include/fst/extensions. DOCUMENTATION: See www.openfst.org for general documentation. See ./NEWS for updates since the last release.
0
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/extensions
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/extensions/compact/compact64_unweighted_acceptor-fst.cc
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. #include <fst/fst.h> #include <fst/compact-fst.h> namespace fst { static FstRegisterer< CompactUnweightedAcceptorFst<StdArc, uint64>> CompactUnweightedAcceptorFst_StdArc_uint64_registerer; static FstRegisterer< CompactUnweightedAcceptorFst<LogArc, uint64>> CompactUnweightedAcceptorFst_LogArc_uint64_registerer; } // namespace fst
0
coqui_public_repos/STT
coqui_public_repos/STT/taskcluster/test-electronjs_v9.2_multiarchpkg-win-cuda-opt.yml
build: template_file: test-win-cuda-opt-base.tyml dependencies: - "node-package-gpu" - "test-training_16k-linux-amd64-py36m-opt" test_model_task: "test-training_16k-linux-amd64-py36m-opt" system_setup: > ${system.sox_win} && ${nodejs.win.prep_12} args: tests_cmdline: "${system.homedir.win}/DeepSpeech/ds/taskcluster/tc-electron-tests.sh 12.x 9.2.0 16k cuda" metadata: name: "DeepSpeech Windows AMD64 CUDA ElectronJS MultiArch Package v9.2 tests" description: "Testing DeepSpeech for Windows/AMD64 on ElectronJS MultiArch Package v9.2, CUDA, optimized version"
0
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/extensions
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/extensions/python/fst.pxd
# See www.openfst.org for extensive documentation on this weighted # finite-state transducer library. from libc.time cimport time_t from libc.time cimport time from libcpp cimport bool from libcpp.vector cimport vector from libcpp.utility cimport pair from libcpp.string cimport string from basictypes cimport int32 from basictypes cimport int64 from basictypes cimport uint32 from basictypes cimport uint64 from ios cimport istream from ios cimport ostream cdef extern from "<fst/util.h>" nogil: # Note that this is a copy, so it should be viewed as read-only. bool FLAGS_fst_error_fatal cdef extern from "<fst/fstlib.h>" namespace "fst" nogil: # FST properties. const uint64 kExpanded const uint64 kMutable const uint64 kError const uint64 kAcceptor const uint64 kNotAcceptor const uint64 kIDeterministic const uint64 kNonIDeterministic const uint64 kODeterministic const uint64 kNonODeterministic const uint64 kEpsilons const uint64 kNoEpsilons const uint64 kIEpsilons const uint64 kNoIEpsilons const uint64 kOEpsilons const uint64 kNoOEpsilons const uint64 kILabelSorted const uint64 kNotILabelSorted const uint64 kOLabelSorted const uint64 kNotOLabelSorted const uint64 kWeighted const uint64 kUnweighted const uint64 kCyclic const uint64 kAcyclic const uint64 kInitialCyclic const uint64 kInitialAcyclic const uint64 kTopSorted const uint64 kNotTopSorted const uint64 kAccessible const uint64 kNotAccessible const uint64 kCoAccessible const uint64 kNotCoAccessible const uint64 kString const uint64 kNotString const uint64 kWeightedCycles const uint64 kUnweightedCycles const uint64 kNullProperties const uint64 kCopyProperties const uint64 kIntrinsicProperties const uint64 kExtrinsicProperties const uint64 kSetStartProperties const uint64 kSetFinalProperties const uint64 kAddStateProperties const uint64 kAddArcProperties const uint64 kSetArcProperties const uint64 kDeleteStatesProperties const uint64 kDeleteArcsProperties const uint64 kStateSortProperties const uint64 kArcSortProperties const uint64 kILabelInvariantProperties const uint64 kOLabelInvariantProperties const uint64 kWeightInvariantProperties const uint64 kAddSuperFinalProperties const uint64 kRmSuperFinalProperties const uint64 kBinaryProperties const uint64 kTrinaryProperties const uint64 kPosTrinaryProperties const uint64 kNegTrinaryProperties const uint64 kFstProperties # ArcIterator flags. const uint32 kArcILabelValue const uint32 kArcOLabelValue const uint32 kArcWeightValue const uint32 kArcNextStateValue const uint32 kArcNoCache const uint32 kArcValueFlags const uint32 kArcFlags # EncodeMapper flags. const uint32 kEncodeLabels const uint32 kEncodeWeights const uint32 kEncodeFlags # Default argument constants. const float kDelta const float kShortestDelta const int kNoLabel const int kNoStateId const int64 kNoSymbol enum ClosureType: CLOSURE_STAR CLOSURE_PLUS enum ComposeFilter: AUTO_FILTER NULL_FILTER SEQUENCE_FILTER ALT_SEQUENCE_FILTER MATCH_FILTER TRIVIAL_FILTER cdef cppclass ComposeOptions: ComposeOptions(bool, ComposeFilter) enum DeterminizeType: DETERMINIZE_FUNCTIONAL DETERMINIZE_NONFUNCTIONAL DETERMINIZE_DISAMBIGUATE enum EncodeType: DECODE ENCODE enum EpsNormalizeType: EPS_NORM_INPUT EPS_NORM_OUTPUT enum ProjectType: PROJECT_INPUT PROJECT_OUTPUT enum QueueType: TRIVIAL_QUEUE FIFO_QUEUE LIFO_QUEUE SHORTEST_FIRST_QUEUE TOP_ORDER_QUEUE STATE_ORDER_QUEUE SCC_QUEUE AUTO_QUEUE OTHER_QUEUE # This is a templated struct at the C++ level, but Cython does not support # templated structs unless we pretend they are full-blown classes. cdef cppclass RandGenOptions[RandArcSelection]: RandGenOptions(const RandArcSelection &, int32, int32, bool, bool) enum ReplaceLabelType: REPLACE_LABEL_NEITHER REPLACE_LABEL_INPUT REPLACE_LABEL_OUTPUT REPLACE_LABEL_BOTH enum ReweightType: REWEIGHT_TO_INITIAL REWEIGHT_TO_FINAL cdef cppclass SymbolTableTextOptions: SymbolTableTextOptions(bool) # Symbol tables. cdef cppclass SymbolTable: SymbolTable() SymbolTable(const string &) @staticmethod SymbolTable *Read(const string &) @staticmethod SymbolTable *ReadText(const string &, const SymbolTableTextOptions &) int64 AddSymbol(const string &, int64) int64 AddSymbol(const string &) SymbolTable *Copy() # Aliased for overload. string FindSymbol "Find"(int64) # Aliased for overload. int64 FindIndex "Find"(string) # Aliased for overload. bool MemberSymbol "Member"(string) # Aliased for overload. bool MemberIndex "Member"(int64) void AddTable(const SymbolTable &) int64 GetNthKey(ssize_t) const string &Name() void SetName(const string &) const string &CheckSum() const string &LabeledCheckSum() bool Write(const string &) bool WriteText(const string &) int64 AvailableKey() size_t NumSymbols() SymbolTable *CompactSymbolTable(const SymbolTable &syms) SymbolTable *MergeSymbolTable(const SymbolTable &, const SymbolTable &, bool *) SymbolTable *FstReadSymbols(const string &, bool) cdef cppclass SymbolTableIterator: SymbolTableIterator(const SymbolTable &) bool Done() void Next() void Reset() string Symbol() int64 Value() cdef extern from "<fst/script/fstscript.h>" namespace "fst::script" nogil: # Weights. cdef cppclass WeightClass: WeightClass() WeightClass(const WeightClass &) WeightClass(const string &, const string &) const string &Type() string ToString() @staticmethod const WeightClass &Zero(const string &) @staticmethod const WeightClass &One(const string &) @staticmethod const WeightClass &NoWeight(const string &) # Alias. cdef bool Eq "operator=="(const WeightClass &, const WeightClass &) # Alias. cdef bool Ne "operator!="(const WeightClass &, const WeightClass &) cdef WeightClass Plus(const WeightClass &, const WeightClass &) cdef WeightClass Times(const WeightClass &, const WeightClass &) cdef WeightClass Divide(const WeightClass &, const WeightClass &) cdef WeightClass Power(const WeightClass &, size_t) # Arcs. cdef cppclass ArcClass: ArcClass(const ArcClass &) ArcClass(int64, int64, const WeightClass &, int64) int64 ilabel int64 olabel WeightClass weight int64 nextstate # FSTs. cdef cppclass FstClass: FstClass(const FstClass &) @staticmethod FstClass *Read(const string &) # Aliased for overload. @staticmethod FstClass *ReadFromStream "Read"(istream &, const string &) int64 Start() WeightClass Final(int64) size_t NumArcs(int64) size_t NumInputEpsilons(int64) size_t NumOutputEpsilons(int64) const string &ArcType() const string &FstType() const SymbolTable *InputSymbols() const SymbolTable *OutputSymbols() const string &WeightType() bool Write(const string &) bool Write(ostream &, const string &) uint64 Properties(uint64, bool) bool ValidStateId(int64) cdef cppclass MutableFstClass(FstClass): bool AddArc(int64, const ArcClass &) int64 AddState() bool DeleteArcs(int64, size_t) bool DeleteArcs(int64) bool DeleteStates(const vector[int64] &) void DeleteStates() SymbolTable *MutableInputSymbols() SymbolTable *MutableOutputSymbols() int64 NumStates() bool ReserveArcs(int64, size_t) void ReserveStates(int64) bool SetStart(int64) bool SetFinal(int64, const WeightClass &) void SetInputSymbols(SymbolTable *) void SetOutputSymbols(SymbolTable *) void SetProperties(uint64, uint64) cdef cppclass VectorFstClass(MutableFstClass): VectorFstClass(const FstClass &) VectorFstClass(const string &) # EncodeMapper. cdef cppclass EncodeMapperClass: EncodeMapperClass(const string &, uint32, EncodeType) # Aliased to __call__ as Cython doesn't have good support for operator(). ArcClass __call__ "operator()"(const ArcClass &) const string &ArcType() uint32 Flags() uint64 Properties(uint64) EncodeType Type() const SymbolTable *InputSymbols() const SymbolTable *OutputSymbols() void SetInputSymbols(const SymbolTable *) void SetOutputSymbols(const SymbolTable *) const string &WeightType() # Iterators. cdef cppclass ArcIteratorClass: ArcIteratorClass(const FstClass &, int64) bool Done() ArcClass Value() void Next() void Reset() void Seek(size_t) size_t Position() uint32 Flags() void SetFlags(uint32, uint32) cdef cppclass MutableArcIteratorClass: MutableArcIteratorClass(MutableFstClass *, int64) bool Done() ArcClass Value() void Next() void Reset() void Seek(size_t) void SetValue(const ArcClass &) size_t Position() uint32 Flags() void SetFlags(uint32, uint32) cdef cppclass StateIteratorClass: StateIteratorClass(const FstClass &) bool Done() int64 Value() void Next() void Reset() ctypedef pair[int64, const FstClass *] LabelFstClassPair ctypedef pair[int64, int64] LabelPair cdef extern from "<fst/script/fstscript.h>" namespace "fst::script" nogil: enum ArcFilterType: ANY_ARC_FILTER EPSILON_ARC_FILTER INPUT_EPSILON_ARC_FILTER OUTPUT_EPSILON_ARC_FILTER enum ArcSortType: ILABEL_SORT OLABEL_SORT cdef void ArcSort(MutableFstClass *, ArcSortType) cdef ClosureType GetClosureType(bool) cdef void Closure(MutableFstClass *, ClosureType) cdef FstClass *CompileFstInternal(istream &, const string &, const string &, const string &, const SymbolTable *, const SymbolTable *, const SymbolTable*, bool, bool, bool, bool, bool) cdef void Compose(FstClass &, FstClass &, MutableFstClass *, const ComposeOptions &) cdef void Concat(MutableFstClass *, const FstClass &) cdef void Connect(MutableFstClass *) cdef FstClass *Convert(const FstClass &, const string &) cdef void Decode(MutableFstClass *, const EncodeMapperClass &) cdef cppclass DeterminizeOptions: DeterminizeOptions(float, const WeightClass &, int64, int64, DeterminizeType, bool) cdef void Determinize(const FstClass &, MutableFstClass *, const DeterminizeOptions &) cdef cppclass DisambiguateOptions: DisambiguateOptions(float, const WeightClass &, int64, int64) cdef void Disambiguate(const FstClass &, MutableFstClass *, const DisambiguateOptions &) cdef void Difference(const FstClass &, const FstClass &, MutableFstClass *, const ComposeOptions &) cdef void DrawFst(const FstClass &fst, const SymbolTable *, const SymbolTable *, const SymbolTable *, bool, const string &, float, float, bool, bool, float, float, int, int, const string &, bool, ostream *, const string &) cdef void Encode(MutableFstClass *, EncodeMapperClass *) cdef EpsNormalizeType GetEpsNormalizeType(bool) cdef void EpsNormalize(const FstClass &, MutableFstClass *, EpsNormalizeType) cdef bool Equal(const FstClass &, const FstClass &, float) cdef bool Equivalent(const FstClass &, const FstClass &, float) cdef void Intersect(const FstClass &, const FstClass &, MutableFstClass *, const ComposeOptions &) cdef void Invert(MutableFstClass *fst) cdef bool Isomorphic(const FstClass &, const FstClass &, float) enum MapType: ARC_SUM_MAPPER IDENTITY_MAPPER INPUT_EPSILON_MAPPER INVERT_MAPPER OUTPUT_EPSILON_MAPPER PLUS_MAPPER QUANTIZE_MAPPER RMWEIGHT_MAPPER SUPERFINAL_MAPPER TIMES_MAPPER TO_LOG_MAPPER TO_LOG64_MAPPER TO_STD_MAPPER cdef FstClass *Map(const FstClass &, MapType, float, double, const WeightClass &) cdef void Minimize(MutableFstClass *, MutableFstClass *, float, bool) cdef ProjectType GetProjectType(bool) cdef void Project(MutableFstClass *, ProjectType) cdef void PrintFst(const FstClass &, ostream &, const string &, const SymbolTable *, const SymbolTable *, const SymbolTable *, bool, bool, const string &) cdef void Prune(const FstClass &, MutableFstClass *, const WeightClass &, int64, float) cdef void Prune(MutableFstClass *, const WeightClass &, int64, float) cdef void Push(const FstClass &, MutableFstClass *, uint32 flags, ReweightType, float) cdef void Push(MutableFstClass *, ReweightType, float, bool) enum RandArcSelection: UNIFORM_ARC_SELECTOR LOG_PROB_ARC_SELECTOR FAST_LOG_PROB_ARC_SELECTOR cdef bool RandEquivalent(const FstClass &, const FstClass &, int32, float, time_t, const RandGenOptions[RandArcSelection] &) cdef void RandGen(const FstClass &, MutableFstClass *, time_t, const RandGenOptions[RandArcSelection] &) cdef void Relabel(MutableFstClass *, const SymbolTable *, const SymbolTable *, const string &, bool, const SymbolTable *, const SymbolTable *, const string &, bool) cdef void Relabel(MutableFstClass *, const vector[LabelPair] &, const vector[LabelPair] &) cdef cppclass ReplaceOptions: ReplaceOptions(int64, ReplaceLabelType, ReplaceLabelType, int64) cdef void Replace(const vector[LabelFstClassPair] &, MutableFstClass *, const ReplaceOptions &) cdef void Reverse(const FstClass &, MutableFstClass *, bool) cdef void Reweight(MutableFstClass *, const vector[WeightClass] &, ReweightType) cdef cppclass RmEpsilonOptions: RmEpsilonOptions(QueueType, bool, const WeightClass &, int64, float) cdef void RmEpsilon(MutableFstClass *, const RmEpsilonOptions &) cdef cppclass ShortestDistanceOptions: ShortestDistanceOptions(QueueType, ArcFilterType, int64, float) cdef void ShortestDistance(const FstClass &, vector[WeightClass] *, const ShortestDistanceOptions &) cdef void ShortestDistance(const FstClass &, vector[WeightClass] *, bool, float) cdef cppclass ShortestPathOptions: ShortestPathOptions(QueueType, int32, bool, float, const WeightClass &, int64) cdef void ShortestPath(const FstClass &, MutableFstClass *, const ShortestPathOptions &) cdef void Synchronize(const FstClass &, MutableFstClass *) cdef bool TopSort(MutableFstClass *) cdef void Union(MutableFstClass *, const FstClass &) cdef bool Verify(const FstClass &) cdef extern from "<fst/script/getters.h>" namespace "fst::script" nogil: cdef bool GetArcSortType(const string &, ArcSortType *) cdef bool GetComposeFilter(const string &, ComposeFilter *) cdef bool GetDeterminizeType(const string &, DeterminizeType *) cdef uint32 GetEncodeFlags(bool, bool) cdef bool GetMapType(const string &, MapType *) cdef uint32 GetPushFlags(bool, bool, bool, bool) cdef bool GetQueueType(const string &, QueueType *) cdef bool GetRandArcSelection(const string &, RandArcSelection *) cdef bool GetReplaceLabelType(string, bool, ReplaceLabelType *) cdef ReweightType GetReweightType(bool) cdef extern from "<fst/extensions/far/far.h>" namespace "fst" nogil: enum FarType: FAR_DEFAULT FAR_STTABLE FAR_STLIST FAR_FST FAR_SSTABLE cdef extern from "<fst/extensions/far/getters.h>" \ namespace "fst" nogil: string GetFarTypeString(FarType) cdef extern from "<fst/extensions/far/getters.h>" \ namespace "fst::script" nogil: FarType GetFarType(const string &) cdef extern from "<fst/extensions/far/far-class.h>" \ namespace "fst::script" nogil: cdef cppclass FarReaderClass: const string &ArcType() bool Done() bool Error() bool Find(const string &) const FstClass *GetFstClass() const string &GetKey() void Next() void Reset() FarType Type() # For simplicity, we always use the multiple-file one. @staticmethod FarReaderClass *Open(const vector[string] &) cdef cppclass FarWriterClass: bool Add(const string &, const FstClass &) bool Error() const string &ArcType() FarType Type() @staticmethod FarWriterClass *Create(const string &, const string &, FarType)
0
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/include
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/include/fst/statesort.h
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. // // Function to sort states of an FST. #ifndef FST_STATESORT_H_ #define FST_STATESORT_H_ #include <algorithm> #include <vector> #include <fst/log.h> #include <fst/mutable-fst.h> namespace fst { // Sorts the input states of an FST. order[i] gives the the state ID after // sorting that corresponds to the state ID i before sorting; it must // therefore be a permutation of the input FST's states ID sequence. template <class Arc> void StateSort(MutableFst<Arc> *fst, const std::vector<typename Arc::StateId> &order) { using StateId = typename Arc::StateId; using Weight = typename Arc::Weight; if (order.size() != fst->NumStates()) { FSTERROR() << "StateSort: Bad order vector size: " << order.size(); fst->SetProperties(kError, kError); return; } if (fst->Start() == kNoStateId) return; const auto props = fst->Properties(kStateSortProperties, false); std::vector<bool> done(order.size(), false); std::vector<Arc> arcsa; std::vector<Arc> arcsb; fst->SetStart(order[fst->Start()]); for (StateIterator<MutableFst<Arc>> siter(*fst); !siter.Done(); siter.Next()) { auto s1 = siter.Value(); StateId s2; if (done[s1]) continue; auto final1 = fst->Final(s1); auto final2 = Weight::Zero(); arcsa.clear(); for (ArcIterator<MutableFst<Arc>> aiter(*fst, s1); !aiter.Done(); aiter.Next()) { arcsa.push_back(aiter.Value()); } for (; !done[s1]; s1 = s2, final1 = final2, std::swap(arcsa, arcsb)) { s2 = order[s1]; if (!done[s2]) { final2 = fst->Final(s2); arcsb.clear(); for (ArcIterator<MutableFst<Arc>> aiter(*fst, s2); !aiter.Done(); aiter.Next()) { arcsb.push_back(aiter.Value()); } } fst->SetFinal(s2, final1); fst->DeleteArcs(s2); for (auto arc : arcsa) { // Copy intended. arc.nextstate = order[arc.nextstate]; fst->AddArc(s2, arc); } done[s1] = true; } } fst->SetProperties(props, kFstProperties); } } // namespace fst #endif // FST_STATESORT_H_
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coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/extensions
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/extensions/compact/Makefile.in
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uninstall-libLTLIBRARIES \ uninstall-libfstLTLIBRARIES # Tell versions [3.59,3.63) of GNU make to not export all variables. # Otherwise a system limit (for SysV at least) may be exceeded. .NOEXPORT:
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coqui_public_repos/inference-engine/third_party/onnxruntime/include/onnxruntime/core
coqui_public_repos/inference-engine/third_party/onnxruntime/include/onnxruntime/core/platform/Barrier.h
// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2016 Dmitry Vyukov <dvyukov@google.com> // // This Source Code Form is subject to the terms of the Mozilla // Public License v. 2.0. If a copy of the MPL was not distributed // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. #include <assert.h> #include "core/common/spin_pause.h" #include "core/platform/ort_mutex.h" #include <mutex> #include <atomic> namespace onnxruntime { class Barrier { public: explicit Barrier(unsigned int count, bool spin = false) : state_(count << 1), notified_(false), spin_(spin) { assert(((count << 1) >> 1) == count); } #ifdef NDEBUG ~Barrier() = default; #else ~Barrier() { assert((state_ >> 1) == 0); } #endif void Notify(unsigned int c = 1) { unsigned int delta = c << 1; unsigned int v = state_.fetch_sub(delta, std::memory_order_acq_rel) - delta; if (v != 1) { // Clear the lowest bit (waiter flag) and check that the original state // value was not zero. If it was zero, it means that notify was called // more times than the original count. assert(((v + delta) & ~1) != 0); return; // either count has not dropped to 0, or waiter is not waiting } std::unique_lock<OrtMutex> l(mu_); assert(!notified_); notified_ = true; cv_.notify_all(); } void Wait() { if (spin_) { while ((state_ >> 1) != 0) { onnxruntime::concurrency::SpinPause(); } } else { unsigned int v = state_.fetch_or(1, std::memory_order_acq_rel); if ((v >> 1) == 0) return; std::unique_lock<OrtMutex> l(mu_); while (!notified_) { cv_.wait(l); } } } private: OrtMutex mu_; OrtCondVar cv_; std::atomic<unsigned int> state_; // low bit is waiter flag bool notified_; const bool spin_; }; // Notification is an object that allows a user to to wait for another // thread to signal a notification that an event has occurred. // // Multiple threads can wait on the same Notification object, // but only one caller must call Notify() on the object. struct Notification : Barrier { Notification() : Barrier(1){}; }; } // namespace onnxruntime
0
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/bin/fstintersect-main.cc
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. // // Intersects two FSTs. #include <cstring> #include <memory> #include <string> #include <fst/flags.h> #include <fst/log.h> #include <fst/script/getters.h> #include <fst/script/intersect.h> DECLARE_string(compose_filter); DECLARE_bool(connect); int fstintersect_main(int argc, char **argv) { namespace s = fst::script; using fst::ComposeFilter; using fst::script::FstClass; using fst::script::VectorFstClass; string usage = "Intersects two FSAs.\n\n Usage: "; usage += argv[0]; usage += " in1.fst in2.fst [out.fst]\n"; usage += " Flags: connect\n"; std::set_new_handler(FailedNewHandler); SET_FLAGS(usage.c_str(), &argc, &argv, true); if (argc < 3 || argc > 4) { ShowUsage(); return 1; } const string in1_name = strcmp(argv[1], "-") == 0 ? "" : argv[1]; const string in2_name = strcmp(argv[2], "-") == 0 ? "" : argv[2]; const string out_name = argc > 3 ? argv[3] : ""; if (in1_name.empty() && in2_name.empty()) { LOG(ERROR) << argv[0] << ": Can't take both inputs from standard input"; return 1; } std::unique_ptr<FstClass> ifst1(FstClass::Read(in1_name)); if (!ifst1) return 1; std::unique_ptr<FstClass> ifst2(FstClass::Read(in2_name)); if (!ifst2) return 1; VectorFstClass ofst(ifst1->ArcType()); ComposeFilter compose_filter; if (!s::GetComposeFilter(FLAGS_compose_filter, &compose_filter)) { LOG(ERROR) << argv[0] << ": Unknown or unsupported compose filter type: " << FLAGS_compose_filter; return 1; } const fst::IntersectOptions opts(FLAGS_connect, compose_filter); s::Intersect(*ifst1, *ifst2, &ofst, opts); return !ofst.Write(out_name); }
0
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/include/fst/extensions
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/include/fst/extensions/special/phi-fst.h
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. #ifndef FST_EXTENSIONS_SPECIAL_PHI_FST_H_ #define FST_EXTENSIONS_SPECIAL_PHI_FST_H_ #include <memory> #include <string> #include <fst/const-fst.h> #include <fst/matcher-fst.h> #include <fst/matcher.h> DECLARE_int64_t(phi_fst_phi_label); DECLARE_bool(phi_fst_phi_loop); DECLARE_string(phi_fst_rewrite_mode); namespace fst { namespace internal { template <class Label> class PhiFstMatcherData { public: PhiFstMatcherData( Label phi_label = FLAGS_phi_fst_phi_label, bool phi_loop = FLAGS_phi_fst_phi_loop, MatcherRewriteMode rewrite_mode = RewriteMode(FLAGS_phi_fst_rewrite_mode)) : phi_label_(phi_label), phi_loop_(phi_loop), rewrite_mode_(rewrite_mode) {} PhiFstMatcherData(const PhiFstMatcherData &data) : phi_label_(data.phi_label_), phi_loop_(data.phi_loop_), rewrite_mode_(data.rewrite_mode_) {} static PhiFstMatcherData<Label> *Read(std::istream &istrm, const FstReadOptions &read) { auto *data = new PhiFstMatcherData<Label>(); ReadType(istrm, &data->phi_label_); ReadType(istrm, &data->phi_loop_); int32_t rewrite_mode; ReadType(istrm, &rewrite_mode); data->rewrite_mode_ = static_cast<MatcherRewriteMode>(rewrite_mode); return data; } bool Write(std::ostream &ostrm, const FstWriteOptions &opts) const { WriteType(ostrm, phi_label_); WriteType(ostrm, phi_loop_); WriteType(ostrm, static_cast<int32_t>(rewrite_mode_)); return !ostrm ? false : true; } Label PhiLabel() const { return phi_label_; } bool PhiLoop() const { return phi_loop_; } MatcherRewriteMode RewriteMode() const { return rewrite_mode_; } private: static MatcherRewriteMode RewriteMode(const string &mode) { if (mode == "auto") return MATCHER_REWRITE_AUTO; if (mode == "always") return MATCHER_REWRITE_ALWAYS; if (mode == "never") return MATCHER_REWRITE_NEVER; LOG(WARNING) << "PhiFst: Unknown rewrite mode: " << mode << ". " << "Defaulting to auto."; return MATCHER_REWRITE_AUTO; } Label phi_label_; bool phi_loop_; MatcherRewriteMode rewrite_mode_; }; } // namespace internal constexpr uint8_t kPhiFstMatchInput = 0x01; // Input matcher is PhiMatcher. constexpr uint8_t kPhiFstMatchOutput = 0x02; // Output matcher is PhiMatcher. template <class M, uint8_t flags = kPhiFstMatchInput | kPhiFstMatchOutput> class PhiFstMatcher : public PhiMatcher<M> { public: using FST = typename M::FST; using Arc = typename M::Arc; using StateId = typename Arc::StateId; using Label = typename Arc::Label; using Weight = typename Arc::Weight; using MatcherData = internal::PhiFstMatcherData<Label>; enum : uint8_t { kFlags = flags }; // This makes a copy of the FST. PhiFstMatcher(const FST &fst, MatchType match_type, std::shared_ptr<MatcherData> data = std::make_shared<MatcherData>()) : PhiMatcher<M>(fst, match_type, PhiLabel(match_type, data ? data->PhiLabel() : MatcherData().PhiLabel()), data ? data->PhiLoop() : MatcherData().PhiLoop(), data ? data->RewriteMode() : MatcherData().RewriteMode()), data_(data) {} // This doesn't copy the FST. PhiFstMatcher(const FST *fst, MatchType match_type, std::shared_ptr<MatcherData> data = std::make_shared<MatcherData>()) : PhiMatcher<M>(fst, match_type, PhiLabel(match_type, data ? data->PhiLabel() : MatcherData().PhiLabel()), data ? data->PhiLoop() : MatcherData().PhiLoop(), data ? data->RewriteMode() : MatcherData().RewriteMode()), data_(data) {} // This makes a copy of the FST. PhiFstMatcher(const PhiFstMatcher<M, flags> &matcher, bool safe = false) : PhiMatcher<M>(matcher, safe), data_(matcher.data_) {} PhiFstMatcher<M, flags> *Copy(bool safe = false) const override { return new PhiFstMatcher<M, flags>(*this, safe); } const MatcherData *GetData() const { return data_.get(); } std::shared_ptr<MatcherData> GetSharedData() const { return data_; } private: static Label PhiLabel(MatchType match_type, Label label) { if (match_type == MATCH_INPUT && flags & kPhiFstMatchInput) return label; if (match_type == MATCH_OUTPUT && flags & kPhiFstMatchOutput) return label; return kNoLabel; } std::shared_ptr<MatcherData> data_; }; extern const char phi_fst_type[]; extern const char input_phi_fst_type[]; extern const char output_phi_fst_type[]; using StdPhiFst = MatcherFst<ConstFst<StdArc>, PhiFstMatcher<SortedMatcher<ConstFst<StdArc>>>, phi_fst_type>; using LogPhiFst = MatcherFst<ConstFst<LogArc>, PhiFstMatcher<SortedMatcher<ConstFst<LogArc>>>, phi_fst_type>; using Log64PhiFst = MatcherFst<ConstFst<Log64Arc>, PhiFstMatcher<SortedMatcher<ConstFst<Log64Arc>>>, input_phi_fst_type>; using StdInputPhiFst = MatcherFst<ConstFst<StdArc>, PhiFstMatcher<SortedMatcher<ConstFst<StdArc>>, kPhiFstMatchInput>, input_phi_fst_type>; using LogInputPhiFst = MatcherFst<ConstFst<LogArc>, PhiFstMatcher<SortedMatcher<ConstFst<LogArc>>, kPhiFstMatchInput>, input_phi_fst_type>; using Log64InputPhiFst = MatcherFst< ConstFst<Log64Arc>, PhiFstMatcher<SortedMatcher<ConstFst<Log64Arc>>, kPhiFstMatchInput>, input_phi_fst_type>; using StdOutputPhiFst = MatcherFst<ConstFst<StdArc>, PhiFstMatcher<SortedMatcher<ConstFst<StdArc>>, kPhiFstMatchOutput>, output_phi_fst_type>; using LogOutputPhiFst = MatcherFst<ConstFst<LogArc>, PhiFstMatcher<SortedMatcher<ConstFst<LogArc>>, kPhiFstMatchOutput>, output_phi_fst_type>; using Log64OutputPhiFst = MatcherFst< ConstFst<Log64Arc>, PhiFstMatcher<SortedMatcher<ConstFst<Log64Arc>>, kPhiFstMatchOutput>, output_phi_fst_type>; } // namespace fst #endif // FST_EXTENSIONS_SPECIAL_PHI_FST_H_
0
coqui_public_repos/TTS/TTS/tts
coqui_public_repos/TTS/TTS/tts/datasets/__init__.py
import os import sys from collections import Counter from pathlib import Path from typing import Callable, Dict, List, Tuple, Union import numpy as np from TTS.tts.datasets.dataset import * from TTS.tts.datasets.formatters import * def split_dataset(items, eval_split_max_size=None, eval_split_size=0.01): """Split a dataset into train and eval. Consider speaker distribution in multi-speaker training. Args: items (List[List]): A list of samples. Each sample is a list of `[audio_path, text, speaker_id]`. eval_split_max_size (int): Number maximum of samples to be used for evaluation in proportion split. Defaults to None (Disabled). eval_split_size (float): If between 0.0 and 1.0 represents the proportion of the dataset to include in the evaluation set. If > 1, represents the absolute number of evaluation samples. Defaults to 0.01 (1%). """ speakers = [item["speaker_name"] for item in items] is_multi_speaker = len(set(speakers)) > 1 if eval_split_size > 1: eval_split_size = int(eval_split_size) else: if eval_split_max_size: eval_split_size = min(eval_split_max_size, int(len(items) * eval_split_size)) else: eval_split_size = int(len(items) * eval_split_size) assert ( eval_split_size > 0 ), " [!] You do not have enough samples for the evaluation set. You can work around this setting the 'eval_split_size' parameter to a minimum of {}".format( 1 / len(items) ) np.random.seed(0) np.random.shuffle(items) if is_multi_speaker: items_eval = [] speakers = [item["speaker_name"] for item in items] speaker_counter = Counter(speakers) while len(items_eval) < eval_split_size: item_idx = np.random.randint(0, len(items)) speaker_to_be_removed = items[item_idx]["speaker_name"] if speaker_counter[speaker_to_be_removed] > 1: items_eval.append(items[item_idx]) speaker_counter[speaker_to_be_removed] -= 1 del items[item_idx] return items_eval, items return items[:eval_split_size], items[eval_split_size:] def add_extra_keys(metadata, language, dataset_name): for item in metadata: # add language name item["language"] = language # add unique audio name relfilepath = os.path.splitext(os.path.relpath(item["audio_file"], item["root_path"]))[0] audio_unique_name = f"{dataset_name}#{relfilepath}" item["audio_unique_name"] = audio_unique_name return metadata def load_tts_samples( datasets: Union[List[Dict], Dict], eval_split=True, formatter: Callable = None, eval_split_max_size=None, eval_split_size=0.01, ) -> Tuple[List[List], List[List]]: """Parse the dataset from the datasets config, load the samples as a List and load the attention alignments if provided. If `formatter` is not None, apply the formatter to the samples else pick the formatter from the available ones based on the dataset name. Args: datasets (List[Dict], Dict): A list of datasets or a single dataset dictionary. If multiple datasets are in the list, they are all merged. eval_split (bool, optional): If true, create a evaluation split. If an eval split provided explicitly, generate an eval split automatically. Defaults to True. formatter (Callable, optional): The preprocessing function to be applied to create the list of samples. It must take the root_path and the meta_file name and return a list of samples in the format of `[[text, audio_path, speaker_id], ...]]`. See the available formatters in `TTS.tts.dataset.formatter` as example. Defaults to None. eval_split_max_size (int): Number maximum of samples to be used for evaluation in proportion split. Defaults to None (Disabled). eval_split_size (float): If between 0.0 and 1.0 represents the proportion of the dataset to include in the evaluation set. If > 1, represents the absolute number of evaluation samples. Defaults to 0.01 (1%). Returns: Tuple[List[List], List[List]: training and evaluation splits of the dataset. """ meta_data_train_all = [] meta_data_eval_all = [] if eval_split else None if not isinstance(datasets, list): datasets = [datasets] for dataset in datasets: formatter_name = dataset["formatter"] dataset_name = dataset["dataset_name"] root_path = dataset["path"] meta_file_train = dataset["meta_file_train"] meta_file_val = dataset["meta_file_val"] ignored_speakers = dataset["ignored_speakers"] language = dataset["language"] # setup the right data processor if formatter is None: formatter = _get_formatter_by_name(formatter_name) # load train set meta_data_train = formatter(root_path, meta_file_train, ignored_speakers=ignored_speakers) assert len(meta_data_train) > 0, f" [!] No training samples found in {root_path}/{meta_file_train}" meta_data_train = add_extra_keys(meta_data_train, language, dataset_name) print(f" | > Found {len(meta_data_train)} files in {Path(root_path).resolve()}") # load evaluation split if set if eval_split: if meta_file_val: meta_data_eval = formatter(root_path, meta_file_val, ignored_speakers=ignored_speakers) meta_data_eval = add_extra_keys(meta_data_eval, language, dataset_name) else: eval_size_per_dataset = eval_split_max_size // len(datasets) if eval_split_max_size else None meta_data_eval, meta_data_train = split_dataset(meta_data_train, eval_size_per_dataset, eval_split_size) meta_data_eval_all += meta_data_eval meta_data_train_all += meta_data_train # load attention masks for the duration predictor training if dataset.meta_file_attn_mask: meta_data = dict(load_attention_mask_meta_data(dataset["meta_file_attn_mask"])) for idx, ins in enumerate(meta_data_train_all): attn_file = meta_data[ins["audio_file"]].strip() meta_data_train_all[idx].update({"alignment_file": attn_file}) if meta_data_eval_all: for idx, ins in enumerate(meta_data_eval_all): attn_file = meta_data[ins["audio_file"]].strip() meta_data_eval_all[idx].update({"alignment_file": attn_file}) # set none for the next iter formatter = None return meta_data_train_all, meta_data_eval_all def load_attention_mask_meta_data(metafile_path): """Load meta data file created by compute_attention_masks.py""" with open(metafile_path, "r", encoding="utf-8") as f: lines = f.readlines() meta_data = [] for line in lines: wav_file, attn_file = line.split("|") meta_data.append([wav_file, attn_file]) return meta_data def _get_formatter_by_name(name): """Returns the respective preprocessing function.""" thismodule = sys.modules[__name__] return getattr(thismodule, name.lower()) def find_unique_chars(data_samples, verbose=True): texts = "".join(item[0] for item in data_samples) chars = set(texts) lower_chars = filter(lambda c: c.islower(), chars) chars_force_lower = [c.lower() for c in chars] chars_force_lower = set(chars_force_lower) if verbose: print(f" > Number of unique characters: {len(chars)}") print(f" > Unique characters: {''.join(sorted(chars))}") print(f" > Unique lower characters: {''.join(sorted(lower_chars))}") print(f" > Unique all forced to lower characters: {''.join(sorted(chars_force_lower))}") return chars_force_lower
0
coqui_public_repos/TTS
coqui_public_repos/TTS/notebooks/Tutorial_2_train_your_first_TTS_model.ipynb
## Install Coqui TTS ! pip install -U pip ! pip install TTSimport os # BaseDatasetConfig: defines name, formatter and path of the dataset. from TTS.tts.configs.shared_configs import BaseDatasetConfig output_path = "tts_train_dir" if not os.path.exists(output_path): os.makedirs(output_path) # Download and extract LJSpeech dataset. !wget -O $output_path/LJSpeech-1.1.tar.bz2 https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2 !tar -xf $output_path/LJSpeech-1.1.tar.bz2 -C $output_pathdataset_config = BaseDatasetConfig( formatter="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "LJSpeech-1.1/") )# GlowTTSConfig: all model related values for training, validating and testing. from TTS.tts.configs.glow_tts_config import GlowTTSConfig config = GlowTTSConfig( batch_size=32, eval_batch_size=16, num_loader_workers=4, num_eval_loader_workers=4, run_eval=True, test_delay_epochs=-1, epochs=100, text_cleaner="phoneme_cleaners", use_phonemes=True, phoneme_language="en-us", phoneme_cache_path=os.path.join(output_path, "phoneme_cache"), print_step=25, print_eval=False, mixed_precision=True, output_path=output_path, datasets=[dataset_config], save_step=1000, )from TTS.utils.audio import AudioProcessor ap = AudioProcessor.init_from_config(config) # Modify sample rate if for a custom audio dataset: # ap.sample_rate = 22050 from TTS.tts.utils.text.tokenizer import TTSTokenizer tokenizer, config = TTSTokenizer.init_from_config(config)from TTS.tts.datasets import load_tts_samples train_samples, eval_samples = load_tts_samples( dataset_config, eval_split=True, eval_split_max_size=config.eval_split_max_size, eval_split_size=config.eval_split_size, )from TTS.tts.models.glow_tts import GlowTTS model = GlowTTS(config, ap, tokenizer, speaker_manager=None)from trainer import Trainer, TrainerArgs trainer = Trainer( TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples )trainer.fit()import glob, os output_path = "tts_train_dir" ckpts = sorted([f for f in glob.glob(output_path+"/*/*.pth")]) configs = sorted([f for f in glob.glob(output_path+"/*/*.json")]) !tts --text "Text for TTS" \ --model_path $test_ckpt \ --config_path $test_config \ --out_path out.wavimport IPython IPython.display.Audio("out.wav")
0
coqui_public_repos/STT
coqui_public_repos/STT/taskcluster/linux-opt-base.tyml
$if: 'event.event in build.allowed' then: taskId: ${taskcluster.taskId} provisionerId: ${taskcluster.docker.provisionerId} workerType: ${build.workerType} taskGroupId: ${taskcluster.taskGroupId} schedulerId: ${taskcluster.schedulerId} dependencies: $map: { $eval: build.dependencies } each(b): $eval: as_slugid(b) created: { $fromNow: '0 sec' } deadline: { $fromNow: '1 day' } expires: $if: '(event.event == "push") || (event.event == "tag")' then: { $fromNow: '6 months' } else: { $fromNow: '7 days' } extra: nc_asset_name: { $eval: build.nc_asset_name } routes: $if: '(event.event == "push") || (event.event == "tag")' then: { $eval: build.routes } payload: maxRunTime: { $eval: to_int(build.maxRunTime) } image: ${build.docker_image} env: TENSORFLOW_BUILD_ARTIFACT: ${build.tensorflow} DEBIAN_FRONTEND: "noninteractive" command: - "/bin/bash" - "--login" - "-cxe" - $let: extraSystemSetup: { $eval: strip(str(build.system_setup)) } extraSystemConfig: { $eval: strip(str(build.system_config)) } in: > adduser --system --home ${system.homedir.linux} ${system.username} && apt-get -qq update && apt-get -qq -y install ${deepspeech.packages_xenial.apt} pixz pkg-config coreutils sudo unzip wget zip && ${extraSystemSetup} && cd ${system.homedir.linux}/ && echo -e "#!/bin/bash\nset -xe\n env && id && (wget -O - $TENSORFLOW_BUILD_ARTIFACT | pixz -d | tar -C ${system.homedir.linux}/ -xf - ) && cd ~/DeepSpeech/ds && git remote set-url origin ${event.head.repo.url} && git fetch origin && git checkout --quiet ${event.head.sha} && git submodule --quiet sync tensorflow/ && git submodule --quiet update tensorflow/ && mkdir -p ${system.homedir.linux}/.cache/node-gyp/ && wget -O - ${system.node_gyp_cache.url} | tar -C ${system.homedir.linux}/.cache/node-gyp/ -xzf - && mkdir -p ${system.homedir.linux}/pyenv-root/ && wget -O - ${system.pyenv.linux.url} | tar -C ${system.homedir.linux}/pyenv-root/ -xzf - && if [ ! -z "${build.gradle_cache.url}" ]; then wget -O - ${build.gradle_cache.url} | tar -C ${system.homedir.linux}/ -xzf - ; fi && if [ ! -z "${build.android_cache.url}" ]; then wget -O - ${build.android_cache.url} | tar -C ${system.homedir.linux}/ -xzf - ; fi;" > /tmp/clone.sh && chmod +x /tmp/clone.sh && sudo -H -u ${system.username} /bin/bash /tmp/clone.sh && ${extraSystemConfig} && sudo -H -u ${system.username} --preserve-env /bin/bash ${system.homedir.linux}/DeepSpeech/ds/${build.scripts.build} && sudo -H -u ${system.username} /bin/bash ${system.homedir.linux}/DeepSpeech/ds/${build.scripts.package} artifacts: "public": type: "directory" path: "/tmp/artifacts/" expires: $if: '(event.event == "push") || (event.event == "tag")' then: { $fromNow: '6 months' } else: { $fromNow: '7 days' } metadata: name: ${build.metadata.name} description: ${build.metadata.description} owner: ${event.head.user.email} source: ${event.head.repo.url}
0
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/bin/fstconvert.cc
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. #include <fst/flags.h> DEFINE_string(fst_type, "vector", "Output FST type"); int fstconvert_main(int argc, char **argv); int main(int argc, char **argv) { return fstconvert_main(argc, argv); }
0
coqui_public_repos/TTS/tests
coqui_public_repos/TTS/tests/text_tests/test_characters.py
import unittest from TTS.tts.utils.text.characters import BaseCharacters, BaseVocabulary, Graphemes, IPAPhonemes # pylint: disable=protected-access class BaseVocabularyTest(unittest.TestCase): def setUp(self): self.phonemes = IPAPhonemes() self.base_vocab = BaseVocabulary( vocab=self.phonemes._vocab, pad=self.phonemes.pad, blank=self.phonemes.blank, bos=self.phonemes.bos, eos=self.phonemes.eos, ) self.empty_vocab = BaseVocabulary({}) def test_pad_id(self): self.assertEqual(self.empty_vocab.pad_id, 0) self.assertEqual(self.base_vocab.pad_id, self.phonemes.pad_id) def test_blank_id(self): self.assertEqual(self.empty_vocab.blank_id, 0) self.assertEqual(self.base_vocab.blank_id, self.phonemes.blank_id) def test_vocab(self): self.assertEqual(self.empty_vocab.vocab, {}) self.assertEqual(self.base_vocab.vocab, self.phonemes._vocab) # def test_init_from_config(self): # ... def test_num_chars(self): self.assertEqual(self.empty_vocab.num_chars, 0) self.assertEqual(self.base_vocab.num_chars, self.phonemes.num_chars) def test_char_to_id(self): try: self.empty_vocab.char_to_id("a") raise Exception("Should have raised KeyError") except: pass for k in self.phonemes.vocab: self.assertEqual(self.base_vocab.char_to_id(k), self.phonemes.char_to_id(k)) def test_id_to_char(self): try: self.empty_vocab.id_to_char(0) raise Exception("Should have raised KeyError") except: pass for k in self.phonemes.vocab: v = self.phonemes.char_to_id(k) self.assertEqual(self.base_vocab.id_to_char(v), self.phonemes.id_to_char(v)) class BaseCharacterTest(unittest.TestCase): def setUp(self): self.characters_empty = BaseCharacters("", "", pad="", eos="", bos="", blank="", is_unique=True, is_sorted=True) def test_default_character_sets(self): # pylint: disable=no-self-use """Test initiation of default character sets""" _ = IPAPhonemes() _ = Graphemes() def test_unique(self): """Test if the unique option works""" self.characters_empty.characters = "abcc" self.characters_empty.punctuations = ".,;:!? " self.characters_empty.pad = "[PAD]" self.characters_empty.eos = "[EOS]" self.characters_empty.bos = "[BOS]" self.characters_empty.blank = "[BLANK]" self.assertEqual( self.characters_empty.num_chars, len(["[PAD]", "[EOS]", "[BOS]", "[BLANK]", "a", "b", "c", ".", ",", ";", ":", "!", "?", " "]), ) def test_unique_sorted(self): """Test if the unique and sorted option works""" self.characters_empty.characters = "cba" self.characters_empty.punctuations = ".,;:!? " self.characters_empty.pad = "[PAD]" self.characters_empty.eos = "[EOS]" self.characters_empty.bos = "[BOS]" self.characters_empty.blank = "[BLANK]" self.assertEqual( self.characters_empty.num_chars, len(["[PAD]", "[EOS]", "[BOS]", "[BLANK]", "a", "b", "c", ".", ",", ";", ":", "!", "?", " "]), ) def test_setters_getters(self): """Test the class setters behaves as expected""" self.characters_empty.characters = "abc" self.assertEqual(self.characters_empty._characters, "abc") self.assertEqual(self.characters_empty.vocab, ["a", "b", "c"]) self.characters_empty.punctuations = ".,;:!? " self.assertEqual(self.characters_empty._punctuations, ".,;:!? ") self.assertEqual(self.characters_empty.vocab, ["a", "b", "c", ".", ",", ";", ":", "!", "?", " "]) self.characters_empty.pad = "[PAD]" self.assertEqual(self.characters_empty._pad, "[PAD]") self.assertEqual(self.characters_empty.vocab, ["[PAD]", "a", "b", "c", ".", ",", ";", ":", "!", "?", " "]) self.characters_empty.eos = "[EOS]" self.assertEqual(self.characters_empty._eos, "[EOS]") self.assertEqual( self.characters_empty.vocab, ["[PAD]", "[EOS]", "a", "b", "c", ".", ",", ";", ":", "!", "?", " "] ) self.characters_empty.bos = "[BOS]" self.assertEqual(self.characters_empty._bos, "[BOS]") self.assertEqual( self.characters_empty.vocab, ["[PAD]", "[EOS]", "[BOS]", "a", "b", "c", ".", ",", ";", ":", "!", "?", " "] ) self.characters_empty.blank = "[BLANK]" self.assertEqual(self.characters_empty._blank, "[BLANK]") self.assertEqual( self.characters_empty.vocab, ["[PAD]", "[EOS]", "[BOS]", "[BLANK]", "a", "b", "c", ".", ",", ";", ":", "!", "?", " "], ) self.assertEqual( self.characters_empty.num_chars, len(["[PAD]", "[EOS]", "[BOS]", "[BLANK]", "a", "b", "c", ".", ",", ";", ":", "!", "?", " "]), ) self.characters_empty.print_log() def test_char_lookup(self): """Test char to ID and ID to char conversion""" self.characters_empty.characters = "abc" self.characters_empty.punctuations = ".,;:!? " self.characters_empty.pad = "[PAD]" self.characters_empty.eos = "[EOS]" self.characters_empty.bos = "[BOS]" self.characters_empty.blank = "[BLANK]" # char to ID self.assertEqual(self.characters_empty.char_to_id("[PAD]"), 0) self.assertEqual(self.characters_empty.char_to_id("[EOS]"), 1) self.assertEqual(self.characters_empty.char_to_id("[BOS]"), 2) self.assertEqual(self.characters_empty.char_to_id("[BLANK]"), 3) self.assertEqual(self.characters_empty.char_to_id("a"), 4) self.assertEqual(self.characters_empty.char_to_id("b"), 5) self.assertEqual(self.characters_empty.char_to_id("c"), 6) self.assertEqual(self.characters_empty.char_to_id("."), 7) self.assertEqual(self.characters_empty.char_to_id(","), 8) self.assertEqual(self.characters_empty.char_to_id(";"), 9) self.assertEqual(self.characters_empty.char_to_id(":"), 10) self.assertEqual(self.characters_empty.char_to_id("!"), 11) self.assertEqual(self.characters_empty.char_to_id("?"), 12) self.assertEqual(self.characters_empty.char_to_id(" "), 13) # ID to char self.assertEqual(self.characters_empty.id_to_char(0), "[PAD]") self.assertEqual(self.characters_empty.id_to_char(1), "[EOS]") self.assertEqual(self.characters_empty.id_to_char(2), "[BOS]") self.assertEqual(self.characters_empty.id_to_char(3), "[BLANK]") self.assertEqual(self.characters_empty.id_to_char(4), "a") self.assertEqual(self.characters_empty.id_to_char(5), "b") self.assertEqual(self.characters_empty.id_to_char(6), "c") self.assertEqual(self.characters_empty.id_to_char(7), ".") self.assertEqual(self.characters_empty.id_to_char(8), ",") self.assertEqual(self.characters_empty.id_to_char(9), ";") self.assertEqual(self.characters_empty.id_to_char(10), ":") self.assertEqual(self.characters_empty.id_to_char(11), "!") self.assertEqual(self.characters_empty.id_to_char(12), "?") self.assertEqual(self.characters_empty.id_to_char(13), " ")
0
coqui_public_repos/TTS/TTS/tts/layers
coqui_public_repos/TTS/TTS/tts/layers/glow_tts/transformer.py
import math import torch from torch import nn from torch.nn import functional as F from TTS.tts.layers.generic.normalization import LayerNorm, LayerNorm2 class RelativePositionMultiHeadAttention(nn.Module): """Multi-head attention with Relative Positional embedding. https://arxiv.org/pdf/1809.04281.pdf It learns positional embeddings for a window of neighbours. For keys and values, it learns different set of embeddings. Key embeddings are agregated with the attention scores and value embeddings are aggregated with the output. Note: Example with relative attention window size 2 - input = [a, b, c, d, e] - rel_attn_embeddings = [e(t-2), e(t-1), e(t+1), e(t+2)] So it learns 4 embedding vectors (in total 8) separately for key and value vectors. Considering the input c - e(t-2) corresponds to c -> a - e(t-2) corresponds to c -> b - e(t-2) corresponds to c -> d - e(t-2) corresponds to c -> e These embeddings are shared among different time steps. So input a, b, d and e also uses the same embeddings. Embeddings are ignored when the relative window is out of limit for the first and the last n items. Args: channels (int): input and inner layer channels. out_channels (int): output channels. num_heads (int): number of attention heads. rel_attn_window_size (int, optional): relation attention window size. If 4, for each time step next and previous 4 time steps are attended. If default, relative encoding is disabled and it is a regular transformer. Defaults to None. heads_share (bool, optional): [description]. Defaults to True. dropout_p (float, optional): dropout rate. Defaults to 0.. input_length (int, optional): intput length for positional encoding. Defaults to None. proximal_bias (bool, optional): enable/disable proximal bias as in the paper. Defaults to False. proximal_init (bool, optional): enable/disable poximal init as in the paper. Init key and query layer weights the same. Defaults to False. """ def __init__( self, channels, out_channels, num_heads, rel_attn_window_size=None, heads_share=True, dropout_p=0.0, input_length=None, proximal_bias=False, proximal_init=False, ): super().__init__() assert channels % num_heads == 0, " [!] channels should be divisible by num_heads." # class attributes self.channels = channels self.out_channels = out_channels self.num_heads = num_heads self.rel_attn_window_size = rel_attn_window_size self.heads_share = heads_share self.input_length = input_length self.proximal_bias = proximal_bias self.dropout_p = dropout_p self.attn = None # query, key, value layers self.k_channels = channels // num_heads self.conv_q = nn.Conv1d(channels, channels, 1) self.conv_k = nn.Conv1d(channels, channels, 1) self.conv_v = nn.Conv1d(channels, channels, 1) # output layers self.conv_o = nn.Conv1d(channels, out_channels, 1) self.dropout = nn.Dropout(dropout_p) # relative positional encoding layers if rel_attn_window_size is not None: n_heads_rel = 1 if heads_share else num_heads rel_stddev = self.k_channels**-0.5 emb_rel_k = nn.Parameter( torch.randn(n_heads_rel, rel_attn_window_size * 2 + 1, self.k_channels) * rel_stddev ) emb_rel_v = nn.Parameter( torch.randn(n_heads_rel, rel_attn_window_size * 2 + 1, self.k_channels) * rel_stddev ) self.register_parameter("emb_rel_k", emb_rel_k) self.register_parameter("emb_rel_v", emb_rel_v) # init layers nn.init.xavier_uniform_(self.conv_q.weight) nn.init.xavier_uniform_(self.conv_k.weight) # proximal bias if proximal_init: self.conv_k.weight.data.copy_(self.conv_q.weight.data) self.conv_k.bias.data.copy_(self.conv_q.bias.data) nn.init.xavier_uniform_(self.conv_v.weight) def forward(self, x, c, attn_mask=None): """ Shapes: - x: :math:`[B, C, T]` - c: :math:`[B, C, T]` - attn_mask: :math:`[B, 1, T, T]` """ q = self.conv_q(x) k = self.conv_k(c) v = self.conv_v(c) x, self.attn = self.attention(q, k, v, mask=attn_mask) x = self.conv_o(x) return x def attention(self, query, key, value, mask=None): # reshape [b, d, t] -> [b, n_h, t, d_k] b, d, t_s, t_t = (*key.size(), query.size(2)) query = query.view(b, self.num_heads, self.k_channels, t_t).transpose(2, 3) key = key.view(b, self.num_heads, self.k_channels, t_s).transpose(2, 3) value = value.view(b, self.num_heads, self.k_channels, t_s).transpose(2, 3) # compute raw attention scores scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels) # relative positional encoding for scores if self.rel_attn_window_size is not None: assert t_s == t_t, "Relative attention is only available for self-attention." # get relative key embeddings key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) rel_logits = self._matmul_with_relative_keys(query, key_relative_embeddings) rel_logits = self._relative_position_to_absolute_position(rel_logits) scores_local = rel_logits / math.sqrt(self.k_channels) scores = scores + scores_local # proximan bias if self.proximal_bias: assert t_s == t_t, "Proximal bias is only available for self-attention." scores = scores + self._attn_proximity_bias(t_s).to(device=scores.device, dtype=scores.dtype) # attention score masking if mask is not None: # add small value to prevent oor error. scores = scores.masked_fill(mask == 0, -1e4) if self.input_length is not None: block_mask = torch.ones_like(scores).triu(-1 * self.input_length).tril(self.input_length) scores = scores * block_mask + -1e4 * (1 - block_mask) # attention score normalization p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s] # apply dropout to attention weights p_attn = self.dropout(p_attn) # compute output output = torch.matmul(p_attn, value) # relative positional encoding for values if self.rel_attn_window_size is not None: relative_weights = self._absolute_position_to_relative_position(p_attn) value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s) output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings) output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t] return output, p_attn @staticmethod def _matmul_with_relative_values(p_attn, re): """ Args: p_attn (Tensor): attention weights. re (Tensor): relative value embedding vector. (a_(i,j)^V) Shapes: -p_attn: :math:`[B, H, T, V]` -re: :math:`[H or 1, V, D]` -logits: :math:`[B, H, T, D]` """ logits = torch.matmul(p_attn, re.unsqueeze(0)) return logits @staticmethod def _matmul_with_relative_keys(query, re): """ Args: query (Tensor): batch of query vectors. (x*W^Q) re (Tensor): relative key embedding vector. (a_(i,j)^K) Shapes: - query: :math:`[B, H, T, D]` - re: :math:`[H or 1, V, D]` - logits: :math:`[B, H, T, V]` """ # logits = torch.einsum('bhld, kmd -> bhlm', [query, re.to(query.dtype)]) logits = torch.matmul(query, re.unsqueeze(0).transpose(-2, -1)) return logits def _get_relative_embeddings(self, relative_embeddings, length): """Convert embedding vestors to a tensor of embeddings""" # Pad first before slice to avoid using cond ops. pad_length = max(length - (self.rel_attn_window_size + 1), 0) slice_start_position = max((self.rel_attn_window_size + 1) - length, 0) slice_end_position = slice_start_position + 2 * length - 1 if pad_length > 0: padded_relative_embeddings = F.pad(relative_embeddings, [0, 0, pad_length, pad_length, 0, 0]) else: padded_relative_embeddings = relative_embeddings used_relative_embeddings = padded_relative_embeddings[:, slice_start_position:slice_end_position] return used_relative_embeddings @staticmethod def _relative_position_to_absolute_position(x): """Converts tensor from relative to absolute indexing for local attention. Shapes: x: :math:`[B, C, T, 2 * T - 1]` Returns: A Tensor of shape :math:`[B, C, T, T]` """ batch, heads, length, _ = x.size() # Pad to shift from relative to absolute indexing. x = F.pad(x, [0, 1, 0, 0, 0, 0, 0, 0]) # Pad extra elements so to add up to shape (len+1, 2*len-1). x_flat = x.view([batch, heads, length * 2 * length]) x_flat = F.pad(x_flat, [0, length - 1, 0, 0, 0, 0]) # Reshape and slice out the padded elements. x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[:, :, :length, length - 1 :] return x_final @staticmethod def _absolute_position_to_relative_position(x): """ Shapes: - x: :math:`[B, C, T, T]` - ret: :math:`[B, C, T, 2*T-1]` """ batch, heads, length, _ = x.size() # padd along column x = F.pad(x, [0, length - 1, 0, 0, 0, 0, 0, 0]) x_flat = x.view([batch, heads, length**2 + length * (length - 1)]) # add 0's in the beginning that will skew the elements after reshape x_flat = F.pad(x_flat, [length, 0, 0, 0, 0, 0]) x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:] return x_final @staticmethod def _attn_proximity_bias(length): """Produce an attention mask that discourages distant attention values. Args: length (int): an integer scalar. Returns: a Tensor with shape :math:`[1, 1, T, T]` """ # L r = torch.arange(length, dtype=torch.float32) # L x L diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) # scale mask values diff = -torch.log1p(torch.abs(diff)) # 1 x 1 x L x L return diff.unsqueeze(0).unsqueeze(0) class FeedForwardNetwork(nn.Module): """Feed Forward Inner layers for Transformer. Args: in_channels (int): input tensor channels. out_channels (int): output tensor channels. hidden_channels (int): inner layers hidden channels. kernel_size (int): conv1d filter kernel size. dropout_p (float, optional): dropout rate. Defaults to 0. """ def __init__(self, in_channels, out_channels, hidden_channels, kernel_size, dropout_p=0.0, causal=False): super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.hidden_channels = hidden_channels self.kernel_size = kernel_size self.dropout_p = dropout_p if causal: self.padding = self._causal_padding else: self.padding = self._same_padding self.conv_1 = nn.Conv1d(in_channels, hidden_channels, kernel_size) self.conv_2 = nn.Conv1d(hidden_channels, out_channels, kernel_size) self.dropout = nn.Dropout(dropout_p) def forward(self, x, x_mask): x = self.conv_1(self.padding(x * x_mask)) x = torch.relu(x) x = self.dropout(x) x = self.conv_2(self.padding(x * x_mask)) return x * x_mask def _causal_padding(self, x): if self.kernel_size == 1: return x pad_l = self.kernel_size - 1 pad_r = 0 padding = [[0, 0], [0, 0], [pad_l, pad_r]] x = F.pad(x, self._pad_shape(padding)) return x def _same_padding(self, x): if self.kernel_size == 1: return x pad_l = (self.kernel_size - 1) // 2 pad_r = self.kernel_size // 2 padding = [[0, 0], [0, 0], [pad_l, pad_r]] x = F.pad(x, self._pad_shape(padding)) return x @staticmethod def _pad_shape(padding): l = padding[::-1] pad_shape = [item for sublist in l for item in sublist] return pad_shape class RelativePositionTransformer(nn.Module): """Transformer with Relative Potional Encoding. https://arxiv.org/abs/1803.02155 Args: in_channels (int): number of channels of the input tensor. out_chanels (int): number of channels of the output tensor. hidden_channels (int): model hidden channels. hidden_channels_ffn (int): hidden channels of FeedForwardNetwork. num_heads (int): number of attention heads. num_layers (int): number of transformer layers. kernel_size (int, optional): kernel size of feed-forward inner layers. Defaults to 1. dropout_p (float, optional): dropout rate for self-attention and feed-forward inner layers_per_stack. Defaults to 0. rel_attn_window_size (int, optional): relation attention window size. If 4, for each time step next and previous 4 time steps are attended. If default, relative encoding is disabled and it is a regular transformer. Defaults to None. input_length (int, optional): input lenght to limit position encoding. Defaults to None. layer_norm_type (str, optional): type "1" uses torch tensor operations and type "2" uses torch layer_norm primitive. Use type "2", type "1: is for backward compat. Defaults to "1". """ def __init__( self, in_channels: int, out_channels: int, hidden_channels: int, hidden_channels_ffn: int, num_heads: int, num_layers: int, kernel_size=1, dropout_p=0.0, rel_attn_window_size: int = None, input_length: int = None, layer_norm_type: str = "1", ): super().__init__() self.hidden_channels = hidden_channels self.hidden_channels_ffn = hidden_channels_ffn self.num_heads = num_heads self.num_layers = num_layers self.kernel_size = kernel_size self.dropout_p = dropout_p self.rel_attn_window_size = rel_attn_window_size self.dropout = nn.Dropout(dropout_p) self.attn_layers = nn.ModuleList() self.norm_layers_1 = nn.ModuleList() self.ffn_layers = nn.ModuleList() self.norm_layers_2 = nn.ModuleList() for idx in range(self.num_layers): self.attn_layers.append( RelativePositionMultiHeadAttention( hidden_channels if idx != 0 else in_channels, hidden_channels, num_heads, rel_attn_window_size=rel_attn_window_size, dropout_p=dropout_p, input_length=input_length, ) ) if layer_norm_type == "1": self.norm_layers_1.append(LayerNorm(hidden_channels)) elif layer_norm_type == "2": self.norm_layers_1.append(LayerNorm2(hidden_channels)) else: raise ValueError(" [!] Unknown layer norm type") if hidden_channels != out_channels and (idx + 1) == self.num_layers: self.proj = nn.Conv1d(hidden_channels, out_channels, 1) self.ffn_layers.append( FeedForwardNetwork( hidden_channels, hidden_channels if (idx + 1) != self.num_layers else out_channels, hidden_channels_ffn, kernel_size, dropout_p=dropout_p, ) ) if layer_norm_type == "1": self.norm_layers_2.append(LayerNorm(hidden_channels if (idx + 1) != self.num_layers else out_channels)) elif layer_norm_type == "2": self.norm_layers_2.append(LayerNorm2(hidden_channels if (idx + 1) != self.num_layers else out_channels)) else: raise ValueError(" [!] Unknown layer norm type") def forward(self, x, x_mask): """ Shapes: - x: :math:`[B, C, T]` - x_mask: :math:`[B, 1, T]` """ attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) for i in range(self.num_layers): x = x * x_mask y = self.attn_layers[i](x, x, attn_mask) y = self.dropout(y) x = self.norm_layers_1[i](x + y) y = self.ffn_layers[i](x, x_mask) y = self.dropout(y) if (i + 1) == self.num_layers and hasattr(self, "proj"): x = self.proj(x) x = self.norm_layers_2[i](x + y) x = x * x_mask return x
0
coqui_public_repos/STT/native_client/kenlm
coqui_public_repos/STT/native_client/kenlm/util/usage.cc
#include "usage.hh" #include "exception.hh" #include <fstream> #include <ostream> #include <sstream> #include <set> #include <string> #include <cstring> #include <cctype> #include <ctime> #if defined(_WIN32) || defined(_WIN64) // This code lifted from physmem.c in gnulib. See the copyright statement // below. # define WIN32_LEAN_AND_MEAN # include <windows.h> /* MEMORYSTATUSEX is missing from older windows headers, so define a local replacement. */ typedef struct { DWORD dwLength; DWORD dwMemoryLoad; DWORDLONG ullTotalPhys; DWORDLONG ullAvailPhys; DWORDLONG ullTotalPageFile; DWORDLONG ullAvailPageFile; DWORDLONG ullTotalVirtual; DWORDLONG ullAvailVirtual; DWORDLONG ullAvailExtendedVirtual; } lMEMORYSTATUSEX; // Is this really supposed to be defined like this? typedef int WINBOOL; typedef WINBOOL (WINAPI *PFN_MS_EX) (lMEMORYSTATUSEX*); #else #include <sys/resource.h> #include <sys/time.h> #include <unistd.h> #endif #if defined(__MACH__) || defined(__APPLE__) #include <sys/types.h> #include <sys/sysctl.h> #include <mach/task.h> #include <mach/mach.h> #include <TargetConditionals.h> #ifndef TARGET_OS_IPHONE #include <libproc.h> #endif #endif namespace util { namespace { #if defined(__MACH__) typedef struct timeval Wall; Wall GetWall() { struct timeval tv; gettimeofday(&tv, NULL); return tv; } #elif defined(_WIN32) || defined(_WIN64) typedef time_t Wall; Wall GetWall() { return time(NULL); } #else typedef struct timespec Wall; Wall GetWall() { Wall ret; UTIL_THROW_IF(-1 == clock_gettime(CLOCK_MONOTONIC, &ret), ErrnoException, "Could not get wall time"); return ret; } #endif // gcc possible-unused function flags #ifdef __GNUC__ double Subtract(time_t first, time_t second) __attribute__ ((unused)); double DoubleSec(time_t tv) __attribute__ ((unused)); #if !defined(_WIN32) && !defined(_WIN64) double Subtract(const struct timeval &first, const struct timeval &second) __attribute__ ((unused)); double Subtract(const struct timespec &first, const struct timespec &second) __attribute__ ((unused)); double DoubleSec(const struct timeval &tv) __attribute__ ((unused)); double DoubleSec(const struct timespec &tv) __attribute__ ((unused)); #endif #endif // Some of these functions are only used on some platforms. #ifdef __clang__ #pragma clang diagnostic push #pragma clang diagnostic ignored "-Wunused-function" #endif // These all assume first > second double Subtract(time_t first, time_t second) { return difftime(first, second); } double DoubleSec(time_t tv) { return static_cast<double>(tv); } #if !defined(_WIN32) && !defined(_WIN64) double Subtract(const struct timeval &first, const struct timeval &second) { return static_cast<double>(first.tv_sec - second.tv_sec) + static_cast<double>(first.tv_usec - second.tv_usec) / 1000000.0; } double Subtract(const struct timespec &first, const struct timespec &second) { return static_cast<double>(first.tv_sec - second.tv_sec) + static_cast<double>(first.tv_nsec - second.tv_nsec) / 1000000000.0; } double DoubleSec(const struct timeval &tv) { return static_cast<double>(tv.tv_sec) + (static_cast<double>(tv.tv_usec) / 1000000.0); } double DoubleSec(const struct timespec &tv) { return static_cast<double>(tv.tv_sec) + (static_cast<double>(tv.tv_nsec) / 1000000000.0); } #endif #ifdef __clang__ #pragma clang diagnostic pop #endif class RecordStart { public: RecordStart() { started_ = GetWall(); } const Wall &Started() const { return started_; } private: Wall started_; }; const RecordStart kRecordStart; const char *SkipSpaces(const char *at) { for (; *at == ' ' || *at == '\t'; ++at) {} return at; } } // namespace double WallTime() { return Subtract(GetWall(), kRecordStart.Started()); } double CPUTime() { #if defined(_WIN32) || defined(_WIN64) return 0.0; #elif defined(__MACH__) || defined(__FreeBSD__) || defined(__APPLE__) struct rusage usage; UTIL_THROW_IF(getrusage(RUSAGE_SELF, &usage), ErrnoException, "getrusage failed"); return DoubleSec(usage.ru_utime) + DoubleSec(usage.ru_stime); #else struct timespec usage; UTIL_THROW_IF(clock_gettime(CLOCK_PROCESS_CPUTIME_ID, &usage), ErrnoException, "clock_gettime failed?!"); return DoubleSec(usage); #endif } double ThreadTime() { #if defined(_WIN32) || defined(_WIN64) // Output parameters for querying thread CPU usage: FILETIME sys_time, user_time; // Unused, but apparently need to be passed: FILETIME c_time, e_time; HANDLE this_thread = GetCurrentThread(); UTIL_THROW_IF(!GetThreadTimes(this_thread, &c_time, &e_time, &sys_time, &user_time), WindowsException, "GetThreadTime"); // Convert LPFILETIME to 64-bit number, and from there to double. ULARGE_INTEGER sys_ticks, user_ticks; sys_ticks.LowPart = sys_time.dwLowDateTime; sys_ticks.HighPart = sys_time.dwHighDateTime; user_ticks.LowPart = user_time.dwLowDateTime; user_ticks.HighPart = user_time.dwHighDateTime; const double ticks = double(sys_ticks.QuadPart + user_ticks.QuadPart); // GetThreadTimes() reports in units of 100 nanoseconds, i.e. ten-millionths // of a second. return ticks / (10 * 1000 * 1000); #elif defined(HAVE_CLOCKGETTIME) struct timespec usage; UTIL_THROW_IF(clock_gettime(CLOCK_THREAD_CPUTIME_ID, &usage), ErrnoException, "clock_gettime failed?!"); return DoubleSec(usage); #elif defined(__MACH__) || defined(__APPLE__) struct task_basic_info t_info; mach_msg_type_number_t t_info_count = TASK_BASIC_INFO_COUNT; task_info(mach_task_self(), TASK_BASIC_INFO, (task_info_t)&t_info, &t_info_count); return 0.0; #endif } uint64_t RSSMax() { #if defined(_WIN32) || defined(_WIN64) return 0; #else struct rusage usage; if (getrusage(RUSAGE_SELF, &usage)) return 0; return static_cast<uint64_t>(usage.ru_maxrss) * 1024; #endif } void PrintUsage(std::ostream &out) { #if !defined(_WIN32) && !defined(_WIN64) #if (defined(__MACH__) || defined(__APPLE__)) && !defined(TARGET_OS_IPHONE) struct mach_task_basic_info t_info; char name[2 * MAXCOMLEN] = {0}; proc_name(getpid(), name, sizeof(name)); mach_msg_type_number_t t_info_count = MACH_TASK_BASIC_INFO_COUNT; task_info(mach_task_self(), MACH_TASK_BASIC_INFO, (task_info_t)&t_info, &t_info_count); out << name << '\t'; out << t_info.resident_size_max << '\t'; out << t_info.resident_size << '\t'; #else // Linux doesn't set memory usage in getrusage :-( std::set<std::string> headers; headers.insert("Name:"); headers.insert("VmPeak:"); headers.insert("VmRSS:"); std::ifstream status("/proc/self/status", std::ios::in); std::string header, value; while ((status >> header) && getline(status, value)) { if (headers.find(header) != headers.end()) { out << header << SkipSpaces(value.c_str()) << '\t'; } } #endif struct rusage usage; if (getrusage(RUSAGE_SELF, &usage)) { perror("getrusage"); return; } out << "RSSMax:" << usage.ru_maxrss << " kB" << '\t'; out << "user:" << DoubleSec(usage.ru_utime) << "\tsys:" << DoubleSec(usage.ru_stime) << '\t'; out << "CPU:" << CPUTime() << '\t'; #endif out << "real:" << WallTime() << '\n'; } /* Adapted from physmem.c in gnulib 831b84c59ef413c57a36b67344467d66a8a2ba70 */ /* Calculate the size of physical memory. Copyright (C) 2000-2001, 2003, 2005-2006, 2009-2013 Free Software Foundation, Inc. This program is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this program. If not, see <http://www.gnu.org/licenses/>. */ /* Written by Paul Eggert. */ uint64_t GuessPhysicalMemory() { #if defined(_SC_PHYS_PAGES) && defined(_SC_PAGESIZE) { long pages = sysconf(_SC_PHYS_PAGES); long page_size = sysconf(_SC_PAGESIZE); if (pages != -1 && page_size != -1) return static_cast<uint64_t>(pages) * static_cast<uint64_t>(page_size); } #endif #ifdef HW_PHYSMEM { /* This works on *bsd and darwin. */ unsigned int physmem; size_t len = sizeof physmem; static int mib[2] = { CTL_HW, HW_PHYSMEM }; if (sysctl (mib, sizeof(mib) / sizeof(mib[0]), &physmem, &len, NULL, 0) == 0 && len == sizeof (physmem)) return static_cast<uint64_t>(physmem); } #endif #if defined(_WIN32) || defined(_WIN64) { /* this works on windows */ PFN_MS_EX pfnex; HMODULE h = GetModuleHandle (TEXT("kernel32.dll")); if (!h) return 0; /* Use GlobalMemoryStatusEx if available. */ if ((pfnex = (PFN_MS_EX) GetProcAddress (h, "GlobalMemoryStatusEx"))) { lMEMORYSTATUSEX lms_ex; lms_ex.dwLength = sizeof lms_ex; if (!pfnex (&lms_ex)) return 0; return lms_ex.ullTotalPhys; } /* Fall back to GlobalMemoryStatus which is always available. but returns wrong results for physical memory > 4GB. */ else { MEMORYSTATUS ms; GlobalMemoryStatus (&ms); return ms.dwTotalPhys; } } #endif return 0; } namespace { class SizeParseError : public Exception { public: explicit SizeParseError(const std::string &str) throw() { *this << "Failed to parse " << str << " into a memory size "; } }; template <class Num> uint64_t ParseNum(const std::string &arg) { std::stringstream stream(arg); Num value; stream >> value; UTIL_THROW_IF_ARG(!stream, SizeParseError, (arg), "for the leading number."); std::string after; stream >> after; UTIL_THROW_IF_ARG(after.size() > 1, SizeParseError, (arg), "because there are more than two characters after the number."); std::string throwaway; UTIL_THROW_IF_ARG(stream >> throwaway, SizeParseError, (arg), "because there was more cruft " << throwaway << " after the number."); // Silly sort, using kilobytes as your default unit. if (after.empty()) after = "K"; if (after == "%") { uint64_t mem = GuessPhysicalMemory(); UTIL_THROW_IF_ARG(!mem, SizeParseError, (arg), "because % was specified but the physical memory size could not be determined."); return static_cast<uint64_t>(static_cast<double>(value) * static_cast<double>(mem) / 100.0); } if (after == "k") after = "K"; std::string units("bKMGTPEZY"); std::string::size_type index = units.find(after[0]); UTIL_THROW_IF_ARG(index == std::string::npos, SizeParseError, (arg), "the allowed suffixes are " << units << "%."); for (std::string::size_type i = 0; i < index; ++i) { value *= 1024; } return static_cast<uint64_t>(value); } } // namespace uint64_t ParseSize(const std::string &arg) { return arg.find('.') == std::string::npos ? ParseNum<double>(arg) : ParseNum<uint64_t>(arg); } } // namespace util
0
coqui_public_repos/TTS
coqui_public_repos/TTS/tests/__init__.py
import os from TTS.config import BaseDatasetConfig from TTS.utils.generic_utils import get_cuda def get_device_id(): use_cuda, _ = get_cuda() if use_cuda: if "CUDA_VISIBLE_DEVICES" in os.environ and os.environ["CUDA_VISIBLE_DEVICES"] != "": GPU_ID = os.environ["CUDA_VISIBLE_DEVICES"].split(",")[0] else: GPU_ID = "0" else: GPU_ID = "" return GPU_ID def get_tests_path(): """Returns the path to the test directory.""" return os.path.dirname(os.path.realpath(__file__)) def get_tests_input_path(): """Returns the path to the test data directory.""" return os.path.join(get_tests_path(), "inputs") def get_tests_data_path(): """Returns the path to the test data directory.""" return os.path.join(get_tests_path(), "data") def get_tests_output_path(): """Returns the path to the directory for test outputs.""" path = os.path.join(get_tests_path(), "outputs") os.makedirs(path, exist_ok=True) return path def run_cli(command): exit_status = os.system(command) assert exit_status == 0, f" [!] command `{command}` failed." def get_test_data_config(): return BaseDatasetConfig(formatter="ljspeech", path="tests/data/ljspeech/", meta_file_train="metadata.csv") def assertHasAttr(test_obj, obj, intendedAttr): # from https://stackoverflow.com/questions/48078636/pythons-unittest-lacks-an-asserthasattr-method-what-should-i-use-instead testBool = hasattr(obj, intendedAttr) test_obj.assertTrue(testBool, msg=f"obj lacking an attribute. obj: {obj}, intendedAttr: {intendedAttr}") def assertHasNotAttr(test_obj, obj, intendedAttr): testBool = hasattr(obj, intendedAttr) test_obj.assertFalse(testBool, msg=f"obj should not have an attribute. obj: {obj}, intendedAttr: {intendedAttr}")
0
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/include/fst
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/include/fst/script/verify.h
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. #ifndef FST_SCRIPT_VERIFY_H_ #define FST_SCRIPT_VERIFY_H_ #include <fst/verify.h> #include <fst/script/arg-packs.h> #include <fst/script/fst-class.h> namespace fst { namespace script { using VerifyArgs = WithReturnValue<bool, const FstClass &>; template <class Arc> void Verify(VerifyArgs *args) { const Fst<Arc> &fst = *(args->args.GetFst<Arc>()); args->retval = Verify(fst); } bool Verify(const FstClass &fst); } // namespace script } // namespace fst #endif // FST_SCRIPT_VERIFY_H_
0
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/extensions/Makefile.am
if HAVE_COMPACT compactdir = compact endif if HAVE_COMPRESS compressdir = compress endif if HAVE_CONST constdir = const endif if HAVE_FAR fardir = far endif if HAVE_GRM fardir = far pdtdir = pdt mpdtdir = mpdt endif if HAVE_LINEAR lineardir = linear endif if HAVE_LOOKAHEAD lookaheaddir = lookahead endif if HAVE_MPDT pdtdir = pdt mpdtdir = mpdt endif if HAVE_NGRAM ngramdir = ngram endif if HAVE_PYTHON fardir = far pywrapfstdir = python endif if HAVE_PDT pdtdir = pdt endif if HAVE_SPECIAL specialdir = special endif SUBDIRS = $(compactdir) $(compressdir) $(constdir) $(fardir) $(lineardir) \ $(lookaheaddir) $(pdtdir) $(mpdtdir) $(ngramdir) $(pywrapfstdir) \ $(specialdir)
0
coqui_public_repos/TTS/tests
coqui_public_repos/TTS/tests/data/dummy_speakers.json
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-0.09324796497821808, -0.006910949945449829, 0.02273549698293209, -0.03458410128951073, 0.0704135149717331, 0.12127451598644257, 0.04999300092458725, -0.12125937640666962 ] } }
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coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/bin/fstcompile.cc
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. #include <fst/flags.h> DEFINE_bool(acceptor, false, "Input in acceptor format"); DEFINE_string(arc_type, "standard", "Output arc type"); DEFINE_string(fst_type, "vector", "Output FST type"); DEFINE_string(isymbols, "", "Input label symbol table"); DEFINE_string(osymbols, "", "Output label symbol table"); DEFINE_string(ssymbols, "", "State label symbol table"); DEFINE_bool(keep_isymbols, false, "Store input label symbol table with FST"); DEFINE_bool(keep_osymbols, false, "Store output label symbol table with FST"); DEFINE_bool(keep_state_numbering, false, "Do not renumber input states"); DEFINE_bool(allow_negative_labels, false, "Allow negative labels (not recommended; may cause conflicts)"); int fstcompile_main(int argc, char **argv); int main(int argc, char **argv) { return fstcompile_main(argc, argv); }
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coqui_public_repos/STT
coqui_public_repos/STT/taskcluster/docs.yml
build: template_file: docs.tyml routes: - "index.project.deepspeech.deepspeech.native_client.${event.head.branchortag}.docs" - "index.project.deepspeech.deepspeech.native_client.${event.head.branchortag}.${event.head.sha}.docs" - "index.project.deepspeech.deepspeech.native_client.docs.${event.head.sha}" system_setup: > ${nodejs.packages_docs_bionic.prep_12} && ${nodejs.packages_docs_bionic.apt_pinning} && apt-get -qq update && apt-get -qq -y install ${nodejs.packages_docs_bionic.apt} ${python.packages_docs_bionic.apt} scripts: build: "taskcluster/docs-build.sh" package: "taskcluster/docs-package.sh" workerType: "${docker.smallTask}" metadata: name: "DeepSpeech API Documentation" description: "Building DeepSpeech API Documentation"
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coqui_public_repos/STT/native_client/dotnet/STTClient
coqui_public_repos/STT/native_client/dotnet/STTClient/Interfaces/ISTT.cs
using STTClient.Models; using System; using System.IO; namespace STTClient.Interfaces { /// <summary> /// Client interface for Coqui STT /// </summary> public interface ISTT : IDisposable { /// <summary> /// Return version of this library. The returned version is a semantic version /// (SemVer 2.0.0). /// </summary> unsafe string Version(); /// <summary> /// Return the sample rate expected by the model. /// </summary> /// <returns>Sample rate.</returns> unsafe int GetModelSampleRate(); /// <summary> /// Get beam width value used by the model. If SetModelBeamWidth was not /// called before, will return the default value loaded from the model /// file. /// </summary> /// <returns>Beam width value used by the model.</returns> unsafe uint GetModelBeamWidth(); /// <summary> /// Set beam width value used by the model. /// </summary> /// <param name="aBeamWidth">The beam width used by the decoder. A larger beam width value generates better results at the cost of decoding time.</param> /// <exception cref="ArgumentException">Thrown on failure.</exception> unsafe void SetModelBeamWidth(uint aBeamWidth); /// <summary> /// Enable decoding using an external scorer. /// </summary> /// <param name="aScorerPath">The path to the external scorer file.</param> /// <exception cref="ArgumentException">Thrown when the native binary failed to enable decoding with an external scorer.</exception> /// <exception cref="FileNotFoundException">Thrown when cannot find the scorer file.</exception> unsafe void EnableExternalScorer(string aScorerPath); /// <summary> /// Add a hot-word. /// </summary> /// <param name="aWord">Some word</param> /// <param name="aBoost">Some boost</param> /// <exception cref="ArgumentException">Thrown on failure.</exception> unsafe void AddHotWord(string aWord, float aBoost); /// <summary> /// Erase entry for a hot-word. /// </summary> /// <param name="aWord">Some word</param> /// <exception cref="ArgumentException">Thrown on failure.</exception> unsafe void EraseHotWord(string aWord); /// <summary> /// Clear all hot-words. /// </summary> /// <exception cref="ArgumentException">Thrown on failure.</exception> unsafe void ClearHotWords(); /// <summary> /// Disable decoding using an external scorer. /// </summary> /// <exception cref="ArgumentException">Thrown when an external scorer is not enabled.</exception> unsafe void DisableExternalScorer(); /// <summary> /// Set hyperparameters alpha and beta of the external scorer. /// </summary> /// <param name="aAlpha">The alpha hyperparameter of the decoder. Language model weight.</param> /// <param name="aBeta">The beta hyperparameter of the decoder. Word insertion weight.</param> /// <exception cref="ArgumentException">Thrown when an external scorer is not enabled.</exception> unsafe void SetScorerAlphaBeta(float aAlpha, float aBeta); /// <summary> /// Use the STT model to perform Speech-To-Text. /// </summary> /// <param name="aBuffer">A 16-bit, mono raw audio signal at the appropriate sample rate (matching what the model was trained on).</param> /// <param name="aBufferSize">The number of samples in the audio signal.</param> /// <returns>The STT result. Returns NULL on error.</returns> unsafe string SpeechToText(short[] aBuffer, uint aBufferSize); /// <summary> /// Use the STT model to perform Speech-To-Text, return results including metadata. /// </summary> /// <param name="aBuffer">A 16-bit, mono raw audio signal at the appropriate sample rate (matching what the model was trained on).</param> /// <param name="aBufferSize">The number of samples in the audio signal.</param> /// <param name="aNumResults">Maximum number of candidate transcripts to return. Returned list might be smaller than this.</param> /// <returns>The extended metadata. Returns NULL on error.</returns> unsafe Metadata SpeechToTextWithMetadata(short[] aBuffer, uint aBufferSize, uint aNumResults); /// <summary> /// Destroy a streaming state without decoding the computed logits. /// This can be used if you no longer need the result of an ongoing streaming /// inference and don't want to perform a costly decode operation. /// </summary> unsafe void FreeStream(STTStream stream); /// <summary> /// Creates a new streaming inference state. /// </summary> unsafe STTStream CreateStream(); /// <summary> /// Feeds audio samples to an ongoing streaming inference. /// </summary> /// <param name="stream">Instance of the stream to feed the data.</param> /// <param name="aBuffer">An array of 16-bit, mono raw audio samples at the appropriate sample rate (matching what the model was trained on).</param> unsafe void FeedAudioContent(STTStream stream, short[] aBuffer, uint aBufferSize); /// <summary> /// Computes the intermediate decoding of an ongoing streaming inference. /// </summary> /// <param name="stream">Instance of the stream to decode.</param> /// <returns>The STT intermediate result.</returns> unsafe string IntermediateDecode(STTStream stream); /// <summary> /// Computes the intermediate decoding of an ongoing streaming inference, including metadata. /// </summary> /// <param name="stream">Instance of the stream to decode.</param> /// <param name="aNumResults">Maximum number of candidate transcripts to return. Returned list might be smaller than this.</param> /// <returns>The extended metadata result.</returns> unsafe Metadata IntermediateDecodeWithMetadata(STTStream stream, uint aNumResults); /// <summary> /// Closes the ongoing streaming inference, returns the STT result over the whole audio signal. /// </summary> /// <param name="stream">Instance of the stream to finish.</param> /// <returns>The STT result.</returns> unsafe string FinishStream(STTStream stream); /// <summary> /// Closes the ongoing streaming inference, returns the STT result over the whole audio signal, including metadata. /// </summary> /// <param name="stream">Instance of the stream to finish.</param> /// <param name="aNumResults">Maximum number of candidate transcripts to return. Returned list might be smaller than this.</param> /// <returns>The extended metadata result.</returns> unsafe Metadata FinishStreamWithMetadata(STTStream stream, uint aNumResults); } }
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coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/script/equal.cc
// See www.openfst.org for extensive documentation on this weighted // finite-state transducer library. #include <fst/script/fst-class.h> #include <fst/script/equal.h> #include <fst/script/script-impl.h> namespace fst { namespace script { bool Equal(const FstClass &fst1, const FstClass &fst2, float delta) { if (!internal::ArcTypesMatch(fst1, fst2, "Equal")) return false; EqualInnerArgs iargs(fst1, fst2, delta); EqualArgs args(iargs); Apply<Operation<EqualArgs>>("Equal", fst1.ArcType(), &args); return args.retval; } REGISTER_FST_OPERATION(Equal, StdArc, EqualArgs); REGISTER_FST_OPERATION(Equal, LogArc, EqualArgs); REGISTER_FST_OPERATION(Equal, Log64Arc, EqualArgs); } // namespace script } // namespace fst
0
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/extensions/Makefile.am
if HAVE_COMPACT compactdir = compact endif if HAVE_COMPRESS compressdir = compress endif if HAVE_CONST constdir = const endif if HAVE_FAR fardir = far endif if HAVE_GRM fardir = far pdtdir = pdt mpdtdir = mpdt endif if HAVE_LINEAR lineardir = linear endif if HAVE_LOOKAHEAD lookaheaddir = lookahead endif if HAVE_MPDT pdtdir = pdt mpdtdir = mpdt endif if HAVE_NGRAM ngramdir = ngram endif if HAVE_PYTHON fardir = far pywrapfstdir = python endif if HAVE_PDT pdtdir = pdt endif if HAVE_SPECIAL specialdir = special endif SUBDIRS = $(compactdir) $(compressdir) $(constdir) $(fardir) $(lineardir) \ $(lookaheaddir) $(pdtdir) $(mpdtdir) $(ngramdir) $(pywrapfstdir) \ $(specialdir)
0