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.gitattributes

@@ -25,7 +25,6 @@
 *.safetensors filter=lfs diff=lfs merge=lfs -text
 saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
 *.tflite filter=lfs diff=lfs merge=lfs -text
 *.tgz filter=lfs diff=lfs merge=lfs -text
 *.wasm filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,2 @@
+.venv
+__pycache__

Dockerfile

@@ -0,0 +1,52 @@
+FROM python:3.9
+
+RUN apt-get update && apt-get install -y gnupg && \ 
+     apt-key adv --recv-keys --keyserver hkp://keyserver.ubuntu.com:80 A3A48C4A && \ 
+     echo "deb http://ppa.launchpad.net/zeehio/festcat/ubuntu bionic main" >> /etc/apt/sources.list && \ 
+     echo "deb-src http://ppa.launchpad.net/zeehio/festcat/ubuntu bionic main" >> /etc/apt/sources.list && \ 
+     apt-get update && \ 
+     apt-get -y install festival festvox-ca-ona-hts festvox-ca-pau-hts lame git make autoconf automake libtool pkg-config gcc libsonic-dev ronn kramdown libpcaudio-dev libatlas-base-dev gfortran
+
+RUN git clone -b ca-to-pr https://github.com/projecte-aina/espeak-ng
+
+RUN cd espeak-ng && \
+ ./autogen.sh && \
+ ./configure --prefix=/usr && \
+ make && \
+ make install
+
+RUN useradd -m -u 1000 user
+
+USER user
+
+
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+
+COPY --chown=user requirements.txt .
+COPY --chown=user models models
+
+RUN pip install -r requirements.txt
+
+RUN git clone https://github.com/jaywalnut310/vits.git && \
+ cd vits && sed s/torch==1.6.0/torch==1.7.0/ requirements.txt > requirements.txt && pip install -r requirements.txt && cd monotonic_align && \
+ python setup.py build_ext --inplace && cd /home/user
+
+ENV PYTHONPATH=$PYTHONPATH:/home/user/app/vits
+
+COPY --chown=user engine.py .
+COPY --chown=user mms.py .
+COPY --chown=user festival.py .
+COPY --chown=user app.py .
+
+RUN mkdir -p cache && chmod 777 cache
+
+ENV NUMBA_CACHE_DIR=/home/user/cache
+ENV MPLCONFIGDIR=/home/user/cache
+
+EXPOSE 7860
+
+CMD ["python", "app.py"]

README.md

@@ -1,11 +1,9 @@
 ---
-title: Catalatest
-emoji: 📚
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
+title: Síntesi en català
+emoji: 👁
+colorFrom: green
+colorTo: red
+sdk: docker
 pinned: false
 ---
 

app.py

@@ -0,0 +1,147 @@
+import tempfile
+import gradio as gr
+import os
+from TTS.utils.synthesizer import Synthesizer
+from espeak_phonemizer import Phonemizer
+from engine import Piper
+from festival import festival_synthesize
+from mms import MMS
+
+MAX_TXT_LEN = 325
+
+fonemitzador = Phonemizer("ca")
+
+def carrega_bsc():
+    model_path = os.getcwd() + "/models/bsc/best_model.pth"
+    config_path = os.getcwd() + "/models/bsc/config.json"
+    speakers_file_path = os.getcwd() + "/models/bsc/speakers.pth"
+    vocoder_path = None
+    vocoder_config_path = None
+
+    synthesizer = Synthesizer(
+        model_path, config_path, speakers_file_path, None, vocoder_path, vocoder_config_path,
+    )
+
+    return synthesizer
+
+def carrega_collectivat():
+    model_path = os.getcwd() + "/models/collectivat/fast-speech_best_model.pth"
+    config_path = os.getcwd() + "/models/collectivat/fast-speech_config.json"
+    vocoder_path = os.getcwd() + "/models/collectivat/ljspeech--hifigan_v2_model_file.pth"
+    vocoder_config_path = os.getcwd() + "/models/collectivat/ljspeech--hifigan_v2_config.json"
+    synthesizer = Synthesizer(
+        model_path, config_path, None, None, vocoder_path, vocoder_config_path
+    )
+
+    return synthesizer
+
+def carrega_piper():
+    return Piper(os.getcwd() + "/models/piper/ca-upc_ona-x-low.onnx")
+
+def carrega_mms():
+    return MMS(os.getcwd() + "/models/mms")
+
+
+model_bsc = carrega_bsc()
+SPEAKERS = model_bsc.tts_model.speaker_manager.speaker_names
+
+model_collectivat = carrega_collectivat()
+
+model_piper = carrega_piper()
+
+model_mms = carrega_mms()
+
+request_count = 0
+
+def tts(text, festival_voice, speaker_idx):
+    if len(text) > MAX_TXT_LEN:
+        text = text[:MAX_TXT_LEN]
+        print(f"Input text was cutoff since it went over the {MAX_TXT_LEN} character limit.")
+    print(text)
+
+    # synthesize
+    wav_bsc = model_bsc.tts(text, speaker_idx)
+    wav_coll = model_collectivat.tts(text)
+    wav_piper = model_piper.synthesize(text)
+
+    fp_bsc = ""
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
+        model_bsc.save_wav(wav_bsc, fp)
+        fp_bsc = fp.name
+
+    fp_coll = ""
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
+        model_collectivat.save_wav(wav_coll, fp)
+        fp_coll = fp.name
+
+    fp_piper = ""
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
+        fp.write(wav_piper)
+        fp_piper = fp.name
+    
+    fp_mms = ""
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
+        model_mms.synthesize(fp.name, text)
+        fp_mms = fp.name
+
+    fonemes = fonemitzador.phonemize(text, keep_clause_breakers=True)
+
+    fp_festival = festival_synthesize(text, festival_voice)
+
+    global request_count
+    request_count += 1
+    print(f"Requests: {request_count}")
+    return fonemes, fp_festival, fp_bsc, fp_coll, fp_piper, fp_mms
+
+
+description="""
+Amb aquesta aplicació podeu sintetitzar text a veu amb els últims models neuronals lliures pel català i amb el motor Festival.
+
+1. Model multi-parlant VITS entrenat pel BSC (Projecte Aina) [enllaç](https://huggingface.co/projecte-aina/tts-ca-coqui-vits-multispeaker)
+2. Model Fastspeech entrenat per Col·lectivat [enllaç](https://github.com/CollectivaT-dev/TTS-API)
+3. Model VITS entrenat per Piper/Home Assistant [enllaç](https://github.com/rhasspy/piper)
+3. Model VITS entrenat per Meta (llicència CC-BY-NC) [enllaç](https://github.com/facebookresearch/fairseq/tree/main/examples/mms)
+
+El primer model ha estat entrenat amb totes les veus de FestCAT, els talls de Common Voice 8 i un altre corpus pel que conté moltes veus de qualitat variable. La veu d'Ona està seleccionada per defecte per la comparativa però podeu provar les altres.
+Els models 2 i 3 han estat entrenats amb la veu d'Ona de FestCAT.
+El model 4, anomenat MMS, de Meta (Facebook) ha estat entrenat a partir de dades d'un [audiollibre](http://live.bible.is/bible/CATBSS/LUK/1) de la Bíblia
+
+Aquesta aplicació fa servir l'últim estat de l'espeak millorat per Carme Armentano del BSC
+https://github.com/projecte-aina/espeak-ng
+
+NOTA: El model de col·lectivat treballa amb grafemes pel que no fa servir espeak com a fonemitzador. Festival conté les seves pròpies normes fonètiques.
+"""
+article= ""
+
+iface = gr.Interface(
+    fn=tts,
+    inputs=[
+        gr.Textbox(
+            label="Text",
+            value="L'Èlia i l'Alí a l'aula.  L'oli i l'ou.  Lulú olorava la lila.",
+        ),
+        gr.Dropdown(label="Parlant del motor Festival", choices=["ona", "pau"], value="ona"),
+        gr.Dropdown(label="Parlant del model VITS multi-parlant del BSC", choices=SPEAKERS, value="ona")
+    ],
+    outputs=[
+        gr.Markdown(label="Fonemes"),
+        gr.Audio(label="Festival",type="filepath"),
+        gr.Audio(label="BSC VITS",type="filepath"),
+        gr.Audio(label="Collectivat Fastspeech",type="filepath"),
+        gr.Audio(label="Piper VITS",type="filepath"),
+        gr.Audio(label="Meta MMS VITS",type="filepath")
+    ],
+    title="Comparativa de síntesi lliure en català️",
+    description=description,
+    article=article,
+    allow_flagging="never",
+    layout="vertical",
+    live=False,
+    examples=[
+        ["Duc pà sec al sac, m'assec on sóc i el suco amb suc", "ona", "ona"],
+        ["Un plat pla blanc, ple de pebre negre n’era. Un plat blanc pla, ple de pebre negre està", "ona", "ona"],
+        ["Visc al bosc i busco vesc i visc del vesc que busco al bosc", "ona", "ona"],
+        ["Una polla xica, pica, pellarica, camatorta i becarica va tenir sis polls xics, pics, pellarics, camacurts i becarics. Si la polla no hagués sigut xica, pica, pellarica, camatorta i becarica, els sis polls no haurien sigut xics, pics, pellarics, camacurts i becarics.", "ona", "ona"]
+    ]
+)
+iface.launch(server_name="0.0.0.0", server_port=7860)

engine.py

@@ -0,0 +1,144 @@
+import io
+import json
+import os
+import wave
+from dataclasses import dataclass
+from pathlib import Path
+from typing import List, Mapping, Optional, Sequence, Union
+
+import numpy as np
+import onnxruntime
+from espeak_phonemizer import Phonemizer
+
+_BOS = "^"
+_EOS = "$"
+_PAD = "_"
+
+
+@dataclass
+class PiperConfig:
+    num_symbols: int
+    num_speakers: int
+    sample_rate: int
+    espeak_voice: str
+    length_scale: float
+    noise_scale: float
+    noise_w: float
+    phoneme_id_map: Mapping[str, Sequence[int]]
+
+
+class Piper:
+    def __init__(
+        self,
+        model_path: Union[str, Path],
+        config_path: Optional[Union[str, Path]] = None,
+        use_cuda: bool = False,
+    ):
+        if config_path is None:
+            config_path = f"{model_path}.json"
+
+        self.config = load_config(config_path)
+        self.phonemizer = Phonemizer(self.config.espeak_voice)
+        self.onnx_options = onnxruntime.SessionOptions()
+        self.onnx_options.intra_op_num_threads = os.cpu_count() - 1
+        self.model = onnxruntime.InferenceSession(
+            str(model_path),
+            sess_options=self.onnx_options,
+            providers=["CPUExecutionProvider"]
+            if not use_cuda
+            else ["CUDAExecutionProvider"],
+        )
+
+    def synthesize(
+        self,
+        text: str,
+        speaker_id: Optional[int] = None,
+        length_scale: Optional[float] = None,
+        noise_scale: Optional[float] = None,
+        noise_w: Optional[float] = None,
+    ) -> bytes:
+        """Synthesize WAV audio from text."""
+        if length_scale is None:
+            length_scale = self.config.length_scale
+
+        if noise_scale is None:
+            noise_scale = self.config.noise_scale
+
+        if noise_w is None:
+            noise_w = self.config.noise_w
+
+        phonemes_str = self.phonemizer.phonemize(text, keep_clause_breakers=True)
+        phonemes = [_BOS] + list(phonemes_str)
+        phoneme_ids: List[int] = []
+
+        for phoneme in phonemes:
+            phoneme_ids.extend(self.config.phoneme_id_map[phoneme])
+            phoneme_ids.extend(self.config.phoneme_id_map[_PAD])
+
+        phoneme_ids.extend(self.config.phoneme_id_map[_EOS])
+
+        phoneme_ids_array = np.expand_dims(np.array(phoneme_ids, dtype=np.int64), 0)
+        phoneme_ids_lengths = np.array([phoneme_ids_array.shape[1]], dtype=np.int64)
+        scales = np.array(
+            [noise_scale, length_scale, noise_w],
+            dtype=np.float32,
+        )
+
+        if (self.config.num_speakers > 1) and (speaker_id is not None):
+            # Default speaker
+            speaker_id = 0
+
+        sid = None
+
+        if speaker_id is not None:
+            sid = np.array([speaker_id], dtype=np.int64)
+
+        # Synthesize through Onnx
+        audio = self.model.run(
+            None,
+            {
+                "input": phoneme_ids_array,
+                "input_lengths": phoneme_ids_lengths,
+                "scales": scales,
+                "sid": sid,
+            },
+        )[0].squeeze((0, 1))
+        audio = audio_float_to_int16(audio.squeeze())
+
+        # Convert to WAV
+        with io.BytesIO() as wav_io:
+            wav_file: wave.Wave_write = wave.open(wav_io, "wb")
+            with wav_file:
+                wav_file.setframerate(self.config.sample_rate)
+                wav_file.setsampwidth(2)
+                wav_file.setnchannels(1)
+                wav_file.writeframes(audio.tobytes())
+
+            return wav_io.getvalue()
+
+
+def load_config(config_path: Union[str, Path]) -> PiperConfig:
+    with open(config_path, "r", encoding="utf-8") as config_file:
+        config_dict = json.load(config_file)
+        inference = config_dict.get("inference", {})
+
+        return PiperConfig(
+            num_symbols=config_dict["num_symbols"],
+            num_speakers=config_dict["num_speakers"],
+            sample_rate=config_dict["audio"]["sample_rate"],
+            espeak_voice=config_dict["espeak"]["voice"],
+            noise_scale=inference.get("noise_scale", 0.667),
+            length_scale=inference.get("length_scale", 1.0),
+            noise_w=inference.get("noise_w", 0.8),
+            phoneme_id_map=config_dict["phoneme_id_map"],
+        )
+
+
+def audio_float_to_int16(
+    audio: np.ndarray, max_wav_value: float = 32767.0
+) -> np.ndarray:
+    """Normalize audio and convert to int16 range"""
+    audio_norm = audio * (max_wav_value / max(0.01, np.max(np.abs(audio))))
+    audio_norm = np.clip(audio_norm, -max_wav_value, max_wav_value)
+    audio_norm = audio_norm.astype("int16")
+    return audio_norm

festival.py

@@ -0,0 +1,65 @@
+#!/usr/bin/env python
+# -*- encoding: utf-8 -*-
+#
+# Copyright (c) 2016 Jordi Mas i Hernandez <jmas@softcatala.org>
+#
+# 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, write to the
+# Free Software Foundation, Inc., 59 Temple Place - Suite 330,
+# Boston, MA 02111-1307, USA.
+
+import subprocess
+import tempfile
+
+festival_voices = {
+    "ona": "voice_upc_ca_ona_hts",
+    "pau": "voice_upc_ca_pau_hts"
+}
+
+def _normalize(result):
+    mapping = {
+                '’' : '\'',
+                'à' : 'à',
+                'í' : 'í',
+                'ó' : 'ó',
+                'è' : 'è',
+                'ò' : 'ò',
+                'ú' : 'ú',
+              }
+
+    for char in mapping.keys():
+        result = result.replace(char, mapping[char])
+
+    return result
+
+
+def festival_synthesize(text, voice):
+    if voice not in ["ona", "pau"]:
+        raise Error
+
+    txt2wave = '/usr/bin/text2wave'
+
+    with tempfile.NamedTemporaryFile() as encoded_file,\
+         tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as wave_file:
+
+        text = _normalize(text)
+        f = open(encoded_file.name, 'wb')
+        f.write(text.encode('ISO-8859-15', 'ignore'))
+        f.close()
+
+        cmd = '{0} -o {1} {2} -eval "({3})"'.\
+              format(txt2wave, wave_file.name, encoded_file.name, festival_voices[voice])
+        p = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE)
+        p.wait()
+
+        return wave_file.name

mms.py

@@ -0,0 +1,84 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import torch
+import commons
+import utils
+from models import SynthesizerTrn
+from scipy.io.wavfile import write
+from pathlib import Path
+from typing import Union
+
+class TextMapper(object):
+    def __init__(self, vocab_file):
+        self.symbols = [x.replace("\n", "") for x in open(vocab_file).readlines()]
+        self.SPACE_ID = self.symbols.index(" ")
+        self._symbol_to_id = {s: i for i, s in enumerate(self.symbols)}
+        self._id_to_symbol = {i: s for i, s in enumerate(self.symbols)}
+
+    def text_to_sequence(self, text, cleaner_names):
+        '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
+        Args:
+        text: string to convert to a sequence
+        cleaner_names: names of the cleaner functions to run the text through
+        Returns:
+        List of integers corresponding to the symbols in the text
+        '''
+        sequence = []
+        clean_text = text.strip()
+        for symbol in clean_text:
+            symbol_id = self._symbol_to_id[symbol]
+            sequence += [symbol_id]
+        return sequence
+
+    def get_text(self, text, hps):
+        text_norm = self.text_to_sequence(text, hps.data.text_cleaners)
+        if hps.data.add_blank:
+            text_norm = commons.intersperse(text_norm, 0)
+        text_norm = torch.LongTensor(text_norm)
+        return text_norm
+
+    def filter_oov(self, text):
+        val_chars = self._symbol_to_id
+        txt_filt = "".join(list(filter(lambda x: x in val_chars, text)))
+        print(f"text after filtering OOV: {txt_filt}")
+        return txt_filt
+
+class MMS():
+    def __init__(self, model_path: Union[str, Path]):
+        ckpt_dir = model_path
+        vocab_file = f"{ckpt_dir}/vocab.txt"
+        config_file = f"{ckpt_dir}/config.json"
+        assert os.path.isfile(config_file), f"{config_file} doesn't exist"
+        self.hps = utils.get_hparams_from_file(config_file)
+        self.text_mapper = TextMapper(vocab_file)
+        self.net_g = SynthesizerTrn(
+            len(self.text_mapper.symbols),
+            self.hps.data.filter_length // 2 + 1,
+            self.hps.train.segment_size // self.hps.data.hop_length,
+            **self.hps.model)
+        g_pth = f"{ckpt_dir}/G_100000.pth"
+        print(f"load {g_pth}")
+
+        _ = utils.load_checkpoint(g_pth, self.net_g, None)
+
+    def synthesize(self, wav_path: str, txt):
+        print(f"text: {txt}")
+        txt = txt.lower()
+        txt = self.text_mapper.filter_oov(txt)
+        stn_tst = self.text_mapper.get_text(txt, self.hps)
+        with torch.no_grad():
+            x_tst = stn_tst.unsqueeze(0)
+            x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
+            hyp = self.net_g.infer(
+                x_tst, x_tst_lengths, noise_scale=.667,
+                noise_scale_w=0.8, length_scale=1.0
+            )[0][0,0].cpu().float().numpy()
+
+        os.makedirs(os.path.dirname(wav_path), exist_ok=True)
+        print(f"wav: {wav_path}")
+        write(wav_path, self.hps.data.sampling_rate, hyp)
+        return

models/bsc/best_model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b15fa7d2052bada1cf421e49d2d03b00e95b49fcd0e42b7af1d92da2880cdecc
+size 1038659133

models/bsc/config.json

@@ -0,0 +1,262 @@
+{
+    "output_path": "/gpfs/projects/bsc88/speech/tts/TTS_v0.8.0/recipes/multispeaker/experiments_from_previous",
+    "logger_uri": null,
+    "run_name": "multispeaker_vits_ca_1e4_1e4_32",
+    "project_name": null,
+    "run_description": "\ud83d\udc38Coqui trainer run.",
+    "print_step": 25,
+    "plot_step": 100,
+    "model_param_stats": false,
+    "wandb_entity": null,
+    "dashboard_logger": "tensorboard",
+    "log_model_step": 1000,
+    "save_step": 1000,
+    "save_n_checkpoints": 5,
+    "save_checkpoints": true,
+    "save_all_best": true,
+    "save_best_after": 10000,
+    "target_loss": null,
+    "print_eval": true,
+    "test_delay_epochs": -1,
+    "run_eval": true,
+    "run_eval_steps": null,
+    "distributed_backend": "nccl",
+    "distributed_url": "tcp://localhost:54321",
+    "mixed_precision": false,
+    "epochs": 1000,
+    "batch_size": 16,
+    "eval_batch_size": 8,
+    "grad_clip": [
+        1000.0,
+        1000.0
+    ],
+    "scheduler_after_epoch": true,
+    "lr": 0.001,
+    "optimizer": "AdamW",
+    "optimizer_params": {
+        "betas": [
+            0.8,
+            0.99
+        ],
+        "eps": 1e-09,
+        "weight_decay": 0.01
+    },
+    "lr_scheduler": "",
+    "lr_scheduler_params": null,
+    "use_grad_scaler": false,
+    "cudnn_enable": true,
+    "cudnn_deterministic": false,
+    "cudnn_benchmark": false,
+    "training_seed": 54321,
+    "model": "vits",
+    "num_loader_workers": 4,
+    "num_eval_loader_workers": 4,
+    "use_noise_augment": false,
+    "audio": {
+        "fft_size": 1024,
+        "sample_rate": 22050,
+        "win_length": 1024,
+        "hop_length": 256,
+        "num_mels": 80,
+        "mel_fmin": 0,
+        "mel_fmax": null
+    },
+    "use_phonemes": true,
+    "phonemizer": "espeak",
+    "phoneme_language": "ca",
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models/collectivat/catotron-ona-TTS-API-entry.json

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+    "model_type": "coqui",
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models/collectivat/fast-speech_config.json

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+            "path": "/home/twbgmy/play/TTS-play/TTS/recipes/catotron/upc_ona",
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+    "r": 1,
+    "f0_cache_path": "/home/twbgmy/play/TTS-play/TTS/recipes/catotron/f0_cache",
+    "restore_path": "/home/twbgmy/.local/share/tts/tts_models--en--ljspeech--fast_pitch/model_file.pth",
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+}

models/collectivat/ljspeech--hifigan_v2_config.json

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+{
+    "run_name": "hifigan",
+    "run_description": "universal hifigan trained on LibriTTS with no spectrogram normalization and using log() for scaling instead of log10()",
+
+
+    // 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": 20,     // reference level db, theoretically 20db is the sound of air.
+        "log_func": "np.log",
+
+        // Silence trimming
+        "do_trim_silence": false,// 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": 0.0,        // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
+        "mel_fmax": 8000.0,     // 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": false,    // 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": 4.0,        // 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
+    },
+
+    // DISTRIBUTED TRAINING
+    "distributed":{
+        "backend": "nccl",
+        "url": "tcp:\/\/localhost:54324"
+    },
+
+    // MODEL PARAMETERS
+    "use_pqmf": false,
+
+    // LOSS PARAMETERS
+    "use_stft_loss": false,
+    "use_subband_stft_loss": false,
+    "use_mse_gan_loss": true,
+    "use_hinge_gan_loss": false,
+    "use_feat_match_loss": true,  // use only with melgan discriminators
+    "use_l1_spec_loss": true,
+
+    // loss weights
+    "stft_loss_weight": 0,
+    "subband_stft_loss_weight": 0,
+    "mse_G_loss_weight": 1,
+    "hinge_G_loss_weight": 0,
+    "feat_match_loss_weight": 10,
+    "l1_spec_loss_weight": 45,
+
+    // multiscale stft loss parameters
+    // "stft_loss_params": {
+    //     "n_ffts": [1024, 2048, 512],
+    //     "hop_lengths": [120, 240, 50],
+    //     "win_lengths": [600, 1200, 240]
+    // },
+
+    "l1_spec_loss_params": {
+        "use_mel": true,
+        "sample_rate": 16000,
+        "n_fft": 1024,
+        "hop_length": 256,
+        "win_length": 1024,
+        "n_mels": 80,
+        "mel_fmin": 0.0,
+        "mel_fmax": null
+    },
+
+    "target_loss": "avg_G_loss",  // loss value to pick the best model to save after each epoch
+
+    // DISCRIMINATOR
+    "discriminator_model": "hifigan_discriminator",
+    //"discriminator_model_params":{
+    //    "peroids": [2, 3, 5, 7, 11],
+    //    "base_channels": 16,
+    //    "max_channels":512,
+    //    "downsample_factors":[4, 4, 4]
+    //},
+    "steps_to_start_discriminator": 0,      // steps required to start GAN trainining.1
+
+    // GENERATOR
+    "generator_model": "hifigan_generator",
+    "generator_model_params": {
+        "resblock_type": "1",
+        "upsample_factors": [8,8,2,2],
+        "upsample_kernel_sizes": [16,16,4,4],
+        "upsample_initial_channel": 128,
+        "resblock_kernel_sizes": [3,7,11],
+        "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]]
+    },
+
+    // DATASET
+    "data_path": "/home/erogol/gdrive/Datasets/non-binary-voice-files/vo_voice_quality_transformation/",
+    "feature_path": null,
+    // "feature_path": "/home/erogol/gdrive/Datasets/non-binary-voice-files/tacotron-DCA/",
+    "seq_len": 8192,
+    "pad_short": 2000,
+    "conv_pad": 0,
+    "use_noise_augment": false,
+    "use_cache": true,
+    "reinit_layers": [],    // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
+
+    // TRAINING
+    "batch_size": 16,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
+
+    // VALIDATION
+    "run_eval": true,
+    "test_delay_epochs": 10,  //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
+    "epochs": 10000,                // total number of epochs to train.
+    "wd": 0.0,                // Weight decay weight.
+    "gen_clip_grad": -1,      // Generator gradient clipping threshold. Apply gradient clipping if > 0
+    "disc_clip_grad": -1,     // Discriminator gradient clipping threshold.
+    // "lr_scheduler_gen": "ExponentialLR",   // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
+    // "lr_scheduler_gen_params": {
+    //    "gamma": 0.999,
+        // "last_epoch": -1
+    // },
+    // "lr_scheduler_disc": "ExponentialLR",   // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
+    // "lr_scheduler_disc_params": {
+    	//   "gamma": 0.999,
+        // "last_epoch": -1
+    // },
+    "lr_gen": 0.00001,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
+    "lr_disc": 0.00001,
+
+    // TENSORBOARD and LOGGING
+    "print_step": 25,       // Number of steps to log traning on console.
+    "print_eval": false,     // If True, it prints loss values for each step in eval run.
+    "save_step": 25000,      // Number of training steps expected to plot training stats on TB and save model checkpoints.
+    "checkpoint": true,     // If true, it saves checkpoints per "save_step"
+    "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
+
+    // DATA LOADING
+    "num_loader_workers": 8,        // number of training data loader processes. Don't set it too big. 4-8 are good values.
+    "num_val_loader_workers": 4,    // number of evaluation data loader processes.
+    "eval_split_size": 10,
+
+    // PATHS
+    "output_path": "/home/erogol/gdrive/Trainings/sam/"
+}
+
+

models/collectivat/ljspeech--hifigan_v2_model_file.pth

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models/mms/G_100000.pth

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models/mms/config.json

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+{
+    "train": {
+        "log_interval": 200,
+        "eval_interval": 1000,
+        "seed": 1234,
+        "epochs": 20000,
+        "learning_rate": 0.0002,
+        "betas": [
+            0.8,
+            0.99
+        ],
+        "eps": 1e-09,
+        "batch_size": 64,
+        "fp16_run": true,
+        "lr_decay": 0.999875,
+        "segment_size": 8192,
+        "init_lr_ratio": 1,
+        "warmup_epochs": 0,
+        "c_mel": 45,
+        "c_kl": 1.0
+    },
+    "data": {
+        "training_files": "train.ltr",
+        "validation_files": "dev.ltr",
+        "text_cleaners": [
+            "transliteration_cleaners"
+        ],
+        "max_wav_value": 32768.0,
+        "sampling_rate": 16000,
+        "filter_length": 1024,
+        "hop_length": 256,
+        "win_length": 1024,
+        "n_mel_channels": 80,
+        "mel_fmin": 0.0,
+        "mel_fmax": null,
+        "add_blank": true,
+        "n_speakers": 0,
+        "cleaned_text": true
+    },
+    "model": {
+        "inter_channels": 192,
+        "hidden_channels": 192,
+        "filter_channels": 768,
+        "n_heads": 2,
+        "n_layers": 6,
+        "kernel_size": 3,
+        "p_dropout": 0.1,
+        "resblock": "1",
+        "resblock_kernel_sizes": [
+            3,
+            7,
+            11
+        ],
+        "resblock_dilation_sizes": [
+            [
+                1,
+                3,
+                5
+            ],
+            [
+                1,
+                3,
+                5
+            ],
+            [
+                1,
+                3,
+                5
+            ]
+        ],
+        "upsample_rates": [
+            8,
+            8,
+            2,
+            2
+        ],
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+        "upsample_kernel_sizes": [
+            16,
+            16,
+            4,
+            4
+        ],
+        "n_layers_q": 3,
+        "use_spectral_norm": false
+    }
+}

models/mms/vocab.txt

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+z
+f
+i
+g
+m
+o
+r
+è
+h
+l
+v
+à
+u
+d
+ú
+ç
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+'
+é
+_
+-
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+a
+—
+x
+ü
+q
+t
+b
+í
+ó
+ï
+ò
+ 
+c
+j
+n
+y

models/piper/MODEL_CARD

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+# Model card for upc_ona (x-low)
+
+* Language: ca (Catalan)
+* Speakers: 1
+* Quality: x-low
+* Samplerate: 16,000Hz
+
+## Dataset
+
+* URL: https://collectivat.cat/asr#upc-festcat-tts-corpora
+* License: CC BY-SA 3.0 ES
+
+## Training
+
+Trained from scratch.

models/piper/ca-upc_ona-x-low.onnx

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+}

requirements.txt

@@ -0,0 +1,4 @@
+git+https://github.com/coqui-ai/TTS@dev#egg=TTS
+gradio
+espeak-phonemizer>=1.1.0,<2 
+onnxruntime~=1.11.0