import itertools import logging import os import zlib from typing import BinaryIO, Iterable, List, NamedTuple, Optional, Tuple, Union import ctranslate2 import numpy as np import tokenizers from faster_whisper.audio import decode_audio from faster_whisper.feature_extractor import FeatureExtractor from faster_whisper.tokenizer import Tokenizer from download_quantized import download_model, format_timestamp, get_logger from faster_whisper.vad import ( SpeechTimestampsMap, VadOptions, collect_chunks, get_speech_timestamps, ) class Word(NamedTuple): start: float end: float word: str probability: float class Segment(NamedTuple): id: int seek: int start: float end: float text: str tokens: List[int] temperature: float avg_logprob: float compression_ratio: float no_speech_prob: float words: Optional[List[Word]] class TranscriptionOptions(NamedTuple): beam_size: int best_of: int patience: float length_penalty: float repetition_penalty: float log_prob_threshold: Optional[float] no_speech_threshold: Optional[float] compression_ratio_threshold: Optional[float] condition_on_previous_text: bool prompt_reset_on_temperature: float temperatures: List[float] initial_prompt: Optional[Union[str, Iterable[int]]] prefix: Optional[str] suppress_blank: bool suppress_tokens: Optional[List[int]] without_timestamps: bool max_initial_timestamp: float word_timestamps: bool prepend_punctuations: str append_punctuations: str class TranscriptionInfo(NamedTuple): language: str language_probability: float duration: float all_language_probs: Optional[List[Tuple[str, float]]] transcription_options: TranscriptionOptions vad_options: VadOptions class WhisperModel: def __init__( self, model_size_or_path: str, device: str = "auto", device_index: Union[int, List[int]] = 0, compute_type: str = "default", cpu_threads: int = 0, num_workers: int = 1, download_root: Optional[str] = None, local_files_only: bool = False, ): """Initializes the Whisper model. Args: model_size_or_path: Size of the model to use (tiny, tiny.en, base, base.en, small, small.en, medium, medium.en, large-v1, or large-v2), a path to a converted model directory, or a CTranslate2-converted Whisper model ID from the Hugging Face Hub. When a size or a model ID is configured, the converted model is downloaded from the Hugging Face Hub. device: Device to use for computation ("cpu", "cuda", "auto"). device_index: Device ID to use. The model can also be loaded on multiple GPUs by passing a list of IDs (e.g. [0, 1, 2, 3]). In that case, multiple transcriptions can run in parallel when transcribe() is called from multiple Python threads (see also num_workers). compute_type: Type to use for computation. See https://opennmt.net/CTranslate2/quantization.html. cpu_threads: Number of threads to use when running on CPU (4 by default). A non zero value overrides the OMP_NUM_THREADS environment variable. num_workers: When transcribe() is called from multiple Python threads, having multiple workers enables true parallelism when running the model (concurrent calls to self.model.generate() will run in parallel). This can improve the global throughput at the cost of increased memory usage. download_root: Directory where the models should be saved. If not set, the models are saved in the standard Hugging Face cache directory. local_files_only: If True, avoid downloading the file and return the path to the local cached file if it exists. """ self.logger = get_logger() if os.path.isdir(model_size_or_path): model_path = model_size_or_path else: model_path = download_model( model_size_or_path, local_files_only=local_files_only, cache_dir=download_root, ) self.model = ctranslate2.models.Whisper( model_path, device=device, device_index=device_index, compute_type=compute_type, intra_threads=cpu_threads, inter_threads=num_workers, ) tokenizer_file = os.path.join(model_path, "tokenizer.json") if os.path.isfile(tokenizer_file): self.hf_tokenizer = tokenizers.Tokenizer.from_file(tokenizer_file) else: self.hf_tokenizer = tokenizers.Tokenizer.from_pretrained( "openai/whisper-tiny" + ("" if self.model.is_multilingual else ".en") ) self.feature_extractor = FeatureExtractor() self.num_samples_per_token = self.feature_extractor.hop_length * 2 self.frames_per_second = ( self.feature_extractor.sampling_rate // self.feature_extractor.hop_length ) self.tokens_per_second = ( self.feature_extractor.sampling_rate // self.num_samples_per_token ) self.input_stride = 2 self.time_precision = 0.02 self.max_length = 448 def transcribe( self, audio: Union[str, BinaryIO, np.ndarray], language: Optional[str] = None, task: str = "transcribe", beam_size: int = 5, best_of: int = 5, patience: float = 1, length_penalty: float = 1, repetition_penalty: float = 1, temperature: Union[float, List[float], Tuple[float, ...]] = [ 0.0, 0.2, 0.4, 0.6, 0.8, 1.0, ], compression_ratio_threshold: Optional[float] = 2.4, log_prob_threshold: Optional[float] = -1.0, no_speech_threshold: Optional[float] = 0.6, condition_on_previous_text: bool = True, prompt_reset_on_temperature: float = 0.5, initial_prompt: Optional[Union[str, Iterable[int]]] = None, prefix: Optional[str] = None, suppress_blank: bool = True, suppress_tokens: Optional[List[int]] = [-1], without_timestamps: bool = False, max_initial_timestamp: float = 1.0, word_timestamps: bool = False, prepend_punctuations: str = "\"'“¿([{-", append_punctuations: str = "\"'.。,，!！?？:：”)]}、", vad_filter: bool = False, vad_parameters: Optional[Union[dict, VadOptions]] = None, ) -> Tuple[Iterable[Segment], TranscriptionInfo]: """Transcribes an input file. Arguments: audio: Path to the input file (or a file-like object), or the audio waveform. language: The language spoken in the audio. It should be a language code such as "en" or "fr". If not set, the language will be detected in the first 30 seconds of audio. task: Task to execute (transcribe or translate). beam_size: Beam size to use for decoding. best_of: Number of candidates when sampling with non-zero temperature. patience: Beam search patience factor. length_penalty: Exponential length penalty constant. repetition_penalty: Penalty applied to the score of previously generated tokens (set > 1 to penalize). temperature: Temperature for sampling. It can be a tuple of temperatures, which will be successively used upon failures according to either `compression_ratio_threshold` or `log_prob_threshold`. compression_ratio_threshold: If the gzip compression ratio is above this value, treat as failed. log_prob_threshold: If the average log probability over sampled tokens is below this value, treat as failed. no_speech_threshold: If the no_speech probability is higher than this value AND the average log probability over sampled tokens is below `log_prob_threshold`, consider the segment as silent. condition_on_previous_text: If True, the previous output of the model is provided as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop, such as repetition looping or timestamps going out of sync. prompt_reset_on_temperature: Resets prompt if temperature is above this value. Arg has effect only if condition_on_previous_text is True. initial_prompt: Optional text string or iterable of token ids to provide as a prompt for the first window. prefix: Optional text to provide as a prefix for the first window. suppress_blank: Suppress blank outputs at the beginning of the sampling. suppress_tokens: List of token IDs to suppress. -1 will suppress a default set of symbols as defined in the model config.json file. without_timestamps: Only sample text tokens. max_initial_timestamp: The initial timestamp cannot be later than this. word_timestamps: Extract word-level timestamps using the cross-attention pattern and dynamic time warping, and include the timestamps for each word in each segment. prepend_punctuations: If word_timestamps is True, merge these punctuation symbols with the next word append_punctuations: If word_timestamps is True, merge these punctuation symbols with the previous word vad_filter: Enable the voice activity detection (VAD) to filter out parts of the audio without speech. This step is using the Silero VAD model https://github.com/snakers4/silero-vad. vad_parameters: Dictionary of Silero VAD parameters or VadOptions class (see available parameters and default values in the class `VadOptions`). Returns: A tuple with: - a generator over transcribed segments - an instance of TranscriptionInfo """ sampling_rate = self.feature_extractor.sampling_rate if not isinstance(audio, np.ndarray): audio = decode_audio(audio, sampling_rate=sampling_rate) duration = audio.shape[0] / sampling_rate self.logger.info( "Processing audio with duration %s", format_timestamp(duration) ) if vad_filter: if vad_parameters is None: vad_parameters = VadOptions() elif isinstance(vad_parameters, dict): vad_parameters = VadOptions(**vad_parameters) speech_chunks = get_speech_timestamps(audio, vad_parameters) audio = collect_chunks(audio, speech_chunks) self.logger.info( "VAD filter removed %s of audio", format_timestamp(duration - (audio.shape[0] / sampling_rate)), ) if self.logger.isEnabledFor(logging.DEBUG): self.logger.debug( "VAD filter kept the following audio segments: %s", ", ".join( "[%s -> %s]" % ( format_timestamp(chunk["start"] / sampling_rate), format_timestamp(chunk["end"] / sampling_rate), ) for chunk in speech_chunks ), ) else: speech_chunks = None features = self.feature_extractor(audio) encoder_output = None all_language_probs = None if language is None: if not self.model.is_multilingual: language = "en" language_probability = 1 else: segment = features[:, : self.feature_extractor.nb_max_frames] encoder_output = self.encode(segment) # results is a list of tuple[str, float] with language names and # probabilities. results = self.model.detect_language(encoder_output)[0] # Parse language names to strip out markers all_language_probs = [(token[2:-2], prob) for (token, prob) in results] # Get top language token and probability language, language_probability = all_language_probs[0] self.logger.info( "Detected language '%s' with probability %.2f", language, language_probability, ) else: language_probability = 1 tokenizer = Tokenizer( self.hf_tokenizer, self.model.is_multilingual, task=task, language=language, ) options = TranscriptionOptions( beam_size=beam_size, best_of=best_of, patience=patience, length_penalty=length_penalty, repetition_penalty=repetition_penalty, log_prob_threshold=log_prob_threshold, no_speech_threshold=no_speech_threshold, compression_ratio_threshold=compression_ratio_threshold, condition_on_previous_text=condition_on_previous_text, prompt_reset_on_temperature=prompt_reset_on_temperature, temperatures=( temperature if isinstance(temperature, (list, tuple)) else [temperature] ), initial_prompt=initial_prompt, prefix=prefix, suppress_blank=suppress_blank, suppress_tokens=get_suppressed_tokens(tokenizer, suppress_tokens), without_timestamps=without_timestamps, max_initial_timestamp=max_initial_timestamp, word_timestamps=word_timestamps, prepend_punctuations=prepend_punctuations, append_punctuations=append_punctuations, ) segments = self.generate_segments(features, tokenizer, options, encoder_output) if speech_chunks: segments = restore_speech_timestamps(segments, speech_chunks, sampling_rate) info = TranscriptionInfo( language=language, language_probability=language_probability, duration=duration, transcription_options=options, vad_options=vad_parameters, all_language_probs=all_language_probs, ) return segments, info def generate_segments( self, features: np.ndarray, tokenizer: Tokenizer, options: TranscriptionOptions, encoder_output: Optional[ctranslate2.StorageView] = None, ) -> Iterable[Segment]: content_frames = features.shape[-1] - self.feature_extractor.nb_max_frames idx = 0 seek = 0 all_tokens = [] prompt_reset_since = 0 if options.initial_prompt is not None: if isinstance(options.initial_prompt, str): initial_prompt = " " + options.initial_prompt.strip() initial_prompt_tokens = tokenizer.encode(initial_prompt) all_tokens.extend(initial_prompt_tokens) else: all_tokens.extend(options.initial_prompt) last_speech_timestamp = 0.0 while seek < content_frames: time_offset = seek * self.feature_extractor.time_per_frame segment = features[:, seek : seek + self.feature_extractor.nb_max_frames] segment_size = min( self.feature_extractor.nb_max_frames, content_frames - seek ) segment_duration = segment_size * self.feature_extractor.time_per_frame if self.logger.isEnabledFor(logging.DEBUG): self.logger.debug( "Processing segment at %s", format_timestamp(time_offset) ) previous_tokens = all_tokens[prompt_reset_since:] prompt = self.get_prompt( tokenizer, previous_tokens, without_timestamps=options.without_timestamps, prefix=options.prefix if seek == 0 else None, ) if encoder_output is None: encoder_output = self.encode(segment) ( result, avg_logprob, temperature, compression_ratio, ) = self.generate_with_fallback(encoder_output, prompt, tokenizer, options) if options.no_speech_threshold is not None: # no voice activity check should_skip = result.no_speech_prob > options.no_speech_threshold if ( options.log_prob_threshold is not None and avg_logprob > options.log_prob_threshold ): # don't skip if the logprob is high enough, despite the no_speech_prob should_skip = False if should_skip: self.logger.debug( "No speech threshold is met (%f > %f)", result.no_speech_prob, options.no_speech_threshold, ) # fast-forward to the next segment boundary seek += segment_size encoder_output = None continue tokens = result.sequences_ids[0] previous_seek = seek current_segments = [] single_timestamp_ending = ( len(tokens) >= 2 and tokens[-2] < tokenizer.timestamp_begin and tokens[-1] >= tokenizer.timestamp_begin ) consecutive_timestamps = [ i for i in range(len(tokens)) if i > 0 and tokens[i] >= tokenizer.timestamp_begin and tokens[i - 1] >= tokenizer.timestamp_begin ] if len(consecutive_timestamps) > 0: slices = list(consecutive_timestamps) if single_timestamp_ending: slices.append(len(tokens)) last_slice = 0 for current_slice in slices: sliced_tokens = tokens[last_slice:current_slice] start_timestamp_position = ( sliced_tokens[0] - tokenizer.timestamp_begin ) end_timestamp_position = ( sliced_tokens[-1] - tokenizer.timestamp_begin ) start_time = ( time_offset + start_timestamp_position * self.time_precision ) end_time = ( time_offset + end_timestamp_position * self.time_precision ) current_segments.append( dict( seek=seek, start=start_time, end=end_time, tokens=sliced_tokens, ) ) last_slice = current_slice if single_timestamp_ending: # single timestamp at the end means no speech after the last timestamp. seek += segment_size else: # otherwise, ignore the unfinished segment and seek to the last timestamp last_timestamp_position = ( tokens[last_slice - 1] - tokenizer.timestamp_begin ) seek += last_timestamp_position * self.input_stride else: duration = segment_duration timestamps = [ token for token in tokens if token >= tokenizer.timestamp_begin ] if len(timestamps) > 0 and timestamps[-1] != tokenizer.timestamp_begin: last_timestamp_position = timestamps[-1] - tokenizer.timestamp_begin duration = last_timestamp_position * self.time_precision current_segments.append( dict( seek=seek, start=time_offset, end=time_offset + duration, tokens=tokens, ) ) seek += segment_size if options.word_timestamps: self.add_word_timestamps( current_segments, tokenizer, encoder_output, segment_size, options.prepend_punctuations, options.append_punctuations, last_speech_timestamp=last_speech_timestamp, ) word_end_timestamps = [ w["end"] for s in current_segments for w in s["words"] ] if len(word_end_timestamps) > 0: last_speech_timestamp = word_end_timestamps[-1] if not single_timestamp_ending and len(word_end_timestamps) > 0: seek_shift = round( (word_end_timestamps[-1] - time_offset) * self.frames_per_second ) if seek_shift > 0: seek = previous_seek + seek_shift encoder_output = None for segment in current_segments: tokens = segment["tokens"] text = tokenizer.decode(tokens) if segment["start"] == segment["end"] or not text.strip(): continue all_tokens.extend(tokens) idx += 1 yield Segment( id=idx, seek=seek, start=segment["start"], end=segment["end"], text=text, tokens=tokens, temperature=temperature, avg_logprob=avg_logprob, compression_ratio=compression_ratio, no_speech_prob=result.no_speech_prob, words=( [Word(**word) for word in segment["words"]] if options.word_timestamps else None ), ) if ( not options.condition_on_previous_text or temperature > options.prompt_reset_on_temperature ): if options.condition_on_previous_text: self.logger.debug( "Reset prompt. prompt_reset_on_temperature threshold is met %f > %f", temperature, options.prompt_reset_on_temperature, ) prompt_reset_since = len(all_tokens) def encode(self, features: np.ndarray) -> ctranslate2.StorageView: # When the model is running on multiple GPUs, the encoder output should be moved # to the CPU since we don't know which GPU will handle the next job. to_cpu = self.model.device == "cuda" and len(self.model.device_index) > 1 features = np.expand_dims(features, 0) features = get_ctranslate2_storage(features) return self.model.encode(features, to_cpu=to_cpu) def generate_with_fallback( self, encoder_output: ctranslate2.StorageView, prompt: List[int], tokenizer: Tokenizer, options: TranscriptionOptions, ) -> Tuple[ctranslate2.models.WhisperGenerationResult, float, float, float]: decode_result = None all_results = [] below_cr_threshold_results = [] max_initial_timestamp_index = int( round(options.max_initial_timestamp / self.time_precision) ) for temperature in options.temperatures: if temperature > 0: kwargs = { "beam_size": 1, "num_hypotheses": options.best_of, "sampling_topk": 0, "sampling_temperature": temperature, } else: kwargs = { "beam_size": options.beam_size, "patience": options.patience, } result = self.model.generate( encoder_output, [prompt], length_penalty=options.length_penalty, repetition_penalty=options.repetition_penalty, max_length=self.max_length, return_scores=True, return_no_speech_prob=True, suppress_blank=options.suppress_blank, suppress_tokens=options.suppress_tokens, max_initial_timestamp_index=max_initial_timestamp_index, **kwargs, )[0] tokens = result.sequences_ids[0] # Recover the average log prob from the returned score. seq_len = len(tokens) cum_logprob = result.scores[0] * (seq_len**options.length_penalty) avg_logprob = cum_logprob / (seq_len + 1) text = tokenizer.decode(tokens).strip() compression_ratio = get_compression_ratio(text) decode_result = ( result, avg_logprob, temperature, compression_ratio, ) all_results.append(decode_result) needs_fallback = False if options.compression_ratio_threshold is not None: if compression_ratio > options.compression_ratio_threshold: needs_fallback = True # too repetitive self.logger.debug( "Compression ratio threshold is not met with temperature %.1f (%f > %f)", temperature, compression_ratio, options.compression_ratio_threshold, ) else: below_cr_threshold_results.append(decode_result) if ( options.log_prob_threshold is not None and avg_logprob < options.log_prob_threshold ): needs_fallback = True # average log probability is too low self.logger.debug( "Log probability threshold is not met with temperature %.1f (%f < %f)", temperature, avg_logprob, options.log_prob_threshold, ) if ( options.no_speech_threshold is not None and result.no_speech_prob > options.no_speech_threshold ): needs_fallback = False # silence if not needs_fallback: break else: # all failed, select the result with the highest average log probability decode_result = max( below_cr_threshold_results or all_results, key=lambda x: x[1] ) return decode_result def get_prompt( self, tokenizer: Tokenizer, previous_tokens: List[int], without_timestamps: bool = False, prefix: Optional[str] = None, ) -> List[int]: prompt = [] if previous_tokens: prompt.append(tokenizer.sot_prev) prompt.extend(previous_tokens[-(self.max_length // 2 - 1) :]) prompt.extend(tokenizer.sot_sequence) if without_timestamps: prompt.append(tokenizer.no_timestamps) if prefix: prefix_tokens = tokenizer.encode(" " + prefix.strip()) if len(prefix_tokens) >= self.max_length // 2: prefix_tokens = prefix_tokens[: self.max_length // 2 - 1] if not without_timestamps: prompt.append(tokenizer.timestamp_begin) prompt.extend(prefix_tokens) return prompt def add_word_timestamps( self, segments: List[dict], tokenizer: Tokenizer, encoder_output: ctranslate2.StorageView, num_frames: int, prepend_punctuations: str, append_punctuations: str, last_speech_timestamp: float, ): if len(segments) == 0: return text_tokens_per_segment = [ [token for token in segment["tokens"] if token < tokenizer.eot] for segment in segments ] text_tokens = list(itertools.chain.from_iterable(text_tokens_per_segment)) alignment = self.find_alignment( tokenizer, text_tokens, encoder_output, num_frames ) word_durations = np.array([word["end"] - word["start"] for word in alignment]) word_durations = word_durations[word_durations.nonzero()] median_duration = np.median(word_durations) if len(word_durations) > 0 else 0.0 max_duration = median_duration * 2 # hack: truncate long words at sentence boundaries. # a better segmentation algorithm based on VAD should be able to replace this. if len(word_durations) > 0: sentence_end_marks = ".。!！?？" # ensure words at sentence boundaries # are not longer than twice the median word duration. for i in range(1, len(alignment)): if alignment[i]["end"] - alignment[i]["start"] > max_duration: if alignment[i]["word"] in sentence_end_marks: alignment[i]["end"] = alignment[i]["start"] + max_duration elif alignment[i - 1]["word"] in sentence_end_marks: alignment[i]["start"] = alignment[i]["end"] - max_duration merge_punctuations(alignment, prepend_punctuations, append_punctuations) time_offset = ( segments[0]["seek"] * self.feature_extractor.hop_length / self.feature_extractor.sampling_rate ) word_index = 0 for segment, text_tokens in zip(segments, text_tokens_per_segment): saved_tokens = 0 words = [] while word_index < len(alignment) and saved_tokens < len(text_tokens): timing = alignment[word_index] if timing["word"]: words.append( dict( word=timing["word"], start=round(time_offset + timing["start"], 2), end=round(time_offset + timing["end"], 2), probability=timing["probability"], ) ) saved_tokens += len(timing["tokens"]) word_index += 1 # hack: truncate long words at segment boundaries. # a better segmentation algorithm based on VAD should be able to replace this. if len(words) > 0: # ensure the first and second word after a pause is not longer than # twice the median word duration. if words[0]["end"] - last_speech_timestamp > median_duration * 4 and ( words[0]["end"] - words[0]["start"] > max_duration or ( len(words) > 1 and words[1]["end"] - words[0]["start"] > max_duration * 2 ) ): if ( len(words) > 1 and words[1]["end"] - words[1]["start"] > max_duration ): boundary = max( words[1]["end"] / 2, words[1]["end"] - max_duration ) words[0]["end"] = words[1]["start"] = boundary words[0]["start"] = max(0, words[0]["end"] - max_duration) # prefer the segment-level start timestamp if the first word is too long. if ( segment["start"] < words[0]["end"] and segment["start"] - 0.5 > words[0]["start"] ): words[0]["start"] = max( 0, min(words[0]["end"] - median_duration, segment["start"]) ) else: segment["start"] = words[0]["start"] # prefer the segment-level end timestamp if the last word is too long. if ( segment["end"] > words[-1]["start"] and segment["end"] + 0.5 < words[-1]["end"] ): words[-1]["end"] = max( words[-1]["start"] + median_duration, segment["end"] ) else: segment["end"] = words[-1]["end"] last_speech_timestamp = segment["end"] segment["words"] = words def find_alignment( self, tokenizer: Tokenizer, text_tokens: List[int], encoder_output: ctranslate2.StorageView, num_frames: int, median_filter_width: int = 7, ) -> List[dict]: if len(text_tokens) == 0: return [] result = self.model.align( encoder_output, tokenizer.sot_sequence, [text_tokens], num_frames, median_filter_width=median_filter_width, )[0] text_token_probs = result.text_token_probs alignments = result.alignments text_indices = np.array([pair[0] for pair in alignments]) time_indices = np.array([pair[1] for pair in alignments]) words, word_tokens = tokenizer.split_to_word_tokens( text_tokens + [tokenizer.eot] ) word_boundaries = np.pad(np.cumsum([len(t) for t in word_tokens[:-1]]), (1, 0)) if len(word_boundaries) <= 1: return [] jumps = np.pad(np.diff(text_indices), (1, 0), constant_values=1).astype(bool) jump_times = time_indices[jumps] / self.tokens_per_second start_times = jump_times[word_boundaries[:-1]] end_times = jump_times[word_boundaries[1:]] word_probabilities = [ np.mean(text_token_probs[i:j]) for i, j in zip(word_boundaries[:-1], word_boundaries[1:]) ] return [ dict( word=word, tokens=tokens, start=start, end=end, probability=probability ) for word, tokens, start, end, probability in zip( words, word_tokens, start_times, end_times, word_probabilities ) ] def restore_speech_timestamps( segments: Iterable[Segment], speech_chunks: List[dict], sampling_rate: int, ) -> Iterable[Segment]: ts_map = SpeechTimestampsMap(speech_chunks, sampling_rate) for segment in segments: if segment.words: words = [] for word in segment.words: # Ensure the word start and end times are resolved to the same chunk. middle = (word.start + word.end) / 2 chunk_index = ts_map.get_chunk_index(middle) word = word._replace( start=ts_map.get_original_time(word.start, chunk_index), end=ts_map.get_original_time(word.end, chunk_index), ) words.append(word) segment = segment._replace( start=words[0].start, end=words[-1].end, words=words, ) else: segment = segment._replace( start=ts_map.get_original_time(segment.start), end=ts_map.get_original_time(segment.end), ) yield segment def get_ctranslate2_storage(segment: np.ndarray) -> ctranslate2.StorageView: segment = np.ascontiguousarray(segment) segment = ctranslate2.StorageView.from_array(segment) return segment def get_compression_ratio(text: str) -> float: text_bytes = text.encode("utf-8") return len(text_bytes) / len(zlib.compress(text_bytes)) def get_suppressed_tokens(tokenizer, suppress_tokens): if not suppress_tokens or -1 in suppress_tokens: return suppress_tokens suppress_tokens = list(suppress_tokens) # Ensure the following special tokens are suppressed when the user does # not use the default set (-1). suppress_tokens.extend( [ tokenizer.transcribe, tokenizer.translate, tokenizer.sot, tokenizer.sot_prev, tokenizer.sot_lm, ] ) return sorted(set(suppress_tokens)) def merge_punctuations(alignment: List[dict], prepended: str, appended: str): # merge prepended punctuations i = len(alignment) - 2 j = len(alignment) - 1 while i >= 0: previous = alignment[i] following = alignment[j] if previous["word"].startswith(" ") and previous["word"].strip() in prepended: # prepend it to the following word following["word"] = previous["word"] + following["word"] following["tokens"] = previous["tokens"] + following["tokens"] previous["word"] = "" previous["tokens"] = [] else: j = i i -= 1 # merge appended punctuations i = 0 j = 1 while j < len(alignment): previous = alignment[i] following = alignment[j] if not previous["word"].endswith(" ") and following["word"] in appended: # append it to the previous word previous["word"] = previous["word"] + following["word"] previous["tokens"] = previous["tokens"] + following["tokens"] following["word"] = "" following["tokens"] = [] else: i = j j += 1