# original https://github.com/guillaumekln/faster-whisper/blob/master/faster_whisper/transcribe.py import itertools import json import logging import os import zlib from dataclasses import asdict, dataclass from inspect import signature from math import ceil from typing import BinaryIO, Iterable, List, Optional, Tuple, Union from warnings import warn import ctranslate2 import numpy as np import tokenizers from tqdm import tqdm from faster_whisper.audio import decode_audio, pad_or_trim from faster_whisper.feature_extractor import FeatureExtractor from faster_whisper.tokenizer import _LANGUAGE_CODES, Tokenizer from faster_whisper.utils import download_model, format_timestamp, get_end, get_logger from faster_whisper.vad import ( SpeechTimestampsMap, VadOptions, collect_chunks, get_speech_timestamps, merge_segments, ) @dataclass class Word: start: float end: float word: str probability: float def _asdict(self): warn( "Word._asdict() method is deprecated, use dataclasses.asdict(Word) instead", DeprecationWarning, 2, ) return asdict(self) @dataclass class Segment: id: int seek: int start: float end: float text: str tokens: List[int] avg_logprob: float compression_ratio: float no_speech_prob: float words: Optional[List[Word]] temperature: Optional[float] def _asdict(self): warn( "Segment._asdict() method is deprecated, use dataclasses.asdict(Segment) instead", DeprecationWarning, 2, ) return asdict(self) @dataclass class TranscriptionOptions: beam_size: int best_of: int patience: float length_penalty: float repetition_penalty: float no_repeat_ngram_size: int 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 multilingual: bool max_new_tokens: Optional[int] clip_timestamps: Union[str, List[float]] hallucination_silence_threshold: Optional[float] hotwords: Optional[str] @dataclass class TranscriptionInfo: language: str language_probability: float duration: float duration_after_vad: float all_language_probs: Optional[List[Tuple[str, float]]] transcription_options: TranscriptionOptions vad_options: VadOptions class BatchedInferencePipeline: def __init__( self, model, ): self.model: WhisperModel = model self.last_speech_timestamp = 0.0 def forward(self, features, tokenizer, chunks_metadata, options): encoder_output, outputs = self.generate_segment_batched( features, tokenizer, options ) segmented_outputs = [] segment_sizes = [] for chunk_metadata, output in zip(chunks_metadata, outputs): duration = chunk_metadata["end_time"] - chunk_metadata["start_time"] segment_size = int(ceil(duration) * self.model.frames_per_second) segment_sizes.append(segment_size) ( subsegments, seek, single_timestamp_ending, ) = self.model._split_segments_by_timestamps( tokenizer=tokenizer, tokens=output["tokens"], time_offset=chunk_metadata["start_time"], segment_size=segment_size, segment_duration=duration, seek=0, ) segmented_outputs.append( [ dict( text=tokenizer.decode(subsegment["tokens"]), avg_logprob=output["avg_logprob"], no_speech_prob=output["no_speech_prob"], tokens=subsegment["tokens"], start=subsegment["start"], end=subsegment["end"], compression_ratio=get_compression_ratio( tokenizer.decode(subsegment["tokens"]) ), seek=int( chunk_metadata["start_time"] * self.model.frames_per_second ), ) for subsegment in subsegments ] ) if options.word_timestamps: self.last_speech_timestamp = self.model.add_word_timestamps( segmented_outputs, tokenizer, encoder_output, segment_sizes, options.prepend_punctuations, options.append_punctuations, self.last_speech_timestamp, ) return segmented_outputs def generate_segment_batched( self, features: np.ndarray, tokenizer: Tokenizer, options: TranscriptionOptions, ): batch_size = features.shape[0] prompt = self.model.get_prompt( tokenizer, previous_tokens=( tokenizer.encode(options.initial_prompt) if options.initial_prompt is not None else [] ), without_timestamps=options.without_timestamps, hotwords=options.hotwords, ) if options.max_new_tokens is not None: max_length = len(prompt) + options.max_new_tokens else: max_length = self.model.max_length if max_length > self.model.max_length: raise ValueError( f"The length of the prompt is {len(prompt)}, and the `max_new_tokens` " f"{max_length - len(prompt)}. Thus, the combined length of the prompt " f"and `max_new_tokens` is: {max_length}. This exceeds the " f"`max_length` of the Whisper model: {self.model.max_length}. " "You should either reduce the length of your prompt, or " "reduce the value of `max_new_tokens`, " f"so that their combined length is less that {self.model.max_length}." ) encoder_output = self.model.encode(features) prompts = [prompt.copy() for _ in range(batch_size)] if options.multilingual: language_tokens = [ tokenizer.tokenizer.token_to_id(segment_langs[0][0]) for segment_langs in self.model.model.detect_language(encoder_output) ] language_token_index = prompt.index(tokenizer.language) for i, language_token in enumerate(language_tokens): prompts[i][language_token_index] = language_token results = self.model.model.generate( encoder_output, prompts, beam_size=options.beam_size, patience=options.patience, length_penalty=options.length_penalty, max_length=max_length, suppress_blank=options.suppress_blank, suppress_tokens=options.suppress_tokens, return_scores=True, return_no_speech_prob=True, sampling_temperature=options.temperatures[0], repetition_penalty=options.repetition_penalty, no_repeat_ngram_size=options.no_repeat_ngram_size, ) output = [] for result in results: # return scores seq_len = len(result.sequences_ids[0]) cum_logprob = result.scores[0] * (seq_len**options.length_penalty) output.append( dict( avg_logprob=cum_logprob / (seq_len + 1), no_speech_prob=result.no_speech_prob, tokens=result.sequences_ids[0], ) ) return encoder_output, output def transcribe( self, audio: Union[str, BinaryIO, np.ndarray], language: Optional[str] = None, task: str = "transcribe", log_progress: bool = False, beam_size: int = 5, best_of: int = 5, patience: float = 1, length_penalty: float = 1, repetition_penalty: float = 1, no_repeat_ngram_size: int = 0, 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 = True, max_initial_timestamp: float = 1.0, word_timestamps: bool = False, prepend_punctuations: str = "\"'“¿([{-", append_punctuations: str = "\"'.。,，!！?？:：”)]}、", multilingual: bool = False, vad_filter: bool = True, vad_parameters: Optional[Union[dict, VadOptions]] = None, max_new_tokens: Optional[int] = None, chunk_length: Optional[int] = None, clip_timestamps: Optional[List[dict]] = None, hallucination_silence_threshold: Optional[float] = None, batch_size: int = 8, hotwords: Optional[str] = None, language_detection_threshold: Optional[float] = 0.5, language_detection_segments: int = 1, ) -> Tuple[Iterable[Segment], TranscriptionInfo]: """transcribe audio in chunks in batched fashion and return with language info. 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). log_progress: whether to show progress bar or not. 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). no_repeat_ngram_size: Prevent repetitions of ngrams with this size (set 0 to disable). temperature: Temperature for sampling. If a list or tuple is passed, only the first value is used. initial_prompt: Optional text string or iterable of token ids to provide as a prompt for the each 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 `tokenizer.non_speech_tokens()`. without_timestamps: Only sample text tokens. 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. Set as False. 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 multilingual: Perform language detection on every segment. 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`). max_new_tokens: Maximum number of new tokens to generate per-chunk. If not set, the maximum will be set by the default max_length. chunk_length: The length of audio segments. If it is not None, it will overwrite the default chunk_length of the FeatureExtractor. clip_timestamps: Optionally provide list of dictionaries each containing "start" and "end" keys that specify the start and end of the voiced region within `chunk_length` boundary. vad_filter will be ignored if clip_timestamps is used. batch_size: the maximum number of parallel requests to model for decoding. hotwords: Hotwords/hint phrases to the model. Has no effect if prefix is not None. language_detection_threshold: If the maximum probability of the language tokens is higher than this value, the language is detected. language_detection_segments: Number of segments to consider for the language detection. Unused Arguments 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. Set as False prompt_reset_on_temperature: Resets prompt if temperature is above this value. Arg has effect only if condition_on_previous_text is True. Set at 0.5 prefix: Optional text to provide as a prefix at the beginning of each window. max_initial_timestamp: The initial timestamp cannot be later than this, set at 0.0. hallucination_silence_threshold: Optional[float] When word_timestamps is True, skip silent periods longer than this threshold (in seconds) when a possible hallucination is detected. set as None. Returns: A tuple with: - a generator over transcribed segments - an instance of TranscriptionInfo """ sampling_rate = self.model.feature_extractor.sampling_rate if multilingual and not self.model.model.is_multilingual: self.model.logger.warning( "The current model is English-only but the multilingual parameter is set to" "True; setting to False instead." ) multilingual = False if not isinstance(audio, np.ndarray): audio = decode_audio(audio, sampling_rate=sampling_rate) duration = audio.shape[0] / sampling_rate chunk_length = chunk_length or self.model.feature_extractor.chunk_length # if no segment split is provided, use vad_model and generate segments if not clip_timestamps: if vad_filter: if vad_parameters is None: vad_parameters = VadOptions( max_speech_duration_s=chunk_length, min_silence_duration_ms=160, ) elif isinstance(vad_parameters, dict): if "max_speech_duration_s" in vad_parameters.keys(): vad_parameters.pop("max_speech_duration_s") vad_parameters = VadOptions( **vad_parameters, max_speech_duration_s=chunk_length ) active_segments = get_speech_timestamps(audio, vad_parameters) clip_timestamps = merge_segments(active_segments, vad_parameters) # run the audio if it is less than 30 sec even without clip_timestamps elif duration < chunk_length: clip_timestamps = [{"start": 0, "end": audio.shape[0]}] else: raise RuntimeError( "No clip timestamps found. " "Set 'vad_filter' to True or provide 'clip_timestamps'." ) duration_after_vad = ( sum((segment["end"] - segment["start"]) for segment in clip_timestamps) / sampling_rate ) audio_chunks, chunks_metadata = collect_chunks(audio, clip_timestamps) features = ( [self.model.feature_extractor(chunk)[..., :-1] for chunk in audio_chunks] if duration_after_vad else [] ) all_language_probs = None # detecting the language if not provided if language is None: if not self.model.model.is_multilingual: language = "en" language_probability = 1 else: ( language, language_probability, all_language_probs, ) = self.model.detect_language( features=np.concatenate( features + [ np.full((self.model.model.n_mels, 1), -1.5, dtype="float32") ], axis=1, ), # add a dummy feature to account for empty audio language_detection_segments=language_detection_segments, language_detection_threshold=language_detection_threshold, ) self.model.logger.info( "Detected language '%s' with probability %.2f", language, language_probability, ) else: if not self.model.model.is_multilingual and language != "en": self.model.logger.warning( "The current model is English-only but the language parameter is set to '%s'; " "using 'en' instead." % language ) language = "en" language_probability = 1 tokenizer = Tokenizer( self.model.hf_tokenizer, self.model.model.is_multilingual, task=task, language=language, ) features = ( np.stack([pad_or_trim(feature) for feature in features]) if features else [] ) options = TranscriptionOptions( beam_size=beam_size, best_of=best_of, patience=patience, length_penalty=length_penalty, repetition_penalty=repetition_penalty, no_repeat_ngram_size=no_repeat_ngram_size, log_prob_threshold=log_prob_threshold, no_speech_threshold=no_speech_threshold, compression_ratio_threshold=compression_ratio_threshold, temperatures=( temperature[:1] 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), prepend_punctuations=prepend_punctuations, append_punctuations=append_punctuations, max_new_tokens=max_new_tokens, hotwords=hotwords, word_timestamps=word_timestamps, hallucination_silence_threshold=None, condition_on_previous_text=False, clip_timestamps=clip_timestamps, prompt_reset_on_temperature=0.5, multilingual=multilingual, without_timestamps=without_timestamps, max_initial_timestamp=0.0, ) info = TranscriptionInfo( language=language, language_probability=language_probability, duration=duration, duration_after_vad=duration_after_vad, transcription_options=options, vad_options=vad_parameters, all_language_probs=all_language_probs, ) segments = self._batched_segments_generator( features, tokenizer, chunks_metadata, batch_size, options, log_progress, ) return segments, info def _batched_segments_generator( self, features, tokenizer, chunks_metadata, batch_size, options, log_progress ): pbar = tqdm(total=len(features), disable=not log_progress, position=0) seg_idx = 0 for i in range(0, len(features), batch_size): results = self.forward( features[i : i + batch_size], tokenizer, chunks_metadata[i : i + batch_size], options, ) for result in results: for segment in result: seg_idx += 1 yield Segment( seek=segment["seek"], id=seg_idx, text=segment["text"], start=round(segment["start"], 3), end=round(segment["end"], 3), words=( None if not options.word_timestamps else [Word(**word) for word in segment["words"]] ), tokens=segment["tokens"], avg_logprob=segment["avg_logprob"], no_speech_prob=segment["no_speech_prob"], compression_ratio=segment["compression_ratio"], temperature=options.temperatures[0], ) pbar.update(1) pbar.close() self.last_speech_timestamp = 0.0 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, files: dict = None, **model_kwargs, ): """Initializes the Whisper model. Args: model_size_or_path: Size of the model to use (tiny, tiny.en, base, base.en, small, small.en, distil-small.en, medium, medium.en, distil-medium.en, large-v1, large-v2, large-v3, large, distil-large-v2, distil-large-v3, large-v3-turbo, or turbo), a path to a converted model directory, or a CTranslate2-converted Whisper model ID from the HF 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. files: Load model files from the memory. This argument is a dictionary mapping file names to file contents as file-like or bytes objects. If this is set, model_path acts as an identifier for this model. """ self.logger = get_logger() tokenizer_bytes, preprocessor_bytes = None, None if files: model_path = model_size_or_path tokenizer_bytes = files.pop("tokenizer.json", None) preprocessor_bytes = files.pop("preprocessor_config.json", None) elif 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, files=files, **model_kwargs, ) tokenizer_file = os.path.join(model_path, "tokenizer.json") if tokenizer_bytes: self.hf_tokenizer = tokenizers.Tokenizer.from_buffer(tokenizer_bytes) elif 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.feat_kwargs = self._get_feature_kwargs(model_path, preprocessor_bytes) self.feature_extractor = FeatureExtractor(**self.feat_kwargs) self.input_stride = 2 self.num_samples_per_token = ( self.feature_extractor.hop_length * self.input_stride ) 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.time_precision = 0.02 self.max_length = 448 @property def supported_languages(self) -> List[str]: """The languages supported by the model.""" return list(_LANGUAGE_CODES) if self.model.is_multilingual else ["en"] def _get_feature_kwargs(self, model_path, preprocessor_bytes=None) -> dict: config = {} try: config_path = os.path.join(model_path, "preprocessor_config.json") if preprocessor_bytes: config = json.loads(preprocessor_bytes) elif os.path.isfile(config_path): with open(config_path, "r", encoding="utf-8") as file: config = json.load(file) else: return config valid_keys = signature(FeatureExtractor.__init__).parameters.keys() return {k: v for k, v in config.items() if k in valid_keys} except json.JSONDecodeError as e: self.logger.warning("Could not load preprocessor config: %s", e) return config def transcribe( self, audio: Union[str, BinaryIO, np.ndarray], language: Optional[str] = None, task: str = "transcribe", log_progress: bool = False, beam_size: int = 5, best_of: int = 5, patience: float = 1, length_penalty: float = 1, repetition_penalty: float = 1, no_repeat_ngram_size: int = 0, 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 = "\"'.。,，!！?？:：”)]}、", multilingual: bool = False, vad_filter: bool = False, vad_parameters: Optional[Union[dict, VadOptions]] = None, max_new_tokens: Optional[int] = None, chunk_length: Optional[int] = None, clip_timestamps: Union[str, List[float]] = "0", hallucination_silence_threshold: Optional[float] = None, hotwords: Optional[str] = None, language_detection_threshold: Optional[float] = 0.5, language_detection_segments: int = 1, ) -> 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). log_progress: whether to show progress bar or not. 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). no_repeat_ngram_size: Prevent repetitions of ngrams with this size (set 0 to disable). 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 `tokenizer.non_speech_tokens()`. 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 multilingual: Perform language detection on every segment. 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`). max_new_tokens: Maximum number of new tokens to generate per-chunk. If not set, the maximum will be set by the default max_length. chunk_length: The length of audio segments. If it is not None, it will overwrite the default chunk_length of the FeatureExtractor. clip_timestamps: Comma-separated list start,end,start,end,... timestamps (in seconds) of clips to process. The last end timestamp defaults to the end of the file. vad_filter will be ignored if clip_timestamps is used. hallucination_silence_threshold: When word_timestamps is True, skip silent periods longer than this threshold (in seconds) when a possible hallucination is detected hotwords: Hotwords/hint phrases to provide the model with. Has no effect if prefix is not None. language_detection_threshold: If the maximum probability of the language tokens is higher than this value, the language is detected. language_detection_segments: Number of segments to consider for the language detection. Returns: A tuple with: - a generator over transcribed segments - an instance of TranscriptionInfo """ sampling_rate = self.feature_extractor.sampling_rate if multilingual and not self.model.is_multilingual: self.logger.warning( "The current model is English-only but the multilingual parameter is set to" "True; setting to False instead." ) multilingual = False if not isinstance(audio, np.ndarray): audio = decode_audio(audio, sampling_rate=sampling_rate) duration = audio.shape[0] / sampling_rate duration_after_vad = duration self.logger.info( "Processing audio with duration %s", format_timestamp(duration) ) if vad_filter and clip_timestamps == "0": 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_chunks, chunks_metadata = collect_chunks(audio, speech_chunks) audio = np.concatenate(audio_chunks, axis=0) duration_after_vad = audio.shape[0] / sampling_rate self.logger.info( "VAD filter removed %s of audio", format_timestamp(duration - duration_after_vad), ) 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 if audio.shape[0] == 0: return None, None features = self.feature_extractor(audio, chunk_length=chunk_length) encoder_output = None all_language_probs = None # detecting the language if not provided if language is None: if not self.model.is_multilingual: language = "en" language_probability = 1 else: start_timestamp = ( float(clip_timestamps.split(",")[0]) if isinstance(clip_timestamps, str) else clip_timestamps[0] ) content_frames = features.shape[-1] - 1 seek = ( int(start_timestamp * self.frames_per_second) if start_timestamp * self.frames_per_second < content_frames else 0 ) ( language, language_probability, all_language_probs, ) = self.detect_language( features=features[..., seek:], language_detection_segments=language_detection_segments, language_detection_threshold=language_detection_threshold, ) self.logger.info( "Detected language '%s' with probability %.2f", language, language_probability, ) else: if not self.model.is_multilingual and language != "en": self.logger.warning( "The current model is English-only but the language parameter is set to '%s'; " "using 'en' instead." % language ) language = "en" 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, no_repeat_ngram_size=no_repeat_ngram_size, 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) if suppress_tokens else suppress_tokens ), without_timestamps=without_timestamps, max_initial_timestamp=max_initial_timestamp, word_timestamps=word_timestamps, prepend_punctuations=prepend_punctuations, append_punctuations=append_punctuations, multilingual=multilingual, max_new_tokens=max_new_tokens, clip_timestamps=clip_timestamps, hallucination_silence_threshold=hallucination_silence_threshold, hotwords=hotwords, ) segments = self.generate_segments( features, tokenizer, options, log_progress, encoder_output ) if speech_chunks: segments = restore_speech_timestamps(segments, speech_chunks, sampling_rate) info = TranscriptionInfo( language=language, language_probability=language_probability, duration=duration, duration_after_vad=duration_after_vad, transcription_options=options, vad_options=vad_parameters, all_language_probs=all_language_probs, ) return segments, info def _split_segments_by_timestamps( self, tokenizer: Tokenizer, tokens: List[int], time_offset: float, segment_size: int, segment_duration: float, seek: int, ) -> List[List[int]]: current_segments = [] single_timestamp_ending = ( len(tokens) >= 2 and tokens[-2] < tokenizer.timestamp_begin <= tokens[-1] ) 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 return current_segments, seek, single_timestamp_ending def generate_segments( self, features: np.ndarray, tokenizer: Tokenizer, options: TranscriptionOptions, log_progress, encoder_output: Optional[ctranslate2.StorageView] = None, ) -> Iterable[Segment]: content_frames = features.shape[-1] - 1 content_duration = float(content_frames * self.feature_extractor.time_per_frame) if isinstance(options.clip_timestamps, str): options.clip_timestamps = [ float(ts) for ts in ( options.clip_timestamps.split(",") if options.clip_timestamps else [] ) ] seek_points: List[int] = [ round(ts * self.frames_per_second) for ts in options.clip_timestamps ] if len(seek_points) == 0: seek_points.append(0) if len(seek_points) % 2 == 1: seek_points.append(content_frames) seek_clips: List[Tuple[int, int]] = list( zip(seek_points[::2], seek_points[1::2]) ) punctuation = "\"'“¿([{-\"'.。,，!！?？:：”)]}、" idx = 0 clip_idx = 0 seek = seek_clips[clip_idx][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) pbar = tqdm(total=content_duration, unit="seconds", disable=not log_progress) last_speech_timestamp = 0.0 all_segments = [] # NOTE: This loop is obscurely flattened to make the diff readable. # A later commit should turn this into a simpler nested loop. # for seek_clip_start, seek_clip_end in seek_clips: # while seek < seek_clip_end while clip_idx < len(seek_clips): seek_clip_start, seek_clip_end = seek_clips[clip_idx] if seek_clip_end > content_frames: seek_clip_end = content_frames if seek < seek_clip_start: seek = seek_clip_start if seek >= seek_clip_end: clip_idx += 1 if clip_idx < len(seek_clips): seek = seek_clips[clip_idx][0] continue time_offset = seek * self.feature_extractor.time_per_frame window_end_time = float( (seek + self.feature_extractor.nb_max_frames) * self.feature_extractor.time_per_frame ) segment_size = min( self.feature_extractor.nb_max_frames, content_frames - seek, seek_clip_end - seek, ) segment = features[:, seek : seek + segment_size] segment_duration = segment_size * self.feature_extractor.time_per_frame segment = pad_or_trim(segment) if self.logger.isEnabledFor(logging.DEBUG): self.logger.debug( "Processing segment at %s", format_timestamp(time_offset) ) previous_tokens = all_tokens[prompt_reset_since:] if seek > 0 or encoder_output is None: encoder_output = self.encode(segment) if options.multilingual: results = self.model.detect_language(encoder_output) language_token, language_probability = results[0][0] language = language_token[2:-2] tokenizer.language = tokenizer.tokenizer.token_to_id(language_token) tokenizer.language_code = language prompt = self.get_prompt( tokenizer, previous_tokens, without_timestamps=options.without_timestamps, prefix=options.prefix if seek == 0 else None, hotwords=options.hotwords, ) ( 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 continue tokens = result.sequences_ids[0] previous_seek = seek # anomalous words are very long/short/improbable def word_anomaly_score(word: dict) -> float: probability = word.get("probability", 0.0) duration = word["end"] - word["start"] score = 0.0 if probability < 0.15: score += 1.0 if duration < 0.133: score += (0.133 - duration) * 15 if duration > 2.0: score += duration - 2.0 return score def is_segment_anomaly(segment: Optional[dict]) -> bool: if segment is None or not segment["words"]: return False words = [w for w in segment["words"] if w["word"] not in punctuation] words = words[:8] score = sum(word_anomaly_score(w) for w in words) return score >= 3 or score + 0.01 >= len(words) def next_words_segment(segments: List[dict]) -> Optional[dict]: return next((s for s in segments if s["words"]), None) ( current_segments, seek, single_timestamp_ending, ) = self._split_segments_by_timestamps( tokenizer=tokenizer, tokens=tokens, time_offset=time_offset, segment_size=segment_size, segment_duration=segment_duration, seek=seek, ) 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, ) if not single_timestamp_ending: last_word_end = get_end(current_segments) if last_word_end is not None and last_word_end > time_offset: seek = round(last_word_end * self.frames_per_second) # skip silence before possible hallucinations if options.hallucination_silence_threshold is not None: threshold = options.hallucination_silence_threshold # if first segment might be a hallucination, skip leading silence first_segment = next_words_segment(current_segments) if first_segment is not None and is_segment_anomaly(first_segment): gap = first_segment["start"] - time_offset if gap > threshold: seek = previous_seek + round(gap * self.frames_per_second) continue # skip silence before any possible hallucination that is surrounded # by silence or more hallucinations hal_last_end = last_speech_timestamp for si in range(len(current_segments)): segment = current_segments[si] if not segment["words"]: continue if is_segment_anomaly(segment): next_segment = next_words_segment( current_segments[si + 1 :] ) if next_segment is not None: hal_next_start = next_segment["words"][0]["start"] else: hal_next_start = time_offset + segment_duration silence_before = ( segment["start"] - hal_last_end > threshold or segment["start"] < threshold or segment["start"] - time_offset < 2.0 ) silence_after = ( hal_next_start - segment["end"] > threshold or is_segment_anomaly(next_segment) or window_end_time - segment["end"] < 2.0 ) if silence_before and silence_after: seek = round( max(time_offset + 1, segment["start"]) * self.frames_per_second ) if content_duration - segment["end"] < threshold: seek = content_frames current_segments[si:] = [] break hal_last_end = segment["end"] last_word_end = get_end(current_segments) if last_word_end is not None: last_speech_timestamp = last_word_end 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 all_segments.append(Segment( id=idx, seek=previous_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) pbar.update( (min(content_frames, seek) - previous_seek) * self.feature_extractor.time_per_frame, ) pbar.close() return all_segments 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 if features.ndim == 2: 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) ) if options.max_new_tokens is not None: max_length = len(prompt) + options.max_new_tokens else: max_length = self.max_length if max_length > self.max_length: raise ValueError( f"The length of the prompt is {len(prompt)}, and the `max_new_tokens` " f"{max_length - len(prompt)}. Thus, the combined length of the prompt " f"and `max_new_tokens` is: {max_length}. This exceeds the " f"`max_length` of the Whisper model: {self.max_length}. " "You should either reduce the length of your prompt, or " "reduce the value of `max_new_tokens`, " f"so that their combined length is less that {self.max_length}." ) 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, no_repeat_ngram_size=options.no_repeat_ngram_size, max_length=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 and options.log_prob_threshold is not None and avg_logprob < options.log_prob_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] ) # to pass final temperature for prompt_reset_on_temperature decode_result = ( decode_result[0], decode_result[1], temperature, decode_result[3], ) return decode_result def get_prompt( self, tokenizer: Tokenizer, previous_tokens: List[int], without_timestamps: bool = False, prefix: Optional[str] = None, hotwords: Optional[str] = None, ) -> List[int]: prompt = [] if previous_tokens or (hotwords and not prefix): prompt.append(tokenizer.sot_prev) if hotwords and not prefix: hotwords_tokens = tokenizer.encode(" " + hotwords.strip()) if len(hotwords_tokens) >= self.max_length // 2: hotwords_tokens = hotwords_tokens[: self.max_length // 2 - 1] prompt.extend(hotwords_tokens) if previous_tokens: 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, ) -> float: if len(segments) == 0: return text_tokens = [] text_tokens_per_segment = [] for segment in segments: segment_tokens = [ [token for token in subsegment["tokens"] if token < tokenizer.eot] for subsegment in segment ] text_tokens.append(list(itertools.chain.from_iterable(segment_tokens))) text_tokens_per_segment.append(segment_tokens) alignments = self.find_alignment( tokenizer, text_tokens, encoder_output, num_frames ) median_max_durations = [] for alignment in alignments: 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 ) median_duration = min(0.7, float(median_duration)) 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) median_max_durations.append((median_duration, max_duration)) for segment_idx, segment in enumerate(segments): word_index = 0 time_offset = segment[0]["seek"] / self.frames_per_second median_duration, max_duration = median_max_durations[segment_idx] for subsegment_idx, subsegment in enumerate(segment): saved_tokens = 0 words = [] while word_index < len(alignments[segment_idx]) and saved_tokens < len( text_tokens_per_segment[segment_idx][subsegment_idx] ): timing = alignments[segment_idx][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 ( subsegment["start"] < words[0]["end"] and subsegment["start"] - 0.5 > words[0]["start"] ): words[0]["start"] = max( 0, min(words[0]["end"] - median_duration, subsegment["start"]), ) else: subsegment["start"] = words[0]["start"] # prefer the segment-level end timestamp if the last word is too long. if ( subsegment["end"] > words[-1]["start"] and subsegment["end"] + 0.5 < words[-1]["end"] ): words[-1]["end"] = max( words[-1]["start"] + median_duration, subsegment["end"] ) else: subsegment["end"] = words[-1]["end"] last_speech_timestamp = subsegment["end"] segments[segment_idx][subsegment_idx]["words"] = words return last_speech_timestamp 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 [] results = self.model.align( encoder_output, tokenizer.sot_sequence, text_tokens, num_frames, median_filter_width=median_filter_width, ) return_list = [] for result, text_token in zip(results, text_tokens): 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_token + [tokenizer.eot] ) if len(word_tokens) <= 1: # return on eot only # >>> np.pad([], (1, 0)) # array([0.]) # This results in crashes when we lookup jump_times with float, like # IndexError: arrays used as indices must be of integer (or boolean) type return_list.append([]) continue word_boundaries = np.pad( np.cumsum([len(t) for t in word_tokens[:-1]]), (1, 0) ) if len(word_boundaries) <= 1: return_list.append([]) continue 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_list.append( [ 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 ) ] ) return return_list def detect_language( self, audio: Optional[np.ndarray] = None, features: Optional[np.ndarray] = None, vad_filter: bool = False, vad_parameters: Union[dict, VadOptions] = None, language_detection_segments: int = 1, language_detection_threshold: float = 0.5, ) -> Tuple[str, float, List[Tuple[str, float]]]: """ Use Whisper to detect the language of the input audio or features. Arguments: audio: Input audio signal, must be a 1D float array sampled at 16khz. features: Input Mel spectrogram features, must be a float array with shape (n_mels, n_frames), if `audio` is provided, the features will be ignored. Either `audio` or `features` must be provided. 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. vad_parameters: Dictionary of Silero VAD parameters or VadOptions class (see available parameters and default values in the class `VadOptions`). language_detection_threshold: If the maximum probability of the language tokens is higher than this value, the language is detected. language_detection_segments: Number of segments to consider for the language detection. Returns: language: Detected language. languege_probability: Probability of the detected language. all_language_probs: List of tuples with all language names and probabilities. """ assert ( audio is not None or features is not None ), "Either `audio` or `features` must be provided." if audio is not None: if vad_filter: speech_chunks = get_speech_timestamps(audio, vad_parameters) audio_chunks, chunks_metadata = collect_chunks(audio, speech_chunks) audio = np.concatenate(audio_chunks, axis=0) audio = audio[ : language_detection_segments * self.feature_extractor.n_samples ] features = self.feature_extractor(audio) features = features[ ..., : language_detection_segments * self.feature_extractor.nb_max_frames ] detected_language_info = {} for i in range(0, features.shape[-1], self.feature_extractor.nb_max_frames): encoder_output = self.encode( pad_or_trim(features[..., i : i + self.feature_extractor.nb_max_frames]) ) # 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] if language_probability > language_detection_threshold: break detected_language_info.setdefault(language, []).append(language_probability) else: # If no language detected for all segments, the majority vote of the highest # projected languages for all segments is used to determine the language. language = max( detected_language_info, key=lambda lang: len(detected_language_info[lang]), ) language_probability = max(detected_language_info[language]) return language, language_probability, all_language_probs 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.start = ts_map.get_original_time(word.start, chunk_index) word.end = ts_map.get_original_time(word.end, chunk_index) words.append(word) segment.start = words[0].start segment.end = words[-1].end segment.words = words else: segment.start = ts_map.get_original_time(segment.start) segment.end = ts_map.get_original_time(segment.end) return segments 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: Tokenizer, suppress_tokens: Tuple[int], ) -> Optional[List[int]]: if -1 in suppress_tokens: suppress_tokens = [t for t in suppress_tokens if t >= 0] suppress_tokens.extend(tokenizer.non_speech_tokens) elif suppress_tokens is None or len(suppress_tokens) == 0: suppress_tokens = [] # interpret empty string as an empty list else: assert isinstance(suppress_tokens, list), "suppress_tokens must be a list" suppress_tokens.extend( [ tokenizer.transcribe, tokenizer.translate, tokenizer.sot, tokenizer.sot_prev, tokenizer.sot_lm, ] ) return tuple(sorted(set(suppress_tokens))) def merge_punctuations(alignment: List[dict], prepended: str, appended: str) -> None: # 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