import itertools import json import logging import os import random import zlib from collections import Counter, defaultdict from inspect import signature from typing import BinaryIO, Iterable, List, NamedTuple, Optional, Tuple, Union import ctranslate2 import numpy as np import tokenizers import torch from pyannote.audio import Model 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_assets_path, get_end, get_logger, ) from faster_whisper.vad import ( SpeechTimestampsMap, VadOptions, VoiceActivitySegmentation, collect_chunks, get_speech_timestamps, merge_chunks, ) 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] avg_logprob: float compression_ratio: float no_speech_prob: float words: Optional[List[Word]] temperature: Optional[float] = 1.0 # Added additional parameters for multilingual videos and fixes below class TranscriptionOptions(NamedTuple): beam_size: int best_of: int patience: float length_penalty: float repetition_penalty: float no_repeat_ngram_size: int log_prob_threshold: Optional[float] log_prob_low_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 output_language: Optional[str] max_new_tokens: Optional[int] clip_timestamps: Union[str, List[float]] hallucination_silence_threshold: Optional[float] hotwords: Optional[str] class TranscriptionInfo(NamedTuple): 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 # The code below is originally from HF pipeline and is used in whisper-x # (https://github.com/m-bain/whisperX) and adapted for faster_whisper class BatchedInferencePipeline: """ Huggingface Pipeline wrapper for WhisperModel. Copyright (c) 2022, Max Bain All rights reserved. Modified by Mobius Labs GmbH """ def __init__( self, model, use_vad_model: bool = True, options: Optional[NamedTuple] = None, tokenizer=None, chunk_length: int = 30, vad_device: Union[int, str, "torch.device"] = "auto", vad_onset: float = 0.500, vad_offset: float = 0.363, language: Optional[str] = None, ): self.model: WhisperModel = model self.tokenizer = tokenizer self.options = options self.preset_language = language self.use_vad_model = use_vad_model self.vad_onset = vad_onset self.vad_offset = vad_offset self.vad_model_path = os.path.join(get_assets_path(), "pyannote_vad_model.bin") if self.use_vad_model: self.vad_device = self.get_device(vad_device) self.vad_model = self.load_vad_model( vad_onset=self.vad_onset, vad_offset=self.vad_offset ) else: self.vad_model = None self.chunk_length = chunk_length # VAD merging size self.last_speech_timestamp = 0.0 def get_device(self, device: Union[int, str, "torch.device"]): """ Converts the input device into a torch.device object. The input can be an integer, a string, or a `torch.device` object. The function handles a special case where the input device is "auto". When "auto" is specified, the device will default to the device of the model (self.model.device). If the model's device is also "auto", it selects "cuda" if a CUDA-capable device is available; otherwise, it selects "cpu". """ if isinstance(device, torch.device): return device elif isinstance(device, str): if device == "auto" and self.model.device == "auto": device = "cuda" if torch.cuda.is_available() else "cpu" elif device == "auto": device = self.model.device return torch.device(device) elif device < 0: return torch.device("cpu") else: return torch.device(f"cuda:{device}") def forward(self, features, segments_metadata, **forward_params): encoder_output, outputs = self.model.generate_segment_batched( features, self.tokenizer, forward_params ) segmented_outputs = [] segment_sizes = [] for segment_metadata, output in zip(segments_metadata, outputs): duration = segment_metadata["end_time"] - segment_metadata["start_time"] segment_size = int(duration * self.model.frames_per_second) segment_sizes.append(segment_size) ( subsegments, seek, single_timestamp_ending, ) = self.model._split_segments_by_timestamps( tokenizer=self.tokenizer, tokens=output["tokens"], time_offset=segment_metadata["start_time"], segment_size=segment_size, segment_duration=duration, seek=0, ) segmented_outputs.append( [ dict( text=self.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( self.tokenizer.decode(subsegment["tokens"]) ), ) for subsegment in subsegments ] ) if forward_params["word_timestamps"]: self.last_speech_timestamp = self.model.add_word_timestamps( segmented_outputs, self.tokenizer, encoder_output, segment_sizes, forward_params["prepend_punctuations"], forward_params["append_punctuations"], self.last_speech_timestamp, ) return segmented_outputs def get_language_and_tokenizer( self, audio, task: Optional[str] = None, language: Optional[str] = None ): all_language_probs = None language_probability = 1.0 if self.tokenizer is None: if not language: ( language, language_probability, all_language_probs, ) = self.model.detect_language(audio) task = task or "transcribe" self.tokenizer = Tokenizer( self.model.hf_tokenizer, self.model.model.is_multilingual, task=task, language=language, ) else: if task is not None: self.tokenizer.task = self.tokenizer.tokenizer.token_to_id( f"<|{task}|>" ) if language is not None: self.tokenizer.language = self.tokenizer.tokenizer.token_to_id( f"<|{language}|>" ) self.tokenizer.language_code = language return language, language_probability, task, all_language_probs @staticmethod def audio_split(audio, segments, sampling_rate): """Returns splitted audio chunks as iterator""" audio_segments = [] segments_metadata = [] for seg in segments: f1 = int(seg["start"] * sampling_rate) f2 = int(seg["end"] * sampling_rate) seg_metadata = { "start_time": seg["start"], "end_time": seg["end"], "stitched_seg": seg["segments"], } audio_segments.append(audio[f1:f2]) segments_metadata.append(seg_metadata) return audio_segments, segments_metadata def load_vad_model(self, vad_onset=0.500, vad_offset=0.363): vad_model = Model.from_pretrained(self.vad_model_path) hyperparameters = { "onset": vad_onset, "offset": vad_offset, "min_duration_on": 0.1, "min_duration_off": 0.1, } vad_pipeline = VoiceActivitySegmentation( segmentation=vad_model, device=torch.device(self.vad_device) ) vad_pipeline.instantiate(hyperparameters) return vad_pipeline def transcribe( self, audio: Union[str, torch.Tensor, np.ndarray], vad_segments: Optional[List[dict]] = None, batch_size: int = 16, language: Optional[str] = None, task: str = None, 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, log_prob_low_threshold: Optional[float] = None, no_speech_threshold: Optional[float] = 0.6, initial_prompt: Optional[Union[str, Iterable[int]]] = None, prefix: Optional[str] = None, suppress_blank: bool = True, suppress_tokens: Optional[List[int]] = [-1], prepend_punctuations: str = "\"'“¿([{-", append_punctuations: str = "\"'.。,，!！?？:：”)]}、", max_new_tokens: Optional[int] = None, hotwords: Optional[str] = None, word_timestamps: bool = False, without_timestamps: bool = True, ) -> Tuple[Iterable[Segment], TranscriptionInfo]: """transcribe audio in chunks in batched fashion and return with language info. Arguments: audio: audio file as numpy array/path for batched transcription. vad_segments: Optionally provide list of dictionaries each containing "start", "end", and "segments" keys. "start" and "end" keys specify the start and end of the voiced region within 30 sec boundary. An additional key "segments" contains all the start and end of voiced regions within that 30sec boundary as a list of tuples. If no vad_segments specified, it uses internal vad model automatically segment them. batch_size: the maximum number of parallel requests to model for decoding. language: The language spoken in the audio. task: either "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. log_prob_low_threshold: This parameter alone is sufficient to skip an output text, whereas log_prob_threshold also looks for appropriate no_speech_threshold value. This value should be less than log_prob_threshold. 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. 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()`. 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 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. hotwords: Hotwords/hint phrases to the model. Has no effect if prefix is not None. 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. without_timestamps: Only sample text tokens. Static params: (Fixed for batched version) max_initial_timestamp: The initial timestamp cannot be later than this, set at 0.0. multilingual: If True, perform transcription on multilingual videos. Set as False. output_language: Valid only if multilingual is set to True. Specifies the string representing the output language. One of 'en' (English) or 'hybrid' (code-switched transcription). set as None. 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 #TODO: support "hallucination_silence_threshold" when "word_timestamps=True" 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. 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. Set as "0". unused: 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. 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`). chunk_length: The length of audio segments. If it is not None, it will overwrite the default chunk_length of the FeatureExtractor. Returns: A tuple with: - a generator over transcribed batched segments. - an instance of TranscriptionInfo. """ sampling_rate = self.model.feature_extractor.sampling_rate if isinstance(audio, np.ndarray): audio = torch.from_numpy(audio) elif not isinstance(audio, torch.Tensor): audio = decode_audio(audio, sampling_rate=sampling_rate) duration = audio.shape[0] / sampling_rate # if no segment split is provided, use vad_model and generate segments if not vad_segments: # run the audio if it is less than 30 sec even without vad_segments if self.use_vad_model: vad_segments = self.vad_model( { "waveform": audio.unsqueeze(0), "sample_rate": 16000, } ) vad_segments = merge_chunks( vad_segments, self.chunk_length, onset=self.vad_onset, offset=self.vad_offset, ) elif duration < self.chunk_length: vad_segments = [ {"start": 0.0, "end": duration, "segments": [(0.0, duration)]} ] else: raise RuntimeError( "No vad segments found. Set 'use_vad_model' to True while loading the model" ) if self.model.model.is_multilingual: language = language or self.preset_language elif language != "en": if language is not None: self.model.logger.warning( f"English-only model is used, but {language} language is" "chosen, setting language to 'en'." ) language = "en" ( language, language_probability, task, all_language_probs, ) = self.get_language_and_tokenizer(audio, task, language) duration_after_vad = sum( segment["end"] - segment["start"] for segment in vad_segments ) # batched options: see the difference with default options in WhisperModel batched_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, log_prob_low_threshold=log_prob_low_threshold, no_speech_threshold=no_speech_threshold, compression_ratio_threshold=compression_ratio_threshold, temperatures=( temperature if isinstance(temperature, (list, tuple)) else [temperature] ), initial_prompt=initial_prompt, prefix=prefix, suppress_blank=suppress_blank, suppress_tokens=get_suppressed_tokens(self.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="0", prompt_reset_on_temperature=0.5, multilingual=False, output_language=None, 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=batched_options, vad_options=None, all_language_probs=all_language_probs, ) audio_segments, segments_metadata = self.audio_split( audio, vad_segments, sampling_rate ) to_cpu = ( self.model.model.device == "cuda" and len(self.model.model.device_index) > 1 ) audio_segments = torch.nested.nested_tensor(audio_segments).to_padded_tensor( padding=0 ) features = torch.stack( [ self.model.feature_extractor(audio_segment, to_cpu=to_cpu)[ ..., : self.model.feature_extractor.nb_max_frames ] for audio_segment in audio_segments ] ) segments = self._batched_segments_generator( features, segments_metadata, batch_size, batched_options, log_progress, ) return segments, info def _batched_segments_generator( self, features, segments_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], segments_metadata[i : i + batch_size], **options._asdict(), ) for result in results: for segment in result: seg_idx += 1 yield Segment( seek=int(result[-1]["end"] * self.model.frames_per_second), 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"], ) pbar.update(1) pbar.close() # revert the tokenizer if multilingual inference is enabled if self.preset_language is None: self.tokenizer = None 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 = 16, 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 or distil-large-v3), 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.device = device # set the random seed to make sure consistency across runs ctranslate2.set_random_seed(42) self.model = ctranslate2.models.Whisper( model_path, device=self.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, device=self.device ) 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, torch.Tensor, 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, 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, log_prob_low_threshold: Optional[float] = None, 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, output_language: Optional[str] = None, 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] = None, 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). 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. log_prob_low_threshold: This parameter alone is sufficient to skip an output text, wheras log_prob_threshold also looks for appropriate no_speech_threshold value. This value should be less than log_prob_threshold. 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: If True, perform transcription on multilingual videos and return the transcript based on the 'output_language' flag. output_language: Valid only if multilingual is set to True. Specifies the string representing the output language. One of 'en' (English) or 'hybrid' (code-switched transcription). 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 isinstance(audio, np.ndarray): audio = torch.from_numpy(audio) elif not isinstance(audio, torch.Tensor): 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 = collect_chunks(audio, speech_chunks) 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 to_cpu = self.model.device == "cuda" and len(self.model.device_index) > 1 features = self.feature_extractor( audio, chunk_length=chunk_length, to_cpu=to_cpu ) encoder_output = None all_language_probs = None # setting output_language for multilingual videos if multilingual: if output_language is None: output_language = "en" elif output_language not in ["en", "hybrid"]: raise ValueError("Output language needs to be one of 'en'/'hybrid'.") # detecting the language if not provided if language is None: if not self.model.is_multilingual: language = "en" language_probability = 1 else: if ( language_detection_segments is None or language_detection_segments < 1 ): language_detection_segments = 1 start_timestamp = ( float(clip_timestamps.split(",")[0]) if isinstance(clip_timestamps, str) else clip_timestamps[0] ) content_frames = ( features.shape[-1] - self.feature_extractor.nb_max_frames ) seek = ( int(start_timestamp * self.frames_per_second) if start_timestamp * self.frames_per_second < content_frames else 0 ) end_frames = min( seek + self.feature_extractor.nb_max_frames * language_detection_segments, content_frames, ) detected_language_info = {} while seek <= end_frames: segment = features[ :, seek : seek + 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] if ( language_detection_threshold is None or language_probability > language_detection_threshold ): break detected_language_info.setdefault(language, []).append( language_probability ) seek += segment.shape[-1] 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]) 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, log_prob_low_threshold=log_prob_low_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, output_language=output_language, 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, 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: torch.Tensor, tokenizer: Tokenizer, options: TranscriptionOptions, encoder_output: Optional[ctranslate2.StorageView] = None, ) -> Iterable[Segment]: content_frames = features.shape[-1] - self.feature_extractor.nb_max_frames content_duration = float(content_frames * self.feature_extractor.time_per_frame) if isinstance(options.clip_timestamps, str): options = options._replace( 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) last_speech_timestamp = 0.0 # 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, self.feature_extractor.nb_max_frames) 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 encoder_output is None: encoder_output = self.encode(segment) # Perform language detection at every segment to update task based on output language, # if the language is english, task is transcribe, # else the task is translate to english (default) # or transcribe if 'output_language' is 'hybrid'. if options.multilingual: results = self.model.detect_language(encoder_output) language_token, language_probability = results[0][0] language = language_token[2:-2] if options.output_language == "en" and language != "en": task = "translate" else: task = "transcribe" # Update tokenizer based on task and language tokenizer.task = tokenizer.tokenizer.token_to_id(f"<|{task}|>") tokenizer.language = tokenizer.tokenizer.token_to_id(language_token) tokenizer.language_code = language # Update prompt based on task and language prompt = self.get_prompt( tokenizer, previous_tokens, without_timestamps=options.without_timestamps, prefix=options.prefix if seek == 0 else None, hotwords=options.hotwords, ) if seek > 0 or 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, ) # Skip if the logprob is very low (below the threshold value), # despite no_speech_prob being low (ex: Too ambiguous outputs) if options.log_prob_low_threshold: if avg_logprob < options.log_prob_low_threshold: should_skip = True self.logger.debug( "log prob low threshold is met (%f > %f)", avg_logprob, options.log_prob_low_threshold, ) if should_skip: # 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 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: torch.Tensor) -> 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 = features.unsqueeze(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]["start"] 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 [] 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_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 generate_segment_batched( self, features: torch.Tensor, tokenizer: Tokenizer, options: dict, ): batch_size = features.shape[0] all_tokens = [] prompt_reset_since = 0 if options["initial_prompt"] is not None: initial_prompt = " " + options["initial_prompt"].strip() initial_prompt_tokens = tokenizer.encode(initial_prompt) all_tokens.extend(initial_prompt_tokens) previous_tokens = all_tokens[prompt_reset_since:] prompt = self.get_prompt( tokenizer, previous_tokens, without_timestamps=options["without_timestamps"], prefix=options["prefix"], ) encoder_output = self.encode(features) result = self.model.generate( encoder_output, [prompt] * batch_size, beam_size=options["beam_size"], patience=options["patience"], length_penalty=options["length_penalty"], max_length=self.max_length, suppress_blank=options["suppress_blank"], suppress_tokens=options["suppress_tokens"], return_scores=True, return_no_speech_prob=True, ) output = [] for res in result: output.append({}) # return scores seq_len = len(res.sequences_ids[0]) cum_logprob = res.scores[0] * (seq_len ** options["length_penalty"]) output[-1]["avg_logprob"] = cum_logprob / (seq_len + 1) # return no speech prob output[-1]["no_speech_prob"] = res.no_speech_prob output[-1]["tokens"] = res.sequences_ids[0] return encoder_output, output def detect_language(self, audio: torch.Tensor): to_cpu = self.model.device == "cuda" and len(self.model.device_index) > 1 segment = self.feature_extractor(audio, padding=True, to_cpu=to_cpu)[ :, : self.feature_extractor.nb_max_frames ] encoder_output = self.encode(segment) results = self.model.detect_language(encoder_output) language_token, language_probability = results[0][0] language = language_token[2:-2] self.logger.info( f"Detected language: {language} ({language_probability:.2f}) in first 30s of audio..." ) all_language_probs = [(token[2:-2], prob) for (token, prob) in results[0]] return language, language_probability, all_language_probs def detect_language_multi_segment( self, audio: Union[str, BinaryIO, torch.Tensor], params: Optional[dict] = None ): """ Detect language based on N highly-confident segments of a language. """ # The threshold is used to decide if the audio is silence or not. # The default is 0.02 (2.0%) i.e, if more than 2.0% of the audio is silent, # the audio is considered as silence. if not params: params = { "multilingual": False, "speech_percentage_threshold": 0.02, "language_detection_segments": 4, "vad_filter": True, "vad_min_silence_duration": 2500, "language_threshold": 0.7, } if params.get("multilingual", False): logging.warning( "lang_id is not supported for multilingual audios, detecting the major language." ) speech_percentage_threshold = params.get("speech_percentage_threshold", 0.02) language_threshold = params.get("language_threshold", 0.7) num_detection_segments = params.get("language_detection_segments", 4) vad_filter_enabled = params.get("vad_filter", True) vad_params = dict( min_silence_duration_ms=params.get("vad_min_silence_duration", 2500) ) if vad_filter_enabled: vad_params = VadOptions(**vad_params) # decode audio if it is not decoded already sampling_rate = self.feature_extractor.sampling_rate if not isinstance(audio, torch.Tensor): audio: torch.Tensor = decode_audio(audio, sampling_rate=sampling_rate) # calculate duration of audio as number of seconds # audio.shape[0] is the number of samples in the audio # sampling_rate is the number of samples per second # if we divide the number of samples by the number of samples per second, # we get the duration in seconds duration = audio.shape[0] / sampling_rate # Check if vad is enabled, and collect voiced segments if vad_filter_enabled: # get chunks of audio that contain speech speech_chunks = get_speech_timestamps(audio, vad_params) # merge chunks of audio that contain speech into a single array audio = collect_chunks(audio, speech_chunks) # calculate new duration of audio without silence duration_vad = audio.shape[0] / sampling_rate logging.debug( f"Lang ID: VAD filter removed {duration - duration_vad} sec of audio" ) # if the audio after VAD is less than 2% of the original audio, consider it as silence if duration_vad / duration < speech_percentage_threshold: return {"language_code": None, "language_confidence": 1.0} # update duration to be the duration after VAD duration = duration_vad # if the duration of the audio is less than 1 second, consider it as silence if duration < 1.0: return {"language_code": None, "language_confidence": 1.0} # number of feature frames in 30 seconds of audio is 3000 nb_max_frames = self.feature_extractor.nb_max_frames # extract features from audio with padding (default) to_cpu = self.model.device == "cuda" and len(self.model.device_index) > 1 features = self.feature_extractor(audio, to_cpu=to_cpu) # number of segments in the audio num_segments = features.shape[-1] // nb_max_frames # more number of segments than possible with the duration of file if num_detection_segments > num_segments: logging.warning( f"Lang ID: Can not have more segments, setting {num_segments} segments." ) num_detection_segments = num_segments # create a list of indices to randomly select segments from indices = list(range(num_detection_segments)) # fix seed to get deterministic results random.seed(0) random.shuffle(indices) detected_languages = [] all_language_probabilities = defaultdict(list) confident_language_probabilities = defaultdict(list) num_confident_segments_per_language = defaultdict(int) # Iterate over the randomly selected indices of the segments. # # For each segment, extract features and detect language. # # If the language is confident, add it to the list of confident segments for that language. # # If the number of confident segments for a language # is greater than or equal to the number of detection segments, # return the language and the average probability of the language. # # If we are unable to get sufficient number of confident predcitions, # return the most frequently detected language with maximum probability. # # We need to get sufficient number of confident predictions per language, not in total. for i in indices: segment_features = features[:, i * nb_max_frames : (i + 1) * nb_max_frames] try: encoder_output = self.encode(segment_features) results = self.model.detect_language(encoder_output)[0] except ValueError as e: # or RuntimeError logging.error(f"Inference error:{e}") # results is the list of classes (languages) and their probabilities (descending), # for eg: [('<|de|>', 0.482177734375),('<|en|>', 0.283447265625),...] # take top language token and probability # and parse language token to strip out markers # for eg: '<|de|>' -> 'de' language_token = results[0][0] language = language_token[2:-2] language_probability = results[0][1] detected_languages.append(language) all_language_probabilities[language].append(language_probability) # only consider if the language prediction is confident if language_probability > language_threshold: num_confident_segments_per_language[language] += 1 # Add language and probability to the list of languages when it is confident confident_language_probabilities[language].append(language_probability) # return the language when sufficient number of confident segments is achieved if ( num_confident_segments_per_language[language] >= num_detection_segments ): # Considering the average probability of only confident segments mean = sum(confident_language_probabilities[language]) / len( confident_language_probabilities[language] ) return { "language_code": language, "language_confidence": mean, } # if we are unable to get sufficient number of confident predictions, # return the most frequently detected language. # if there is a tie, return the one with maximum average probability. counter = Counter(detected_languages) # Define the key function to select frequent language with attached probabilities def key_func(language): # Calculate the frequency of the language frequency = counter[language] # Calculate the average probability of the language prob_avg = sum(all_language_probabilities[language]) / len( all_language_probabilities[language] ) return frequency, prob_avg if detected_languages: # Use the key function to find the language with maximum frequency and probability max_language = max(detected_languages, key=key_func) max_probability = sum(all_language_probabilities[max_language]) / len( all_language_probabilities[max_language] ) # Do additional checks for silence for non-confident case # calculate RMS amplitude and DC offset dc_offset = audio.mean() audio_minus_dc_offset = audio - dc_offset is_silent = ( torch.all(audio.abs() < 0.01) or torch.sqrt(torch.mean(audio_minus_dc_offset**2)) < 0.01 ) if is_silent: return {"language_code": None, "language_confidence": 1.0} return { "language_code": max_language, "language_confidence": max_probability, } # Language is not detected for any segment and none of prev conditions met return {"language_code": None, "language_confidence": 1.0} 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: torch.Tensor) -> ctranslate2.StorageView: segment = segment.contiguous() segment = ctranslate2.StorageView.from_array( segment if segment.is_cuda else segment.numpy() ) # torch cpu tensors don't implement __array_interface__ # https://github.com/pytorch/pytorch/issues/51156 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"] if "tokens" in alignment[0].keys(): following["tokens"] = previous["tokens"] + following["tokens"] previous["tokens"] = [] previous["word"] = "" 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"] if "tokens" in alignment[0].keys(): previous["tokens"] = previous["tokens"] + following["tokens"] following["tokens"] = [] following["word"] = "" else: i = j j += 1