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| import os | |
| from typing import List, Union | |
| from faster_whisper import WhisperModel, download_model | |
| from src.config import ModelConfig, VadInitialPromptMode | |
| from src.hooks.progressListener import ProgressListener | |
| from src.languages import get_language_from_name | |
| from src.modelCache import ModelCache | |
| from src.prompts.abstractPromptStrategy import AbstractPromptStrategy | |
| from src.whisper.abstractWhisperContainer import AbstractWhisperCallback, AbstractWhisperContainer | |
| from src.utils import format_timestamp | |
| class FasterWhisperContainer(AbstractWhisperContainer): | |
| def __init__(self, model_name: str, device: str = None, compute_type: str = "float16", | |
| download_root: str = None, | |
| cache: ModelCache = None, models: List[ModelConfig] = []): | |
| super().__init__(model_name, device, compute_type, download_root, cache, models) | |
| def ensure_downloaded(self): | |
| """ | |
| Ensure that the model is downloaded. This is useful if you want to ensure that the model is downloaded before | |
| passing the container to a subprocess. | |
| """ | |
| model_config = self._get_model_config() | |
| if os.path.isdir(model_config.url): | |
| model_config.path = model_config.url | |
| else: | |
| model_config.path = download_model(model_config.url, output_dir=self.download_root) | |
| def _get_model_config(self) -> ModelConfig: | |
| """ | |
| Get the model configuration for the model. | |
| """ | |
| for model in self.models: | |
| if model.name == self.model_name: | |
| return model | |
| return None | |
| def _create_model(self): | |
| print("Loading faster whisper model " + self.model_name + " for device " + str(self.device)) | |
| model_config = self._get_model_config() | |
| model_url = model_config.url | |
| if model_config.type == "whisper": | |
| if model_url not in ["tiny", "base", "small", "medium", "large", "large-v1", "large-v2", "large-v3"]: | |
| raise Exception("FasterWhisperContainer does not yet support Whisper models. Use ct2-transformers-converter to convert the model to a faster-whisper model.") | |
| if model_url == "large": | |
| # large is an alias for large-v3 | |
| model_url = "large-v3" | |
| device = self.device | |
| if (device is None): | |
| device = "auto" | |
| model = WhisperModel(model_url, device=device, compute_type=self.compute_type) | |
| return model | |
| def create_callback(self, language: str = None, task: str = None, | |
| prompt_strategy: AbstractPromptStrategy = None, | |
| **decodeOptions: dict) -> AbstractWhisperCallback: | |
| """ | |
| Create a WhisperCallback object that can be used to transcript audio files. | |
| Parameters | |
| ---------- | |
| language: str | |
| The target language of the transcription. If not specified, the language will be inferred from the audio content. | |
| task: str | |
| The task - either translate or transcribe. | |
| prompt_strategy: AbstractPromptStrategy | |
| The prompt strategy to use. If not specified, the prompt from Whisper will be used. | |
| decodeOptions: dict | |
| Additional options to pass to the decoder. Must be pickleable. | |
| Returns | |
| ------- | |
| A WhisperCallback object. | |
| """ | |
| return FasterWhisperCallback(self, language=language, task=task, prompt_strategy=prompt_strategy, **decodeOptions) | |
| class FasterWhisperCallback(AbstractWhisperCallback): | |
| def __init__(self, model_container: FasterWhisperContainer, language: str = None, task: str = None, | |
| prompt_strategy: AbstractPromptStrategy = None, | |
| **decodeOptions: dict): | |
| self.model_container = model_container | |
| self.language = language | |
| self.task = task | |
| self.prompt_strategy = prompt_strategy | |
| self.decodeOptions = decodeOptions | |
| self._printed_warning = False | |
| def invoke(self, audio, segment_index: int, prompt: str, detected_language: str, progress_listener: ProgressListener = None): | |
| """ | |
| Peform the transcription of the given audio file or data. | |
| Parameters | |
| ---------- | |
| audio: Union[str, np.ndarray, torch.Tensor] | |
| The audio file to transcribe, or the audio data as a numpy array or torch tensor. | |
| segment_index: int | |
| The target language of the transcription. If not specified, the language will be inferred from the audio content. | |
| task: str | |
| The task - either translate or transcribe. | |
| progress_listener: ProgressListener | |
| A callback to receive progress updates. | |
| """ | |
| model: WhisperModel = self.model_container.get_model() | |
| language_code = self._lookup_language_code(self.language) if self.language else None | |
| # Copy decode options and remove options that are not supported by faster-whisper | |
| decodeOptions = self.decodeOptions.copy() | |
| verbose = decodeOptions.pop("verbose", None) | |
| logprob_threshold = decodeOptions.pop("logprob_threshold", None) | |
| patience = decodeOptions.pop("patience", None) | |
| length_penalty = decodeOptions.pop("length_penalty", None) | |
| suppress_tokens = decodeOptions.pop("suppress_tokens", None) | |
| if (decodeOptions.pop("fp16", None) is not None): | |
| if not self._printed_warning: | |
| print("WARNING: fp16 option is ignored by faster-whisper - use compute_type instead.") | |
| self._printed_warning = True | |
| # Fix up decode options | |
| if (logprob_threshold is not None): | |
| decodeOptions["log_prob_threshold"] = logprob_threshold | |
| decodeOptions["patience"] = float(patience) if patience is not None else 1.0 | |
| decodeOptions["length_penalty"] = float(length_penalty) if length_penalty is not None else 1.0 | |
| # See if supress_tokens is a string - if so, convert it to a list of ints | |
| decodeOptions["suppress_tokens"] = self._split_suppress_tokens(suppress_tokens) | |
| initial_prompt = self.prompt_strategy.get_segment_prompt(segment_index, prompt, detected_language) \ | |
| if self.prompt_strategy else prompt | |
| segments_generator, info = model.transcribe(audio, \ | |
| language=language_code if language_code else detected_language, task=self.task, \ | |
| initial_prompt=initial_prompt, \ | |
| **decodeOptions | |
| ) | |
| segments = [] | |
| for segment in segments_generator: | |
| segments.append(segment) | |
| if progress_listener is not None: | |
| progress_listener.on_progress(segment.end, info.duration) | |
| if verbose: | |
| print("[{}->{}] {}".format(format_timestamp(segment.start, True), format_timestamp(segment.end, True), | |
| segment.text)) | |
| text = " ".join([segment.text for segment in segments]) | |
| # Convert the segments to a format that is easier to serialize | |
| whisper_segments = [{ | |
| "text": segment.text, | |
| "start": segment.start, | |
| "end": segment.end, | |
| # Extra fields added by faster-whisper | |
| "words": [{ | |
| "start": word.start, | |
| "end": word.end, | |
| "word": word.word, | |
| "probability": word.probability | |
| } for word in (segment.words if segment.words is not None else []) ] | |
| } for segment in segments] | |
| result = { | |
| "segments": whisper_segments, | |
| "text": text, | |
| "language": info.language if info else None, | |
| # Extra fields added by faster-whisper | |
| "language_probability": info.language_probability if info else None, | |
| "duration": info.duration if info else None | |
| } | |
| # If we have a prompt strategy, we need to increment the current prompt | |
| if self.prompt_strategy: | |
| self.prompt_strategy.on_segment_finished(segment_index, prompt, detected_language, result) | |
| if progress_listener is not None: | |
| progress_listener.on_finished() | |
| return result | |
| def _split_suppress_tokens(self, suppress_tokens: Union[str, List[int]]): | |
| if (suppress_tokens is None): | |
| return None | |
| if (isinstance(suppress_tokens, list)): | |
| return suppress_tokens | |
| return [int(token) for token in suppress_tokens.split(",")] | |
| def _lookup_language_code(self, language: str): | |
| language = get_language_from_name(language) | |
| if language is None: | |
| raise ValueError("Invalid language: " + language) | |
| return language.code | |