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| # External programs | |
| import whisper | |
| class WhisperModelCache: | |
| def __init__(self): | |
| self._cache = dict() | |
| def get(self, model_name, device: str = None): | |
| key = model_name + ":" + (device if device else '') | |
| result = self._cache.get(key) | |
| if result is None: | |
| print("Loading whisper model " + model_name) | |
| result = whisper.load_model(name=model_name, device=device) | |
| self._cache[key] = result | |
| return result | |
| def clear(self): | |
| self._cache.clear() | |
| # A global cache of models. This is mainly used by the daemon processes to avoid loading the same model multiple times. | |
| GLOBAL_WHISPER_MODEL_CACHE = WhisperModelCache() | |
| class WhisperContainer: | |
| def __init__(self, model_name: str, device: str = None, cache: WhisperModelCache = None): | |
| self.model_name = model_name | |
| self.device = device | |
| self.cache = cache | |
| # Will be created on demand | |
| self.model = None | |
| def get_model(self): | |
| if self.model is None: | |
| if (self.cache is None): | |
| print("Loading whisper model " + self.model_name) | |
| self.model = whisper.load_model(self.model_name, device=self.device) | |
| else: | |
| self.model = self.cache.get(self.model_name, device=self.device) | |
| return self.model | |
| def create_callback(self, language: str = None, task: str = None, initial_prompt: str = None, **decodeOptions: dict): | |
| """ | |
| 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. | |
| initial_prompt: str | |
| The initial prompt to use for the transcription. | |
| decodeOptions: dict | |
| Additional options to pass to the decoder. Must be pickleable. | |
| Returns | |
| ------- | |
| A WhisperCallback object. | |
| """ | |
| return WhisperCallback(self, language=language, task=task, initial_prompt=initial_prompt, **decodeOptions) | |
| # This is required for multiprocessing | |
| def __getstate__(self): | |
| return { "model_name": self.model_name, "device": self.device } | |
| def __setstate__(self, state): | |
| self.model_name = state["model_name"] | |
| self.device = state["device"] | |
| self.model = None | |
| # Depickled objects must use the global cache | |
| self.cache = GLOBAL_WHISPER_MODEL_CACHE | |
| class WhisperCallback: | |
| def __init__(self, model_container: WhisperContainer, language: str = None, task: str = None, initial_prompt: str = None, **decodeOptions: dict): | |
| self.model_container = model_container | |
| self.language = language | |
| self.task = task | |
| self.initial_prompt = initial_prompt | |
| self.decodeOptions = decodeOptions | |
| def invoke(self, audio, segment_index: int, prompt: str, detected_language: str): | |
| """ | |
| 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. | |
| prompt: str | |
| The prompt to use for the transcription. | |
| detected_language: str | |
| The detected language of the audio file. | |
| Returns | |
| ------- | |
| The result of the Whisper call. | |
| """ | |
| model = self.model_container.get_model() | |
| return model.transcribe(audio, \ | |
| language=self.language if self.language else detected_language, task=self.task, \ | |
| initial_prompt=self._concat_prompt(self.initial_prompt, prompt) if segment_index == 0 else prompt, \ | |
| **self.decodeOptions) | |
| def _concat_prompt(self, prompt1, prompt2): | |
| if (prompt1 is None): | |
| return prompt2 | |
| elif (prompt2 is None): | |
| return prompt1 | |
| else: | |
| return prompt1 + " " + prompt2 |