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| import abc | |
| from typing import List | |
| from src.config import ModelConfig, VadInitialPromptMode | |
| from src.hooks.progressListener import ProgressListener | |
| from src.modelCache import GLOBAL_MODEL_CACHE, ModelCache | |
| from src.prompts.abstractPromptStrategy import AbstractPromptStrategy | |
| class AbstractWhisperCallback: | |
| def __init__(self): | |
| self.__prompt_mode_gpt = None | |
| 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. | |
| """ | |
| raise NotImplementedError() | |
| class AbstractWhisperContainer: | |
| def __init__(self, model_name: str, device: str = None, compute_type: str = "float16", | |
| download_root: str = None, | |
| cache: ModelCache = None, models: List[ModelConfig] = []): | |
| self.model_name = model_name | |
| self.device = device | |
| self.compute_type = compute_type | |
| self.download_root = download_root | |
| self.cache = cache | |
| # Will be created on demand | |
| self.model = None | |
| # List of known models | |
| self.models = models | |
| def get_model(self): | |
| if self.model is None: | |
| if (self.cache is None): | |
| self.model = self._create_model() | |
| else: | |
| model_key = "WhisperContainer." + self.model_name + ":" + (self.device if self.device else '') | |
| self.model = self.cache.get(model_key, self._create_model) | |
| return self.model | |
| def _create_model(self): | |
| raise NotImplementedError() | |
| def ensure_downloaded(self): | |
| pass | |
| 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 for the transcription. | |
| decodeOptions: dict | |
| Additional options to pass to the decoder. Must be pickleable. | |
| Returns | |
| ------- | |
| A WhisperCallback object. | |
| """ | |
| raise NotImplementedError() | |
| # This is required for multiprocessing | |
| def __getstate__(self): | |
| return { | |
| "model_name": self.model_name, | |
| "device": self.device, | |
| "download_root": self.download_root, | |
| "models": self.models, | |
| "compute_type": self.compute_type | |
| } | |
| def __setstate__(self, state): | |
| self.model_name = state["model_name"] | |
| self.device = state["device"] | |
| self.download_root = state["download_root"] | |
| self.models = state["models"] | |
| self.compute_type = state["compute_type"] | |
| self.model = None | |
| # Depickled objects must use the global cache | |
| self.cache = GLOBAL_MODEL_CACHE |