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# External programs | |
import whisper | |
from src.modelCache import GLOBAL_MODEL_CACHE, ModelCache | |
class WhisperContainer: | |
def __init__(self, model_name: str, device: str = None, download_root: str = None, cache: ModelCache = None): | |
self.model_name = model_name | |
self.device = device | |
self.download_root = download_root | |
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): | |
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): | |
print("Loading whisper model " + self.model_name) | |
return whisper.load_model(self.model_name, device=self.device, download_root=self.download_root) | |
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, "download_root": self.download_root } | |
def __setstate__(self, state): | |
self.model_name = state["model_name"] | |
self.device = state["device"] | |
self.download_root = state["download_root"] | |
self.model = None | |
# Depickled objects must use the global cache | |
self.cache = GLOBAL_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 |