aadnk commited on
Commit
6250a98
1 Parent(s): 052fe7e

Adding a dummy implementation of Whisper for testing

Browse files
app.py CHANGED
@@ -624,4 +624,5 @@ if __name__ == '__main__':
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  if (threads := args.pop("threads")) > 0:
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  torch.set_num_threads(threads)
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  create_ui(app_config=updated_config)
 
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  if (threads := args.pop("threads")) > 0:
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  torch.set_num_threads(threads)
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+ print("Using whisper implementation: " + updated_config.whisper_implementation)
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  create_ui(app_config=updated_config)
src/whisper/dummyWhisperContainer.py ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ from typing import List
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+
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+ import ffmpeg
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+ from src.config import ModelConfig
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+ from src.hooks.progressListener import ProgressListener
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+ from src.modelCache import ModelCache
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+ from src.prompts.abstractPromptStrategy import AbstractPromptStrategy
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+ from src.whisper.abstractWhisperContainer import AbstractWhisperCallback, AbstractWhisperContainer
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+
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+ class DummyWhisperContainer(AbstractWhisperContainer):
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+ def __init__(self, model_name: str, device: str = None, compute_type: str = "float16",
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+ download_root: str = None,
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+ cache: ModelCache = None, models: List[ModelConfig] = []):
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+ super().__init__(model_name, device, compute_type, download_root, cache, models)
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+
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+ def ensure_downloaded(self):
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+ """
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+ Ensure that the model is downloaded. This is useful if you want to ensure that the model is downloaded before
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+ passing the container to a subprocess.
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+ """
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+ print("[Dummy] Ensuring that the model is downloaded")
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+
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+ def _create_model(self):
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+ print("[Dummy] Creating dummy whisper model " + self.model_name + " for device " + str(self.device))
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+ return None
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+
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+ def create_callback(self, language: str = None, task: str = None,
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+ prompt_strategy: AbstractPromptStrategy = None,
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+ **decodeOptions: dict) -> AbstractWhisperCallback:
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+ """
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+ Create a WhisperCallback object that can be used to transcript audio files.
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+
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+ Parameters
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+ ----------
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+ language: str
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+ The target language of the transcription. If not specified, the language will be inferred from the audio content.
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+ task: str
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+ The task - either translate or transcribe.
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+ prompt_strategy: AbstractPromptStrategy
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+ The prompt strategy to use. If not specified, the prompt from Whisper will be used.
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+ decodeOptions: dict
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+ Additional options to pass to the decoder. Must be pickleable.
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+
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+ Returns
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+ -------
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+ A WhisperCallback object.
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+ """
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+ return DummyWhisperCallback(self, language=language, task=task, prompt_strategy=prompt_strategy, **decodeOptions)
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+
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+ class DummyWhisperCallback(AbstractWhisperCallback):
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+ def __init__(self, model_container: DummyWhisperContainer, **decodeOptions: dict):
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+ self.model_container = model_container
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+ self.decodeOptions = decodeOptions
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+
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+ def invoke(self, audio, segment_index: int, prompt: str, detected_language: str, progress_listener: ProgressListener = None):
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+ """
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+ Peform the transcription of the given audio file or data.
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+
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+ Parameters
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+ ----------
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+ audio: Union[str, np.ndarray, torch.Tensor]
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+ The audio file to transcribe, or the audio data as a numpy array or torch tensor.
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+ segment_index: int
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+ The target language of the transcription. If not specified, the language will be inferred from the audio content.
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+ task: str
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+ The task - either translate or transcribe.
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+ progress_listener: ProgressListener
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+ A callback to receive progress updates.
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+ """
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+ print("[Dummy] Invoking dummy whisper callback for segment " + str(segment_index))
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+
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+ # Estimate length
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+ if isinstance(audio, str):
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+ audio_length = ffmpeg.probe(audio)["format"]["duration"]
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+ # Format is pcm_s16le at a sample rate of 16000, loaded as a float32 array.
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+ else:
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+ audio_length = len(audio) / 16000
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+
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+ # Convert the segments to a format that is easier to serialize
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+ whisper_segments = [{
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+ "text": "Dummy text for segment " + str(segment_index),
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+ "start": 0,
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+ "end": audio_length,
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+
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+ # Extra fields added by faster-whisper
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+ "words": []
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+ }]
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+
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+ result = {
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+ "segments": whisper_segments,
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+ "text": "Dummy text for segment " + str(segment_index),
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+ "language": "en" if detected_language is None else detected_language,
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+
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+ # Extra fields added by faster-whisper
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+ "language_probability": 1.0,
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+ "duration": audio_length,
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+ }
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+
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+ if progress_listener is not None:
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+ progress_listener.on_finished()
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+ return result
src/whisper/whisperFactory.py CHANGED
@@ -15,5 +15,9 @@ def create_whisper_container(whisper_implementation: str,
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  elif (whisper_implementation == "faster-whisper" or whisper_implementation == "faster_whisper"):
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  from src.whisper.fasterWhisperContainer import FasterWhisperContainer
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  return FasterWhisperContainer(model_name=model_name, device=device, compute_type=compute_type, download_root=download_root, cache=cache, models=models)
 
 
 
 
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  else:
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  raise ValueError("Unknown Whisper implementation: " + whisper_implementation)
 
15
  elif (whisper_implementation == "faster-whisper" or whisper_implementation == "faster_whisper"):
16
  from src.whisper.fasterWhisperContainer import FasterWhisperContainer
17
  return FasterWhisperContainer(model_name=model_name, device=device, compute_type=compute_type, download_root=download_root, cache=cache, models=models)
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+ elif (whisper_implementation == "dummy-whisper" or whisper_implementation == "dummy_whisper" or whisper_implementation == "dummy"):
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+ # This is useful for testing
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+ from src.whisper.dummyWhisperContainer import DummyWhisperContainer
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+ return DummyWhisperContainer(model_name=model_name, device=device, compute_type=compute_type, download_root=download_root, cache=cache, models=models)
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  else:
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  raise ValueError("Unknown Whisper implementation: " + whisper_implementation)