Sviatoslav Tsariov
37adf74745
Deleted message sender and promt service Implemented fast whisper, but it is not working WavStack refactored into QueueStack, which can use different strategies for proccessing
59 lines
1.5 KiB
Python
59 lines
1.5 KiB
Python
import sys
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import whisper
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from faster_whisper import WhisperModel
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import config
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from recognizer.strategies import BaseRecognizerStrategy
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class FastWhisperStrategy(BaseRecognizerStrategy):
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def __init__(self) -> None:
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self._model = WhisperModel(
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model_size_or_path=config.HARPYIA_MODEL,
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device=config.DEVICE,
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num_workers=config.WHISPER_NUM_WORKERS,
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cpu_threads=config.WHISPER_CPU_THREADS
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)
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def recognize(self, file, language, prompt) -> any:
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audio = self._prepare_file(file.name)
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return self._transcribe(audio, language, prompt)
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def _prepare_file(self, filename: str):
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audio = whisper.load_audio(filename, sr=config.HARPYIA_SAMPLE_RATE)
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audio = whisper.pad_or_trim(audio)
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return audio
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def _transcribe(self, audio, language, prompt):
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segments, _ = self._model.transcribe(
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audio,
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language=language,
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initial_prompt=prompt,
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condition_on_previous_text=False,
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vad_filter=True,
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beam_size=config.WHISPER_BEAM_SIZE,
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)
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print('Segments:', file=sys.stderr)
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for i in segments:
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print(i, file=sys.stderr)
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words = []
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for segment in list(segments):
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words.append(segment.text)
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return {
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'text': ' '.join(words),
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'segments': {
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'id': None,
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'seek': None,
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'start': None,
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'end': None,
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'text': None,
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'tokens': None,
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'temperature': None,
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'avg_logprob': None,
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'compression_ratio': None,
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'no_speech_prob': None,
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}
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} |