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from pywhispercpp.model import Model |
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import soundfile |
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import config |
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import numpy as np |
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from logging import getLogger |
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logger = getLogger(__name__) |
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class WhisperCPP: |
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def __init__(self, warmup=True) -> None: |
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models_dir = config.MODEL_DIR.as_posix() |
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self.model = Model( |
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model=config.WHISPER_MODEL, |
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models_dir=models_dir, |
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print_realtime=False, |
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print_progress=False, |
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print_timestamps=False, |
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translate=False |
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) |
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if warmup: |
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self.warmup() |
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def warmup(cls, warmup_steps=1): |
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mel, _, = soundfile.read(f"{config.ASSERT_DIR}/jfk.flac") |
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for _ in range(warmup_steps): |
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cls.model.transcribe(mel, print_progress=False) |
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@staticmethod |
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def config_language(language): |
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if language == "zh": |
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return config.MAX_LENTH_ZH, config.WHISPER_PROMPT_ZH |
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elif language == "en": |
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return config.MAX_LENGTH_EN, config.WHISPER_PROMPT_EN |
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raise ValueError(f"Unsupported language : {language}") |
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def transcribe(self, audio_buffer:bytes, language): |
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max_len, prompt = self.config_language(language) |
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audio_buffer = np.frombuffer(audio_buffer, dtype=np.float32) |
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try: |
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output = self.model.transcribe( |
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audio_buffer, |
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initial_prompt=prompt, |
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language=language, |
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token_timestamps=True, |
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max_len=max_len |
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) |
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return output |
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except Exception as e: |
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logger.error(e) |
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return [] |
