mirror of
https://github.com/shirayu/whispering.git
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92 lines
2.2 KiB
Python
92 lines
2.2 KiB
Python
#!/usr/bin/env python3
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import argparse
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import queue
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from logging import INFO, getLogger
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import sounddevice as sd
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import torch
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from whisper import available_models
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from whisper.audio import N_FRAMES, SAMPLE_RATE
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from whisper.tokenizer import LANGUAGES, TO_LANGUAGE_CODE
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from whisper_streaming.schema import WhisperConfig
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from whisper_streaming.transcriber import WhisperStreamingTranscriber
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logger = getLogger(__name__)
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def transcribe_from_mic(config: WhisperConfig) -> None:
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sd_device = None
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wsp = WhisperStreamingTranscriber(config=config)
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q = queue.Queue()
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def sd_callback(indata, frames, time, status):
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if status:
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logger.warning(status)
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q.put(indata.ravel())
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logger.info("Ready to transcribe")
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with sd.InputStream(
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samplerate=SAMPLE_RATE,
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blocksize=N_FRAMES * 10, # FIXME
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device=sd_device,
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dtype="float32",
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channels=1,
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callback=sd_callback,
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):
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while True:
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segment = q.get()
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r = wsp.transcribe(segment=segment)
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if r is not None:
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print(r.text)
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def get_opts() -> argparse.Namespace:
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--language",
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type=str,
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default=None,
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choices=sorted(LANGUAGES.keys())
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+ sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]),
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required=True,
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)
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parser.add_argument(
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"--model",
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type=str,
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choices=available_models(),
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required=True,
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)
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parser.add_argument(
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"--device",
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default="cuda" if torch.cuda.is_available() else "cpu",
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help="device to use for PyTorch inference",
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)
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parser.add_argument(
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"--beam_size",
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"-b",
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type=int,
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default=5,
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)
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return parser.parse_args()
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def main() -> None:
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opts = get_opts()
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logger.setLevel(INFO)
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if opts.beam_size <= 0:
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opts.beam_size = None
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config = WhisperConfig(
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model_name=opts.model,
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language=opts.language,
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device=opts.device,
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beam_size=opts.beam_size,
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)
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transcribe_from_mic(config=config)
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if __name__ == "__main__":
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main()
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