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- import whisper
- import ffmpeg
- import numpy as np
- SAMPLE_RATE = 16000
- def load_audio(data: bytes):
- try:
- # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
- # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
- out, _ = (
- ffmpeg.input("pipe:", threads=0)
- .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=SAMPLE_RATE)
- .run(cmd="ffmpeg", capture_stdout=True, capture_stderr=True, input=data)
- )
- except ffmpeg.Error as e:
- raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
- return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
- class ASR():
- def __init__(self, model = "tiny"):
- self.model = whisper.load_model(model)
- def transcribe(self, audio: bytes):
- audio = load_audio(audio)
- return self.model.transcribe(audio)
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