mirror of
https://github.com/shirayu/whispering.git
synced 2024-05-09 11:52:59 +00:00
72 lines
2 KiB
Python
72 lines
2 KiB
Python
#!/usr/bin/env python3
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from logging import getLogger
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from typing import Iterator, Optional
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import numpy as np
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import torch
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from whisper.audio import N_FRAMES, SAMPLE_RATE
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from whispering.schema import SpeechSegment
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logger = getLogger(__name__)
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class VAD:
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def __init__(
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self,
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):
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self.vad_model, _ = torch.hub.load(
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repo_or_dir="snakers4/silero-vad",
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model="silero_vad",
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)
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def __call__(
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self,
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*,
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audio: np.ndarray,
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threshold: float,
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total_block_number: Optional[int] = None,
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) -> Iterator[SpeechSegment]:
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# audio.shape should be multiple of (N_FRAMES,)
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def my_ret(
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*,
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start_block_idx: int,
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idx: int,
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) -> SpeechSegment:
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return SpeechSegment(
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start_block_idx=start_block_idx,
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end_block_idx=idx,
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audio=audio[N_FRAMES * start_block_idx : N_FRAMES * idx],
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)
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if total_block_number is None:
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total_block_number = int(audio.shape[0] / N_FRAMES)
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block_unit: int = audio.shape[0] // total_block_number
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start_block_idx = None
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for idx in range(total_block_number):
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start: int = block_unit * idx
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end: int = block_unit * (idx + 1)
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vad_prob = self.vad_model(
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torch.from_numpy(audio[start:end]),
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SAMPLE_RATE,
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).item()
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logger.debug(f"VAD: {vad_prob} (threshold={threshold})")
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if vad_prob > threshold:
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if start_block_idx is None:
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start_block_idx = idx
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else:
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if start_block_idx is not None:
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yield my_ret(
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start_block_idx=start_block_idx,
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idx=idx,
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)
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start_block_idx = None
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if start_block_idx is not None:
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yield my_ret(
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start_block_idx=start_block_idx,
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idx=total_block_number,
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)
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