132 lines
3.2 KiB
Python
Executable File
132 lines
3.2 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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This file shows how to remove non-speech segments
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and merge all speech segments into a large segment
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and save it to a file.
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Usage
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python3 ./vad-remove-non-speech-segments-from-file.py \
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--silero-vad-model silero_vad.onnx \
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input.wav \
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output.wav
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Please visit
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https://github.com/snakers4/silero-vad/raw/master/src/silero_vad/data/silero_vad.onnx
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to download silero_vad.onnx
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For instance,
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wget https://github.com/snakers4/silero-vad/raw/master/src/silero_vad/data/silero_vad.onnx
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"""
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import argparse
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from pathlib import Path
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from typing import Tuple
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import numpy as np
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import sherpa_onnx
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import soundfile as sf
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def assert_file_exists(filename: str):
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assert Path(filename).is_file(), (
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f"{filename} does not exist!\n"
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"Please refer to "
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"https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html to download it"
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)
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def get_args():
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument(
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"--silero-vad-model",
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type=str,
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required=True,
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help="Path to silero_vad.onnx",
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)
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parser.add_argument(
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"input",
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type=str,
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help="Path to input.wav",
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)
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parser.add_argument(
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"output",
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type=str,
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help="Path to output.wav",
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)
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return parser.parse_args()
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def load_audio(filename: str) -> Tuple[np.ndarray, int]:
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data, sample_rate = sf.read(
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filename,
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always_2d=True,
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dtype="float32",
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)
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data = data[:, 0] # use only the first channel
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samples = np.ascontiguousarray(data)
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return samples, sample_rate
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def main():
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args = get_args()
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assert_file_exists(args.silero_vad_model)
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assert_file_exists(args.input)
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samples, sample_rate = load_audio(args.input)
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if sample_rate != 16000:
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import librosa
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samples = librosa.resample(samples, orig_sr=sample_rate, target_sr=16000)
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sample_rate = 16000
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config = sherpa_onnx.VadModelConfig()
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config.silero_vad.model = args.silero_vad_model
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config.silero_vad.threshold = 0.5
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config.silero_vad.min_silence_duration = 0.25 # seconds
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config.silero_vad.min_speech_duration = 0.25 # seconds
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# If the current segment is larger than this value, then it increases
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# the threshold to 0.9 internally. After detecting this segment,
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# it resets the threshold to its original value.
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config.silero_vad.max_speech_duration = 5 # seconds
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config.sample_rate = sample_rate
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window_size = config.silero_vad.window_size
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vad = sherpa_onnx.VoiceActivityDetector(config, buffer_size_in_seconds=30)
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speech_samples = []
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while len(samples) > window_size:
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vad.accept_waveform(samples[:window_size])
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samples = samples[window_size:]
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while not vad.empty():
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speech_samples.extend(vad.front.samples)
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vad.pop()
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vad.flush()
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while not vad.empty():
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speech_samples.extend(vad.front.samples)
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vad.pop()
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speech_samples = np.array(speech_samples, dtype=np.float32)
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sf.write(args.output, speech_samples, samplerate=sample_rate)
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print(f"Saved to {args.output}")
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if __name__ == "__main__":
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main()
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