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114 lines
3.7 KiB
Python
114 lines
3.7 KiB
Python
#!/usr/bin/env python3
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"""
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This file shows how to use the speech enhancement API with VoxLock and a voice
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sample.
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VoxLock is a target-speaker voice isolation model. It keeps the voice of one
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speaker, e.g., the caller in a phone call, and removes the other voices.
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The model is from https://github.com/arbazsiddiqui/voxlock
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This script provides a voice sample of the speaker to keep, so VoxLock isolates
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that speaker from the first sample. The voice sample must have at least 3
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seconds of the speaker's voice; from a longer file, VoxLock uses the loudest
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3 seconds within its first 30 seconds.
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If you don't have a voice sample, please see
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./offline-speech-enhancement-voxlock.py instead.
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Please download files used in this script from
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https://github.com/k2-fsa/sherpa-onnx/releases/tag/speech-enhancement-models
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Example:
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wget https://github.com/k2-fsa/sherpa-onnx/releases/download/speech-enhancement-models/sherpa-onnx-voxlock-int8.tar.bz2
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tar xvf sherpa-onnx-voxlock-int8.tar.bz2
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"""
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import time
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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 create_speech_denoiser():
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enroll_model = "./sherpa-onnx-voxlock-int8/enroll.onnx"
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chunk_model = "./sherpa-onnx-voxlock-int8/chunk_int8.onnx"
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voice_sample = "./sherpa-onnx-voxlock-int8/test_wavs/en-voice-sample-f.wav"
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if not Path(enroll_model).is_file() or not Path(chunk_model).is_file():
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raise ValueError(
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"Please first download the models from "
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"https://github.com/k2-fsa/sherpa-onnx/releases/tag/speech-enhancement-models"
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)
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if not Path(voice_sample).is_file():
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raise ValueError(
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f"{voice_sample} does not exist. You can download it from "
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"https://github.com/k2-fsa/sherpa-onnx/releases/tag/speech-enhancement-models"
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)
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config = sherpa_onnx.OfflineSpeechDenoiserConfig(
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model=sherpa_onnx.OfflineSpeechDenoiserModelConfig(
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voxlock=sherpa_onnx.OfflineSpeechDenoiserVoxLockModelConfig(
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enroll_model=enroll_model,
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chunk_model=chunk_model,
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voice_sample=voice_sample,
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),
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debug=False,
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num_threads=1,
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provider="cpu",
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)
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)
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if not config.validate():
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print(config)
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raise ValueError("Errors in config. Please check previous error logs")
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return sherpa_onnx.OfflineSpeechDenoiser(config)
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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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sd = create_speech_denoiser()
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test_wave = "./sherpa-onnx-voxlock-int8/test_wavs/en-combined.wav"
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if not Path(test_wave).is_file():
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raise ValueError(
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f"{test_wave} does not exist. You can download it from "
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"https://github.com/k2-fsa/sherpa-onnx/releases/tag/speech-enhancement-models"
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)
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samples, sample_rate = load_audio(test_wave)
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start = time.time()
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denoised = sd(samples, sample_rate)
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end = time.time()
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elapsed_seconds = end - start
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audio_duration = len(samples) / sample_rate
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real_time_factor = elapsed_seconds / audio_duration
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sf.write(
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"./voxlock-enhanced-with-voice-sample-16k.wav",
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denoised.samples,
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denoised.sample_rate,
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)
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print("Saved to ./voxlock-enhanced-with-voice-sample-16k.wav")
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print(f"Elapsed seconds: {elapsed_seconds:.3f}")
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print(f"Audio duration in seconds: {audio_duration:.3f}")
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print(f"RTF: {elapsed_seconds:.3f}/{audio_duration:.3f} = {real_time_factor:.3f}")
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if __name__ == "__main__":
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main()
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