b5093e27f9
Save APKs for each release in a separate directory. Huggingface requires that each directory cannot contain more than 1000 files. Since we have so many tts models and for each model we need to build APKs of 4 different ABIs, it is a workaround for the huggingface's constraint by placing them into separate directories for different releases.
139 lines
3.5 KiB
Python
Executable File
139 lines
3.5 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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Different from ./vad-remove-non-speech-segments.py, this file supports only
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Linux.
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Usage
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python3 ./vad-remove-non-speech-segments-alsa.py \
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--silero-vad-model silero_vad.onnx
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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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import time
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from pathlib import Path
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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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"--device-name",
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type=str,
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required=True,
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help="""
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The device name specifies which microphone to use in case there are several
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on your system. You can use
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arecord -l
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to find all available microphones on your computer. For instance, if it outputs
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**** List of CAPTURE Hardware Devices ****
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card 3: UACDemoV10 [UACDemoV1.0], device 0: USB Audio [USB Audio]
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Subdevices: 1/1
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Subdevice #0: subdevice #0
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and if you want to select card 3 and device 0 on that card, please use:
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plughw:3,0
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as the device_name.
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""",
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)
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return parser.parse_args()
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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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device_name = args.device_name
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print(f"device_name: {device_name}")
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alsa = sherpa_onnx.Alsa(device_name)
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sample_rate = 16000
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samples_per_read = int(0.1 * sample_rate) # 0.1 second = 100 ms
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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.sample_rate = sample_rate
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window_size = config.silero_vad.window_size
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buffer = []
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vad = sherpa_onnx.VoiceActivityDetector(config, buffer_size_in_seconds=30)
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all_samples = []
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print("Started! Please speak. Press Ctrl C to exit")
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try:
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while True:
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samples = alsa.read(samples_per_read) # a blocking read
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samples = np.array(samples)
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buffer = np.concatenate([buffer, samples])
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all_samples = np.concatenate([all_samples, samples])
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while len(buffer) > window_size:
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vad.accept_waveform(buffer[:window_size])
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buffer = buffer[window_size:]
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except KeyboardInterrupt:
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print("\nCaught Ctrl + C. Saving & Exiting")
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speech_samples = []
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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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filename_for_speech = time.strftime("%Y%m%d-%H%M%S-speech.wav")
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sf.write(filename_for_speech, speech_samples, samplerate=sample_rate)
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filename_for_all = time.strftime("%Y%m%d-%H%M%S-all.wav")
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sf.write(filename_for_all, all_samples, samplerate=sample_rate)
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print(f"Saved to {filename_for_speech} and {filename_for_all}")
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if __name__ == "__main__":
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main()
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