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.
126 lines
3.6 KiB
Python
Executable File
126 lines
3.6 KiB
Python
Executable File
#!/usr/bin/env python3
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import argparse
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import os
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import sys
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from pathlib import Path
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try:
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import sounddevice as sd
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except ImportError:
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print("Please install sounddevice first. You can use")
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print()
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print(" pip install sounddevice")
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print()
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print("to install it")
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sys.exit(-1)
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import sherpa_onnx
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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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return parser.parse_args()
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def main():
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args = get_args()
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if not Path(args.silero_vad_model).is_file():
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raise RuntimeError(
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f"{args.silero_vad_model} does not exist. Please download it from "
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"https://github.com/snakers4/silero-vad/raw/master/src/silero_vad/data/silero_vad.onnx"
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)
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mic_sample_rate = 16000
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if "SHERPA_ONNX_MIC_SAMPLE_RATE" in os.environ:
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mic_sample_rate = int(os.environ.get("SHERPA_ONNX_MIC_SAMPLE_RATE"))
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print(f"Change microphone sample rate to {mic_sample_rate}")
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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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vad = sherpa_onnx.VoiceActivityDetector(config, buffer_size_in_seconds=30)
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# python3 -m sounddevice
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# can also be used to list all devices
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devices = sd.query_devices()
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if len(devices) == 0:
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print("No microphone devices found")
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print(
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"If you are using Linux and you are sure there is a microphone "
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"on your system, please use "
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"./vad-alsa.py"
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)
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sys.exit(0)
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print(devices)
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if "SHERPA_ONNX_MIC_DEVICE" in os.environ:
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input_device_idx = int(os.environ.get("SHERPA_ONNX_MIC_DEVICE"))
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sd.default.device[0] = input_device_idx
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print(f'Use selected device: {devices[input_device_idx]["name"]}')
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else:
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input_device_idx = sd.default.device[0]
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print(f'Use default device: {devices[input_device_idx]["name"]}')
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print("Started! Please speak. Press Ctrl C to exit")
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printed = False
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k = 0
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try:
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with sd.InputStream(
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channels=1, dtype="float32", samplerate=mic_sample_rate
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) as s:
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while True:
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samples, _ = s.read(samples_per_read) # a blocking read
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samples = samples.reshape(-1)
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if mic_sample_rate != sample_rate:
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import librosa
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samples = librosa.resample(
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samples, orig_sr=mic_sample_rate, target_sr=sample_rate
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)
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vad.accept_waveform(samples)
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if vad.is_speech_detected() and not printed:
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print("Detected speech")
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printed = True
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if not vad.is_speech_detected():
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printed = False
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while not vad.empty():
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samples = vad.front.samples
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duration = len(samples) / sample_rate
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filename = f"seg-{k}-{duration:.3f}-seconds.wav"
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k += 1
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sherpa_onnx.write_wave(filename, samples, sample_rate)
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print(f"Duration: {duration:.3f} seconds")
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print(f"Saved to {filename}")
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print("----------")
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vad.pop()
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except KeyboardInterrupt:
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print("\nCaught Ctrl + C. Exit")
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if __name__ == "__main__":
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main()
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