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.
132 lines
3.5 KiB
Python
Executable File
132 lines
3.5 KiB
Python
Executable File
#!/usr/bin/env python3
|
|
|
|
"""
|
|
This file shows how to remove non-speech segments
|
|
and merge all speech segments into a large segment
|
|
and save it to a file.
|
|
|
|
Usage
|
|
|
|
python3 ./vad-remove-non-speech-segments.py \
|
|
--silero-vad-model silero_vad.onnx
|
|
|
|
Please visit
|
|
https://github.com/snakers4/silero-vad/raw/master/src/silero_vad/data/silero_vad.onnx
|
|
to download silero_vad.onnx
|
|
|
|
For instance,
|
|
|
|
wget https://github.com/snakers4/silero-vad/raw/master/src/silero_vad/data/silero_vad.onnx
|
|
"""
|
|
|
|
import argparse
|
|
import sys
|
|
import time
|
|
from pathlib import Path
|
|
|
|
import numpy as np
|
|
import sherpa_onnx
|
|
import soundfile as sf
|
|
|
|
try:
|
|
import sounddevice as sd
|
|
except ImportError:
|
|
print("Please install sounddevice first. You can use")
|
|
print()
|
|
print(" pip install sounddevice")
|
|
print()
|
|
print("to install it")
|
|
sys.exit(-1)
|
|
|
|
|
|
def assert_file_exists(filename: str):
|
|
assert Path(filename).is_file(), (
|
|
f"{filename} does not exist!\n"
|
|
"Please refer to "
|
|
"https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html to download it"
|
|
)
|
|
|
|
|
|
def get_args():
|
|
parser = argparse.ArgumentParser(
|
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--silero-vad-model",
|
|
type=str,
|
|
required=True,
|
|
help="Path to silero_vad.onnx",
|
|
)
|
|
|
|
return parser.parse_args()
|
|
|
|
|
|
def main():
|
|
devices = sd.query_devices()
|
|
if len(devices) == 0:
|
|
print("No microphone devices found")
|
|
print(
|
|
"If you are using Linux and you are sure there is a microphone "
|
|
"on your system, please use "
|
|
"./vad-remove-non-speech-segments-alsa.py"
|
|
)
|
|
sys.exit(0)
|
|
|
|
print(devices)
|
|
default_input_device_idx = sd.default.device[0]
|
|
print(f'Use default device: {devices[default_input_device_idx]["name"]}')
|
|
|
|
args = get_args()
|
|
assert_file_exists(args.silero_vad_model)
|
|
|
|
sample_rate = 16000
|
|
samples_per_read = int(0.1 * sample_rate) # 0.1 second = 100 ms
|
|
|
|
config = sherpa_onnx.VadModelConfig()
|
|
config.silero_vad.model = args.silero_vad_model
|
|
config.sample_rate = sample_rate
|
|
|
|
window_size = config.silero_vad.window_size
|
|
|
|
buffer = []
|
|
vad = sherpa_onnx.VoiceActivityDetector(config, buffer_size_in_seconds=30)
|
|
|
|
all_samples = []
|
|
|
|
print("Started! Please speak. Press Ctrl C to exit")
|
|
|
|
try:
|
|
with sd.InputStream(channels=1, dtype="float32", samplerate=sample_rate) as s:
|
|
while True:
|
|
samples, _ = s.read(samples_per_read) # a blocking read
|
|
samples = samples.reshape(-1)
|
|
buffer = np.concatenate([buffer, samples])
|
|
|
|
all_samples = np.concatenate([all_samples, samples])
|
|
|
|
while len(buffer) > window_size:
|
|
vad.accept_waveform(buffer[:window_size])
|
|
buffer = buffer[window_size:]
|
|
except KeyboardInterrupt:
|
|
print("\nCaught Ctrl + C. Saving & Exiting")
|
|
|
|
speech_samples = []
|
|
while not vad.empty():
|
|
speech_samples.extend(vad.front.samples)
|
|
vad.pop()
|
|
|
|
speech_samples = np.array(speech_samples, dtype=np.float32)
|
|
|
|
filename_for_speech = time.strftime("%Y%m%d-%H%M%S-speech.wav")
|
|
sf.write(filename_for_speech, speech_samples, samplerate=sample_rate)
|
|
|
|
filename_for_all = time.strftime("%Y%m%d-%H%M%S-all.wav")
|
|
sf.write(filename_for_all, all_samples, samplerate=sample_rate)
|
|
|
|
print(f"Saved to {filename_for_speech} and {filename_for_all}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|