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import sys, os
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import torch
import librosa
import argparse
import numpy as np
from scipy.io.wavfile import write
from vad.utils import init_jit_model, get_speech_timestamps
def load_audio(file: str, sr: int = 16000):
x, sr = librosa.load(file, sr=sr)
return x
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--ref', type=str, required=True,
help="Path of ref audio.")
parser.add_argument('--svc', type=str, required=True,
help="Path of svc audio.")
parser.add_argument('--out', type=str, required=True,
help="Path of out audio.")
args = parser.parse_args()
print("svc in wave :", args.ref)
print("svc out wave :", args.svc)
print("svc post wave :", args.out)
model = init_jit_model(os.path.join('vad/assets', 'silero_vad.jit'))
model.eval()
ref_wave = load_audio(args.ref, sr=16000)
tmp_wave = torch.from_numpy(ref_wave).squeeze(0)
tag_wave = get_speech_timestamps(
tmp_wave, model, threshold=0.2, sampling_rate=16000)
ref_wave[:] = 0
for tag in tag_wave:
ref_wave[tag["start"]:tag["end"]] = 1
ref_wave = np.repeat(ref_wave, 2, -1)
svc_wave = load_audio(args.svc, sr=32000)
min_len = min(len(ref_wave), len(svc_wave))
ref_wave = ref_wave[:min_len]
svc_wave = svc_wave[:min_len]
svc_wave[ref_wave == 0] = 0
write(args.out, 32000, svc_wave)
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