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# %%
import os
import torch
import torchaudio
import librosa
import numpy as np
from tqdm import tqdm
# %%
wav_path = r'C:\Users\ni-user\Downloads\New folder\whatsapp'
wav_new_path = r'C:\Users\ni-user\Downloads\New folder\newdata'
sr_target = 22050
silence_audio_size = 256 * 3
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# device = 'cuda'
wav_fpaths = [f.path for f in os.scandir(wav_path) if f.name.split('.')[0]+".opus"]
# waves = make_dataset_from_subdirs(wavs_path)
if not os.path.exists(wav_new_path):
os.makedirs(wav_new_path)
print(f"Created folder @ {wav_new_path}")
# %%
for wav_fpath in tqdm(wav_fpaths):
fname = os.path.basename(wav_fpath)
fpath = os.path.join(wav_path, fname)
wave, sr = torchaudio.load(fpath)
if sr != sr_target:
wave = wave.to(device)
wave = torchaudio.functional.resample(wave, sr, sr_target,
lowpass_filter_width=1024)
wave_ = wave[0].cpu().numpy()
wave_ = wave_ / np.abs(wave_).max() * 0.999
wave_ = librosa.effects.trim(
wave_, top_db=23, frame_length=1024, hop_length=256)[0]
wave_ = np.append(wave_, [0.]*silence_audio_size)
torchaudio.save(f"{wav_new_path}/{fname.split('.')[0]+'.wav'}",
torch.Tensor(wave_).unsqueeze(0), sr_target)
# %%
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