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from scipy.io.wavfile import read
import torch
import numpy as np
import os
from multiprocessing import Pool
from tqdm import tqdm
# Change here
base="jp_dataset/basic5000/wav"
hann_window = {}
def load_wav_to_torch(full_path):
sampling_rate, data = read(full_path)
# data, sampling_rate = librosa.load(full_path)
return torch.FloatTensor(data.astype(np.float32)), sampling_rate
def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
if torch.min(y) < -1.:
print('min value is ', torch.min(y))
if torch.max(y) > 1.:
print('max value is ', torch.max(y))
global hann_window
dtype_device = str(y.dtype) + '_' + str(y.device)
wnsize_dtype_device = str(win_size) + '_' + dtype_device
if wnsize_dtype_device not in hann_window:
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
y = y.squeeze(1)
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=True)
spec = torch.view_as_real(spec)
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
return spec
def get_audio(filename):
max_wave_length = 32768.0
filter_length = 1024
hop_length = 256
win_length = 1024
audio, sampling_rate = load_wav_to_torch(filename)
audio_norm = audio / max_wave_length
audio_norm = audio_norm.unsqueeze(0)
spec_filename = filename.replace(".wav", ".spec.pt")
spec = spectrogram_torch(audio_norm, filter_length,
sampling_rate, hop_length, win_length,
center=False)
spec = torch.squeeze(spec, 0)
torch.save(spec, spec_filename)
if __name__=="__main__":
waves = []
batch_size = 16
for wav_name in os.listdir(base):
wav_path = os.path.join(base, wav_name)
if wav_path.endswith(".wav"):
waves.append(wav_path)
with Pool(batch_size) as p:
print(list((tqdm(p.imap(get_audio, waves), total=len(waves)))))
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