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import argparse
import torch
import torch.utils.data
import numpy as np
import librosa
from omegaconf import OmegaConf
from librosa.filters import mel as librosa_mel_fn
MAX_WAV_VALUE = 32768.0
def load_wav_to_torch(full_path, sample_rate):
wav, _ = librosa.load(full_path, sr=sample_rate)
wav = wav / np.abs(wav).max() * 0.6
return torch.FloatTensor(wav)
def dynamic_range_compression(x, C=1, clip_val=1e-5):
return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
def dynamic_range_decompression(x, C=1):
return np.exp(x) / C
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
return torch.log(torch.clamp(x, min=clip_val) * C)
def dynamic_range_decompression_torch(x, C=1):
return torch.exp(x) / C
def spectral_normalize_torch(magnitudes):
output = dynamic_range_compression_torch(magnitudes)
return output
def spectral_de_normalize_torch(magnitudes):
output = dynamic_range_decompression_torch(magnitudes)
return output
mel_basis = {}
hann_window = {}
def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, 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 mel_basis, hann_window
if fmax not in mel_basis:
mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
hann_window[str(y.device)] = torch.hann_window(win_size).to(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)
# complex tensor as default, then use view_as_real for future pytorch compatibility
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[str(y.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-9))
spec = torch.matmul(mel_basis[str(fmax)+'_'+str(y.device)], spec)
spec = spectral_normalize_torch(spec)
return spec
def mel_spectrogram_file(path, hps):
audio = load_wav_to_torch(path, hps.data.sampling_rate)
audio = audio.unsqueeze(0)
# match audio length to self.hop_length * n for evaluation
if (audio.size(1) % hps.data.hop_length) != 0:
audio = audio[:, :-(audio.size(1) % hps.data.hop_length)]
mel = mel_spectrogram(audio, hps.data.filter_length, hps.data.mel_channels, hps.data.sampling_rate,
hps.data.hop_length, hps.data.win_length, hps.data.mel_fmin, hps.data.mel_fmax, center=False)
return mel
def print_mel(mel, path="mel.png"):
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(12, 4))
if isinstance(mel, torch.Tensor):
mel = mel.cpu().numpy()
plt.pcolor(mel)
plt.savefig(path, format="png")
plt.close(fig)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-w", "--wav", help="wav", dest="wav")
parser.add_argument("-m", "--mel", help="mel", dest="mel") # csv for excel
args = parser.parse_args()
print(args.wav)
print(args.mel)
hps = OmegaConf.load(f"./configs/base.yaml")
mel = mel_spectrogram_file(args.wav, hps)
# TODO
mel = torch.squeeze(mel, 0)
# [100, length]
torch.save(mel, args.mel)
print_mel(mel, "debug.mel.png")
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