Spaces:
Running
Running
File size: 2,364 Bytes
506a2b4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
"""mel-spectrogram extraction in Matcha-TTS"""
from librosa.filters import mel as librosa_mel_fn
import torch
import numpy as np
# NOTE: they decalred these global vars
mel_basis = {}
hann_window = {}
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
return torch.log(torch.clamp(x, min=clip_val) * C)
def spectral_normalize_torch(magnitudes):
output = dynamic_range_compression_torch(magnitudes)
return output
"""
feat_extractor: !name:matcha.utils.audio.mel_spectrogram
n_fft: 1920
num_mels: 80
sampling_rate: 24000
hop_size: 480
win_size: 1920
fmin: 0
fmax: 8000
center: False
"""
def mel_spectrogram(y, n_fft=1920, num_mels=80, sampling_rate=24000, hop_size=480, win_size=1920,
fmin=0, fmax=8000, center=False):
"""Copied from https://github.com/shivammehta25/Matcha-TTS/blob/main/matcha/utils/audio.py
Set default values according to Cosyvoice's config.
"""
if isinstance(y, np.ndarray):
y = torch.tensor(y).float()
if len(y.shape) == 1:
y = y[None, ]
if torch.min(y) < -1.0:
print("min value is ", torch.min(y))
if torch.max(y) > 1.0:
print("max value is ", torch.max(y))
global mel_basis, hann_window # pylint: disable=global-statement,global-variable-not-assigned
if f"{str(fmax)}_{str(y.device)}" 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)
spec = torch.view_as_real(
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.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
|