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| """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 | |