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| """from logging import getLogger | |
| import torch | |
| import torch.utils.data | |
| import torchaudio | |
| LOG = getLogger(__name__) | |
| from ..hparams import HParams | |
| def spectrogram_torch(audio: torch.Tensor, hps: HParams) -> torch.Tensor: | |
| return torchaudio.transforms.Spectrogram( | |
| n_fft=hps.data.filter_length, | |
| win_length=hps.data.win_length, | |
| hop_length=hps.data.hop_length, | |
| power=1.0, | |
| window_fn=torch.hann_window, | |
| normalized=False, | |
| ).to(audio.device)(audio) | |
| def spec_to_mel_torch(spec: torch.Tensor, hps: HParams) -> torch.Tensor: | |
| return torchaudio.transforms.MelScale( | |
| n_mels=hps.data.n_mel_channels, | |
| sample_rate=hps.data.sampling_rate, | |
| f_min=hps.data.mel_fmin, | |
| f_max=hps.data.mel_fmax, | |
| ).to(spec.device)(spec) | |
| def mel_spectrogram_torch(audio: torch.Tensor, hps: HParams) -> torch.Tensor: | |
| return torchaudio.transforms.MelSpectrogram( | |
| sample_rate=hps.data.sampling_rate, | |
| n_fft=hps.data.filter_length, | |
| n_mels=hps.data.n_mel_channels, | |
| win_length=hps.data.win_length, | |
| hop_length=hps.data.hop_length, | |
| f_min=hps.data.mel_fmin, | |
| f_max=hps.data.mel_fmax, | |
| power=1.0, | |
| window_fn=torch.hann_window, | |
| normalized=False, | |
| ).to(audio.device)(audio)""" | |
| from logging import getLogger | |
| import torch | |
| import torch.utils.data | |
| from librosa.filters import mel as librosa_mel_fn | |
| LOG = getLogger(__name__) | |
| MAX_WAV_VALUE = 32768.0 | |
| def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): | |
| """ | |
| PARAMS | |
| ------ | |
| C: compression factor | |
| """ | |
| return torch.log(torch.clamp(x, min=clip_val) * C) | |
| def dynamic_range_decompression_torch(x, C=1): | |
| """ | |
| PARAMS | |
| ------ | |
| C: compression factor used to compress | |
| """ | |
| 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 spectrogram_torch(y, hps, center=False): | |
| if torch.min(y) < -1.0: | |
| LOG.info("min value is ", torch.min(y)) | |
| if torch.max(y) > 1.0: | |
| LOG.info("max value is ", torch.max(y)) | |
| n_fft = hps.data.filter_length | |
| hop_size = hps.data.hop_length | |
| win_size = hps.data.win_length | |
| 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=False, | |
| ) | |
| spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) | |
| return spec | |
| def spec_to_mel_torch(spec, hps): | |
| sampling_rate = hps.data.sampling_rate | |
| n_fft = hps.data.filter_length | |
| num_mels = hps.data.n_mel_channels | |
| fmin = hps.data.mel_fmin | |
| fmax = hps.data.mel_fmax | |
| global mel_basis | |
| dtype_device = str(spec.dtype) + "_" + str(spec.device) | |
| fmax_dtype_device = str(fmax) + "_" + dtype_device | |
| if fmax_dtype_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[fmax_dtype_device] = torch.from_numpy(mel).to( | |
| dtype=spec.dtype, device=spec.device | |
| ) | |
| spec = torch.matmul(mel_basis[fmax_dtype_device], spec) | |
| spec = spectral_normalize_torch(spec) | |
| return spec | |
| def mel_spectrogram_torch(y, hps, center=False): | |
| sampling_rate = hps.data.sampling_rate | |
| n_fft = hps.data.filter_length | |
| num_mels = hps.data.n_mel_channels | |
| fmin = hps.data.mel_fmin | |
| fmax = hps.data.mel_fmax | |
| hop_size = hps.data.hop_length | |
| win_size = hps.data.win_length | |
| if torch.min(y) < -1.0: | |
| LOG.info(f"min value is {torch.min(y)}") | |
| if torch.max(y) > 1.0: | |
| LOG.info(f"max value is {torch.max(y)}") | |
| global mel_basis, hann_window | |
| dtype_device = str(y.dtype) + "_" + str(y.device) | |
| fmax_dtype_device = str(fmax) + "_" + dtype_device | |
| wnsize_dtype_device = str(win_size) + "_" + dtype_device | |
| if fmax_dtype_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[fmax_dtype_device] = torch.from_numpy(mel).to( | |
| dtype=y.dtype, device=y.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=False, | |
| ) | |
| spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) | |
| spec = torch.matmul(mel_basis[fmax_dtype_device], spec) | |
| spec = spectral_normalize_torch(spec) | |
| return spec | |