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import torch
import torch.nn.functional as F
from librosa.filters import mel as librosa_mel_fn
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):
return dynamic_range_compression_torch(magnitudes)
def spectral_de_normalize_torch(magnitudes):
return dynamic_range_decompression_torch(magnitudes)
mel_basis, hann_window = {}, {}
def spectrogram_torch(y, n_fft, hop_size, win_size, center=False):
global hann_window
wnsize_dtype_device = str(win_size) + "_" + str(y.dtype) + "_" + str(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)
pad = F.pad(y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), mode="reflect").squeeze(1)
if str(y.device).startswith("ocl"): pad = pad.cpu()
spec = torch.stft(pad, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device].to(pad.device), center=center, pad_mode="reflect", normalized=False, onesided=True, return_complex=True)
spec = torch.sqrt(spec.real.pow(2) + spec.imag.pow(2) + 1e-6)
return spec.to(y.device)
def spec_to_mel_torch(spec, n_fft, num_mels, sample_rate, fmin, fmax):
global mel_basis
fmax_dtype_device = str(fmax) + "_" + str(spec.dtype) + "_" + str(spec.device)
if fmax_dtype_device not in mel_basis: mel_basis[fmax_dtype_device] = torch.from_numpy(librosa_mel_fn(sr=sample_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)).to(dtype=spec.dtype, device=spec.device)
return spectral_normalize_torch(torch.matmul(mel_basis[fmax_dtype_device], spec))
def mel_spectrogram_torch(y, n_fft, num_mels, sample_rate, hop_size, win_size, fmin, fmax, center=False):
return spec_to_mel_torch(spectrogram_torch(y, n_fft, hop_size, win_size, center), n_fft, num_mels, sample_rate, fmin, fmax) |