| """ from https://github.com/jik876/hifi-gan """ |
|
|
| import math |
| import os |
| import random |
|
|
| import numpy as np |
| import torch |
| import torch.utils.data |
| from librosa.filters import mel as librosa_mel_fn |
| from librosa.util import normalize |
| from scipy.io.wavfile import read |
|
|
| MAX_WAV_VALUE = 32768.0 |
|
|
|
|
| def load_wav(full_path): |
| sampling_rate, data = read(full_path) |
| return data, sampling_rate |
|
|
|
|
| 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) < -1.0: |
| 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 |
| if fmax not in mel_basis: |
| mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, 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 |
|
|
|
|
| def get_dataset_filelist(a): |
| with open(a.input_training_file, encoding="utf-8") as fi: |
| training_files = [ |
| os.path.join(a.input_wavs_dir, x.split("|")[0] + ".wav") for x in fi.read().split("\n") if len(x) > 0 |
| ] |
|
|
| with open(a.input_validation_file, encoding="utf-8") as fi: |
| validation_files = [ |
| os.path.join(a.input_wavs_dir, x.split("|")[0] + ".wav") for x in fi.read().split("\n") if len(x) > 0 |
| ] |
| return training_files, validation_files |
|
|
|
|
| class MelDataset(torch.utils.data.Dataset): |
| def __init__( |
| self, |
| training_files, |
| segment_size, |
| n_fft, |
| num_mels, |
| hop_size, |
| win_size, |
| sampling_rate, |
| fmin, |
| fmax, |
| split=True, |
| shuffle=True, |
| n_cache_reuse=1, |
| device=None, |
| fmax_loss=None, |
| fine_tuning=False, |
| base_mels_path=None, |
| ): |
| self.audio_files = training_files |
| random.seed(1234) |
| if shuffle: |
| random.shuffle(self.audio_files) |
| self.segment_size = segment_size |
| self.sampling_rate = sampling_rate |
| self.split = split |
| self.n_fft = n_fft |
| self.num_mels = num_mels |
| self.hop_size = hop_size |
| self.win_size = win_size |
| self.fmin = fmin |
| self.fmax = fmax |
| self.fmax_loss = fmax_loss |
| self.cached_wav = None |
| self.n_cache_reuse = n_cache_reuse |
| self._cache_ref_count = 0 |
| self.device = device |
| self.fine_tuning = fine_tuning |
| self.base_mels_path = base_mels_path |
|
|
| def __getitem__(self, index): |
| filename = self.audio_files[index] |
| if self._cache_ref_count == 0: |
| audio, sampling_rate = load_wav(filename) |
| audio = audio / MAX_WAV_VALUE |
| if not self.fine_tuning: |
| audio = normalize(audio) * 0.95 |
| self.cached_wav = audio |
| if sampling_rate != self.sampling_rate: |
| raise ValueError(f"{sampling_rate} SR doesn't match target {self.sampling_rate} SR") |
| self._cache_ref_count = self.n_cache_reuse |
| else: |
| audio = self.cached_wav |
| self._cache_ref_count -= 1 |
|
|
| audio = torch.FloatTensor(audio) |
| audio = audio.unsqueeze(0) |
|
|
| if not self.fine_tuning: |
| if self.split: |
| if audio.size(1) >= self.segment_size: |
| max_audio_start = audio.size(1) - self.segment_size |
| audio_start = random.randint(0, max_audio_start) |
| audio = audio[:, audio_start : audio_start + self.segment_size] |
| else: |
| audio = torch.nn.functional.pad(audio, (0, self.segment_size - audio.size(1)), "constant") |
|
|
| mel = mel_spectrogram( |
| audio, |
| self.n_fft, |
| self.num_mels, |
| self.sampling_rate, |
| self.hop_size, |
| self.win_size, |
| self.fmin, |
| self.fmax, |
| center=False, |
| ) |
| else: |
| mel = np.load(os.path.join(self.base_mels_path, os.path.splitext(os.path.split(filename)[-1])[0] + ".npy")) |
| mel = torch.from_numpy(mel) |
|
|
| if len(mel.shape) < 3: |
| mel = mel.unsqueeze(0) |
|
|
| if self.split: |
| frames_per_seg = math.ceil(self.segment_size / self.hop_size) |
|
|
| if audio.size(1) >= self.segment_size: |
| mel_start = random.randint(0, mel.size(2) - frames_per_seg - 1) |
| mel = mel[:, :, mel_start : mel_start + frames_per_seg] |
| audio = audio[:, mel_start * self.hop_size : (mel_start + frames_per_seg) * self.hop_size] |
| else: |
| mel = torch.nn.functional.pad(mel, (0, frames_per_seg - mel.size(2)), "constant") |
| audio = torch.nn.functional.pad(audio, (0, self.segment_size - audio.size(1)), "constant") |
|
|
| mel_loss = mel_spectrogram( |
| audio, |
| self.n_fft, |
| self.num_mels, |
| self.sampling_rate, |
| self.hop_size, |
| self.win_size, |
| self.fmin, |
| self.fmax_loss, |
| center=False, |
| ) |
|
|
| return (mel.squeeze(), audio.squeeze(0), filename, mel_loss.squeeze()) |
|
|
| def __len__(self): |
| return len(self.audio_files) |
|
|
