# Copyright (c) 2024 NVIDIA CORPORATION. # Licensed under the MIT license. # Adapted from https://github.com/jik876/hifi-gan under the MIT license. # LICENSE is in incl_licenses directory. import math import os import random import torch import torch.utils.data import numpy as np from librosa.util import normalize from scipy.io.wavfile import read from librosa.filters import mel as librosa_mel_fn import pathlib from tqdm import tqdm MAX_WAV_VALUE = 32767.0 # NOTE: 32768.0 -1 to prevent int16 overflow (results in popping sound in corner cases) def load_wav(full_path, sr_target): sampling_rate, data = read(full_path) if sampling_rate != sr_target: raise RuntimeError("Sampling rate of the file {} is {} Hz, but the model requires {} Hz". format(full_path, sampling_rate, sr_target)) 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.: print('min value is ', torch.min(y)) if torch.max(y) > 1.: print('max value is ', torch.max(y)) global mel_basis, hann_window if fmax not in mel_basis: mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax) str_key_mel_basis = str(fmax)+'_'+str(y.device) mel_basis[str_key_mel_basis] = 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) # complex tensor as default, then use view_as_real for future pytorch compatibility spec = 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.view_as_real(spec) spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9)) spec = torch.matmul(mel_basis[str_key_mel_basis], spec) spec = spectral_normalize_torch(spec) return spec def get_dataset_filelist(a): with open(a.input_training_file, 'r', 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] print("first training file: {}".format(training_files[0])) with open(a.input_validation_file, 'r', 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] print("first validation file: {}".format(validation_files[0])) list_unseen_validation_files = [] for i in range(len(a.list_input_unseen_validation_file)): with open(a.list_input_unseen_validation_file[i], 'r', encoding='utf-8') as fi: unseen_validation_files = [os.path.join(a.list_input_unseen_wavs_dir[i], x.split('|')[0] + '.wav') for x in fi.read().split('\n') if len(x) > 0] print("first unseen {}th validation fileset: {}".format(i, unseen_validation_files[0])) list_unseen_validation_files.append(unseen_validation_files) return training_files, validation_files, list_unseen_validation_files class MelDataset(torch.utils.data.Dataset): def __init__(self, training_files, hparams, 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, is_seen=True): self.audio_files = training_files random.seed(1234) if shuffle: random.shuffle(self.audio_files) self.hparams = hparams self.is_seen = is_seen if self.is_seen: self.name = pathlib.Path(self.audio_files[0]).parts[0] else: self.name = '-'.join(pathlib.Path(self.audio_files[0]).parts[:2]).strip("/") 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 print("INFO: checking dataset integrity...") for i in tqdm(range(len(self.audio_files))): assert os.path.exists(self.audio_files[i]), "{} not found".format(self.audio_files[i]) def __getitem__(self, index): filename = self.audio_files[index] if self._cache_ref_count == 0: audio, sampling_rate = load_wav(filename, self.sampling_rate) 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("{} SR doesn't match target {} SR".format( sampling_rate, self.sampling_rate)) 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: # validation step # match audio length to self.hop_size * n for evaluation if (audio.size(1) % self.hop_size) != 0: audio = audio[:, :-(audio.size(1) % self.hop_size)] 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) assert audio.shape[1] == mel.shape[2] * self.hop_size, "audio shape {} mel shape {}".format(audio.shape, mel.shape) 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)