import math import os import json import random import torch # from torchvision.transforms.functional import resize import torch.utils.data import numpy as np import librosa from librosa.util import normalize from scipy.io.wavfile import read from librosa.filters import mel as librosa_mel_fn # from speechbrain.lobes.models.FastSpeech2 import mel_spectogram 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.: # 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) 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) # 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(fmax)+'_'+str(y.device)], spec) spec = spectral_normalize_torch(spec) return spec def get_dataset_filelist(a): training_files =[] validation_files =[] total_files = 0 audio_dir = "dataset/audio" with open("filelists/train.txt") as f: training_files = f.readlines() for i, line in enumerate(training_files): spk, basename = line.strip().split('|') training_files[i] = f"{audio_dir}/{spk}/{basename}.wav" with open("filelists/val.txt") as f: validation_files = f.readlines() for i, line in enumerate(validation_files): spk, basename = line.strip().split('|') validation_files[i] = f"{audio_dir}/{spk}/{basename}.wav" random.seed(1234) random.shuffle(training_files) random.shuffle(validation_files) 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, shuffle=True, n_cache_reuse=1, device=None, fmax_loss=None, use_aug=False): 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.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.use_aug = use_aug with open("filelists/spk2id.json") as f: self.spk2id = json.load(f) 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 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 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) 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) spk_path = filename.replace("audio", "spk").replace(".wav", ".npy") spk_emb = torch.from_numpy(np.load(spk_path)) # (256) spk = filename.split("/")[-2] spk_id = self.spk2id[spk] spk_id = torch.LongTensor([spk_id]) if not self.use_aug: return (mel.squeeze(), audio.squeeze(0), filename, mel_loss.squeeze(), spk_emb, spk_id) mel_aug, _ = mel_spectogram( audio=audio.squeeze(), sample_rate=16000, hop_length=256, win_length=1024, n_mels=80, n_fft=1024, f_min=0.0, f_max=8000.0, power=1, normalized=False, min_max_energy_norm=True, norm="slaney", mel_scale="slaney", compression=True ) mel_aug = self.resize_mel(mel_aug.unsqueeze(0)).squeeze(0) return (mel_aug.squeeze(), mel.squeeze(), audio.squeeze(0), filename, mel_loss.squeeze(), spk_emb, spk_id) def __len__(self): return len(self.audio_files) def resize_mel(self, mel): ratio = 0.85 + 0.3 * torch.rand(1) # 0.85 ~ 1.15 height = int(mel.size(-2) * ratio) width = mel.size(-1) mel_r = resize(mel, (height, width), antialias=True) if height >= mel.size(-2): mel_r = mel_r[:, :mel.size(-2), :] else: pad = mel_r[:, -1:, :].repeat(1, mel.size(-2) - height, 1) pad += torch.randn_like(pad) / 1e3 mel_r = torch.cat((mel_r, pad), 1) return mel_r