import math import os import random from pathlib import Path import librosa import numpy as np import pandas as pd import torch import torch.nn.functional as F import torch.utils.data import torchaudio from librosa.filters import mel as librosa_mel_fn from librosa.util import normalize from scipy.io.wavfile import read def load_wav(full_path): #sampling_rate, data = read(full_path) #return data, sampling_rate data, sampling_rate = librosa.load(full_path, sr=None) 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 = {} class LogMelSpectrogram(torch.nn.Module): def __init__(self, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): super().__init__() self.melspctrogram = torchaudio.transforms.MelSpectrogram( sample_rate=sampling_rate, n_fft=n_fft, win_length=win_size, hop_length=hop_size, center=center, power=1.0, norm="slaney", onesided=True, n_mels=num_mels, mel_scale="slaney", f_min=fmin, f_max=fmax ) self.n_fft = n_fft self.hop_size = hop_size def forward(self, wav): wav = F.pad(wav, ((self.n_fft - self.hop_size) // 2, (self.n_fft - self.hop_size) // 2), "reflect") mel = self.melspctrogram(wav) logmel = torch.log(torch.clamp(mel, min=1e-5)) return logmel 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(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) # print("Padding by", int((n_fft - hop_size)/2), y.shape) # pre-padding n_pad = hop_size - ( y.shape[1] % hop_size ) y = F.pad(y.unsqueeze(1), (0, n_pad), mode='reflect').squeeze(1) # print("intermediate:", y.shape) y = F.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[str(y.device)], center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=True) spec = spec.abs().clamp_(3e-5) # print("Post: ", y.shape, spec.shape) spec = torch.matmul(mel_basis[str(fmax)+'_'+str(y.device)], spec) spec = spectral_normalize_torch(spec) return spec def get_dataset_filelist(a): train_df = pd.read_csv(a.input_training_file) valid_df = pd.read_csv(a.input_validation_file) return train_df, valid_df 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, audio_root_path=None, feat_root_path=None, use_alt_melcalc=False): self.audio_files = training_files if shuffle: self.audio_files = self.audio_files.sample(frac=1, random_state=1234) 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.audio_root_path = Path(audio_root_path) self.feat_root_path = Path(feat_root_path) self.alt_melspec = LogMelSpectrogram(n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax) self.use_alt_melcalc = use_alt_melcalc def __getitem__(self, index): row = self.audio_files.iloc[index] if self._cache_ref_count == 0: audio, sampling_rate = load_wav(self.audio_root_path/row.audio_path) 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.tensor(audio, dtype=torch.float32) 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') if self.use_alt_melcalc: mel = self.alt_melspec(audio) else: mel1 = 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 = mel.permute(0, 2, 1) # (1, dim, seq_len) --> (1, seq_len, dim) else: mel = torch.load(self.feat_root_path/row.feat_path, map_location='cpu').float() if len(mel.shape) < 3: mel = mel.unsqueeze(0) # (1, seq_len, dim) 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(1) - 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, 0, 0, frames_per_seg - mel.size(2)), 'constant') audio = torch.nn.functional.pad(audio, (0, self.segment_size - audio.size(1)), 'constant') if self.use_alt_melcalc: mel_loss = self.alt_melspec(audio) else: 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), str(row.audio_path), mel_loss.squeeze()) def __len__(self): return len(self.audio_files)