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| from torch.utils.data import Dataset | |
| from pathlib import Path | |
| from vocoder.wavernn import audio | |
| import vocoder.wavernn.hparams as hp | |
| import numpy as np | |
| import torch | |
| class VocoderDataset(Dataset): | |
| def __init__(self, metadata_fpath: Path, mel_dir: Path, wav_dir: Path): | |
| print("Using inputs from:\n\t%s\n\t%s\n\t%s" % (metadata_fpath, mel_dir, wav_dir)) | |
| with metadata_fpath.open("r") as metadata_file: | |
| metadata = [line.split("|") for line in metadata_file] | |
| gta_fnames = [x[1] for x in metadata if int(x[4])] | |
| gta_fpaths = [mel_dir.joinpath(fname) for fname in gta_fnames] | |
| wav_fnames = [x[0] for x in metadata if int(x[4])] | |
| wav_fpaths = [wav_dir.joinpath(fname) for fname in wav_fnames] | |
| self.samples_fpaths = list(zip(gta_fpaths, wav_fpaths)) | |
| print("Found %d samples" % len(self.samples_fpaths)) | |
| def __getitem__(self, index): | |
| mel_path, wav_path = self.samples_fpaths[index] | |
| # Load the mel spectrogram and adjust its range to [-1, 1] | |
| mel = np.load(mel_path).T.astype(np.float32) / hp.mel_max_abs_value | |
| # Load the wav | |
| wav = np.load(wav_path) | |
| if hp.apply_preemphasis: | |
| wav = audio.pre_emphasis(wav) | |
| wav = np.clip(wav, -1, 1) | |
| # Fix for missing padding # TODO: settle on whether this is any useful | |
| r_pad = (len(wav) // hp.hop_length + 1) * hp.hop_length - len(wav) | |
| wav = np.pad(wav, (0, r_pad), mode='constant') | |
| assert len(wav) >= mel.shape[1] * hp.hop_length | |
| wav = wav[:mel.shape[1] * hp.hop_length] | |
| assert len(wav) % hp.hop_length == 0 | |
| # Quantize the wav | |
| if hp.voc_mode == 'RAW': | |
| if hp.mu_law: | |
| quant = audio.encode_mu_law(wav, mu=2 ** hp.bits) | |
| else: | |
| quant = audio.float_2_label(wav, bits=hp.bits) | |
| elif hp.voc_mode == 'MOL': | |
| quant = audio.float_2_label(wav, bits=16) | |
| return mel.astype(np.float32), quant.astype(np.int64) | |
| def __len__(self): | |
| return len(self.samples_fpaths) | |
| def collate_vocoder(batch): | |
| mel_win = hp.voc_seq_len // hp.hop_length + 2 * hp.voc_pad | |
| max_offsets = [x[0].shape[-1] -2 - (mel_win + 2 * hp.voc_pad) for x in batch] | |
| mel_offsets = [np.random.randint(0, offset) for offset in max_offsets] | |
| sig_offsets = [(offset + hp.voc_pad) * hp.hop_length for offset in mel_offsets] | |
| mels = [x[0][:, mel_offsets[i]:mel_offsets[i] + mel_win] for i, x in enumerate(batch)] | |
| labels = [x[1][sig_offsets[i]:sig_offsets[i] + hp.voc_seq_len + 1] for i, x in enumerate(batch)] | |
| mels = np.stack(mels).astype(np.float32) | |
| labels = np.stack(labels).astype(np.int64) | |
| mels = torch.tensor(mels) | |
| labels = torch.tensor(labels).long() | |
| x = labels[:, :hp.voc_seq_len] | |
| y = labels[:, 1:] | |
| bits = 16 if hp.voc_mode == 'MOL' else hp.bits | |
| x = audio.label_2_float(x.float(), bits) | |
| if hp.voc_mode == 'MOL' : | |
| y = audio.label_2_float(y.float(), bits) | |
| return x, y, mels |