from torch.utils.data import Dataset from pathlib import Path from models.vocoder.wavernn import audio import models.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