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