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b56ec5ee1b
Fix a UserWarning in synthesizer/synthesizer_dataset.py, because of converting list of numpy array to torch tensor at Ln.85.
94 lines
3.4 KiB
Python
94 lines
3.4 KiB
Python
import torch
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from torch.utils.data import Dataset
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import numpy as np
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from pathlib import Path
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from synthesizer.utils.text import text_to_sequence
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class SynthesizerDataset(Dataset):
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def __init__(self, metadata_fpath: Path, mel_dir: Path, embed_dir: Path, hparams):
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print("Using inputs from:\n\t%s\n\t%s\n\t%s" % (metadata_fpath, mel_dir, embed_dir))
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with metadata_fpath.open("r", encoding="utf-8") as metadata_file:
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metadata = [line.split("|") for line in metadata_file]
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mel_fnames = [x[1] for x in metadata if int(x[4])]
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mel_fpaths = [mel_dir.joinpath(fname) for fname in mel_fnames]
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embed_fnames = [x[2] for x in metadata if int(x[4])]
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embed_fpaths = [embed_dir.joinpath(fname) for fname in embed_fnames]
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self.samples_fpaths = list(zip(mel_fpaths, embed_fpaths))
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self.samples_texts = [x[5].strip() for x in metadata if int(x[4])]
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self.metadata = metadata
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self.hparams = hparams
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print("Found %d samples" % len(self.samples_fpaths))
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def __getitem__(self, index):
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# Sometimes index may be a list of 2 (not sure why this happens)
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# If that is the case, return a single item corresponding to first element in index
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if index is list:
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index = index[0]
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mel_path, embed_path = self.samples_fpaths[index]
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mel = np.load(mel_path).T.astype(np.float32)
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# Load the embed
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embed = np.load(embed_path)
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# Get the text and clean it
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text = text_to_sequence(self.samples_texts[index], self.hparams.tts_cleaner_names)
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# Convert the list returned by text_to_sequence to a numpy array
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text = np.asarray(text).astype(np.int32)
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return text, mel.astype(np.float32), embed.astype(np.float32), index
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def __len__(self):
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return len(self.samples_fpaths)
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def collate_synthesizer(batch):
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# Text
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x_lens = [len(x[0]) for x in batch]
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max_x_len = max(x_lens)
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chars = [pad1d(x[0], max_x_len) for x in batch]
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chars = np.stack(chars)
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# Mel spectrogram
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spec_lens = [x[1].shape[-1] for x in batch]
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max_spec_len = max(spec_lens) + 1
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if max_spec_len % 2 != 0: # FIXIT: Hardcoded due to incompatibility with Windows (no lambda)
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max_spec_len += 2 - max_spec_len % 2
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# WaveRNN mel spectrograms are normalized to [0, 1] so zero padding adds silence
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# By default, SV2TTS uses symmetric mels, where -1*max_abs_value is silence.
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# if hparams.symmetric_mels:
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# mel_pad_value = -1 * hparams.max_abs_value
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# else:
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# mel_pad_value = 0
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mel_pad_value = -4 # FIXIT: Hardcoded due to incompatibility with Windows (no lambda)
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mel = [pad2d(x[1], max_spec_len, pad_value=mel_pad_value) for x in batch]
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mel = np.stack(mel)
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# Speaker embedding (SV2TTS)
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embeds = [x[2] for x in batch]
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embeds = np.stack(embeds)
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# Index (for vocoder preprocessing)
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indices = [x[3] for x in batch]
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# Convert all to tensor
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chars = torch.tensor(chars).long()
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mel = torch.tensor(mel)
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embeds = torch.tensor(embeds)
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return chars, mel, embeds, indices
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def pad1d(x, max_len, pad_value=0):
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return np.pad(x, (0, max_len - len(x)), mode="constant", constant_values=pad_value)
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def pad2d(x, max_len, pad_value=0):
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return np.pad(x, ((0, 0), (0, max_len - x.shape[-1])), mode="constant", constant_values=pad_value)
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