mirror of https://github.com/coqui-ai/TTS.git
89 lines
2.8 KiB
Python
89 lines
2.8 KiB
Python
import torch
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import numpy as np
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from torch.utils.data import Dataset
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class WaveRNNDataset(Dataset):
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"""
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WaveRNN Dataset searchs for all the wav files under root path.
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"""
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def __init__(self,
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ap,
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items,
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seq_len,
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hop_len,
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pad,
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mode,
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is_training=True,
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verbose=False,
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):
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self.ap = ap
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self.item_list = items
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self.seq_len = seq_len
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self.hop_len = hop_len
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self.pad = pad
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self.mode = mode
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self.is_training = is_training
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self.verbose = verbose
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def __len__(self):
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return len(self.item_list)
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def __getitem__(self, index):
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item = self.load_item(index)
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return item
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def load_item(self, index):
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wavpath, feat_path = self.item_list[index]
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m = np.load(feat_path.replace("/quant/", "/mel/"))
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# x = self.wav_cache[index]
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if m.shape[-1] < 5:
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print(" [!] Instance is too short! : {}".format(wavpath))
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self.item_list[index] = self.item_list[index + 1]
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feat_path = self.item_list[index]
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m = np.load(feat_path.replace("/quant/", "/mel/"))
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if self.mode in ["gauss", "mold"]:
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# x = np.load(feat_path.replace("/mel/", "/quant/"))
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x = self.ap.load_wav(wavpath)
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elif isinstance(self.mode, int):
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x = np.load(feat_path.replace("/mel/", "/quant/"))
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else:
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raise RuntimeError("Unknown dataset mode - ", self.mode)
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return m, x
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def collate(self, batch):
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mel_win = self.seq_len // self.hop_len + 2 * self.pad
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max_offsets = [x[0].shape[-1] -
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(mel_win + 2 * self.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 + self.pad) *
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self.hop_len for offset in mel_offsets]
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mels = [
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x[0][:, mel_offsets[i]: mel_offsets[i] + mel_win]
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for i, x in enumerate(batch)
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]
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coarse = [
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x[1][sig_offsets[i]: sig_offsets[i] + self.seq_len + 1]
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for i, x in enumerate(batch)
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]
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mels = np.stack(mels).astype(np.float32)
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if self.mode in ["gauss", "mold"]:
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coarse = np.stack(coarse).astype(np.float32)
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coarse = torch.FloatTensor(coarse)
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x_input = coarse[:, : self.seq_len]
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elif isinstance(self.mode, int):
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coarse = np.stack(coarse).astype(np.int64)
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coarse = torch.LongTensor(coarse)
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x_input = (
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2 * coarse[:, : self.seq_len].float() /
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(2 ** self.mode - 1.0) - 1.0
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)
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y_coarse = coarse[:, 1:]
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mels = torch.FloatTensor(mels)
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return x_input, mels, y_coarse
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