mirror of https://github.com/coqui-ai/TTS.git
Remove TTSDatasetCached. It is merged to TTSDataset.py
This commit is contained in:
Eren Golge
parent
345e87b181
commit
37476e136c
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@ -25,10 +25,10 @@
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"do_trim_silence": true // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
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},
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"embedding_size": 256,
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"embedding_size": 256,
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"text_cleaner": "english_cleaners",
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"epochs": 1000,
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"lr": 0.001,
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"lr_decay": false,
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"warmup_steps": 4000,
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@ -52,4 +52,4 @@
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"output_path": "../keep/", // output path for all training outputs.
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"num_loader_workers": 8, // number of training data loader processes. Don't set it too big. 4-8 are good values.
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"num_val_loader_workers": 4 // number of evaluation data loader processes.
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}
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}
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@ -1,162 +0,0 @@
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import os
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import random
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import numpy as np
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import collections
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import librosa
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import torch
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from torch.utils.data import Dataset
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from utils.text import text_to_sequence
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from datasets.preprocess import tts_cache
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from utils.data import (prepare_data, pad_per_step, prepare_tensor,
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prepare_stop_target)
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class MyDataset(Dataset):
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# TODO: Not finished yet.
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def __init__(self,
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root_path,
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meta_file,
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outputs_per_step,
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text_cleaner,
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ap,
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batch_group_size=0,
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min_seq_len=0,
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**kwargs
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):
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self.ap = ap
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self.root_path = root_path
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self.batch_group_size = batch_group_size
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self.feat_dir = os.path.join(root_path, 'loader_data')
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self.items = tts_cache(root_path, meta_file)
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self.outputs_per_step = outputs_per_step
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self.sample_rate = ap.sample_rate
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self.cleaners = text_cleaner
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self.min_seq_len = min_seq_len
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print(" > Reading LJSpeech from - {}".format(root_path))
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print(" | > Number of instances : {}".format(len(self.items)))
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self.sort_items()
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def load_wav(self, filename):
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try:
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audio = self.ap.load_wav(filename)
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return audio
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except RuntimeError as e:
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print(" !! Cannot read file : {}".format(filename))
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def load_np(self, filename):
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data = np.load(filename).astype('float32')
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return data
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def sort_items(self):
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r"""Sort text sequences in ascending order"""
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lengths = np.array([len(ins[-1]) for ins in self.items])
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print(" | > Max length sequence {}".format(np.max(lengths)))
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print(" | > Min length sequence {}".format(np.min(lengths)))
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print(" | > Avg length sequence {}".format(np.mean(lengths)))
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idxs = np.argsort(lengths)
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new_frames = []
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ignored = []
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for i, idx in enumerate(idxs):
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length = lengths[idx]
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if length < self.min_seq_len:
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ignored.append(idx)
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else:
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new_frames.append(self.items[idx])
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print(" | > {} instances are ignored by min_seq_len ({})".format(
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len(ignored), self.min_seq_len))
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# shuffle batch groups
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if self.batch_group_size > 0:
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print(" | > Batch group shuffling is active.")
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for i in range(len(new_frames) // self.batch_group_size):
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offset = i * self.batch_group_size
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end_offset = offset + self.batch_group_size
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temp_frames = new_frames[offset : end_offset]
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random.shuffle(temp_frames)
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new_frames[offset : end_offset] = temp_frames
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self.items = new_frames
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def __len__(self):
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return len(self.items)
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def __getitem__(self, idx):
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wav_name = self.items[idx][0]
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mel_name = self.items[idx][1]
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linear_name = self.items[idx][2]
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text = self.items[idx][-1]
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text = np.asarray(
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text_to_sequence(text, [self.cleaners]), dtype=np.int32)
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if wav_name.split('.')[-1] == 'npy':
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wav = self.load_np(wav_name)
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else:
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wav = np.asarray(self.load_wav(wav_name), dtype=np.float32)
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mel = self.load_np(mel_name)
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linear = self.load_np(linear_name)
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sample = {
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'text': text,
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'wav': wav,
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'item_idx': self.items[idx][0],
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'mel': mel,
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'linear': linear
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}
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return sample
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def collate_fn(self, batch):
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r"""
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Perform preprocessing and create a final data batch:
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1. PAD sequences with the longest sequence in the batch
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2. Convert Audio signal to Spectrograms.
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3. PAD sequences that can be divided by r.
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4. Convert Numpy to Torch tensors.
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"""
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# Puts each data field into a tensor with outer dimension batch size
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if isinstance(batch[0], collections.Mapping):
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keys = list()
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wav = [d['wav'] for d in batch]
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item_idxs = [d['item_idx'] for d in batch]
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text = [d['text'] for d in batch]
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mel = [d['mel'] for d in batch]
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linear = [d['linear'] for d in batch]
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text_lenghts = np.array([len(x) for x in text])
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max_text_len = np.max(text_lenghts)
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mel_lengths = [m.shape[1] + 1 for m in mel] # +1 for zero-frame
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# compute 'stop token' targets
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stop_targets = [
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np.array([0.] * (mel_len - 1)) for mel_len in mel_lengths
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]
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# PAD stop targets
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stop_targets = prepare_stop_target(stop_targets,
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self.outputs_per_step)
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# PAD sequences with largest length of the batch
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text = prepare_data(text).astype(np.int32)
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wav = prepare_data(wav)
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# PAD features with largest length + a zero frame
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linear = prepare_tensor(linear, self.outputs_per_step)
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mel = prepare_tensor(mel, self.outputs_per_step)
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timesteps = mel.shape[2]
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# B x T x D
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linear = linear.transpose(0, 2, 1)
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mel = mel.transpose(0, 2, 1)
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# convert things to pytorch
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text_lenghts = torch.LongTensor(text_lenghts)
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text = torch.LongTensor(text)
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linear = torch.FloatTensor(linear).contiguous()
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mel = torch.FloatTensor(mel).contiguous()
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mel_lengths = torch.LongTensor(mel_lengths)
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stop_targets = torch.FloatTensor(stop_targets)
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return text, text_lenghts, linear, mel, mel_lengths, stop_targets, item_idxs
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raise TypeError(("batch must contain tensors, numbers, dicts or lists;\
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found {}".format(type(batch[0]))))
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@ -14,7 +14,7 @@ from utils.data import (prepare_data, pad_per_step, prepare_tensor,
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class MyDataset(Dataset):
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# TODO: Not finished yet.
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# TODO: Merge to TTSDataset.py, but it is not fast as it is supposed to be
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def __init__(self,
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root_path,
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meta_file,
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