245 lines
10 KiB
Python
245 lines
10 KiB
Python
import os
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import os
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import random
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import torch
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import torch.nn.functional as F
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import torch.utils.data
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import torchaudio
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from tqdm import tqdm
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from data.audio.unsupervised_audio_dataset import load_audio
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from data.util import find_files_of_type, is_audio_file
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from models.tacotron2.taco_utils import load_filepaths_and_text
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from models.tacotron2.text import text_to_sequence
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from utils.util import opt_get
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def load_tsv(filename):
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with open(filename, encoding='utf-8') as f:
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components = [line.strip().split('\t') for line in f]
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base = os.path.dirname(filename)
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filepaths_and_text = [[os.path.join(base, f'{component[1]}'), component[0]] for component in components]
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return filepaths_and_text
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def load_mozilla_cv(filename):
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with open(filename, encoding='utf-8') as f:
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components = [line.strip().split('\t') for line in f][1:] # First line is the header
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base = os.path.dirname(filename)
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filepaths_and_text = [[os.path.join(base, f'clips/{component[1]}'), component[2]] for component in components]
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return filepaths_and_text
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def load_voxpopuli(filename):
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with open(filename, encoding='utf-8') as f:
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lines = [line.strip().split('\t') for line in f][1:] # First line is the header
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base = os.path.dirname(filename)
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filepaths_and_text = []
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for line in lines:
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if len(line) == 0:
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continue
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file, raw_text, norm_text, speaker_id, split, gender = line
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year = file[:4]
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filepaths_and_text.append([os.path.join(base, year, f'{file}.ogg.wav'), raw_text])
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return filepaths_and_text
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class TextWavLoader(torch.utils.data.Dataset):
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def __init__(self, hparams):
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self.path = hparams['path']
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if not isinstance(self.path, list):
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self.path = [self.path]
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fetcher_mode = opt_get(hparams, ['fetcher_mode'], 'lj')
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if not isinstance(fetcher_mode, list):
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fetcher_mode = [fetcher_mode]
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assert len(self.path) == len(fetcher_mode)
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self.load_conditioning = opt_get(hparams, ['load_conditioning'], False)
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self.conditioning_candidates = opt_get(hparams, ['num_conditioning_candidates'], 3)
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self.conditioning_length = opt_get(hparams, ['conditioning_length'], 44100)
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self.audiopaths_and_text = []
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for p, fm in zip(self.path, fetcher_mode):
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if fm == 'lj' or fm == 'libritts':
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fetcher_fn = load_filepaths_and_text
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elif fm == 'tsv':
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fetcher_fn = load_tsv
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elif fm == 'mozilla_cv':
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assert not self.load_conditioning # Conditioning inputs are incompatible with mozilla_cv
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fetcher_fn = load_mozilla_cv
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elif fm == 'voxpopuli':
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assert not self.load_conditioning # Conditioning inputs are incompatible with voxpopuli
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fetcher_fn = load_voxpopuli
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else:
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raise NotImplementedError()
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self.audiopaths_and_text.extend(fetcher_fn(p))
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self.text_cleaners = hparams.text_cleaners
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self.sample_rate = hparams.sample_rate
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random.seed(hparams.seed)
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random.shuffle(self.audiopaths_and_text)
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self.max_wav_len = opt_get(hparams, ['max_wav_length'], None)
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self.max_text_len = opt_get(hparams, ['max_text_length'], None)
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# If needs_collate=False, all outputs will be aligned and padded at maximum length.
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self.needs_collate = opt_get(hparams, ['needs_collate'], True)
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if not self.needs_collate:
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assert self.max_wav_len is not None and self.max_text_len is not None
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def get_wav_text_pair(self, audiopath_and_text):
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# separate filename and text
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audiopath, text = audiopath_and_text[0], audiopath_and_text[1]
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text_seq = self.get_text(text)
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wav = load_audio(audiopath, self.sample_rate)
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return (text_seq, wav, text, audiopath_and_text[0])
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def get_text(self, text):
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text_norm = torch.IntTensor(text_to_sequence(text, self.text_cleaners))
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return text_norm
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def load_conditioning_candidates(self, path):
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candidates = find_files_of_type('img', os.path.dirname(path), qualifier=is_audio_file)[0]
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assert len(candidates) < 50000 # Sanity check to ensure we aren't loading "related files" that aren't actually related.
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if len(candidates) == 0:
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print(f"No conditioning candidates found for {path} (not even the clip itself??)")
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raise NotImplementedError()
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# Sample with replacement. This can get repeats, but more conveniently handles situations where there are not enough candidates.
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related_clips = []
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for k in range(self.conditioning_candidates):
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rel_clip = load_audio(random.choice(candidates), self.sample_rate)
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gap = rel_clip.shape[-1] - self.conditioning_length
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if gap < 0:
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rel_clip = F.pad(rel_clip, pad=(0, abs(gap)))
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elif gap > 0:
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rand_start = random.randint(0, gap)
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rel_clip = rel_clip[:, rand_start:rand_start+self.conditioning_length]
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related_clips.append(rel_clip)
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return torch.stack(related_clips, dim=0)
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def __getitem__(self, index):
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try:
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tseq, wav, text, path = self.get_wav_text_pair(self.audiopaths_and_text[index])
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cond = self.load_conditioning_candidates(self.audiopaths_and_text[index][0]) if self.load_conditioning else None
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except:
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print(f"error loading {self.audiopaths_and_text[index][0]}")
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return self[index+1]
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if wav is None or \
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(self.max_wav_len is not None and wav.shape[-1] > self.max_wav_len) or \
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(self.max_text_len is not None and tseq.shape[0] > self.max_text_len):
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# Basically, this audio file is nonexistent or too long to be supported by the dataset.
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# It's hard to handle this situation properly. Best bet is to return the a random valid token and skew the dataset somewhat as a result.
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#if wav is not None:
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# print(f"Exception {index} wav_len:{wav.shape[-1]} text_len:{tseq.shape[0]} fname: {path}")
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rv = random.randint(0,len(self)-1)
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return self[rv]
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orig_output = wav.shape[-1]
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orig_text_len = tseq.shape[0]
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if not self.needs_collate:
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if wav.shape[-1] != self.max_wav_len:
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wav = F.pad(wav, (0, self.max_wav_len - wav.shape[-1]))
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if tseq.shape[0] != self.max_text_len:
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tseq = F.pad(tseq, (0, self.max_text_len - tseq.shape[0]))
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res = {
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'real_text': text,
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'padded_text': tseq,
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'text_lengths': torch.tensor(orig_text_len, dtype=torch.long),
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'wav': wav,
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'wav_lengths': torch.tensor(orig_output, dtype=torch.long),
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'filenames': path
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}
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if self.load_conditioning:
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res['conditioning'] = cond
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return res
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return tseq, wav, path, text, cond
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def __len__(self):
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return len(self.audiopaths_and_text)
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class TextMelCollate():
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""" Zero-pads model inputs and targets based on number of frames per step
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"""
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def __call__(self, batch):
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"""Collate's training batch from normalized text and wav
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PARAMS
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------
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batch: [text_normalized, wav, filename, text]
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"""
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# Right zero-pad all one-hot text sequences to max input length
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input_lengths, ids_sorted_decreasing = torch.sort(
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torch.LongTensor([len(x[0]) for x in batch]),
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dim=0, descending=True)
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max_input_len = input_lengths[0]
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text_padded = torch.LongTensor(len(batch), max_input_len)
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text_padded.zero_()
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filenames = []
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real_text = []
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conds = []
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for i in range(len(ids_sorted_decreasing)):
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text = batch[ids_sorted_decreasing[i]][0]
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text_padded[i, :text.size(0)] = text
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filenames.append(batch[ids_sorted_decreasing[i]][2])
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real_text.append(batch[ids_sorted_decreasing[i]][3])
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c = batch[ids_sorted_decreasing[i]][4]
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if c is not None:
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conds.append(c)
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# Right zero-pad wav
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num_wavs = batch[0][1].size(0)
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max_target_len = max([x[1].size(1) for x in batch])
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# include mel padded and gate padded
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wav_padded = torch.FloatTensor(len(batch), num_wavs, max_target_len)
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wav_padded.zero_()
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output_lengths = torch.LongTensor(len(batch))
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for i in range(len(ids_sorted_decreasing)):
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wav = batch[ids_sorted_decreasing[i]][1]
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wav_padded[i, :, :wav.size(1)] = wav
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output_lengths[i] = wav.size(1)
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res = {
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'padded_text': text_padded,
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'text_lengths': input_lengths,
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'wav': wav_padded,
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'wav_lengths': output_lengths,
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'filenames': filenames,
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'real_text': real_text,
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}
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if len(conds) > 0:
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res['conditioning'] = torch.stack(conds)
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return res
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if __name__ == '__main__':
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batch_sz = 8
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params = {
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'mode': 'nv_tacotron',
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'path': ['Z:\\bigasr_dataset\\libritts\\test-clean_list.txt'],
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'fetcher_mode': ['libritts'],
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'phase': 'train',
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'n_workers': 0,
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'batch_size': batch_sz,
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'needs_collate': False,
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'max_wav_length': 255995,
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'max_text_length': 200,
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'sample_rate': 22050,
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'load_conditioning': True,
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'num_conditioning_candidates': 3,
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'conditioning_length': 44100,
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}
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from data import create_dataset, create_dataloader
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ds, c = create_dataset(params, return_collate=True)
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dl = create_dataloader(ds, params, collate_fn=c)
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i = 0
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m = None
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for i, b in tqdm(enumerate(dl)):
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if i > 5:
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break
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w = b['wav']
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for ib in range(batch_sz):
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print(f'{i} {ib} {b["real_text"][ib]}')
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torchaudio.save(f'{i}_clip_{ib}.wav', b['wav'][ib], ds.sample_rate)
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for c in range(3):
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torchaudio.save(f'{i}_clip_{ib}_cond{c}.wav', b['conditioning'][ib, c], ds.sample_rate)
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