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import matplotlib |
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matplotlib.use('Agg') |
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from tasks.base_task import data_loader |
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from tasks.tts.fs2 import FastSpeech2Task |
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from tasks.tts.dataset_utils import FastSpeechDataset, BaseTTSDataset |
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import glob |
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import importlib |
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from utils.pitch_utils import norm_interp_f0, denorm_f0, f0_to_coarse |
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from inference.base_tts_infer import load_data_preprocessor |
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from data_gen.tts.emotion import inference as EmotionEncoder |
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from data_gen.tts.emotion.inference import embed_utterance as Embed_utterance |
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from data_gen.tts.emotion.inference import preprocess_wav |
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from tqdm import tqdm |
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from utils.hparams import hparams |
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from data_gen.tts.data_gen_utils import build_phone_encoder, build_word_encoder |
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import random |
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import torch |
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import torch.optim |
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import torch.nn.functional as F |
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import torch.utils.data |
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from utils.indexed_datasets import IndexedDataset |
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from resemblyzer import VoiceEncoder |
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import torch.distributions |
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import numpy as np |
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import utils |
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import os |
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class GenerSpeech_dataset(BaseTTSDataset): |
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def __init__(self, prefix, shuffle=False, test_items=None, test_sizes=None, data_dir=None): |
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super().__init__(prefix, shuffle, test_items, test_sizes, data_dir) |
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self.f0_mean, self.f0_std = hparams.get('f0_mean', None), hparams.get('f0_std', None) |
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if prefix == 'valid': |
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indexed_ds = IndexedDataset(f'{self.data_dir}/train') |
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sizes = np.load(f'{self.data_dir}/train_lengths.npy') |
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index = [i for i in range(len(indexed_ds))] |
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random.shuffle(index) |
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index = index[:300] |
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self.sizes = sizes[index] |
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self.indexed_ds = [] |
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for i in index: |
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self.indexed_ds.append(indexed_ds[i]) |
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self.avail_idxs = list(range(len(self.sizes))) |
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if hparams['min_frames'] > 0: |
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self.avail_idxs = [x for x in self.avail_idxs if self.sizes[x] >= hparams['min_frames']] |
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self.sizes = [self.sizes[i] for i in self.avail_idxs] |
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if prefix == 'test' and hparams['test_input_dir'] != '': |
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self.preprocessor, self.preprocess_args = load_data_preprocessor() |
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self.indexed_ds, self.sizes = self.load_test_inputs(hparams['test_input_dir']) |
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self.avail_idxs = [i for i, _ in enumerate(self.sizes)] |
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def load_test_inputs(self, test_input_dir): |
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inp_wav_paths = sorted(glob.glob(f'{test_input_dir}/*.wav') + glob.glob(f'{test_input_dir}/*.mp3')) |
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binarizer_cls = hparams.get("binarizer_cls", 'data_gen.tts.base_binarizerr.BaseBinarizer') |
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pkg = ".".join(binarizer_cls.split(".")[:-1]) |
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cls_name = binarizer_cls.split(".")[-1] |
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binarizer_cls = getattr(importlib.import_module(pkg), cls_name) |
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phone_encoder = build_phone_encoder(hparams['binary_data_dir']) |
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word_encoder = build_word_encoder(hparams['binary_data_dir']) |
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voice_encoder = VoiceEncoder().cuda() |
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encoder = [phone_encoder, word_encoder] |
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sizes = [] |
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items = [] |
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EmotionEncoder.load_model(hparams['emotion_encoder_path']) |
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preprocessor, preprocess_args = self.preprocessor, self.preprocess_args |
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for wav_fn in tqdm(inp_wav_paths): |
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item_name = wav_fn[len(test_input_dir) + 1:].replace("/", "_") |
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spk_id = emotion = 0 |
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item2tgfn = wav_fn.replace('.wav', '.TextGrid') |
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txtpath = wav_fn.replace('.wav', '.txt') |
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with open(txtpath, 'r') as f: |
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text_raw = f.readlines() |
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f.close() |
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ph, txt = preprocessor.txt_to_ph(preprocessor.txt_processor, text_raw[0], preprocess_args) |
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item = binarizer_cls.process_item(item_name, ph, txt, item2tgfn, wav_fn, spk_id, emotion, encoder, hparams['binarization_args']) |
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item['emo_embed'] = Embed_utterance(preprocess_wav(item['wav_fn'])) |
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item['spk_embed'] = voice_encoder.embed_utterance(item['wav']) |
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items.append(item) |
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sizes.append(item['len']) |
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return items, sizes |
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def _get_item(self, index): |
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if hasattr(self, 'avail_idxs') and self.avail_idxs is not None: |
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index = self.avail_idxs[index] |
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if self.indexed_ds is None: |
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self.indexed_ds = IndexedDataset(f'{self.data_dir}/{self.prefix}') |
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return self.indexed_ds[index] |
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def __getitem__(self, index): |
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hparams = self.hparams |
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item = self._get_item(index) |
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assert len(item['mel']) == self.sizes[index], (len(item['mel']), self.sizes[index]) |
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max_frames = hparams['max_frames'] |
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spec = torch.Tensor(item['mel'])[:max_frames] |
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max_frames = spec.shape[0] // hparams['frames_multiple'] * hparams['frames_multiple'] |
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spec = spec[:max_frames] |
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phone = torch.LongTensor(item['phone'][:hparams['max_input_tokens']]) |
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sample = { |
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"id": index, |
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"item_name": item['item_name'], |
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"text": item['txt'], |
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"txt_token": phone, |
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"mel": spec, |
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"mel_nonpadding": spec.abs().sum(-1) > 0, |
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} |
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spec = sample['mel'] |
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T = spec.shape[0] |
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sample['mel2ph'] = mel2ph = torch.LongTensor(item['mel2ph'])[:T] if 'mel2ph' in item else None |
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if hparams['use_pitch_embed']: |
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assert 'f0' in item |
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if hparams.get('normalize_pitch', False): |
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f0 = item["f0"] |
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if len(f0 > 0) > 0 and f0[f0 > 0].std() > 0: |
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f0[f0 > 0] = (f0[f0 > 0] - f0[f0 > 0].mean()) / f0[f0 > 0].std() * hparams['f0_std'] + \ |
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hparams['f0_mean'] |
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f0[f0 > 0] = f0[f0 > 0].clip(min=60, max=500) |
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pitch = f0_to_coarse(f0) |
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pitch = torch.LongTensor(pitch[:max_frames]) |
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else: |
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pitch = torch.LongTensor(item.get("pitch"))[:max_frames] if "pitch" in item else None |
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f0, uv = norm_interp_f0(item["f0"][:max_frames], hparams) |
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uv = torch.FloatTensor(uv) |
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f0 = torch.FloatTensor(f0) |
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else: |
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f0 = uv = torch.zeros_like(mel2ph) |
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pitch = None |
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sample["f0"], sample["uv"], sample["pitch"] = f0, uv, pitch |
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sample["spk_embed"] = torch.Tensor(item['spk_embed']) |
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sample["emotion"] = item['emotion'] |
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sample["emo_embed"] = torch.Tensor(item['emo_embed']) |
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if hparams.get('use_word', False): |
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sample["ph_words"] = item["ph_words"] |
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sample["word_tokens"] = torch.LongTensor(item["word_tokens"]) |
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sample["mel2word"] = torch.LongTensor(item.get("mel2word"))[:max_frames] |
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sample["ph2word"] = torch.LongTensor(item['ph2word'][:hparams['max_input_tokens']]) |
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return sample |
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def collater(self, samples): |
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if len(samples) == 0: |
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return {} |
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hparams = self.hparams |
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id = torch.LongTensor([s['id'] for s in samples]) |
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item_names = [s['item_name'] for s in samples] |
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text = [s['text'] for s in samples] |
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txt_tokens = utils.collate_1d([s['txt_token'] for s in samples], 0) |
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mels = utils.collate_2d([s['mel'] for s in samples], 0.0) |
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txt_lengths = torch.LongTensor([s['txt_token'].numel() for s in samples]) |
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mel_lengths = torch.LongTensor([s['mel'].shape[0] for s in samples]) |
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batch = { |
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'id': id, |
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'item_name': item_names, |
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'nsamples': len(samples), |
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'text': text, |
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'txt_tokens': txt_tokens, |
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'txt_lengths': txt_lengths, |
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'mels': mels, |
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'mel_lengths': mel_lengths, |
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} |
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f0 = utils.collate_1d([s['f0'] for s in samples], 0.0) |
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pitch = utils.collate_1d([s['pitch'] for s in samples]) if samples[0]['pitch'] is not None else None |
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uv = utils.collate_1d([s['uv'] for s in samples]) |
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mel2ph = utils.collate_1d([s['mel2ph'] for s in samples], 0.0) if samples[0]['mel2ph'] is not None else None |
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batch.update({ |
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'mel2ph': mel2ph, |
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'pitch': pitch, |
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'f0': f0, |
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'uv': uv, |
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}) |
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spk_embed = torch.stack([s['spk_embed'] for s in samples]) |
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batch['spk_embed'] = spk_embed |
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emo_embed = torch.stack([s['emo_embed'] for s in samples]) |
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batch['emo_embed'] = emo_embed |
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if hparams.get('use_word', False): |
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ph_words = [s['ph_words'] for s in samples] |
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batch['ph_words'] = ph_words |
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word_tokens = utils.collate_1d([s['word_tokens'] for s in samples], 0) |
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batch['word_tokens'] = word_tokens |
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mel2word = utils.collate_1d([s['mel2word'] for s in samples], 0) |
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batch['mel2word'] = mel2word |
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ph2word = utils.collate_1d([s['ph2word'] for s in samples], 0) |
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batch['ph2word'] = ph2word |
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return batch |
