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