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| import torch | |
| from inference.tts.base_tts_infer import BaseTTSInfer | |
| from modules.tts.syntaspeech.syntaspeech import SyntaSpeech | |
| from utils.commons.ckpt_utils import load_ckpt | |
| from utils.commons.hparams import hparams | |
| from modules.tts.syntaspeech.syntactic_graph_buider import Sentence2GraphParser | |
| class SyntaSpeechInfer(BaseTTSInfer): | |
| def __init__(self, hparams, device=None): | |
| super().__init__(hparams, device) | |
| if hparams['ds_name'] in ['biaobei']: | |
| self.syntactic_graph_builder = Sentence2GraphParser(language='zh') | |
| elif hparams['ds_name'] in ['ljspeech', 'libritts']: | |
| self.syntactic_graph_builder = Sentence2GraphParser(language='en') | |
| def build_model(self): | |
| ph_dict_size = len(self.ph_encoder) | |
| word_dict_size = len(self.word_encoder) | |
| model = SyntaSpeech(ph_dict_size, word_dict_size, self.hparams) | |
| load_ckpt(model, hparams['work_dir'], 'model') | |
| model.to(self.device) | |
| with torch.no_grad(): | |
| model.store_inverse_all() | |
| model.eval() | |
| return model | |
| def input_to_batch(self, item): | |
| item_names = [item['item_name']] | |
| text = [item['text']] | |
| ph = [item['ph']] | |
| txt_tokens = torch.LongTensor(item['ph_token'])[None, :].to(self.device) | |
| txt_lengths = torch.LongTensor([txt_tokens.shape[1]]).to(self.device) | |
| word_tokens = torch.LongTensor(item['word_token'])[None, :].to(self.device) | |
| word_lengths = torch.LongTensor([word_tokens.shape[1]]).to(self.device) | |
| ph2word = torch.LongTensor(item['ph2word'])[None, :].to(self.device) | |
| spk_ids = torch.LongTensor(item['spk_id'])[None, :].to(self.device) | |
| dgl_graph, etypes = self.syntactic_graph_builder.parse(item['text'], words=item['words'].split(" "), ph_words=item['ph_words'].split(" ")) | |
| dgl_graph = dgl_graph.to(self.device) | |
| etypes = etypes.to(self.device) | |
| batch = { | |
| 'item_name': item_names, | |
| 'text': text, | |
| 'ph': ph, | |
| 'txt_tokens': txt_tokens, | |
| 'txt_lengths': txt_lengths, | |
| 'word_tokens': word_tokens, | |
| 'word_lengths': word_lengths, | |
| 'ph2word': ph2word, | |
| 'spk_ids': spk_ids, | |
| 'graph_lst': [dgl_graph], | |
| 'etypes_lst': [etypes] | |
| } | |
| return batch | |
| def forward_model(self, inp): | |
| sample = self.input_to_batch(inp) | |
| with torch.no_grad(): | |
| output = self.model( | |
| sample['txt_tokens'], | |
| sample['word_tokens'], | |
| ph2word=sample['ph2word'], | |
| word_len=sample['word_lengths'].max(), | |
| infer=True, | |
| forward_post_glow=True, | |
| spk_id=sample.get('spk_ids'), | |
| graph_lst=sample['graph_lst'], | |
| etypes_lst=sample['etypes_lst'] | |
| ) | |
| mel_out = output['mel_out'] | |
| wav_out = self.run_vocoder(mel_out) | |
| wav_out = wav_out.cpu().numpy() | |
| return wav_out[0] | |
| if __name__ == '__main__': | |
| SyntaSpeechInfer.example_run() | |