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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,12 +1,12 @@ | ||
| title: 'NATSpeech/PortaSpeech' | ||
| title: 'yerfor/SyntaSpeech' | ||
| description: | | ||
| Gradio demo for NATSpeech/PortaSpeech. To use it, simply add your audio, or click one of the examples to load them. Note: This space is running on CPU, inference times will be higher. | ||
| Gradio demo for yerfor/SyntaSpeech. To use it, simply add your audio, or click one of the examples to load them. Note: This space is running on CPU, inference times will be higher. | ||
| article: | | ||
| Link to <a href='https://github.com/NATSpeech/NATSpeech/blob/main/docs/portaspeech.md' style='color:blue;' target='_blank\'>Github REPO</a> | ||
| Link to <a href='https://github.com/yerfor/SyntaSpeech' style='color:blue;' target='_blank\'>Github REPO</a> | ||
| example_inputs: | ||
| - |- | ||
| the invention of movable metal letters in the middle of the fifteenth century may justly be considered as the invention of the art of printing. | ||
| - |- | ||
| produced the block books, which were the immediate predecessors of the true printed book, | ||
| inference_cls: inference.tts.ps_flow.PortaSpeechFlowInfer | ||
| exp_name: ps_normal_exp | ||
| inference_cls: inference.tts.synta.SyntaSpeechInfer | ||
| exp_name: lj_synta |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,76 @@ | ||
| 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() |