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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()
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