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import os
import torch
from modules.vocoder.hifigan.hifigan import HifiGanGenerator
from tasks.tts.dataset_utils import FastSpeechWordDataset
from tasks.tts.tts_utils import load_data_preprocessor
from utils.commons.ckpt_utils import load_ckpt
from utils.commons.hparams import set_hparams
class BaseTTSInfer:
def __init__(self, hparams, device=None):
if device is None:
device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.hparams = hparams
self.device = device
self.data_dir = hparams['binary_data_dir']
self.preprocessor, self.preprocess_args = load_data_preprocessor()
self.ph_encoder, self.word_encoder = self.preprocessor.load_dict(self.data_dir)
self.spk_map = self.preprocessor.load_spk_map(self.data_dir)
self.ds_cls = FastSpeechWordDataset
self.model = self.build_model()
self.model.eval()
self.model.to(self.device)
self.vocoder = self.build_vocoder()
self.vocoder.eval()
self.vocoder.to(self.device)
def build_model(self):
raise NotImplementedError
def forward_model(self, inp):
raise NotImplementedError
def build_vocoder(self):
base_dir = self.hparams['vocoder_ckpt']
config_path = f'{base_dir}/config.yaml'
config = set_hparams(config_path, global_hparams=False)
vocoder = HifiGanGenerator(config)
load_ckpt(vocoder, base_dir, 'model_gen')
return vocoder
def run_vocoder(self, c):
c = c.transpose(2, 1)
y = self.vocoder(c)[:, 0]
return y
def preprocess_input(self, inp):
"""
:param inp: {'text': str, 'item_name': (str, optional), 'spk_name': (str, optional)}
:return:
"""
preprocessor, preprocess_args = self.preprocessor, self.preprocess_args
text_raw = inp['text']
item_name = inp.get('item_name', '<ITEM_NAME>')
spk_name = inp.get('spk_name', '<SINGLE_SPK>')
ph, txt, word, ph2word, ph_gb_word = preprocessor.txt_to_ph(
preprocessor.txt_processor, text_raw, preprocess_args)
word_token = self.word_encoder.encode(word)
ph_token = self.ph_encoder.encode(ph)
spk_id = self.spk_map[spk_name]
item = {'item_name': item_name, 'text': txt, 'ph': ph, 'spk_id': spk_id,
'ph_token': ph_token, 'word_token': word_token, 'ph2word': ph2word,
'ph_words':ph_gb_word, 'words': word}
item['ph_len'] = len(item['ph_token'])
return item
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([txt_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)
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,
}
return batch
def postprocess_output(self, output):
return output
def infer_once(self, inp):
inp = self.preprocess_input(inp)
output = self.forward_model(inp)
output = self.postprocess_output(output)
return output
@classmethod
def example_run(cls):
from utils.commons.hparams import set_hparams
from utils.commons.hparams import hparams as hp
from utils.audio.io import save_wav
set_hparams()
if hp['ds_name'] in ['lj', 'libritts']:
inp = {
'text': '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.'
}
elif hp['ds_name'] in ['biaobei']:
inp = {
'text': '如果我想你三遍，天上乌云就散一片。'
}
infer_ins = cls(hp)
out = infer_ins.infer_once(inp)
os.makedirs('infer_out', exist_ok=True)
save_wav(out, f'infer_out/example_out.wav', hp['audio_sample_rate'])
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