import torch from inference.base_tts_infer import BaseTTSInfer from utils.ckpt_utils import load_ckpt, get_last_checkpoint from utils.hparams import hparams from modules.GenerSpeech.model.generspeech import GenerSpeech from usr.diff.net import DiffNet import os import numpy as np from functools import partial class GenerSpeechInfer(BaseTTSInfer): def build_model(self): f0_stats_fn = f'{hparams["binary_data_dir"]}/train_f0s_mean_std.npy' if os.path.exists(f0_stats_fn): hparams['f0_mean'], hparams['f0_std'] = np.load(f0_stats_fn) hparams['f0_mean'] = float(hparams['f0_mean']) hparams['f0_std'] = float(hparams['f0_std']) model = GenerSpeech(self.ph_encoder) model.eval() load_ckpt(model, hparams['work_dir'], 'model') return model def forward_model(self, inp): sample = self.input_to_batch(inp) txt_tokens = sample['txt_tokens'] # [B, T_t] with torch.no_grad(): output = self.model(txt_tokens, ref_mel2ph=sample['mel2ph'], ref_mel2word=sample['mel2word'], ref_mels=sample['mels'], spk_embed=sample['spk_embed'], emo_embed=sample['emo_embed'], global_steps=300000, infer=True) mel_out = output['mel_out'] wav_out = self.run_vocoder(mel_out) wav_out = wav_out.squeeze().cpu().numpy() return wav_out if __name__ == '__main__': GenerSpeechInfer.example_run()