import torch # from inference.tts.fs import FastSpeechInfer # from modules.tts.fs2_orig import FastSpeech2Orig from inference.svs.base_svs_infer import BaseSVSInfer from utils import load_ckpt from utils.hparams import hparams from usr.diff.shallow_diffusion_tts import GaussianDiffusion from usr.diffsinger_task import DIFF_DECODERS from modules.fastspeech.pe import PitchExtractor import utils class DiffSingerE2EInfer(BaseSVSInfer): def build_model(self): model = GaussianDiffusion( phone_encoder=self.ph_encoder, out_dims=hparams['audio_num_mel_bins'], denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams), timesteps=hparams['timesteps'], K_step=hparams['K_step'], loss_type=hparams['diff_loss_type'], spec_min=hparams['spec_min'], spec_max=hparams['spec_max'], ) model.eval() load_ckpt(model, hparams['work_dir'], 'model') if hparams.get('pe_enable') is not None and hparams['pe_enable']: self.pe = PitchExtractor().to(self.device) utils.load_ckpt(self.pe, hparams['pe_ckpt'], 'model', strict=True) self.pe.eval() return model def forward_model(self, inp): sample = self.input_to_batch(inp) txt_tokens = sample['txt_tokens'] # [B, T_t] spk_id = sample.get('spk_ids') with torch.no_grad(): output = self.model(txt_tokens, spk_id=spk_id, ref_mels=None, infer=True, pitch_midi=sample['pitch_midi'], midi_dur=sample['midi_dur'], is_slur=sample['is_slur']) mel_out = output['mel_out'] # [B, T,80] if hparams.get('pe_enable') is not None and hparams['pe_enable']: f0_pred = self.pe(mel_out)['f0_denorm_pred'] # pe predict from Pred mel else: f0_pred = output['f0_denorm'] wav_out = self.run_vocoder(mel_out, f0=f0_pred) wav_out = wav_out.cpu().numpy() return wav_out[0] if __name__ == '__main__': inp = { 'text': '小酒窝长睫毛AP是你最美的记号', 'notes': 'C#4/Db4 | F#4/Gb4 | G#4/Ab4 | A#4/Bb4 F#4/Gb4 | F#4/Gb4 C#4/Db4 | C#4/Db4 | rest | C#4/Db4 | A#4/Bb4 | G#4/Ab4 | A#4/Bb4 | G#4/Ab4 | F4 | C#4/Db4', 'notes_duration': '0.407140 | 0.376190 | 0.242180 | 0.509550 0.183420 | 0.315400 0.235020 | 0.361660 | 0.223070 | 0.377270 | 0.340550 | 0.299620 | 0.344510 | 0.283770 | 0.323390 | 0.360340', 'input_type': 'word' } # user input: Chinese characters c = { 'text': '小酒窝长睫毛AP是你最美的记号', 'ph_seq': 'x iao j iu w o ch ang ang j ie ie m ao AP sh i n i z ui m ei d e j i h ao', 'note_seq': 'C#4/Db4 C#4/Db4 F#4/Gb4 F#4/Gb4 G#4/Ab4 G#4/Ab4 A#4/Bb4 A#4/Bb4 F#4/Gb4 F#4/Gb4 F#4/Gb4 C#4/Db4 C#4/Db4 C#4/Db4 rest C#4/Db4 C#4/Db4 A#4/Bb4 A#4/Bb4 G#4/Ab4 G#4/Ab4 A#4/Bb4 A#4/Bb4 G#4/Ab4 G#4/Ab4 F4 F4 C#4/Db4 C#4/Db4', 'note_dur_seq': '0.407140 0.407140 0.376190 0.376190 0.242180 0.242180 0.509550 0.509550 0.183420 0.315400 0.315400 0.235020 0.361660 0.361660 0.223070 0.377270 0.377270 0.340550 0.340550 0.299620 0.299620 0.344510 0.344510 0.283770 0.283770 0.323390 0.323390 0.360340 0.360340', 'is_slur_seq': '0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0', 'input_type': 'phoneme' } # input like Opencpop dataset. DiffSingerE2EInfer.example_run(inp) # python inference/svs/ds_e2e.py --config usr/configs/midi/e2e/opencpop/ds100_adj_rel.yaml --exp_name 0228_opencpop_ds100_rel