wehub-resource-sync
67 lines
3.5 KiB
Python
67 lines
3.5 KiB
Python
import torch
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# from inference.tts.fs import FastSpeechInfer
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# from modules.tts.fs2_orig import FastSpeech2Orig
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from inference.svs.base_svs_infer import BaseSVSInfer
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from utils import load_ckpt
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from utils.hparams import hparams
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from usr.diff.shallow_diffusion_tts import GaussianDiffusion
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from usr.diffsinger_task import DIFF_DECODERS
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from modules.fastspeech.pe import PitchExtractor
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import utils
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class DiffSingerE2EInfer(BaseSVSInfer):
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def build_model(self):
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model = GaussianDiffusion(
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phone_encoder=self.ph_encoder,
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out_dims=hparams['audio_num_mel_bins'], denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams),
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timesteps=hparams['timesteps'],
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K_step=hparams['K_step'],
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loss_type=hparams['diff_loss_type'],
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spec_min=hparams['spec_min'], spec_max=hparams['spec_max'],
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)
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model.eval()
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load_ckpt(model, hparams['work_dir'], 'model')
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if hparams.get('pe_enable') is not None and hparams['pe_enable']:
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self.pe = PitchExtractor().to(self.device)
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utils.load_ckpt(self.pe, hparams['pe_ckpt'], 'model', strict=True)
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self.pe.eval()
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return model
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def forward_model(self, inp):
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sample = self.input_to_batch(inp)
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txt_tokens = sample['txt_tokens'] # [B, T_t]
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spk_id = sample.get('spk_ids')
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with torch.no_grad():
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output = self.model(txt_tokens, spk_id=spk_id, ref_mels=None, infer=True,
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pitch_midi=sample['pitch_midi'], midi_dur=sample['midi_dur'],
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is_slur=sample['is_slur'])
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mel_out = output['mel_out'] # [B, T,80]
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if hparams.get('pe_enable') is not None and hparams['pe_enable']:
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f0_pred = self.pe(mel_out)['f0_denorm_pred'] # pe predict from Pred mel
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else:
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f0_pred = output['f0_denorm']
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wav_out = self.run_vocoder(mel_out, f0=f0_pred)
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wav_out = wav_out.cpu().numpy()
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return wav_out[0]
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if __name__ == '__main__':
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inp = {
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'text': '小酒窝长睫毛AP是你最美的记号',
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'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',
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'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',
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'input_type': 'word'
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} # user input: Chinese characters
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c = {
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'text': '小酒窝长睫毛AP是你最美的记号',
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'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',
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'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',
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'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',
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'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',
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'input_type': 'phoneme'
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} # input like Opencpop dataset.
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DiffSingerE2EInfer.example_run(inp)
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# python inference/svs/ds_e2e.py --config usr/configs/midi/e2e/opencpop/ds100_adj_rel.yaml --exp_name 0228_opencpop_ds100_rel |