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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 modules.diff.shallow_diffusion_tts import GaussianDiffusion
from tasks.svs.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
inp = {
'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)
# CUDA_VISIBLE_DEVICES=3 python inference/svs/ds_e2e.py --config egs/egs_bases/svs/midi/e2e/opencpop/ds100_adj_rel.yaml --exp_name 0228_opencpop_ds100_rel |