deviandice/DL-Art-School
1
0
Fork 0
forked from mrq/DL-Art-School
Code Issues Pull Requests Packages Projects Releases Wiki Activity

mdf for shortened mel test

Browse Source
This commit is contained in:
James Betker 2022-05-22 19:29:20 -06:00
parent 1095248caf
commit c0bc466aad
1 changed files with 13 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

24
codes/trainer/eval/music_diffusion_fid.py
View File

@ -68,6 +68,7 @@ class MusicDiffusionFid(evaluator.Evaluator):
self.diffusion_fn = self.perform_diffusion_from_codes
self.local_modules['codegen'] = get_music_codegen()
self.spec_fn = TorchMelSpectrogramInjector({'n_mel_channels': 256, 'mel_fmax': 22000, 'normalize': True, 'in': 'in', 'out': 'out'}, {})
self.spec_100_fn = TorchMelSpectrogramInjector({'n_mel_channels': 100, 'mel_fmax': 22000, 'normalize': True, 'in': 'in', 'out': 'out'}, {})
def load_data(self, path):
return list(glob(f'{path}/*.wav'))
@ -166,20 +167,21 @@ class MusicDiffusionFid(evaluator.Evaluator):
mel = self.spec_fn({'in': audio})['out']
codegen = self.local_modules['codegen'].to(mel.device)
codes = codegen.get_codes(mel)
mel_norm = normalize_mel(mel)
precomputed_codes, precomputed_cond = self.model.timestep_independent(codes=codes, conditioning_input=mel_norm[:,:,:112],
expected_seq_len=mel_norm.shape[-1], return_code_pred=False)
gen_mel = self.diffuser.p_sample_loop(self.model, mel_norm.shape, noise=torch.zeros_like(mel_norm),
mel100 = self.spec_100_fn({'in': audio})['out']
mel100_norm = normalize_mel(mel100)
precomputed_codes, precomputed_cond = self.model.timestep_independent(codes=codes, conditioning_input=mel100_norm[:,:,:112],
expected_seq_len=mel100_norm.shape[-1], return_code_pred=False)
gen_mel = self.diffuser.p_sample_loop(self.model, mel100_norm.shape,
model_kwargs={'precomputed_code_embeddings': precomputed_codes, 'precomputed_cond_embeddings': precomputed_cond})
gen_mel_denorm = denormalize_mel(gen_mel)
output_shape = (1,16,audio.shape[-1]//16)
self.spec_decoder = self.spec_decoder.to(audio.device)
gen_wav = self.diffuser.p_sample_loop(self.spec_decoder, output_shape, model_kwargs={'aligned_conditioning': gen_mel_denorm})
gen_wav = pixel_shuffle_1d(gen_wav, 16)
return gen_wav, real_resampled, gen_mel, mel_norm, sample_rate
#gen_mel_denorm = denormalize_mel(gen_mel)
#output_shape = (1,16,audio.shape[-1]//16)
#self.spec_decoder = self.spec_decoder.to(audio.device)
#gen_wav = self.diffuser.p_sample_loop(self.spec_decoder, output_shape, model_kwargs={'aligned_conditioning': gen_mel_denorm})
#gen_wav = pixel_shuffle_1d(gen_wav, 16)
#return gen_wav, real_resampled, gen_mel, mel_norm, sample_rate
return real_resampled, real_resampled, gen_mel, mel100_norm, sample_rate
def project(self, sample, sample_rate):
sample = torchaudio.functional.resample(sample, sample_rate, 22050)