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readd one mdf function

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This commit is contained in:
James Betker 2022-07-19 15:04:36 -06:00
parent c00398e955
commit 17a07b2e33
1 changed files with 30 additions and 0 deletions
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codes/trainer/eval/music_diffusion_fid.py
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@ -70,6 +70,8 @@ class MusicDiffusionFid(evaluator.Evaluator):
elif 'from_codes' == mode: elif 'from_codes' == mode:
self.diffusion_fn = self.perform_diffusion_from_codes self.diffusion_fn = self.perform_diffusion_from_codes
self.local_modules['codegen'] = get_music_codegen() self.local_modules['codegen'] = get_music_codegen()
elif 'from_codes_quant' == mode:
self.diffusion_fn = self.perform_diffusion_from_codes_quant
elif 'cheater_gen' == mode: elif 'cheater_gen' == mode:
self.diffusion_fn = self.perform_reconstruction_from_cheater_gen self.diffusion_fn = self.perform_reconstruction_from_cheater_gen
self.local_modules['cheater_encoder'] = get_cheater_encoder() self.local_modules['cheater_encoder'] = get_cheater_encoder()
@ -137,6 +139,34 @@ class MusicDiffusionFid(evaluator.Evaluator):
return gen_wav, real_resampled, gen_mel, mel_norm, sample_rate return gen_wav, real_resampled, gen_mel, mel_norm, sample_rate
def perform_diffusion_from_codes_quant(self, audio, sample_rate=22050):
real_resampled = audio
audio = audio.unsqueeze(0)
mel = self.spec_fn({'in': audio})['out']
mel_norm = normalize_mel(mel)
#def denoising_fn(x):
# q9 = torch.quantile(x, q=.95, dim=-1).unsqueeze(-1)
# s = q9.clamp(1, 9999999999)
# x = x.clamp(-s, s) / s
# return x
gen_mel = self.diffuser.p_sample_loop(self.model, mel_norm.shape, #denoised_fn=denoising_fn, clip_denoised=False,
model_kwargs={'truth_mel': mel_norm,
'conditioning_input': mel_norm,
'disable_diversity': True})
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.spectral_diffuser.p_sample_loop(self.spec_decoder, output_shape,
model_kwargs={'aligned_conditioning': gen_mel_denorm})
gen_wav = pixel_shuffle_1d(gen_wav, 16)
real_wav = self.spectral_diffuser.p_sample_loop(self.spec_decoder, output_shape,
model_kwargs={'aligned_conditioning': mel})
real_wav = pixel_shuffle_1d(real_wav, 16)
return gen_wav, real_wav.squeeze(0), gen_mel, mel_norm, sample_rate
def perform_reconstruction_from_cheater_gen(self, audio, sample_rate=22050): def perform_reconstruction_from_cheater_gen(self, audio, sample_rate=22050):
audio = audio.unsqueeze(0) audio = audio.unsqueeze(0)