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Restore old MDF functionality for cheater gen

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This commit is contained in:
James Betker 2022-07-09 08:01:32 -06:00
parent 694400a45b
commit 5138d61767
1 changed files with 18 additions and 31 deletions
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49
codes/trainer/eval/music_diffusion_fid.py
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@ -223,36 +223,23 @@ class MusicDiffusionFid(evaluator.Evaluator):
cheater = self.local_modules['cheater_encoder'].to(audio.device)(mel_norm)
# 1. Generate the cheater latent using the input as a reference.
gen_cheater = self.diffuser.ddim_sample_loop(self.model, cheater.shape, progress=True,
model_kwargs={'conditioning_input': cheater},
causal=self.causal, causal_slope=self.causal_slope)
gen_cheater = self.diffuser.ddim_sample_loop(self.model, cheater.shape, progress=True, model_kwargs={'conditioning_input': cheater})
# 2. Decode the cheater into a MEL. This operation and the next need to be chunked to make them feasible to perform within GPU memory.
chunks = torch.split(gen_cheater, 64, dim=-1)
gen_mels = []
gen_wavs = []
for chunk in tqdm(chunks):
gen_mel = self.cheater_decoder_diffuser.ddim_sample_loop(self.local_modules['cheater_decoder'].diff.to(audio.device), (1,256,chunk.shape[-1]*16), progress=True,
model_kwargs={'codes': chunk.permute(0,2,1)})
gen_mels.append(gen_mel)
# 2. Decode the cheater into a MEL
gen_mel = self.cheater_decoder_diffuser.ddim_sample_loop(self.local_modules['cheater_decoder'].diff.to(audio.device), (1,256,gen_cheater.shape[-1]*16), progress=True,
model_kwargs={'codes': gen_cheater.permute(0,2,1)})
# 3. And then the MEL back into a spectrogram
output_shape = (1,16,audio.shape[-1]//(16*len(chunks)))
self.spec_decoder = self.spec_decoder.to(audio.device)
gen_mel_denorm = denormalize_mel(gen_mel)
gen_wav = self.spectral_diffuser.p_sample_loop(self.spec_decoder, output_shape,
model_kwargs={'codes': gen_mel_denorm})
gen_wav = pixel_shuffle_1d(gen_wav, 16)
gen_wavs.append(gen_wav)
gen_mel = torch.cat(gen_mels, dim=-1)
gen_wav = torch.cat(gen_wavs, dim=-1)
# 3. And then the MEL back into a spectrogram
output_shape = (1,16,audio.shape[-1]//16)
self.spec_decoder = self.spec_decoder.to(audio.device)
gen_mel_denorm = denormalize_mel(gen_mel)
gen_wav = self.spectral_diffuser.p_sample_loop(self.spec_decoder, output_shape,
model_kwargs={'codes': gen_mel_denorm})
gen_wav = pixel_shuffle_1d(gen_wav, 16)
if audio.shape[-1] < 40 * 22050:
real_wav = self.spectral_diffuser.p_sample_loop(self.spec_decoder, output_shape,
model_kwargs={'codes': mel})
real_wav = pixel_shuffle_1d(real_wav, 16)
else:
real_wav = audio # TODO: chunk like above.
real_wav = self.spectral_diffuser.p_sample_loop(self.spec_decoder, output_shape,
model_kwargs={'codes': mel})
real_wav = pixel_shuffle_1d(real_wav, 16)
return gen_wav, real_wav.squeeze(0), gen_mel, mel_norm, sample_rate
@ -432,18 +419,18 @@ class MusicDiffusionFid(evaluator.Evaluator):
if __name__ == '__main__':
diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_cheater_gen_r8.yml', 'generator',
diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_cheater_gen_v5\\train.yml', 'generator',
also_load_savepoint=False,
load_path='X:\\dlas\\experiments\\train_music_cheater_gen_v5_causal\\models\\1000_generator.pth'
load_path='X:\\dlas\\experiments\\train_music_cheater_gen_v5\\models\\206000_generator_ema.pth'
).cuda()
opt_eval = {'path': 'Y:\\split\\yt-music-eval', # eval music, mostly electronica. :)
#'path': 'E:\\music_eval', # this is music from the training dataset, including a lot more variety.
'diffusion_steps': 64,
'conditioning_free': True, 'conditioning_free_k': 1, 'use_ddim': True, 'clip_audio': False,
'diffusion_schedule': 'linear', 'diffusion_type': 'cheater_gen',
'causal': True, 'causal_slope': 4,
#'causal': True, 'causal_slope': 4,
#'partial_low': 128, 'partial_high': 192
}
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 232, 'device': 'cuda', 'opt': {}}
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 235, 'device': 'cuda', 'opt': {}}
eval = MusicDiffusionFid(diffusion, opt_eval, env)
print(eval.perform_eval())