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resample real inputs for music_diffusion_fid

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James Betker 2022-06-18 10:40:48 -06:00
parent c000e489fa
commit cb7569ee5e
1 changed files with 10 additions and 6 deletions
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16
codes/trainer/eval/music_diffusion_fid.py
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@ -52,7 +52,7 @@ class MusicDiffusionFid(evaluator.Evaluator):
model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', 4000), model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', 4000),
conditioning_free=False, conditioning_free_k=1) conditioning_free=False, conditioning_free_k=1)
self.dev = self.env['device'] self.dev = self.env['device']
mode = opt_get(opt_eval, ['diffusion_type'], 'tts') mode = opt_get(opt_eval, ['diffusion_type'], 'spec_decode')
self.spec_decoder = get_mel2wav_model() self.spec_decoder = get_mel2wav_model()
self.projector = ContrastiveAudio(model_dim=512, transformer_heads=8, dropout=0, encoder_depth=8, mel_channels=256) self.projector = ContrastiveAudio(model_dim=512, transformer_heads=8, dropout=0, encoder_depth=8, mel_channels=256)
@ -141,7 +141,11 @@ class MusicDiffusionFid(evaluator.Evaluator):
model_kwargs={'aligned_conditioning': gen_mel_denorm}) model_kwargs={'aligned_conditioning': gen_mel_denorm})
gen_wav = pixel_shuffle_1d(gen_wav, 16) gen_wav = pixel_shuffle_1d(gen_wav, 16)
return gen_wav, real_resampled, gen_mel, mel_norm, sample_rate 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_partial_diffusion_from_codes_quant(self, audio, sample_rate=22050, partial_low=0, partial_high=256): def perform_partial_diffusion_from_codes_quant(self, audio, sample_rate=22050, partial_low=0, partial_high=256):
if sample_rate != sample_rate: if sample_rate != sample_rate:
@ -271,15 +275,15 @@ class MusicDiffusionFid(evaluator.Evaluator):
if __name__ == '__main__': if __name__ == '__main__':
diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_diffusion_tfd_quant.yml', 'generator', diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_diffusion_tfd_quant.yml', 'generator',
also_load_savepoint=False, also_load_savepoint=False,
load_path='X:\\dlas\\experiments\\train_music_diffusion_tfd11\\models\\24000_generator_ema.pth' load_path='X:\\dlas\\experiments\\train_music_diffusion_tfd12\\models\\41500_generator_ema.pth'
).cuda() ).cuda()
opt_eval = {'path': 'Y:\\split\\yt-music-eval', # eval music, mostly electronica. :) 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. #'path': 'E:\\music_eval', # this is music from the training dataset, including a lot more variety.
'diffusion_steps': 200, 'diffusion_steps': 200,
'conditioning_free': False, 'conditioning_free_k': 1, 'conditioning_free': True, 'conditioning_free_k': 2,
'diffusion_schedule': 'cosine', 'diffusion_type': 'from_codes_quant', 'diffusion_schedule': 'linear', 'diffusion_type': 'from_codes_quant',
#'partial_low': 128, 'partial_high': 192 #'partial_low': 128, 'partial_high': 192
} }
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 600, 'device': 'cuda', 'opt': {}} env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 605, 'device': 'cuda', 'opt': {}}
eval = MusicDiffusionFid(diffusion, opt_eval, env) eval = MusicDiffusionFid(diffusion, opt_eval, env)
print(eval.perform_eval()) print(eval.perform_eval())