Restore causal decoding
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@ -253,7 +253,8 @@ class MusicDiffusionFid(evaluator.Evaluator):
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cheater_codes = self.kmeans_inj({'in': cheater})['out']
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ar_latent = self.local_modules['ar_prior'].to(audio.device)(cheater_codes, cheater, return_latent=True)
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gen_mel = self.diffuser.ddim_sample_loop(self.model, mel_norm.shape, model_kwargs={'codes': ar_latent}, progress=True)
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gen_mel = self.diffuser.ddim_sample_loop(self.model, mel_norm.shape, model_kwargs={'codes': ar_latent},
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causal=self.causal, causal_slope=self.causal_slope, progress=True)
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gen_mel_denorm = denormalize_mel(gen_mel)
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output_shape = (1,16,audio.shape[-1]//16)
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@ -419,18 +420,18 @@ class MusicDiffusionFid(evaluator.Evaluator):
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if __name__ == '__main__':
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diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_cheater_gen_v5\\train.yml', 'generator',
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diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_cheater_gen.yml', 'generator',
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also_load_savepoint=False,
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load_path='X:\\dlas\\experiments\\train_music_cheater_gen_v5\\models\\206000_generator_ema.pth'
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load_path='X:\\dlas\\experiments\\train_music_cheater_gen_v5_causal_retrain\\models\\12000_generator.pth'
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).cuda()
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opt_eval = {'path': 'Y:\\split\\yt-music-eval', # eval music, mostly electronica. :)
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#'path': 'E:\\music_eval', # this is music from the training dataset, including a lot more variety.
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'diffusion_steps': 64,
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'conditioning_free': True, 'conditioning_free_k': 1, 'use_ddim': True, 'clip_audio': False,
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'diffusion_schedule': 'linear', 'diffusion_type': 'cheater_gen',
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#'causal': True, 'causal_slope': 4,
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'causal': True, 'causal_slope': 4,
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#'partial_low': 128, 'partial_high': 192
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}
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env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 235, 'device': 'cuda', 'opt': {}}
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env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 236, 'device': 'cuda', 'opt': {}}
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eval = MusicDiffusionFid(diffusion, opt_eval, env)
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print(eval.perform_eval())
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