go all in on m2wv3
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@ -78,7 +78,6 @@ class MusicDiffusionFid(evaluator.Evaluator):
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self.spectral_diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [16]), model_mean_type='epsilon',
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self.spectral_diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [16]), model_mean_type='epsilon',
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model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', 4000),
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model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', 4000),
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conditioning_free=False, conditioning_free_k=1)
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conditioning_free=False, conditioning_free_k=1)
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self.spec_decoder = get_mel2wav_v3_model() # The only reason the other functions don't use v3 is because earlier models were trained with v1 and I want to keep metrics consistent.
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self.local_modules['spec_decoder'] = self.spec_decoder
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self.local_modules['spec_decoder'] = self.spec_decoder
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elif 'from_ar_prior' == mode:
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elif 'from_ar_prior' == mode:
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self.diffusion_fn = self.perform_diffusion_from_codes_ar_prior
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self.diffusion_fn = self.perform_diffusion_from_codes_ar_prior
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@ -89,15 +88,12 @@ class MusicDiffusionFid(evaluator.Evaluator):
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conditioning_free=True, conditioning_free_k=1)
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conditioning_free=True, conditioning_free_k=1)
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self.kmeans_inj = KmeansQuantizerInjector({'centroids': '../experiments/music_k_means_centroids.pth', 'in': 'in', 'out': 'out'}, {})
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self.kmeans_inj = KmeansQuantizerInjector({'centroids': '../experiments/music_k_means_centroids.pth', 'in': 'in', 'out': 'out'}, {})
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self.local_modules['ar_prior'] = get_ar_prior()
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self.local_modules['ar_prior'] = get_ar_prior()
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self.spec_decoder = get_mel2wav_v3_model()
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self.local_modules['spec_decoder'] = self.spec_decoder
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self.local_modules['spec_decoder'] = self.spec_decoder
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elif 'chained_sr' == mode:
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elif 'chained_sr' == mode:
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self.diffusion_fn = self.perform_chained_sr
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self.diffusion_fn = self.perform_chained_sr
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self.spec_decoder = get_mel2wav_v3_model()
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self.local_modules['spec_decoder'] = self.spec_decoder
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if not hasattr(self, 'spec_decoder'):
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self.spec_decoder = get_mel2wav_model()
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self.local_modules['spec_decoder'] = self.spec_decoder
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self.local_modules['spec_decoder'] = self.spec_decoder
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self.spec_decoder = get_mel2wav_v3_model()
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self.local_modules['spec_decoder'] = self.spec_decoder
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self.spec_fn = TorchMelSpectrogramInjector({'n_mel_channels': 256, 'mel_fmax': 11000, 'filter_length': 16000,
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self.spec_fn = TorchMelSpectrogramInjector({'n_mel_channels': 256, 'mel_fmax': 11000, 'filter_length': 16000,
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'normalize': True, 'in': 'in', 'out': 'out'}, {})
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'normalize': True, 'in': 'in', 'out': 'out'}, {})
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