forked from mrq/DL-Art-School
music diffusion fid adjustments
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@ -62,9 +62,11 @@ class MusicDiffusionFid(evaluator.Evaluator):
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real_resampled = torchaudio.functional.resample(audio, 22050, sample_rate).unsqueeze(0)
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else:
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real_resampled = audio
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mel = self.spec_fn({'in': real_resampled})['out']
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output_shape = (1, 1, mel.shape[-1] * 256)
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gen = self.diffuser.p_sample_loop(self.model, output_shape, model_kwargs={'aligned_conditioning': mel})
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audio = audio.unsqueeze(0)
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output_shape = audio.shape
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gen = self.diffuser.p_sample_loop(self.model, output_shape, model_kwargs={'conditioning': audio,
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'return_surrogate': False})
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#_, surrogate = self.model(audio, torch.tensor([0], device=audio.device), audio)
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return gen, real_resampled, sample_rate
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def load_projector(self):
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@ -139,9 +141,9 @@ 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_waveform_gen.yml', 'generator',
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diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_waveform_gen2.yml', 'generator',
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also_load_savepoint=False,
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load_path='X:\\dlas\\experiments\\train_music_waveform_gen_r3\\models\\11200_generator_ema.pth').cuda()
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load_path='X:\\dlas\\experiments\\train_music_waveform_gen2\\models\\59000_generator_ema.pth').cuda()
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opt_eval = {'path': 'Y:\\split\\yt-music-eval', 'diffusion_steps': 500,
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'conditioning_free': False, 'conditioning_free_k': 1,
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'diffusion_schedule': 'linear', 'diffusion_type': 'standard'}
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