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This commit is contained in:
James Betker 2022-05-23 10:38:28 -06:00
parent 874de1775d
commit f4a97ca0a7
1 changed files with 4 additions and 3 deletions
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7
codes/trainer/eval/music_diffusion_fid.py
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@ -68,7 +68,8 @@ class MusicDiffusionFid(evaluator.Evaluator):
elif 'from_codes' == mode: elif 'from_codes' == mode:
self.diffusion_fn = self.perform_diffusion_from_codes self.diffusion_fn = self.perform_diffusion_from_codes
self.local_modules['codegen'] = get_music_codegen() self.local_modules['codegen'] = get_music_codegen()
self.spec_fn = TorchMelSpectrogramInjector({'n_mel_channels': 256, 'mel_fmax': 11000, 'filter_length': 16000, 'normalize': True, 'in': 'in', 'out': 'out'}, {}) self.spec_fn = TorchMelSpectrogramInjector({'n_mel_channels': 256, 'mel_fmax': 11000, 'filter_length': 16000,
'normalize': True, 'do_normalization': True, 'in': 'in', 'out': 'out'}, {})
def load_data(self, path): def load_data(self, path):
return list(glob(f'{path}/*.wav')) return list(glob(f'{path}/*.wav'))
@ -85,7 +86,7 @@ class MusicDiffusionFid(evaluator.Evaluator):
model_kwargs={'aligned_conditioning': mel}) model_kwargs={'aligned_conditioning': mel})
gen = pixel_shuffle_1d(gen, 16) gen = pixel_shuffle_1d(gen, 16)
return gen, real_resampled, normalize_mel(self.spec_fn({'in': gen})['out']), normalize_mel(mel), sample_rate return gen, real_resampled, self.spec_fn({'in': gen})['out'], mel, sample_rate
def gen_freq_gap(self, mel, band_range=(60,100)): def gen_freq_gap(self, mel, band_range=(60,100)):
gap_start, gap_end = band_range gap_start, gap_end = band_range
@ -118,7 +119,7 @@ class MusicDiffusionFid(evaluator.Evaluator):
output_shape = (1, 16, audio.shape[-1] // 16) output_shape = (1, 16, audio.shape[-1] // 16)
self.spec_decoder = self.spec_decoder.to(audio.device) self.spec_decoder = self.spec_decoder.to(audio.device)
# Cool fact: we can re-use the diffuser for the spectrogram diffuser since it has the same parametrization. # Cool fact: we can re-use the diffuser for the spectrogram diffuser since it has the same parametrization.
gen = self.diffuser.p_sample_loop(self.spec_decoder, output_shape, noise=torch.zeros(*output_shape, device=audio.device), gen = self.diffuser.p_sample_loop(self.spec_decoder, output_shape,
model_kwargs={'aligned_conditioning': spec}) model_kwargs={'aligned_conditioning': spec})
gen = pixel_shuffle_1d(gen, 16) gen = pixel_shuffle_1d(gen, 16)