few things with gap filling

codes/models/audio/music/music_gen_fill_gaps.py

@@ -186,15 +186,14 @@ class MusicGenerator(nn.Module):
 
 
     def timestep_independent(self, truth, expected_seq_len, return_code_pred):
-        code_emb = self.conditioner(truth)
+        truth_emb = self.conditioner(truth)
         # Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
         if self.training and self.unconditioned_percentage > 0:
-            unconditioned_batches = torch.rand((code_emb.shape[0], 1, 1),
+            unconditioned_batches = torch.rand((truth_emb.shape[0], 1, 1),
-                                               device=code_emb.device) < self.unconditioned_percentage
+                                               device=truth_emb.device) < self.unconditioned_percentage
-            code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(truth.shape[0], 1, 1),
+            truth_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(truth.shape[0], 1, 1),
-                                   code_emb)
+                                   truth_emb)
-        expanded_code_emb = F.interpolate(code_emb, size=expected_seq_len, mode='nearest')
+        return truth_emb
-        return expanded_code_emb
 
 
     def forward(self, x, timesteps, truth=None, precomputed_aligned_embeddings=None, conditioning_free=False):
@@ -212,20 +211,21 @@ class MusicGenerator(nn.Module):
 
         unused_params = []
         if conditioning_free:
-            code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, x.shape[-1])
+            truth_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, x.shape[-1])
             unused_params.extend(list(self.conditioner.parameters()))
         else:
             if precomputed_aligned_embeddings is not None:
-                code_emb = precomputed_aligned_embeddings
+                truth_emb = precomputed_aligned_embeddings
             else:
-                truth = self.do_masking(truth)
+                if self.training:
-                code_emb = self.timestep_independent(truth, x.shape[-1], True)
+                    truth = self.do_masking(truth)
+                truth_emb = self.timestep_independent(truth, x.shape[-1], True)
             unused_params.append(self.unconditioned_embedding)
 
         time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
-        code_emb = self.conditioning_timestep_integrator(code_emb, time_emb)
+        truth_emb = self.conditioning_timestep_integrator(truth_emb, time_emb)
         x = self.inp_block(x)
-        x = torch.cat([x, code_emb], dim=1)
+        x = torch.cat([x, truth_emb], dim=1)
         x = self.integrating_conv(x)
         for i, lyr in enumerate(self.layers):
             # Do layer drop where applicable. Do not drop first and last layers.

codes/models/audio/music/unet_diffusion_waveform_gen.py

@@ -95,7 +95,7 @@ class ResBlock(TimestepBlock):
             h = self.out_layers(h)
         return self.skip_connection(x) + h
 
-class DiffusionTts(nn.Module):
+class DiffusionWaveformGen(nn.Module):
     """
     The full UNet model with attention and timestep embedding.
 
@@ -465,7 +465,7 @@ class DiffusionTts(nn.Module):
 
 @register_model
 def register_unet_diffusion_waveform_gen(opt_net, opt):
-    return DiffusionTts(**opt_net['kwargs'])
+    return DiffusionWaveformGen(**opt_net['kwargs'])
 
 
 if __name__ == '__main__':
@@ -473,17 +473,17 @@ if __name__ == '__main__':
     aligned_latent = torch.randn(2,388,1024)
     aligned_sequence = torch.randn(2,120,220)
     ts = torch.LongTensor([600, 600])
-    model = DiffusionTts(128,
+    model = DiffusionWaveformGen(128,
-                         channel_mult=[1,1.5,2, 3, 4, 6, 8],
+                                 channel_mult=[1,1.5,2, 3, 4, 6, 8],
-                         num_res_blocks=[2, 2, 2, 2, 2, 2, 1],
+                                 num_res_blocks=[2, 2, 2, 2, 2, 2, 1],
-                         token_conditioning_resolutions=[1,4,16,64],
+                                 token_conditioning_resolutions=[1,4,16,64],
-                         attention_resolutions=[],
+                                 attention_resolutions=[],
-                         num_heads=8,
+                                 num_heads=8,
-                         kernel_size=3,
+                                 kernel_size=3,
-                         scale_factor=2,
+                                 scale_factor=2,
-                         time_embed_dim_multiplier=4,
+                                 time_embed_dim_multiplier=4,
-                         super_sampling=False,
+                                 super_sampling=False,
-                         efficient_convs=False)
+                                 efficient_convs=False)
     # Test with latent aligned conditioning
     o = model(clip, ts, aligned_latent)
     # Test with sequence aligned conditioning

codes/trainer/eval/audio_diffusion_fid.py

@@ -17,7 +17,7 @@ from models.clip.mel_text_clip import MelTextCLIP
 from models.audio.tts.tacotron2 import text_to_sequence
 from scripts.audio.gen.speech_synthesis_utils import load_discrete_vocoder_diffuser, wav_to_mel, load_speech_dvae, \
     convert_mel_to_codes, load_univnet_vocoder, wav_to_univnet_mel
-from trainer.injectors.audio_injectors import denormalize_tacotron_mel
+from trainer.injectors.audio_injectors import denormalize_mel
 from utils.util import ceil_multiple, opt_get, load_model_from_config, pad_or_truncate
 
 
@@ -161,7 +161,7 @@ class AudioDiffusionFid(evaluator.Evaluator):
                                     model_kwargs={'aligned_conditioning': mel_codes,
                                                   'conditioning_input': univnet_mel})
         # denormalize mel
-        gen_mel = denormalize_tacotron_mel(gen_mel)
+        gen_mel = denormalize_mel(gen_mel)
 
         gen_wav = self.local_modules['vocoder'].inference(gen_mel)
         real_dec = self.local_modules['vocoder'].inference(univnet_mel)

codes/trainer/eval/music_diffusion_fid.py

@@ -16,13 +16,15 @@ import trainer.eval.evaluator as evaluator
 from data.audio.paired_voice_audio_dataset import load_tsv_aligned_codes
 from data.audio.unsupervised_audio_dataset import load_audio
 from data.audio.voice_tokenizer import VoiceBpeTokenizer
+from models.audio.music.unet_diffusion_waveform_gen import DiffusionWaveformGen
 from models.clip.mel_text_clip import MelTextCLIP
 from models.audio.tts.tacotron2 import text_to_sequence
 from models.diffusion.gaussian_diffusion import get_named_beta_schedule
 from models.diffusion.respace import space_timesteps, SpacedDiffusion
 from scripts.audio.gen.speech_synthesis_utils import load_discrete_vocoder_diffuser, wav_to_mel, load_speech_dvae, \
     convert_mel_to_codes, load_univnet_vocoder, wav_to_univnet_mel
-from trainer.injectors.audio_injectors import denormalize_tacotron_mel, TorchMelSpectrogramInjector, pixel_shuffle_1d
+from trainer.injectors.audio_injectors import denormalize_mel, TorchMelSpectrogramInjector, pixel_shuffle_1d, \
+    normalize_mel
 from utils.util import ceil_multiple, opt_get, load_model_from_config, pad_or_truncate
 
 
@@ -50,15 +52,28 @@ class MusicDiffusionFid(evaluator.Evaluator):
                            conditioning_free=conditioning_free_diffusion_enabled, conditioning_free_k=conditioning_free_k)
         self.dev = self.env['device']
         mode = opt_get(opt_eval, ['diffusion_type'], 'tts')
-        self.local_modules = {}
+
-        if mode == 'standard':
+        self.spec_decoder = DiffusionWaveformGen(model_channels=256, in_channels=16, in_mel_channels=256, out_channels=32,
-            self.diffusion_fn = self.perform_diffusion_standard
+                                                 channel_mult=[1,2,3,4], num_res_blocks=[3,3,3,3], token_conditioning_resolutions=[1,4],
+                                                 num_heads=8,
+                                                 dropout=0, kernel_size=3, scale_factor=2, time_embed_dim_multiplier=4, unconditioned_percentage=0)
+        self.spec_decoder.load_state_dict(torch.load('../experiments/music_waveform_gen.pth', map_location=torch.device('cpu')))
+        self.local_modules = {'spec_decoder': self.spec_decoder}
+
+        if mode == 'spec_decode':
+            self.diffusion_fn = self.perform_diffusion_spec_decode
+        elif 'gap_fill_' in mode:
+            self.diffusion_fn = self.perform_diffusion_gap_fill
+            if '_freq' in mode:
+                self.gap_gen_fn = self.gen_freq_gap
+            else:
+                self.gap_gen_fn = self.gen_time_gap
         self.spec_fn = TorchMelSpectrogramInjector({'n_mel_channels': 256, 'mel_fmax': 22000, 'normalize': True, 'in': 'in', 'out': 'out'}, {})
 
     def load_data(self, path):
         return list(glob(f'{path}/*.wav'))
 
-    def perform_diffusion_standard(self, audio, sample_rate=22050):
+    def perform_diffusion_spec_decode(self, audio, sample_rate=22050):
         if sample_rate != sample_rate:
             real_resampled = torchaudio.functional.resample(audio, 22050, sample_rate).unsqueeze(0)
         else:
@@ -69,7 +84,47 @@ class MusicDiffusionFid(evaluator.Evaluator):
         gen = self.diffuser.p_sample_loop(self.model, output_shape, noise=torch.zeros(*output_shape, device=audio.device),
                                           model_kwargs={'aligned_conditioning': mel})
         gen = pixel_shuffle_1d(gen, 16)
-        real_resampled = real_resampled + torch.FloatTensor(real_resampled.shape).uniform_(0.0, 1e-5).to(real_resampled.device)
+
+        return gen, real_resampled, sample_rate
+
+    def gen_freq_gap(self, mel, band_range=(130,150)):
+        gap_start, gap_end = band_range
+        mel[:, gap_start:gap_end] = 0
+        return mel
+
+    def gen_time_gap(self, mel):
+        mel[:, :, 22050*5:22050*6] = 0
+        return mel
+
+    def perform_diffusion_gap_fill(self, audio, sample_rate=22050, band_range=(130,150)):
+        if sample_rate != sample_rate:
+            real_resampled = torchaudio.functional.resample(audio, 22050, sample_rate).unsqueeze(0)
+        else:
+            real_resampled = audio
+        audio = audio.unsqueeze(0)
+
+        # Fetch the MEL and mask out the requested bands.
+        mel = self.spec_fn({'in': audio})['out']
+        mel = normalize_mel(mel)
+        mel = self.gap_gen_fn(mel)
+        output_shape = (1, mel.shape[1], mel.shape[2])
+
+        # Repair the MEL with the given model.
+        spec = self.diffuser.p_sample_loop(self.model, output_shape, noise=torch.zeros(*output_shape, device=audio.device),
+                                          model_kwargs={'truth': mel})
+        import torchvision
+        torchvision.utils.save_image((spec.unsqueeze(1) + 1) / 2, 'gen.png')
+        torchvision.utils.save_image((mel.unsqueeze(1) + 1) / 2, 'mel.png')
+        spec = denormalize_mel(spec)
+
+        # Re-convert the resulting MEL back into audio using the spectrogram decoder.
+        output_shape = (1, 16, audio.shape[-1] // 16)
+        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.
+        gen = self.diffuser.p_sample_loop(self.spec_decoder, output_shape, noise=torch.zeros(*output_shape, device=audio.device),
+                                          model_kwargs={'aligned_conditioning': spec})
+        gen = pixel_shuffle_1d(gen, 16)
+
         return gen, real_resampled, sample_rate
 
     def load_projector(self):
@@ -148,12 +203,12 @@ class MusicDiffusionFid(evaluator.Evaluator):
 
 
 if __name__ == '__main__':
-    diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_waveform_gen3.yml', 'generator',
+    diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_gap_filler.yml', 'generator',
                                        also_load_savepoint=False,
-                                       load_path='X:\\dlas\\experiments\\train_music_waveform_gen3_r1\\models\\10000_generator_ema.pth').cuda()
+                                       load_path='X:\\dlas\\experiments\\train_music_gap_filler\\models\\5000_generator.pth').cuda()
-    opt_eval = {'path': 'Y:\\split\\yt-music-eval', 'diffusion_steps': 50,
+    opt_eval = {'path': 'Y:\\split\\yt-music-eval', 'diffusion_steps': 100,
                 'conditioning_free': False, 'conditioning_free_k': 1,
-                'diffusion_schedule': 'linear', 'diffusion_type': 'standard'}
+                'diffusion_schedule': 'linear', 'diffusion_type': 'gap_fill_freq'}
-    env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 1, 'device': 'cuda', 'opt': {}}
+    env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 2, 'device': 'cuda', 'opt': {}}
     eval = MusicDiffusionFid(diffusion, opt_eval, env)
     print(eval.perform_eval())

codes/trainer/injectors/audio_injectors.py

@@ -282,6 +282,20 @@ class ConditioningLatentDistributionDivergenceInjector(Injector):
         return {self.output: mean_loss, self.var_loss_key: var_loss}
 
 
+class RandomScaleInjector(Injector):
+    def __init__(self, opt, env):
+        super().__init__(opt, env)
+        self.min_samples = opt['min_samples']
+
+    def forward(self, state):
+        inp = state[self.input]
+        if self.min_samples < inp.shape[-1]:
+            samples = random.randint(self.min_samples, inp.shape[-1])
+            start = random.randint(0, inp.shape[-1]-samples)
+            inp = inp[:, :, start:start+samples]
+        return {self.output: inp}
+
+
 def pixel_shuffle_1d(x, upscale_factor):
     batch_size, channels, steps = x.size()
     channels //= upscale_factor