fixes

codes/data/audio/unsupervised_audio_dataset.py

@@ -10,7 +10,7 @@ from audio2numpy import open_audio
 from tqdm import tqdm
 
 from data.util import find_files_of_type, is_audio_file, load_paths_from_cache
-from models.audio.tts.tacotron2 import load_wav_to_torch
+from models.audio.tts.tacotron2.taco_utils import load_wav_to_torch
 from utils.util import opt_get
 
 

codes/data/audio/wav_aug.py

@@ -3,7 +3,7 @@ import random
 import torch
 import torchaudio.sox_effects
 
-from models.audio.tts.tacotron2 import load_wav_to_torch
+from models.audio.tts.tacotron2.taco_utils import load_wav_to_torch
 
 
 # Returns random double on [l,h] as a string

codes/models/audio/tts/tacotron2/__init__.py

@@ -0,0 +1,5 @@
+from models.audio.tts.tacotron2.taco_utils import *
+from models.audio.tts.tacotron2.text import *
+from models.audio.tts.tacotron2.tacotron2 import *
+from models.audio.tts.tacotron2.stft import *
+from models.audio.tts.tacotron2.layers import *

codes/models/audio/vocoders/univnet/generator.py

@@ -84,19 +84,15 @@ class UnivNetGenerator(nn.Module):
     def inference(self, c, z=None):
         # pad input mel with zeros to cut artifact
         # see https://github.com/seungwonpark/melgan/issues/8
-        zero = torch.full((1, self.mel_channel, 10), -11.5129).to(c.device)
+        zero = torch.full((c.shape[0], self.mel_channel, 10), -11.5129).to(c.device)
         mel = torch.cat((c, zero), dim=2)
 
         if z is None:
-            z = torch.randn(1, self.noise_dim, mel.size(2)).to(mel.device)
+            z = torch.randn(c.shape[0], self.noise_dim, mel.size(2)).to(mel.device)
 
         audio = self.forward(mel, z)
-        audio = audio.squeeze()  # collapse all dimension except time axis
+        audio = audio[:, :, :-(self.hop_length * 10)]
-        audio = audio[:-(self.hop_length * 10)]
+        audio = audio.clamp(min=-1, max=1)
-        audio = MAX_WAV_VALUE * audio
-        audio = audio.clamp(min=-MAX_WAV_VALUE, max=MAX_WAV_VALUE - 1)
-        audio = audio.short()
-
         return audio
 
 

codes/scripts/audio/test_audio_similarity.py

@@ -5,7 +5,7 @@ import torch.nn.functional as F
 
 from data.util import is_wav_file, find_files_of_type
 from models.audio_resnet import resnet50
-from models.audio.tts.tacotron2 import load_wav_to_torch
+from models.audio.tts.tacotron2.taco_utils import load_wav_to_torch
 from scripts.byol.byol_extract_wrapped_model import extract_byol_model_from_state_dict
 
 if __name__ == '__main__':

codes/trainer/eval/audio_diffusion_fid.py

@@ -125,20 +125,21 @@ class AudioDiffusionFid(evaluator.Evaluator):
         real_resampled = torchaudio.functional.resample(audio, 22050, SAMPLE_RATE).unsqueeze(0)
         univnet_mel = wav_to_univnet_mel(audio)  # to be used for a conditioning input
 
-        output_size = real_resampled.shape[-1]
+        output_size = univnet_mel.shape[-1]
         aligned_codes_compression_factor = output_size // mel_codes.shape[-1]
-        padded_size = ceil_multiple(output_size, 2048)
+        padded_size = ceil_multiple(output_size, self.model.alignment_size)
         padding_added = padded_size - output_size
         padding_needed_for_codes = padding_added // aligned_codes_compression_factor
         if padding_needed_for_codes > 0:
             mel_codes = F.pad(mel_codes, (0, padding_needed_for_codes))
-        output_shape = (1, 1, padded_size)
+        output_shape = (1, 100, padded_size)
         gen_mel = self.diffuser.p_sample_loop(self.model, output_shape,
                                     model_kwargs={'aligned_conditioning': mel_codes,
                                                   'conditioning_input': univnet_mel})
 
-        gen_wav = self.local_modules['vocoder'](gen_mel)
+        gen_wav = self.local_modules['vocoder'].inference(gen_mel)
-        return gen_wav, real_resampled, SAMPLE_RATE
+        real_dec = self.local_modules['vocoder'].inference(univnet_mel)
+        return gen_wav.float(), real_dec, SAMPLE_RATE
 
     def load_projector(self):
         """
@@ -257,9 +258,9 @@ if __name__ == '__main__':
 if __name__ == '__main__':
     from utils.util import load_model_from_config
 
-    diffusion = load_model_from_config('X:\\dlas\\experiments\\train_diffusion_tts9.yml', 'generator',
+    diffusion = load_model_from_config('X:\\dlas\\experiments\\train_diffusion_tts9_mel.yml', 'generator',
                                        also_load_savepoint=False,
-                                       load_path='X:\\dlas\\experiments\\train_diffusion_tts9\\models\\7500_generator_ema.pth').cuda()
+                                       load_path='X:\\dlas\\experiments\\train_diffusion_tts9_mel\\models\\10000_generator_ema.pth').cuda()
     opt_eval = {'eval_tsv': 'Y:\\libritts\\test-clean\\transcribed-brief-w2v.tsv', 'diffusion_steps': 100,
                 'conditioning_free': False, 'conditioning_free_k': 1,
                 'diffusion_schedule': 'linear', 'diffusion_type': 'tts9_mel'}