Auto-normalize wav files by data type

codes/data/audio/nv_tacotron_dataset.py

@@ -49,7 +49,6 @@ class TextMelLoader(torch.utils.data.Dataset):
                 raise NotImplementedError()
             self.audiopaths_and_text.extend(fetcher_fn(p))
         self.text_cleaners = hparams.text_cleaners
-        self.max_wav_value = hparams.max_wav_value
         self.sampling_rate = hparams.sampling_rate
         self.load_mel_from_disk = opt_get(hparams, ['load_mel_from_disk'], False)
         self.return_wavs = opt_get(hparams, ['return_wavs'], False)
@@ -83,7 +82,6 @@ class TextMelLoader(torch.utils.data.Dataset):
         else:
             if filename.endswith('.wav'):
                 audio, sampling_rate = load_wav_to_torch(filename)
-                audio = (audio / self.max_wav_value)
             else:
                 audio, sampling_rate = audio2numpy.audio_from_file(filename)
                 audio = torch.tensor(audio)
@@ -108,8 +106,6 @@ class TextMelLoader(torch.utils.data.Dataset):
             else:
                 melspec = self.stft.mel_spectrogram(audio_norm)
                 melspec = torch.squeeze(melspec, 0)
-        else:
-
 
         return melspec
 

codes/data/audio/wavfile_dataset.py

@@ -15,23 +15,22 @@ from utils.util import opt_get
 class WavfileDataset(torch.utils.data.Dataset):
 
     def __init__(self, opt):
-        cache_path = opt_get(opt, ['cache_path'], os.path.join(self.path, 'cache.pth'))  # Will fail when multiple paths specified, must be specified in this case.
+        path = opt['path']
-        self.path = os.path.dirname(opt['path'])
+        cache_path = opt['cache_path']  # Will fail when multiple paths specified, must be specified in this case.
-        if not isinstance(self.path, list):
+        if not isinstance(path, list):
-            self.path = [self.path]
+            path = [path]
         if os.path.exists(cache_path):
             self.audiopaths = torch.load(cache_path)
         else:
             print("Building cache..")
             self.audiopaths = []
-            for p in self.path:
+            for p in path:
                 self.audiopaths.extend(find_files_of_type('img', p, qualifier=is_wav_file)[0])
             torch.save(self.audiopaths, cache_path)
 
         # Parse options
         self.sampling_rate = opt_get(opt, ['sampling_rate'], 24000)
         self.augment = opt_get(opt, ['do_augmentation'], False)
-        self.max_wav_value = 32768.0
 
         self.window = 2 * self.sampling_rate
         if self.augment:
@@ -39,13 +38,20 @@ class WavfileDataset(torch.utils.data.Dataset):
 
     def get_audio_for_index(self, index):
         audiopath = self.audiopaths[index]
-        filename = os.path.join(self.path, audiopath)
+        audio, sampling_rate = load_wav_to_torch(audiopath)
-        audio, sampling_rate = load_wav_to_torch(filename)
         if sampling_rate != self.sampling_rate:
-            raise ValueError(f"Input sampling rate does not match specified rate {self.sampling_rate}")
+            if sampling_rate < self.sampling_rate:
-        audio_norm = audio / self.max_wav_value
+                print(f'{audiopath} has a sample rate of {sampling_rate} which is lower than the requested sample rate of {self.sampling_rate}. This is not a good idea.')
-        audio_norm = audio_norm.unsqueeze(0)
+            audio = torch.nn.functional.interpolate(audio.unsqueeze(0).unsqueeze(1), scale_factor=self.sampling_rate/sampling_rate, mode='nearest', recompute_scale_factor=False).squeeze()
-        return audio_norm, audiopath
+
+        # Check some assumptions about audio range. This should be automatically fixed in load_wav_to_torch, but might not be in some edge cases, where we should squawk.
+        # '2' is arbitrarily chosen since it seems like audio will often "overdrive" the [-1,1] bounds.
+        if torch.any(audio > 2) or not torch.any(audio < 0):
+            print(f"Error with {audiopath}. Max={audio.max()} min={audio.min()}")
+        audio.clip_(-1, 1)
+
+        audio = audio.unsqueeze(0)
+        return audio, audiopath
 
     def __getitem__(self, index):
         clip1, clip2 = None, None

codes/models/tacotron2/taco_utils.py

@@ -14,7 +14,13 @@ def get_mask_from_lengths(lengths, max_len=None):
 
 def load_wav_to_torch(full_path):
     sampling_rate, data = read(full_path)
-    return torch.FloatTensor(data.astype(np.float32)), sampling_rate
+    if data.dtype == np.int16:
+        norm_fix = 32768
+    elif data.dtype == np.float16 or data.dtype == np.float32:
+        norm_fix = 1.
+    else:
+        raise NotImplemented(f"Provided data dtype not supported: {data.dtype}")
+    return (torch.FloatTensor(data.astype(np.float32)) / norm_fix, sampling_rate)
 
 
 def load_filepaths_and_text(filename, split="|"):

codes/scripts/audio/random_mp3_splitter.py

@@ -23,7 +23,7 @@ if __name__ == '__main__':
     separator = Separator('spleeter:2stems')
     files = find_audio_files(src_dir, include_nonwav=True)
     for e, file in enumerate(tqdm(files)):
-        if e < 1:
+        if e < 1092:
             continue
         file_basis = osp.relpath(file, src_dir)\
             .replace('/', '_')\

codes/train.py

@@ -234,7 +234,7 @@ class Trainer:
             if self.rank <= 0:
                 for k, v in reduced_metrics.items():
                     val = torch.stack(v).mean().item()
-                    self.tb_logger.add_scalar(k, val, self.current_step)
+                    self.tb_logger.add_scalar(f'val_{k}', val, self.current_step)
                     print(f">>Eval {k}: {val}")
                 if opt['wandb']:
                     import wandb
@@ -282,7 +282,7 @@ class Trainer:
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_gpt_asr_mozcv.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_lrdvae_audio_clips.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)
     args = parser.parse_args()