only divide by MAX_WAV_VALUE if int16, pad mel/audio if smaller than segment_size, switch tqdm, crop mel/wav in train if necessary

Author: lars76

meldataset.py

@@ -247,7 +247,8 @@ def __getitem__(self, index):
         filename = self.audio_files[index]
         if self._cache_ref_count == 0:
             audio, sampling_rate = load_wav(filename, self.sampling_rate)
-            audio = audio / MAX_WAV_VALUE
+            if np.abs(audio).max() > 1:
+                audio = audio / MAX_WAV_VALUE
             if not self.fine_tuning:
                 audio = normalize(audio) * 0.95
             self.cached_wav = audio
@@ -328,13 +329,13 @@ def __getitem__(self, index):
                         * self.hop_size : (mel_start + frames_per_seg)
                         * self.hop_size,
                     ]
-                else:
-                    mel = torch.nn.functional.pad(
-                        mel, (0, frames_per_seg - mel.size(2)), "constant"
-                    )
-                    audio = torch.nn.functional.pad(
-                        audio, (0, self.segment_size - audio.size(1)), "constant"
-                    )
+
+                mel = torch.nn.functional.pad(
+                    mel, (0, frames_per_seg - mel.size(2)), "constant"
+                )
+                audio = torch.nn.functional.pad(
+                    audio, (0, self.segment_size - audio.size(1)), "constant"
+                )
 
         mel_loss = mel_spectrogram(
             audio,

train.py

@@ -308,7 +308,7 @@ def validate(rank, a, h, loader, mode="seen"):
             print(f"step {steps} {mode} speaker validation...")
 
             # Loop over validation set and compute metrics
-            for j, batch in tqdm(enumerate(loader)):
+            for j, batch in enumerate(tqdm(loader)):
                 x, y, _, y_mel = batch
                 y = y.to(device)
                 if hasattr(generator, "module"):
@@ -326,7 +326,8 @@ def validate(rank, a, h, loader, mode="seen"):
                     h.fmin,
                     h.fmax_for_loss,
                 )
-                val_err_tot += F.l1_loss(y_mel, y_g_hat_mel).item()
+                min_t = min(y_mel.size(-1), y_g_hat_mel.size(-1))
+                val_err_tot += F.l1_loss(y_mel[...,:min_t], y_g_hat_mel[...,:min_t]).item()
 
                 # PESQ calculation. only evaluate PESQ if it's speech signal (nonspeech PESQ will error out)
                 if (
@@ -343,7 +344,8 @@ def validate(rank, a, h, loader, mode="seen"):
                     val_pesq_tot += pesq(16000, y_int_16k, y_g_hat_int_16k, "wb")
 
                 # MRSTFT calculation
-                val_mrstft_tot += loss_mrstft(y_g_hat, y).item()
+                min_t = min(y.size(-1), y_g_hat.size(-1))
+                val_mrstft_tot += loss_mrstft(y_g_hat[...,:min_t], y[...,:min_t]).item()
 
                 # Log audio and figures to Tensorboard
                 if j % a.eval_subsample == 0:  # Subsample every nth from validation set