update model params

configs/48k.json

@@ -11,10 +11,10 @@
       "eval_interval": 1000,
       "seed": 1234,
       "max_epochs": 20000,
-      "learning_rate": 2e-4,
+      "learning_rate": 8e-4,
       "betas": [0.8, 0.99],
       "eps": 1e-9,
-      "batch_size": 16,
+      "batch_size": 32,
       "fp16_run": true,
       "lr_decay": 0.999875,
       "segment_size": 16384,
@@ -49,7 +49,7 @@
       ],
       "upsample_rates": [8,8,4,2],
       "upsample_initial_channel": 512,
-      "upsample_kernel_sizes": [16,16,4,4],
+      "upsample_kernel_sizes": [16,16,8,4],
       "use_spectral_norm": false
     }
   }

hifigan/hub/__init__.py

@@ -19,7 +19,7 @@ def hifigan_48k(
         ],
         upsample_rates=[8,8,4,2],
         upsample_initial_channel=512,
-        upsample_kernel_sizes=[16,16,4,4]
+        upsample_kernel_sizes=[16,16,8,4]
         )
     if pretrained:
         checkpoint = torch.hub.load_state_dict_from_url(

test.py

@@ -9,7 +9,7 @@ def load_local():
     if os.path.exists("logs/lightning_logs"):
         versions = glob.glob("logs/lightning_logs/version_*")
         if len(list(versions)) > 0:
-            last_ver = sorted(list(versions))[-1]
+            last_ver = sorted(list(versions), key=lambda p: int(p.split("_")[-1]))[-1]
             last_ckpt = os.path.join(last_ver, "checkpoints/last.ckpt")
             if os.path.exists(last_ckpt):
                 ckpt_path = last_ckpt
@@ -32,7 +32,7 @@ def load_remote():
 hifigan = load_local().to(device)
 
 # Load audio
-wav, sr = torchaudio.load("test.wav")
+wav, sr = torchaudio.load("zszy_48k.wav")
 assert sr == 48000
 
 mel = mel_spectrogram_torch(wav, 2048, 256, 48000, 512, 2048, 0, None, False)

train.py

@@ -47,8 +47,9 @@ def main():
     valid_loader = DataLoader(valid_dataset, batch_size=1, num_workers=16, shuffle=False, pin_memory=True, collate_fn=collate_fn)
 
     model = HifiGAN(**hparams)
+    # model = HifiGAN.load_from_checkpoint(checkpoint_path="logs/lightning_logs/version_10/checkpoints/last.ckpt", strict=False)
 
-    checkpoint_callback = ModelCheckpoint(dirpath=None, save_last=True, every_n_train_steps=2000)
+    checkpoint_callback = ModelCheckpoint(dirpath=None, save_last=True, every_n_train_steps=2000, save_weights_only=False)
 
     devices = [int(n.strip()) for n in args.device.split(",")]
     trainer_params = {
@@ -76,7 +77,7 @@ def main():
     if os.path.exists("logs/lightning_logs"):
         versions = glob.glob("logs/lightning_logs/version_*")
         if len(list(versions)) > 0:
-            last_ver = sorted(list(versions))[-1]
+            last_ver = sorted(list(versions), key=lambda p: int(p.split("_")[-1]))[-1]
             last_ckpt = os.path.join(last_ver, "checkpoints/last.ckpt")
             if os.path.exists(last_ckpt):
                 ckpt_path = last_ckpt