add lots of logging

voice_conversion/so-vits-svc-paul/src/so_vits_svc_fork/fakeyou_infer.py

@@ -238,6 +238,9 @@ def infer(
     config_path = Path(config_path)
     if cluster_model_path is not None:
         cluster_model_path = Path(cluster_model_path)
+    
+    LOG.info("Calling infer()...")
+
     infer(
         input_path=input_path,
         output_path=output_path,

voice_conversion/so-vits-svc-paul/src/so_vits_svc_fork/inference/infer_tool.py

@@ -111,22 +111,45 @@ class Svc:
         else:
             self.dev = torch.device(device)
         self.net_g_ms = None
+
+        LOG.info("Calling utils.get_hparams_from_file() ...")
+
         self.hps_ms = utils.get_hparams_from_file(config_path)
         self.target_sample = self.hps_ms.data.sampling_rate
         self.hop_size = self.hps_ms.data.hop_length
         self.spk2id = self.hps_ms.spk
+
+        LOG.info("setting self.hubert_model = utils.get_hubert_model() ... ")
         self.hubert_model = utils.get_hubert_model().to(self.dev)
+
+        LOG.info("done setting self.hubert_model = utils.get_hubert_model() ... ")
+
+        LOG.info("Calling self.load_model() ...")
+
         self.load_model()
+
+        LOG.info("Done with: Calling self.load_model() ...")
+
         if cluster_model_path is not None and Path(cluster_model_path).exists():
+            LOG.warn("cluster.get_cluster_model() ... ")
+
             self.cluster_model = cluster.get_cluster_model(cluster_model_path)
 
+            LOG.warn("done with: cluster.get_cluster_model() ... ")
+
     def load_model(self):
         self.net_g_ms = SynthesizerTrn(
             self.hps_ms.data.filter_length // 2 + 1,
             self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
             **self.hps_ms.model,
         )
+
+        LOG.warn("load_model() -> utils.load_checkpoint() ... ")
+
         _ = utils.load_checkpoint(self.net_g_path, self.net_g_ms, None)
+
+        LOG.warn("done with: load_model() -> utils.load_checkpoint() ... ")
+
         if "half" in self.net_g_path and torch.cuda.is_available():
             _ = self.net_g_ms.half().eval().to(self.dev)
         else:
@@ -260,6 +283,9 @@ class Svc:
             )
             // 2
         )
+
+        LOG.info("LOOP: for chunk in split_silence() ... ")
+
         for chunk in split_silence(
             audio,
             top_db=-db_thresh,
@@ -280,6 +306,9 @@ class Svc:
                         np.zeros([pad_len], dtype=np.float32),
                     ]
                 )
+
+                LOG.info("self.infer() [inside loop]")
+
                 audio_chunk_pad_infer_tensor, _ = self.infer(
                     speaker,
                     transpose,

voice_conversion/so-vits-svc-paul/src/so_vits_svc_fork/inference_main.py

@@ -44,6 +44,9 @@ def infer(
     input_path = Path(input_path)
     config_path = Path(config_path)
     cluster_model_path = Path(cluster_model_path) if cluster_model_path else None
+
+    LOG.info("Calling Svc()...")
+
     svc_model = Svc(
         net_g_path=model_path.as_posix(),
         config_path=config_path.as_posix(),
@@ -53,7 +56,12 @@ def infer(
         device=device,
     )
 
+    LOG.info("Calling librosa.load()...")
+
     audio, _ = librosa.load(input_path, sr=svc_model.target_sample)
+
+    LOG.info("Calling svc_model.infer_silence()...")
+
     audio = svc_model.infer_silence(
         audio.astype(np.float32),
         speaker=speaker,
@@ -68,6 +76,8 @@ def infer(
         absolute_thresh=absolute_thresh,
     )
 
+    LOG.info("Calling soundfile.write()...")
+
     soundfile.write(output_path, audio, svc_model.target_sample)
 
 

voice_conversion/so-vits-svc-paul/src/so_vits_svc_fork/utils.py

@@ -330,20 +330,33 @@ def ensure_hubert_model() -> Path:
     if not vec_path.exists():
         # url = "http://obs.cstcloud.cn/share/obs/sankagenkeshi/checkpoint_best_legacy_500.pt"
         # url = "https://huggingface.co/innnky/contentvec/resolve/main/checkpoint_best_legacy_500.pt"
+
+        LOG.warn("DOWNLOADING HUBERT! NOT ON FILESYSTEM!")
+        LOG.warn("download_file() ...")
+
         url = "https://huggingface.co/therealvul/so-vits-svc-4.0-init/resolve/main/checkpoint_best_legacy_500.pt"
         download_file(url, vec_path, desc="Downloading Hubert model")
+
+        LOG.warn("done with download_file()")
+
     return vec_path
 
 
 def get_hubert_model():
+    LOG.warn("ensure_hubert_model() ... ")
+
     vec_path = ensure_hubert_model()
     from fairseq import checkpoint_utils
 
+    LOG.warn("checkpoint_utils.load_model_ensemble_and_task() ... ")
+
     models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
         [vec_path.as_posix()],
         suffix="",
     )
     model = models[0]
+
+    LOG.warn("model.eval() ... ")
     model.eval()
     return model