Update app.py

app.py

@@ -42,7 +42,7 @@ default_cluster_infer_ratio = 0.5
 duration_limit = int(os.environ.get("MAX_DURATION_SECONDS", 9e9))
 ###################################################################
 
-models = {}
+models = []
 all_speakers = []
 for repo_id in repo_ids:
     # Figure out the latest generator by taking highest value one.
@@ -57,7 +57,7 @@ for repo_id in repo_ids:
         )[-1]
         ckpt_name = f"G_{latest_id}.pth"
 
-    cluster_model_name = cluster_model_name or "kmeans.pt"
+    cluster_model_name = "kmeans.pt"
     if cluster_model_name in list_repo_files(repo_id):
         print(f"Found Cluster model - Downloading {cluster_model_name} from {repo_id}")
         cluster_model_path = hf_hub_download(repo_id, cluster_model_name)
@@ -70,16 +70,13 @@ for repo_id in repo_ids:
     config_path = hf_hub_download(repo_id, "config.json")
     hparams = HParams(**json.loads(Path(config_path).read_text()))
     speakers = list(hparams.spk.keys())
+    all_speakers.extend(speakers)
     device = "cuda" if torch.cuda.is_available() else "cpu"
     model = Svc(net_g_path=generator_path, config_path=config_path, device=device, cluster_model_path=cluster_model_path)
-    
-    for speaker in speakers:
-        models[speaker] = model
-        all_speakers.append(speaker)
+    models.append(model)
 
-    # Reset ckpt_name and cluster_model_name for the next iteration
+    # Reset ckpt_name for the next iteration
     ckpt_name = None
-    cluster_model_name = None
 
 demucs_model = get_model(DEFAULT_MODEL)