Upload app.py

app.py

@@ -31,17 +31,24 @@ def generate_tts(text: str, voice: str):
     m = get_model()
     clean_voice = voice.replace("pocket-", "").replace("-", "_")
     
-    # 1. Get the voice state dict for the selected voice
-    if hasattr(m, "get_voice_state"):
-        voice_state = m.get_voice_state(clean_voice)
-    elif hasattr(m, "load_voice_state"):
-        voice_state = m.load_voice_state(clean_voice)
+    # Initialize the model state with the specified voice
+    if hasattr(m, "init_model_state"):
+        try:
+            model_state = m.init_model_state(voice=clean_voice)
+        except TypeError:
+            model_state = m.init_model_state(clean_voice)
+    elif hasattr(m, "get_voice_state"):
+        model_state = m.get_voice_state(clean_voice)
     else:
-        # Fallback to empty dict or voice lookup
-        voice_state = m.get_state_for_voice(clean_voice) if hasattr(m, "get_state_for_voice") else {}
+        # Check all available methods on m for state initialization
+        init_fn = getattr(m, "get_initial_state", getattr(m, "new_state", None))
+        if init_fn:
+            model_state = init_fn(clean_voice)
+        else:
+            raise RuntimeError(f"Available methods on TTSModel: {[fn for fn in dir(m) if not fn.startswith('_')]}")
 
-    # 2. Generate audio with (voice_state_dict, text)
-    audio = m.generate_audio(voice_state, clean_text)
+    # Generate audio
+    audio = m.generate_audio(model_state, clean_text)
     
     if isinstance(audio, torch.Tensor):
         audio_np = audio.detach().cpu().numpy()
@@ -54,7 +61,7 @@ def generate_tts(text: str, voice: str):
 demo = gr.Interface(
     fn=generate_tts,
     inputs=[
-        gr.Textbox(label="Text to speak", value="Hello! This is genuine Kyutai Pocket TTS running smoothly."),
+        gr.Textbox(label="Text to speak", value="Hello! This is Kyutai Pocket TTS running smoothly."),
         gr.Dropdown(choices=VOICE_LIST, value="stuart_bell", label="Voice"),
     ],
     outputs=gr.Audio(label="Generated Audio", type="numpy"),