Update app.py

app.py

@@ -1,34 +1,13 @@
 import os
 os.environ["CUDA_VISIBLE_DEVICES"] = ""
-os.environ["PYTORCH_CUDA_ALLOC_CONF"] = ""
-os.environ["HF_HUB_DISABLE_TELEMETRY"] = "1"
 
 import tempfile
 import requests
-import gradio as gr
 import torch
 import torchaudio as ta
+import gradio as gr
 from threading import Lock
 
-# ===== HARD CPU PATCH =====
-# 1) paksa torch.cuda.is_available() false
-torch.cuda.is_available = lambda: False
-
-# 2) paksa semua torch.load -> map_location=cpu
-_orig_torch_load = torch.load
-def _cpu_torch_load(*args, **kwargs):
-    kwargs["map_location"] = torch.device("cpu")
-    return _orig_torch_load(*args, **kwargs)
-torch.load = _cpu_torch_load
-
-# 3) paksa restore location serializer ke CPU
-import torch.serialization
-_orig_restore = torch.serialization.default_restore_location
-def _restore_cpu(storage, location):
-    return _orig_restore(storage, "cpu")
-torch.serialization.default_restore_location = _restore_cpu
-# ==========================
-
 from chatterbox.tts import ChatterboxTTS
 from huggingface_hub import hf_hub_download
 from safetensors.torch import load_file
@@ -44,21 +23,16 @@ def get_model():
     if _model is None:
         with _lock:
             if _model is None:
-                print("Loading model on CPU...")
                 m = ChatterboxTTS.from_pretrained(device="cpu")
-
-                # overwrite t3 dengan checkpoint indo
                 ckpt = hf_hub_download(repo_id=MODEL_REPO, filename=CHECKPOINT_FILENAME)
                 t3_state = load_file(ckpt, device="cpu")
                 m.t3.load_state_dict(t3_state)
-
                 m = m.to("cpu")
                 m.eval()
                 _model = m
-                print("Model ready.")
     return _model
 
-def _download_wav(url: str) -> str:
+def _download_wav(url: str):
     r = requests.get(url, timeout=90)
     r.raise_for_status()
     f = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
@@ -66,21 +40,14 @@ def _download_wav(url: str) -> str:
     f.close()
     return f.name
 
-def clone_voice(text: str, audio_file, audio_url: str):
+def clone_voice(text, audio_file, audio_url):
     if not text or not text.strip():
-        raise gr.Error("Text prompt tidak boleh kosong.")
-
-    prompt_path = None
-    if audio_file:
-        prompt_path = audio_file
-    elif audio_url and audio_url.strip():
-        prompt_path = _download_wav(audio_url.strip())
-
+        raise gr.Error("Text prompt kosong.")
+    prompt_path = audio_file or ( _download_wav(audio_url.strip()) if audio_url and audio_url.strip() else None )
     if not prompt_path:
-        raise gr.Error("Upload file WAV atau isi URL WAV.")
+        raise gr.Error("Upload WAV atau isi URL WAV.")
 
     model = get_model()
-
     with torch.no_grad():
         wav = model.generate(text.strip(), audio_prompt_path=prompt_path)
 
@@ -91,20 +58,13 @@ def clone_voice(text: str, audio_file, audio_url: str):
     ta.save(out, wav.cpu(), model.sr)
     return out
 
-with gr.Blocks(title="Chatterbox ID Voice Clone CPU") as demo:
-    gr.Markdown("## Chatterbox Indonesian Voice Cloning (CPU)")
-    text_in = gr.Textbox(label="Text Prompt", lines=4)
-    wav_in = gr.Audio(label="Upload WAV Prompt", type="filepath")
-    url_in = gr.Textbox(label="Audio URL WAV (opsional)")
+with gr.Blocks() as demo:
+    text = gr.Textbox(label="Text Prompt", lines=4)
+    wav = gr.Audio(label="Upload WAV", type="filepath")
+    url = gr.Textbox(label="WAV URL (opsional)")
     btn = gr.Button("Generate")
-    out_audio = gr.Audio(label="Output WAV", type="filepath")
-
-    btn.click(
-        fn=clone_voice,
-        inputs=[text_in, wav_in, url_in],
-        outputs=[out_audio],
-        api_name="clone_voice"
-    )
+    out = gr.Audio(label="Output", type="filepath")
+    btn.click(clone_voice, [text, wav, url], out, api_name="clone_voice")
 
 if __name__ == "__main__":
     port = int(os.getenv("PORT", "7860"))