Update app.py

app.py

@@ -20,7 +20,6 @@ os.system("python -m unidic download")
 HF_TOKEN = None
 api = HfApi(token=HF_TOKEN)
 
-print("Downloading if not downloaded viXTTS")
 checkpoint_dir = "model/"
 repo_id = "capleaf/viXTTS"
 use_deepspeed = False
@@ -87,7 +86,7 @@ def calculate_keep_len(text, lang):
     return -1
 
 
-@spaces.GPU
+@spaces.GPU(duration=0)
 def predict(
     prompt,
     language,
@@ -139,7 +138,6 @@ def predict(
         if normalize_text and language == "vi":
             prompt = normalize_vietnamese_text(prompt)
 
-        print("Generando nuevo audio...")
         t0 = time.time()
         out = MODEL.inference(
             prompt,
@@ -151,12 +149,10 @@ def predict(
             enable_text_splitting=True,
         )
         inference_time = time.time() - t0
-        print(f"Tiempo de generación de audio: {round(inference_time*1000)} milisegundos")
         metrics_text += (
             f"Tiempo de generación de audio: {round(inference_time*1000)} milisegundos\n"
         )
         real_time_factor = (time.time() - t0) / out["wav"].shape[-1] * 24000
-        print(f"Factor de tiempo real (RTF): {real_time_factor}")
         metrics_text += f"Factor de tiempo real (RTF): {real_time_factor:.2f}\n"
 
         keep_len = calculate_keep_len(prompt, language)
@@ -166,13 +162,6 @@ def predict(
 
     except RuntimeError as e:
         if "device-side assert" in str(e):
-            print(
-                f"Exit due to: Unrecoverable exception caused by language:{language} prompt:{prompt}",
-                flush=True,
-            )
-            gr.Warning("Se ha producido un error. Por favor, inténtalo de nuevo en un minuto.")
-            print("Cuda device-assert Runtime encountered need restart")
-
             error_time = datetime.datetime.now().strftime("%d-%m-%Y-%H:%M:%S")
             error_data = [
                 error_time,
@@ -181,14 +170,11 @@ def predict(
                 audio_file_pth,
             ]
             error_data = [str(e) if type(e) != str else e for e in error_data]
-            print(error_data)
-            print(speaker_wav)
             write_io = StringIO()
             csv.writer(write_io).writerows([error_data])
             csv_upload = write_io.getvalue().encode()
 
             filename = error_time + "_" + str(uuid.uuid4()) + ".csv"
-            print("Writing error csv")
             error_api = HfApi()
             error_api.upload_file(
                 path_or_fileobj=csv_upload,
@@ -197,7 +183,6 @@ def predict(
                 repo_type="dataset",
             )
 
-            print("Writing error reference audio")
             speaker_filename = error_time + "_reference_" + str(uuid.uuid4()) + ".wav"
             error_api = HfApi()
             error_api.upload_file(
@@ -210,17 +195,13 @@ def predict(
             space = api.get_space_runtime(repo_id=repo_id)
             if space.stage != "BUILDING":
                 api.restart_space(repo_id=repo_id)
-            else:
-                print("TRIED TO RESTART but space is building")
 
         else:
             if "Failed to decode" in str(e):
-                print("Speaker encoding error", str(e))
                 metrics_text = gr.Warning(
                     metrics_text="Parece que hay un problema con la referencia de audio. ¿Has activado el micrófono?"
                 )
             else:
-                print("RuntimeError: non device-side assert error:", str(e))
                 metrics_text = gr.Warning(
                     "Se ha producido un error inesperado. Por favor, inténtalo de nuevo."
                 )