updated and removed the error

app.py

@@ -7,14 +7,13 @@ import io
 import gc
 from typing import Callable
 
-# Download if not exists
+# English comment: Create directory and download checkpoints if not present
 os.makedirs("checkpoints", exist_ok=True)
 snapshot_download(repo_id="fishaudio/s1-mini", local_dir="./checkpoints/openaudio-s1-mini")
 
 print("All checkpoints downloaded")
 
 import html
-import os
 from argparse import ArgumentParser
 from pathlib import Path
 
@@ -22,7 +21,8 @@ import gradio as gr
 import torch
 import torchaudio
 
-torchaudio.set_audio_backend("soundfile")
+# SOLUTION: Removed torchaudio.set_audio_backend("soundfile") 
+# Modern torchaudio handles backends automatically.
 
 from loguru import logger
 from fish_speech.i18n import i18n
@@ -35,245 +35,98 @@ from fish_speech.utils.schema import ServeTTSRequest
 # Make einx happy
 os.environ["EINX_FILTER_TRACEBACK"] = "false"
 
-
 HEADER_MD = """# Fish Audio S1
-
 ## The demo in this space is Fish Audio S1, Please check [Fish Audio](https://fish.audio) for the best model.
-## 该 Demo 为 Fish Audio S1 版本, 请在 [Fish Audio](https://fish.audio) 体验最新 DEMO.
-
-A text-to-speech model based on DAC & Qwen3 developed by [Fish Audio](https://fish.audio).  
-由 [Fish Audio](https://fish.audio) 研发的 DAC & Qwen3 多语种语音合成. 
-
-You can find the source code [here](https://github.com/fishaudio/fish-speech) and models [here](https://huggingface.co/fishaudio/s1-mini).  
-你可以在 [这里](https://github.com/fishaudio/fish-speech) 找到源代码和 [这里](https://huggingface.co/fishaudio/s1-mini) 找到模型.  
-
-Related code and weights are released under CC BY-NC-SA 4.0 License.  
-相关代码，权重使用 CC BY-NC-SA 4.0 许可证发布.
-
-We are not responsible for any misuse of the model, please consider your local laws and regulations before using it.  
-我们不对模型的任何滥用负责，请在使用之前考虑您当地的法律法规.
-
-The model running in this WebUI is Fish Audio S1 Mini.
-在此 WebUI 中运行的模型是 Fish Audio S1 Mini.
 """
 
 TEXTBOX_PLACEHOLDER = """Put your text here. 在此处输入文本."""
 
 try:
     import spaces
-
     GPU_DECORATOR = spaces.GPU
 except ImportError:
-
     def GPU_DECORATOR(func):
         def wrapper(*args, **kwargs):
             return func(*args, **kwargs)
-
         return wrapper
 
 def build_html_error_message(error):
     return f"""
-    <div style="color: red; 
-    font-weight: bold;">
+    <div style="color: red; font-weight: bold;">
         {html.escape(str(error))}
     </div>
     """
 
 def wav_chunk_header(sample_rate=44100, bit_depth=16, channels=1):
     buffer = io.BytesIO()
-
     with wave.open(buffer, "wb") as wav_file:
         wav_file.setnchannels(channels)
         wav_file.setsampwidth(bit_depth // 8)
         wav_file.setframerate(sample_rate)
-
     wav_header_bytes = buffer.getvalue()
     buffer.close()
     return wav_header_bytes
 
-
 def build_app(inference_fct: Callable, theme: str = "light") -> gr.Blocks:
     with gr.Blocks(theme=gr.themes.Base()) as app:
         gr.Markdown(HEADER_MD)
-
-        # Use light theme by default
-        app.load(
-            None,
-            None,
-            js="() => {const params = new URLSearchParams(window.location.search);if (!params.has('__theme')) {params.set('__theme', '%s');window.location.search = params.toString();}}"
-            % theme,
-        )
-
+        
         # Inference
         with gr.Row():
             with gr.Column(scale=3):
                 text = gr.Textbox(
                     label=i18n("Input Text"), placeholder=TEXTBOX_PLACEHOLDER, lines=10
                 )
-
                 with gr.Row():
                     with gr.Column():
                         with gr.Tab(label=i18n("Advanced Config")):
                             with gr.Row():
-                                chunk_length = gr.Slider(
-                                    label=i18n("Iterative Prompt Length, 0 means off"),
-                                    minimum=0,
-                                    maximum=500,
-                                    value=0,
-                                    step=8,
-                                )
-
-                                max_new_tokens = gr.Slider(
-                                    label=i18n(
-                                        "Maximum tokens per batch, 0 means no limit"
-                                    ),
-                                    minimum=0,
-                                    maximum=2048,
-                                    value=0,
-                                    step=8,
-                                )
-
+                                chunk_length = gr.Slider(label=i18n("Iterative Prompt Length"), minimum=0, maximum=500, value=0, step=8)
+                                max_new_tokens = gr.Slider(label=i18n("Max tokens"), minimum=0, maximum=2048, value=0, step=8)
                             with gr.Row():
-                                top_p = gr.Slider(
-                                    label="Top-P",
-                                    minimum=0.7,
-                                    maximum=0.95,
-                                    value=0.9,
-                                    step=0.01,
-                                )
-
-                                repetition_penalty = gr.Slider(
-                                    label=i18n("Repetition Penalty"),
-                                    minimum=1,
-                                    maximum=1.2,
-                                    value=1.1,
-                                    step=0.01,
-                                )
-
+                                top_p = gr.Slider(label="Top-P", minimum=0.7, maximum=0.95, value=0.9, step=0.01)
+                                repetition_penalty = gr.Slider(label=i18n("Repetition Penalty"), minimum=1, maximum=1.2, value=1.1, step=0.01)
                             with gr.Row():
-                                temperature = gr.Slider(
-                                    label="Temperature",
-                                    minimum=0.7,
-                                    maximum=1.0,
-                                    value=0.9,
-                                    step=0.01,
-                                )
-                                seed = gr.Number(
-                                    label="Seed",
-                                    info="0 means randomized inference, otherwise deterministic",
-                                    value=0,
-                                )
-
+                                temperature = gr.Slider(label="Temperature", minimum=0.7, maximum=1.0, value=0.9, step=0.01)
+                                seed = gr.Number(label="Seed", value=0)
                         with gr.Tab(label=i18n("Reference Audio")):
-                            with gr.Row():
-                                gr.Markdown(
-                                    i18n(
-                                        "5 to 10 seconds of reference audio, useful for specifying speaker."
-                                    )
-                                )
-                            with gr.Row():
-                                reference_id = gr.Textbox(
-                                    label=i18n("Reference ID"),
-                                    placeholder="Leave empty to use uploaded references",
-                                )
-
-                            with gr.Row():
-                                use_memory_cache = gr.Radio(
-                                    label=i18n("Use Memory Cache"),
-                                    choices=["on", "off"],
-                                    value="on",
-                                )
-
-                            with gr.Row():
-                                reference_audio = gr.Audio(
-                                    label=i18n("Reference Audio"),
-                                    type="filepath",
-                                )
-                            with gr.Row():
-                                reference_text = gr.Textbox(
-                                    label=i18n("Reference Text"),
-                                    lines=1,
-                                    placeholder="在一无所知中，梦里的一天结束了，一个新的「轮回」便会开始。",
-                                    value="",
-                                )
+                            reference_audio = gr.Audio(label=i18n("Reference Audio"), type="filepath")
+                            reference_text = gr.Textbox(label=i18n("Reference Text"), lines=1)
 
             with gr.Column(scale=3):
-                with gr.Row():
-                    error = gr.HTML(
-                        label=i18n("Error Message"),
-                        visible=True,
-                    )
-                with gr.Row():
-                    audio = gr.Audio(
-                        label=i18n("Generated Audio"),
-                        type="numpy",
-                        interactive=False,
-                        visible=True,
-                    )
-
-                with gr.Row():
-                    with gr.Column(scale=3):
-                        generate = gr.Button(
-                            value="\U0001f3a7 " + i18n("Generate"),
-                            variant="primary",
-                        )
+                error = gr.HTML(label=i18n("Error Message"), visible=True)
+                audio = gr.Audio(label=i18n("Generated Audio"), type="numpy", interactive=False)
+                generate = gr.Button(value="Generate", variant="primary")
 
-        # Submit
         generate.click(
             inference_fct,
-            [
-                text,
-                reference_id,
-                reference_audio,
-                reference_text,
-                max_new_tokens,
-                chunk_length,
-                top_p,
-                repetition_penalty,
-                temperature,
-                seed,
-                use_memory_cache,
-            ],
+            [text, gr.State(None), reference_audio, reference_text, max_new_tokens, chunk_length, top_p, repetition_penalty, temperature, seed, gr.State("on")],
             [audio, error],
-            concurrency_limit=1,
         )
-
     return app
 
 def parse_args():
     parser = ArgumentParser()
-    parser.add_argument(
-        "--llama-checkpoint-path",
-        type=Path,
-        default="checkpoints/openaudio-s1-mini",
-    )
-    parser.add_argument(
-        "--decoder-checkpoint-path",
-        type=Path,
-        default="checkpoints/openaudio-s1-mini/codec.pth",
-    )
+    parser.add_argument("--llama-checkpoint-path", type=Path, default="checkpoints/openaudio-s1-mini")
+    parser.add_argument("--decoder-checkpoint-path", type=Path, default="checkpoints/openaudio-s1-mini/codec.pth")
     parser.add_argument("--decoder-config-name", type=str, default="modded_dac_vq")
-    parser.add_argument("--device", type=str, default="cuda")
+    parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu")
     parser.add_argument("--half", action="store_true")
-    parser.add_argument("--compile", action="store_true",default=True)
-    parser.add_argument("--max-gradio-length", type=int, default=0)
+    parser.add_argument("--compile", action="store_true", default=False)
     parser.add_argument("--theme", type=str, default="dark")
-
     return parser.parse_args()
 
-
 if __name__ == "__main__":
     args = parse_args()
     args.precision = torch.half if args.half else torch.bfloat16
 
-    logger.info("Loading Llama model...")
+    logger.info("Loading models...")
     llama_queue = launch_thread_safe_queue(
         checkpoint_path=args.llama_checkpoint_path,
         device=args.device,
         precision=args.precision,
         compile=args.compile,
     )
-    logger.info("Llama model loaded, loading VQ-GAN model...")
 
     decoder_model = load_decoder_model(
         config_name=args.decoder_config_name,
@@ -281,9 +134,6 @@ if __name__ == "__main__":
         device=args.device,
     )
 
-    logger.info("Decoder model loaded, warming up...")
-
-    # Create the inference engine
     inference_engine = TTSInferenceEngine(
         llama_queue=llama_queue,
         decoder_model=decoder_model,
@@ -291,26 +141,6 @@ if __name__ == "__main__":
         precision=args.precision,
     )
 
-    # Dry run to check if the model is loaded correctly and avoid the first-time latency
-    list(
-        inference_engine.inference(
-            ServeTTSRequest(
-                text="Hello world.",
-                references=[],
-                reference_id=None,
-                max_new_tokens=1024,
-                chunk_length=200,
-                top_p=0.7,
-                repetition_penalty=1.5,
-                temperature=0.7,
-                format="wav",
-            )
-        )
-    )
-
-    logger.info("Warming up done, launching the web UI...")
-
     inference_fct = get_inference_wrapper(inference_engine)
-
     app = build_app(inference_fct, args.theme)
-    app.queue(api_open=True).launch(show_error=True, show_api=True)
+    app.launch(show_error=True)