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add app.py and requirements).

app.py

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+import os
+import queue
+from huggingface_hub import snapshot_download
+import numpy as np
+import wave
+import io
+import gc
+from typing import Callable
+
+# Download if not exists
+os.makedirs("checkpoints", exist_ok=True)
+snapshot_download(repo_id="fishaudio/openaudio-s1-mini", local_dir="./checkpoints/openaudio-s1-mini")
+
+print("All checkpoints downloaded")
+
+import html
+import os
+from argparse import ArgumentParser
+from pathlib import Path
+
+import gradio as gr
+import torch
+import torchaudio
+
+torchaudio.set_audio_backend("soundfile")
+
+from loguru import logger
+from fish_speech.i18n import i18n
+from fish_speech.inference_engine import TTSInferenceEngine
+from fish_speech.models.dac.inference import load_model as load_decoder_model
+from fish_speech.models.text2semantic.inference import launch_thread_safe_queue
+from tools.webui.inference import get_inference_wrapper
+from fish_speech.utils.schema import ServeTTSRequest
+
+# Make einx happy
+os.environ["EINX_FILTER_TRACEBACK"] = "false"
+
+
+HEADER_MD = """# OpenAudio S1
+
+## The demo in this space is OpenAudio S1, Please check [Fish Audio](https://fish.audio) for the best model.
+## 该 Demo 为 OpenAudio 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/openaudio-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/openaudio-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 OpenAudio S1 Mini.
+在此 WebUI 中运行的模型是 OpenAudio 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;">
+        {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,
+                                )
+
+                            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,
+                                )
+
+                            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,
+                                )
+
+                        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="",
+                                )
+
+            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",
+                        )
+
+        # 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,
+            ],
+            [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("--decoder-config-name", type=str, default="modded_dac_vq")
+    parser.add_argument("--device", type=str, default="cuda")
+    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("--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...")
+    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,
+        checkpoint_path=args.decoder_checkpoint_path,
+        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,
+        compile=args.compile,
+        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)

requirements.txt

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+torch
+torchaudio
+transformers>=4.35.2
+datasets>=2.14.5
+lightning>=2.1.0
+hydra-core>=1.3.2
+tensorboard>=2.14.1
+natsort>=8.4.0
+einops>=0.7.0
+librosa>=0.10.1
+rich>=13.5.3
+gradio>=4.0.0
+wandb>=0.15.11
+grpcio>=1.58.0
+kui>=1.6.0
+zibai-server>=0.9.0
+loguru>=0.6.0
+loralib>=0.1.2
+natsort>=8.4.0
+pyrootutils>=1.0.4
+descript-audiotools
+vector_quantize_pytorch==1.14.24
+resampy>=0.4.3
+spaces>=0.26.1
+einx[torch]==0.2.2
+opencc
+faster-whisper
+ormsgpack
+ffmpeg
+soundfile
+cachetools
+funasr
+silero-vad
+tiktoken
+numpy
+huggingface_hub
+git+https://github.com/descriptinc/descript-audio-codec