Update tools/webui.py

tools/webui.py

@@ -1,621 +1,546 @@
-import gc
-import html
-import io
-import os
-import queue
-import wave
-from argparse import ArgumentParser
-from functools import partial
-from pathlib import Path
-from tools.api import decode_vq_tokens, encode_reference
-import gradio as gr
-import librosa
-import numpy as np
-import pyrootutils
-import torch
-from loguru import logger
-from transformers import AutoTokenizer
-import spaces
-pyrootutils.setup_root(__file__, indicator=".project-root", pythonpath=True)
-
-
-from fish_speech.i18n import i18n
-from fish_speech.text.chn_text_norm.text import Text as ChnNormedText
-from fish_speech.utils import autocast_exclude_mps
-from tools.auto_rerank import batch_asr, calculate_wer, is_chinese, load_model
-from tools.llama.generate import (
-    GenerateRequest,
-    GenerateResponse,
-    WrappedGenerateResponse,
-    launch_thread_safe_queue,
-)
-from tools.vqgan.inference import load_model as load_decoder_model
-import torchaudio
-
-
-torchaudio.set_audio_backend("soundfile")
-
-# Make einx happy
-os.environ["EINX_FILTER_TRACEBACK"] = "false"
-
-
-HEADER_MD = f"""# Fish Speech
-
-{i18n("A text-to-speech model based on VQ-GAN and Llama developed by [Fish Audio](https://fish.audio).")}  
-
-{i18n("You can find the source code [here](https://github.com/fishaudio/fish-speech) and models [here](https://huggingface.co/fishaudio/fish-speech-1.4).")}  
-
-{i18n("Related code and weights are released under CC BY-NC-SA 4.0 License.")}  
-
-{i18n("We are not responsible for any misuse of the model, please consider your local laws and regulations before using it.")}  
-"""
-
-TEXTBOX_PLACEHOLDER = i18n("Put your text here.")
-SPACE_IMPORTED = False
-
-def build_html_error_message(error):
-    return f"""
-    <div style="color: red; 
-    font-weight: bold;">
-        {html.escape(str(error))}
-    </div>
-    """
-
-
-@torch.inference_mode()
-def inference(
-    text,
-    enable_reference_audio=False,
-    reference_audio=None,
-    reference_text="",
-    max_new_tokens=0,
-    chunk_length=100,
-    top_p=0.7,
-    repetition_penalty=1.2,
-    temperature=0.7,
-    streaming=False,
-):
-    if args.max_gradio_length > 0 and len(text) > args.max_gradio_length:
-        return (
-            None,
-            None,
-            i18n("Text is too long, please keep it under {} characters.").format(
-                args.max_gradio_length
-            ),
-        )
-
-    # Parse reference audio aka prompt
-    prompt_tokens = encode_reference(
-        decoder_model=decoder_model,
-        reference_audio=reference_audio,
-        enable_reference_audio=enable_reference_audio,
-    )
-
-    # LLAMA Inference
-    request = dict(
-        device=decoder_model.device,
-        max_new_tokens=max_new_tokens,
-        text=text,
-        top_p=top_p,
-        repetition_penalty=repetition_penalty,
-        temperature=temperature,
-        compile=args.compile,
-        iterative_prompt=chunk_length > 0,
-        chunk_length=chunk_length,
-        max_length=2048,
-        prompt_tokens=prompt_tokens if enable_reference_audio else None,
-        prompt_text=reference_text if enable_reference_audio else None,
-    )
-
-    response_queue = queue.Queue()
-    llama_queue.put(
-        GenerateRequest(
-            request=request,
-            response_queue=response_queue,
-        )
-    )
-
-    if streaming:
-        yield wav_chunk_header(), None, None
-
-    segments = []
-
-    while True:
-        result: WrappedGenerateResponse = response_queue.get()
-        if result.status == "error":
-            yield None, None, build_html_error_message(result.response)
-            break
-
-        result: GenerateResponse = result.response
-        if result.action == "next":
-            break
-
-        with autocast_exclude_mps(
-            device_type=decoder_model.device.type, dtype=args.precision
-        ):
-            fake_audios = decode_vq_tokens(
-                decoder_model=decoder_model,
-                codes=result.codes,
-            )
-
-        fake_audios = fake_audios.float().cpu().numpy()
-        segments.append(fake_audios)
-
-        if streaming:
-            yield (fake_audios * 32768).astype(np.int16).tobytes(), None, None
-
-    if len(segments) == 0:
-        return (
-            None,
-            None,
-            build_html_error_message(
-                i18n("No audio generated, please check the input text.")
-            ),
-        )
-
-    # No matter streaming or not, we need to return the final audio
-    audio = np.concatenate(segments, axis=0)
-    yield None, (decoder_model.spec_transform.sample_rate, audio), None
-
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-        gc.collect()
-
-
-def inference_with_auto_rerank(
-    text,
-    enable_reference_audio,
-    reference_audio,
-    reference_text,
-    max_new_tokens,
-    chunk_length,
-    top_p,
-    repetition_penalty,
-    temperature,
-    use_auto_rerank,
-    streaming=False,
-):
-
-    max_attempts = 2 if use_auto_rerank else 1
-    best_wer = float("inf")
-    best_audio = None
-    best_sample_rate = None
-
-    for attempt in range(max_attempts):
-        audio_generator = inference(
-            text,
-            enable_reference_audio,
-            reference_audio,
-            reference_text,
-            max_new_tokens,
-            chunk_length,
-            top_p,
-            repetition_penalty,
-            temperature,
-            streaming=False,
-        )
-
-        # 获取音频数据
-        for _ in audio_generator:
-            pass
-        _, (sample_rate, audio), message = _
-
-        if audio is None:
-            return None, None, message
-
-        if not use_auto_rerank:
-            return None, (sample_rate, audio), None
-
-        asr_result = batch_asr(asr_model, [audio], sample_rate)[0]
-        wer = calculate_wer(text, asr_result["text"])
-        if wer <= 0.3 and not asr_result["huge_gap"]:
-            return None, (sample_rate, audio), None
-
-        if wer < best_wer:
-            best_wer = wer
-            best_audio = audio
-            best_sample_rate = sample_rate
-
-        if attempt == max_attempts - 1:
-            break
-
-    return None, (best_sample_rate, best_audio), None
-
-
-inference_stream = partial(inference, streaming=True)
-
-n_audios = 4
-
-global_audio_list = []
-global_error_list = []
-
-
-def inference_wrapper(
-    text,
-    enable_reference_audio,
-    reference_audio,
-    reference_text,
-    max_new_tokens,
-    chunk_length,
-    top_p,
-    repetition_penalty,
-    temperature,
-    batch_infer_num,
-    if_load_asr_model,
-):
-    audios = []
-    errors = []
-
-    for _ in range(batch_infer_num):
-        result = inference_with_auto_rerank(
-            text,
-            enable_reference_audio,
-            reference_audio,
-            reference_text,
-            max_new_tokens,
-            chunk_length,
-            top_p,
-            repetition_penalty,
-            temperature,
-            if_load_asr_model,
-        )
-
-        _, audio_data, error_message = result
-
-        audios.append(
-            gr.Audio(value=audio_data if audio_data else None, visible=True),
-        )
-        errors.append(
-            gr.HTML(value=error_message if error_message else None, visible=True),
-        )
-
-    for _ in range(batch_infer_num, n_audios):
-        audios.append(
-            gr.Audio(value=None, visible=False),
-        )
-        errors.append(
-            gr.HTML(value=None, visible=False),
-        )
-
-    return None, *audios, *errors
-
-
-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 normalize_text(user_input, use_normalization):
-    if use_normalization:
-        return ChnNormedText(raw_text=user_input).normalize()
-    else:
-        return user_input
-
-
-asr_model = None
-
-
-def change_if_load_asr_model(if_load):
-    global asr_model
-
-    if if_load:
-        gr.Warning("Loading faster whisper model...")
-        if asr_model is None:
-            asr_model = load_model()
-        return gr.Checkbox(label="Unload faster whisper model", value=if_load)
-
-    if if_load is False:
-        gr.Warning("Unloading faster whisper model...")
-        del asr_model
-        asr_model = None
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-            gc.collect()
-        return gr.Checkbox(label="Load faster whisper model", value=if_load)
-
-
-def change_if_auto_label(if_load, if_auto_label, enable_ref, ref_audio, ref_text):
-    if if_load and asr_model is not None:
-        if (
-            if_auto_label
-            and enable_ref
-            and ref_audio is not None
-            and ref_text.strip() == ""
-        ):
-            data, sample_rate = librosa.load(ref_audio)
-            res = batch_asr(asr_model, [data], sample_rate)[0]
-            ref_text = res["text"]
-    else:
-        gr.Warning("Whisper model not loaded!")
-
-    return gr.Textbox(value=ref_text)
-
-
-def build_app():
-    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();}}"
-            % args.theme,
-        )
-
-        # Inference
-        with gr.Row():
-            with gr.Column(scale=3):
-                text = gr.Textbox(
-                    label=i18n("Input Text"), placeholder=TEXTBOX_PLACEHOLDER, lines=10
-                )
-                refined_text = gr.Textbox(
-                    label=i18n("Realtime Transform Text"),
-                    placeholder=i18n(
-                        "Normalization Result Preview (Currently Only Chinese)"
-                    ),
-                    lines=5,
-                    interactive=False,
-                )
-
-                with gr.Row():
-                    if_refine_text = gr.Checkbox(
-                        label=i18n("Text Normalization"),
-                        value=False,
-                        scale=1,
-                    )
-
-                    if_load_asr_model = gr.Checkbox(
-                        label=i18n("Load / Unload ASR model for auto-reranking"),
-                        value=False,
-                        scale=3,
-                    )
-
-                with gr.Row():
-                    with gr.Tab(label=i18n("Advanced Config")):
-                        chunk_length = gr.Slider(
-                            label=i18n("Iterative Prompt Length, 0 means off"),
-                            minimum=50,
-                            maximum=300,
-                            value=200,
-                            step=8,
-                        )
-
-                        max_new_tokens = gr.Slider(
-                            label=i18n("Maximum tokens per batch, 0 means no limit"),
-                            minimum=0,
-                            maximum=2048,
-                            value=1024,  # 0 means no limit
-                            step=8,
-                        )
-
-                        top_p = gr.Slider(
-                            label="Top-P",
-                            minimum=0.6,
-                            maximum=0.9,
-                            value=0.7,
-                            step=0.01,
-                        )
-
-                        repetition_penalty = gr.Slider(
-                            label=i18n("Repetition Penalty"),
-                            minimum=1,
-                            maximum=1.5,
-                            value=1.2,
-                            step=0.01,
-                        )
-
-                        temperature = gr.Slider(
-                            label="Temperature",
-                            minimum=0.6,
-                            maximum=0.9,
-                            value=0.7,
-                            step=0.01,
-                        )
-
-                    with gr.Tab(label=i18n("Reference Audio")):
-                        gr.Markdown(
-                            i18n(
-                                "5 to 10 seconds of reference audio, useful for specifying speaker."
-                            )
-                        )
-
-                        enable_reference_audio = gr.Checkbox(
-                            label=i18n("Enable Reference Audio"),
-                        )
-                        reference_audio = gr.Audio(
-                            label=i18n("Reference Audio"),
-                            type="filepath",
-                        )
-                        with gr.Row():
-                            if_auto_label = gr.Checkbox(
-                                label=i18n("Auto Labeling"),
-                                min_width=100,
-                                scale=0,
-                                value=False,
-                            )
-                            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.Tab(label=i18n("Batch Inference")):
-                        batch_infer_num = gr.Slider(
-                            label="Batch infer nums",
-                            minimum=1,
-                            maximum=n_audios,
-                            step=1,
-                            value=1,
-                        )
-
-            with gr.Column(scale=3):
-                for _ in range(n_audios):
-                    with gr.Row():
-                        error = gr.HTML(
-                            label=i18n("Error Message"),
-                            visible=True if _ == 0 else False,
-                        )
-                        global_error_list.append(error)
-                    with gr.Row():
-                        audio = gr.Audio(
-                            label=i18n("Generated Audio"),
-                            type="numpy",
-                            interactive=False,
-                            visible=True if _ == 0 else False,
-                        )
-                        global_audio_list.append(audio)
-
-                with gr.Row():
-                    stream_audio = gr.Audio(
-                        label=i18n("Streaming Audio"),
-                        streaming=True,
-                        autoplay=True,
-                        interactive=False,
-                        show_download_button=True,
-                    )
-                with gr.Row():
-                    with gr.Column(scale=3):
-                        generate = gr.Button(
-                            value="\U0001F3A7 " + i18n("Generate"), variant="primary"
-                        )
-                        generate_stream = gr.Button(
-                            value="\U0001F3A7 " + i18n("Streaming Generate"),
-                            variant="primary",
-                        )
-
-        text.input(
-            fn=normalize_text, inputs=[text, if_refine_text], outputs=[refined_text]
-        )
-
-        if_load_asr_model.change(
-            fn=change_if_load_asr_model,
-            inputs=[if_load_asr_model],
-            outputs=[if_load_asr_model],
-        )
-
-        if_auto_label.change(
-            fn=lambda: gr.Textbox(value=""),
-            inputs=[],
-            outputs=[reference_text],
-        ).then(
-            fn=change_if_auto_label,
-            inputs=[
-                if_load_asr_model,
-                if_auto_label,
-                enable_reference_audio,
-                reference_audio,
-                reference_text,
-            ],
-            outputs=[reference_text],
-        )
-
-        # # Submit
-        generate.click(
-            inference_wrapper,
-            [
-                refined_text,
-                enable_reference_audio,
-                reference_audio,
-                reference_text,
-                max_new_tokens,
-                chunk_length,
-                top_p,
-                repetition_penalty,
-                temperature,
-                batch_infer_num,
-                if_load_asr_model,
-            ],
-            [stream_audio, *global_audio_list, *global_error_list],
-            concurrency_limit=1,
-        )
-
-        generate_stream.click(
-            inference_stream,
-            [
-                refined_text,
-                enable_reference_audio,
-                reference_audio,
-                reference_text,
-                max_new_tokens,
-                chunk_length,
-                top_p,
-                repetition_penalty,
-                temperature,
-            ],
-            [stream_audio, global_audio_list[0], global_error_list[0]],
-            concurrency_limit=10,
-        )
-    return app
-
-
-def parse_args():
-    parser = ArgumentParser()
-    parser.add_argument(
-        "--llama-checkpoint-path",
-        type=Path,
-        default="checkpoints/fish-speech-1.4",
-    )
-    parser.add_argument(
-        "--decoder-checkpoint-path",
-        type=Path,
-        default="checkpoints/fish-speech-1.4/firefly-gan-vq-fsq-8x1024-21hz-generator.pth",
-    )
-    parser.add_argument("--decoder-config-name", type=str, default="firefly_gan_vq")
-    parser.add_argument("--device", type=str, default="cuda")
-    parser.add_argument("--half", action="store_true")
-    parser.add_argument("--compile", action="store_true")
-    parser.add_argument("--max-gradio-length", type=int, default=0)
-    parser.add_argument("--theme", type=str, default="light")
-
-    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...")
-
-    # Dry run to check if the model is loaded correctly and avoid the first-time latency
-    list(
-        inference(
-            text="",
-            enable_reference_audio=False,
-            reference_audio=None,
-            reference_text="",
-            max_new_tokens=0,
-            chunk_length=100,
-            top_p=0.7,
-            repetition_penalty=1.2,
-            temperature=0.7,
-        )
-    )
-
-    logger.info("Warming up done, launching the web UI...")
-
-    app = build_app()
-    app.launch(show_api=True)
+import gc
+import html
+import io
+import os
+import queue
+import wave
+from argparse import ArgumentParser
+from functools import partial
+from pathlib import Path
+
+import gradio as gr
+import librosa
+import numpy as np
+import pyrootutils
+import torch
+from loguru import logger
+from transformers import AutoTokenizer
+
+pyrootutils.setup_root(__file__, indicator=".project-root", pythonpath=True)
+
+
+from fish_speech.i18n import i18n
+from fish_speech.text.chn_text_norm.text import Text as ChnNormedText
+from fish_speech.utils import autocast_exclude_mps, set_seed
+from tools.api import decode_vq_tokens, encode_reference
+from tools.file import AUDIO_EXTENSIONS, list_files
+from tools.llama.generate import (
+    GenerateRequest,
+    GenerateResponse,
+    WrappedGenerateResponse,
+    launch_thread_safe_queue,
+)
+from tools.vqgan.inference import load_model as load_decoder_model
+
+# Make einx happy
+os.environ["EINX_FILTER_TRACEBACK"] = "false"
+
+
+HEADER_MD = f"""# Fish Speech
+
+{i18n("A text-to-speech model based on VQ-GAN and Llama developed by [Fish Audio](https://fish.audio).")}  
+
+{i18n("You can find the source code [here](https://github.com/fishaudio/fish-speech) and models [here](https://huggingface.co/fishaudio/fish-speech-1.4).")}  
+
+{i18n("Related code and weights are released under CC BY-NC-SA 4.0 License.")}  
+
+{i18n("We are not responsible for any misuse of the model, please consider your local laws and regulations before using it.")}  
+"""
+
+TEXTBOX_PLACEHOLDER = i18n("Put your text here.")
+SPACE_IMPORTED = False
+
+
+def build_html_error_message(error):
+    return f"""
+    <div style="color: red; 
+    font-weight: bold;">
+        {html.escape(str(error))}
+    </div>
+    """
+
+
+@torch.inference_mode()
+def inference(
+    text,
+    enable_reference_audio,
+    reference_audio,
+    reference_text,
+    max_new_tokens,
+    chunk_length,
+    top_p,
+    repetition_penalty,
+    temperature,
+    seed="0",
+    streaming=False,
+):
+    if args.max_gradio_length > 0 and len(text) > args.max_gradio_length:
+        return (
+            None,
+            None,
+            i18n("Text is too long, please keep it under {} characters.").format(
+                args.max_gradio_length
+            ),
+        )
+
+    seed = int(seed)
+    if seed != 0:
+        set_seed(seed)
+        logger.warning(f"set seed: {seed}")
+
+    # Parse reference audio aka prompt
+    prompt_tokens = encode_reference(
+        decoder_model=decoder_model,
+        reference_audio=reference_audio,
+        enable_reference_audio=enable_reference_audio,
+    )
+
+    # LLAMA Inference
+    request = dict(
+        device=decoder_model.device,
+        max_new_tokens=max_new_tokens,
+        text=text,
+        top_p=top_p,
+        repetition_penalty=repetition_penalty,
+        temperature=temperature,
+        compile=args.compile,
+        iterative_prompt=chunk_length > 0,
+        chunk_length=chunk_length,
+        max_length=2048,
+        prompt_tokens=prompt_tokens if enable_reference_audio else None,
+        prompt_text=reference_text if enable_reference_audio else None,
+    )
+
+    response_queue = queue.Queue()
+    llama_queue.put(
+        GenerateRequest(
+            request=request,
+            response_queue=response_queue,
+        )
+    )
+
+    if streaming:
+        yield wav_chunk_header(), None, None
+
+    segments = []
+
+    while True:
+        result: WrappedGenerateResponse = response_queue.get()
+        if result.status == "error":
+            yield None, None, build_html_error_message(result.response)
+            break
+
+        result: GenerateResponse = result.response
+        if result.action == "next":
+            break
+
+        with autocast_exclude_mps(
+            device_type=decoder_model.device.type, dtype=args.precision
+        ):
+            fake_audios = decode_vq_tokens(
+                decoder_model=decoder_model,
+                codes=result.codes,
+            )
+
+        fake_audios = fake_audios.float().cpu().numpy()
+        segments.append(fake_audios)
+
+        if streaming:
+            yield (fake_audios * 32768).astype(np.int16).tobytes(), None, None
+
+    if len(segments) == 0:
+        return (
+            None,
+            None,
+            build_html_error_message(
+                i18n("No audio generated, please check the input text.")
+            ),
+        )
+
+    # No matter streaming or not, we need to return the final audio
+    audio = np.concatenate(segments, axis=0)
+    yield None, (decoder_model.spec_transform.sample_rate, audio), None
+
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+        gc.collect()
+
+
+inference_stream = partial(inference, streaming=True)
+
+n_audios = 4
+
+global_audio_list = []
+global_error_list = []
+
+
+def inference_wrapper(
+    text,
+    enable_reference_audio,
+    reference_audio,
+    reference_text,
+    max_new_tokens,
+    chunk_length,
+    top_p,
+    repetition_penalty,
+    temperature,
+    seed,
+    batch_infer_num,
+):
+    audios = []
+    errors = []
+
+    for _ in range(batch_infer_num):
+        result = inference(
+            text,
+            enable_reference_audio,
+            reference_audio,
+            reference_text,
+            max_new_tokens,
+            chunk_length,
+            top_p,
+            repetition_penalty,
+            temperature,
+            seed,
+        )
+
+        _, audio_data, error_message = next(result)
+
+        audios.append(
+            gr.Audio(value=audio_data if audio_data else None, visible=True),
+        )
+        errors.append(
+            gr.HTML(value=error_message if error_message else None, visible=True),
+        )
+
+    for _ in range(batch_infer_num, n_audios):
+        audios.append(
+            gr.Audio(value=None, visible=False),
+        )
+        errors.append(
+            gr.HTML(value=None, visible=False),
+        )
+
+    return None, *audios, *errors
+
+
+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 normalize_text(user_input, use_normalization):
+    if use_normalization:
+        return ChnNormedText(raw_text=user_input).normalize()
+    else:
+        return user_input
+
+
+def update_examples():
+    examples_dir = Path("references")
+    examples_dir.mkdir(parents=True, exist_ok=True)
+    example_audios = list_files(examples_dir, AUDIO_EXTENSIONS, recursive=True)
+    return gr.Dropdown(choices=example_audios + [""])
+
+
+def build_app():
+    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();}}"
+            % args.theme,
+        )
+
+        # Inference
+        with gr.Row():
+            with gr.Column(scale=3):
+                text = gr.Textbox(
+                    label=i18n("Input Text"), placeholder=TEXTBOX_PLACEHOLDER, lines=10
+                )
+                refined_text = gr.Textbox(
+                    label=i18n("Realtime Transform Text"),
+                    placeholder=i18n(
+                        "Normalization Result Preview (Currently Only Chinese)"
+                    ),
+                    lines=5,
+                    interactive=False,
+                )
+
+                with gr.Row():
+                    if_refine_text = gr.Checkbox(
+                        label=i18n("Text Normalization"),
+                        value=False,
+                        scale=1,
+                    )
+
+                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=50,
+                                    maximum=300,
+                                    value=200,
+                                    step=8,
+                                )
+
+                                max_new_tokens = gr.Slider(
+                                    label=i18n(
+                                        "Maximum tokens per batch, 0 means no limit"
+                                    ),
+                                    minimum=0,
+                                    maximum=2048,
+                                    value=0,  # 0 means no limit
+                                    step=8,
+                                )
+
+                            with gr.Row():
+                                top_p = gr.Slider(
+                                    label="Top-P",
+                                    minimum=0.6,
+                                    maximum=0.9,
+                                    value=0.7,
+                                    step=0.01,
+                                )
+
+                                repetition_penalty = gr.Slider(
+                                    label=i18n("Repetition Penalty"),
+                                    minimum=1,
+                                    maximum=1.5,
+                                    value=1.2,
+                                    step=0.01,
+                                )
+
+                            with gr.Row():
+                                temperature = gr.Slider(
+                                    label="Temperature",
+                                    minimum=0.6,
+                                    maximum=0.9,
+                                    value=0.7,
+                                    step=0.01,
+                                )
+                                seed = gr.Textbox(
+                                    label="Seed",
+                                    info="0 means randomized inference, otherwise deterministic",
+                                    placeholder="any 32-bit-integer",
+                                    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():
+                                enable_reference_audio = gr.Checkbox(
+                                    label=i18n("Enable Reference Audio"),
+                                )
+
+                            with gr.Row():
+                                example_audio_dropdown = gr.Dropdown(
+                                    label=i18n("Select Example Audio"),
+                                    choices=[""],
+                                    value="",
+                                    interactive=True,
+                                    allow_custom_value=True,
+                                )
+                            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.Tab(label=i18n("Batch Inference")):
+                            with gr.Row():
+                                batch_infer_num = gr.Slider(
+                                    label="Batch infer nums",
+                                    minimum=1,
+                                    maximum=n_audios,
+                                    step=1,
+                                    value=1,
+                                )
+
+            with gr.Column(scale=3):
+                for _ in range(n_audios):
+                    with gr.Row():
+                        error = gr.HTML(
+                            label=i18n("Error Message"),
+                            visible=True if _ == 0 else False,
+                        )
+                        global_error_list.append(error)
+                    with gr.Row():
+                        audio = gr.Audio(
+                            label=i18n("Generated Audio"),
+                            type="numpy",
+                            interactive=False,
+                            visible=True if _ == 0 else False,
+                        )
+                        global_audio_list.append(audio)
+
+                with gr.Row():
+                    stream_audio = gr.Audio(
+                        label=i18n("Streaming Audio"),
+                        streaming=True,
+                        autoplay=True,
+                        interactive=False,
+                        show_download_button=True,
+                    )
+                with gr.Row():
+                    with gr.Column(scale=3):
+                        generate = gr.Button(
+                            value="\U0001F3A7 " + i18n("Generate"), variant="primary"
+                        )
+                        generate_stream = gr.Button(
+                            value="\U0001F3A7 " + i18n("Streaming Generate"),
+                            variant="primary",
+                        )
+
+        text.input(
+            fn=normalize_text, inputs=[text, if_refine_text], outputs=[refined_text]
+        )
+
+        def select_example_audio(audio_path):
+            audio_path = Path(audio_path)
+            if audio_path.is_file():
+                lab_file = Path(audio_path.with_suffix(".lab"))
+
+                if lab_file.exists():
+                    lab_content = lab_file.read_text(encoding="utf-8").strip()
+                else:
+                    lab_content = ""
+
+                return str(audio_path), lab_content, True
+            return None, "", False
+
+        # Connect the dropdown to update reference audio and text
+        example_audio_dropdown.change(
+            fn=update_examples, inputs=[], outputs=[example_audio_dropdown]
+        ).then(
+            fn=select_example_audio,
+            inputs=[example_audio_dropdown],
+            outputs=[reference_audio, reference_text, enable_reference_audio],
+        )
+
+        # # Submit
+        generate.click(
+            inference_wrapper,
+            [
+                refined_text,
+                enable_reference_audio,
+                reference_audio,
+                reference_text,
+                max_new_tokens,
+                chunk_length,
+                top_p,
+                repetition_penalty,
+                temperature,
+                seed,
+                batch_infer_num,
+            ],
+            [stream_audio, *global_audio_list, *global_error_list],
+            concurrency_limit=1,
+        )
+
+        generate_stream.click(
+            inference_stream,
+            [
+                refined_text,
+                enable_reference_audio,
+                reference_audio,
+                reference_text,
+                max_new_tokens,
+                chunk_length,
+                top_p,
+                repetition_penalty,
+                temperature,
+                seed,
+            ],
+            [stream_audio, global_audio_list[0], global_error_list[0]],
+            concurrency_limit=1,
+        )
+    return app
+
+
+def parse_args():
+    parser = ArgumentParser()
+    parser.add_argument(
+        "--llama-checkpoint-path",
+        type=Path,
+        default="checkpoints/fish-speech-1.4",
+    )
+    parser.add_argument(
+        "--decoder-checkpoint-path",
+        type=Path,
+        default="checkpoints/fish-speech-1.4/firefly-gan-vq-fsq-8x1024-21hz-generator.pth",
+    )
+    parser.add_argument("--decoder-config-name", type=str, default="firefly_gan_vq")
+    parser.add_argument("--device", type=str, default="cuda")
+    parser.add_argument("--half", action="store_true")
+    parser.add_argument("--compile", action="store_true")
+    parser.add_argument("--max-gradio-length", type=int, default=0)
+    parser.add_argument("--theme", type=str, default="light")
+
+    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...")
+
+    # Dry run to check if the model is loaded correctly and avoid the first-time latency
+    list(
+        inference(
+            text="Hello, world!",
+            enable_reference_audio=False,
+            reference_audio=None,
+            reference_text="",
+            max_new_tokens=0,
+            chunk_length=200,
+            top_p=0.7,
+            repetition_penalty=1.2,
+            temperature=0.7,
+        )
+    )
+
+    logger.info("Warming up done, launching the web UI...")
+
+    app = build_app()
+    app.launch(show_api=True)