docs: add research/testing disclaimer to Gradio UI header

app.py

@@ -1,719 +1,724 @@
-"""
-LongCat-AudioDiT Enhanced – Gradio Web UI
-
-Primary workflow: Voice Cloning
-  1. Upload reference audio → auto-transcribe with Whisper
-  2. Type text to synthesise in the cloned voice
-  3. Generate → save to Voice Library with a name
-  4. Reuse any saved voice from the dropdown
-
-All actions are exposed as Gradio REST API endpoints.
-
-Usage:
-    python app.py
-    python app.py --port 7860 --share
-    python app.py --device cpu
-"""
-
-import argparse
-import logging
-import os
-import socket
-import time
-from pathlib import Path
-
-import gradio as gr
-import numpy as np
-import soundfile as sf
-import torch
-import torch.nn.functional as F
-
-from utils import normalize_text, load_audio, approx_duration_from_text
-from memory_manager import ModelMemoryManager
-from voice_library import get_library
-from download_models import (
-    download_audiodit, download_whisper,
-    _audiodit_present, _whisper_present,
-    AUDIODIT_MODELS, WHISPER_MODELS,
-    AUDIODIT_DIR, WHISPER_DIR,
-)
-
-logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
-logger = logging.getLogger(__name__)
-
-OUTPUT_DIR = Path(__file__).parent / "outputs"
-OUTPUT_DIR.mkdir(exist_ok=True)
-
-# ---------------------------------------------------------------------------
-# Memory manager
-# ---------------------------------------------------------------------------
-_mgr: ModelMemoryManager = None
-
-def get_manager(mode: str = "auto") -> ModelMemoryManager:
-    global _mgr
-    if _mgr is None or _mgr.mode.value != mode:
-        if _mgr is not None:
-            _mgr.release_all()
-        _mgr = ModelMemoryManager(mode=mode)
-    return _mgr
-
-# ---------------------------------------------------------------------------
-# Port helpers
-# ---------------------------------------------------------------------------
-def _port_free(port: int) -> bool:
-    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
-        s.settimeout(1)
-        return s.connect_ex(("127.0.0.1", port)) != 0
-
-def find_free_port(start: int = 7860, end: int = 7960) -> int:
-    for p in range(start, end):
-        if _port_free(p):
-            return p
-    raise RuntimeError(f"No free port found in {start}-{end}")
-
-# ---------------------------------------------------------------------------
-# Core: transcribe reference audio
-# ---------------------------------------------------------------------------
-def transcribe_reference(audio_path, whisper_size: str, language: str, memory_mode: str, device: str):
-    """
-    Transcribe a reference audio file with Whisper.
-    Returns (transcription_text, status_msg).
-    """
-    if audio_path is None:
-        return "", "Upload a reference audio file first."
-
-    mgr = get_manager(memory_mode)
-    try:
-        whisper = mgr.get_whisper(whisper_size=whisper_size)
-    except Exception as e:
-        return "", f"Failed to load Whisper: {e}"
-
-    lang_arg = language if language and language != "auto" else None
-    try:
-        text, detected = whisper.transcribe(str(audio_path), language=lang_arg)
-    except Exception as e:
-        return "", f"Transcription failed: {e}"
-
-    return text, f"Transcribed [{detected}] — {len(text)} characters"
-
-
-# ---------------------------------------------------------------------------
-# Core: clone voice (reference audio + transcription → new speech)
-# ---------------------------------------------------------------------------
-def clone_voice(
-    text: str,
-    ref_audio_path,
-    ref_transcription: str,
-    audiodit_size: str,
-    nfe: int,
-    guidance_strength: float,
-    guidance_method: str,
-    seed: int,
-    memory_mode: str,
-    device: str,
-):
-    """
-    Synthesise `text` in the voice captured from `ref_audio_path`.
-    Returns (output_audio_path, status_msg).
-    """
-    if not text or not text.strip():
-        return None, "Enter text to synthesise."
-    if ref_audio_path is None:
-        return None, "Upload a reference audio file."
-    if not ref_transcription or not ref_transcription.strip():
-        return None, "Reference transcription is empty. Use 'Auto-Transcribe' first."
-
-    mgr = get_manager(memory_mode)
-    try:
-        model, tokenizer = mgr.get_tts(audiodit_size=audiodit_size, device=device)
-    except Exception as e:
-        return None, f"Failed to load TTS model: {e}"
-
-    torch.manual_seed(seed)
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed(seed)
-
-    sr        = model.config.sampling_rate
-    full_hop  = model.config.latent_hop
-    max_dur   = model.config.max_wav_duration
-
-    synth_text = normalize_text(text)
-    ref_text   = normalize_text(ref_transcription)
-    full_text  = f"{ref_text} {synth_text}"
-
-    inputs = tokenizer([full_text], padding="longest", return_tensors="pt")
-    inputs = {k: v.to(device) for k, v in inputs.items()}
-
-    # Encode reference audio to get prompt duration
-    try:
-        off = 3
-        pw = load_audio(str(ref_audio_path), sr)
-        if pw.shape[-1] % full_hop != 0:
-            pw = F.pad(pw, (0, full_hop - pw.shape[-1] % full_hop))
-        pw_padded = F.pad(pw, (0, full_hop * off))
-        with torch.no_grad():
-            plt = model.vae.encode(pw_padded.unsqueeze(0).to(device))
-        if off:
-            plt = plt[..., :-off]
-        prompt_dur = plt.shape[-1]
-        prompt_wav = load_audio(str(ref_audio_path), sr).unsqueeze(0)
-    except Exception as e:
-        return None, f"Failed to process reference audio: {e}"
-
-    prompt_time = prompt_dur * full_hop / sr
-    dur_sec = approx_duration_from_text(synth_text, max_duration=max_dur - prompt_time)
-    try:
-        approx_pd = approx_duration_from_text(ref_text, max_duration=max_dur)
-        ratio = np.clip(prompt_time / approx_pd, 1.0, 1.5)
-        dur_sec = dur_sec * ratio
-    except Exception:
-        pass
-
-    duration = int(dur_sec * sr // full_hop)
-    duration = min(duration + prompt_dur, int(max_dur * sr // full_hop))
-
-    try:
-        with torch.no_grad():
-            output = model(
-                input_ids=inputs["input_ids"],
-                attention_mask=inputs["attention_mask"],
-                prompt_audio=prompt_wav,
-                duration=duration,
-                steps=nfe,
-                cfg_strength=guidance_strength,
-                guidance_method=guidance_method,
-            )
-    except Exception as e:
-        return None, f"Generation failed: {e}"
-
-    wav = output.waveform.squeeze().detach().cpu().numpy()
-    out_path = OUTPUT_DIR / f"clone_{int(time.time())}.wav"
-    sf.write(str(out_path), wav, sr)
-
-    return str(out_path), f"Done — {len(wav)/sr:.2f}s generated"
-
-
-# ---------------------------------------------------------------------------
-# Core: plain TTS (no reference voice)
-# ---------------------------------------------------------------------------
-def plain_tts(
-    text: str,
-    audiodit_size: str,
-    nfe: int,
-    guidance_strength: float,
-    guidance_method: str,
-    seed: int,
-    memory_mode: str,
-    device: str,
-):
-    """Synthesise text with no voice reference (random voice)."""
-    if not text or not text.strip():
-        return None, "Enter text to synthesise."
-
-    mgr = get_manager(memory_mode)
-    try:
-        model, tokenizer = mgr.get_tts(audiodit_size=audiodit_size, device=device)
-    except Exception as e:
-        return None, f"Failed to load TTS model: {e}"
-
-    torch.manual_seed(seed)
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed(seed)
-
-    sr       = model.config.sampling_rate
-    full_hop = model.config.latent_hop
-    max_dur  = model.config.max_wav_duration
-
-    t = normalize_text(text)
-    inputs = tokenizer([t], padding="longest", return_tensors="pt")
-    inputs = {k: v.to(device) for k, v in inputs.items()}
-
-    dur_sec  = approx_duration_from_text(t, max_duration=max_dur)
-    duration = int(dur_sec * sr // full_hop)
-    duration = min(duration, int(max_dur * sr // full_hop))
-
-    try:
-        with torch.no_grad():
-            output = model(
-                input_ids=inputs["input_ids"],
-                attention_mask=inputs["attention_mask"],
-                prompt_audio=None,
-                duration=duration,
-                steps=nfe,
-                cfg_strength=guidance_strength,
-                guidance_method=guidance_method,
-            )
-    except Exception as e:
-        return None, f"Generation failed: {e}"
-
-    wav = output.waveform.squeeze().detach().cpu().numpy()
-    out_path = OUTPUT_DIR / f"tts_{int(time.time())}.wav"
-    sf.write(str(out_path), wav, sr)
-    return str(out_path), f"Done — {len(wav)/sr:.2f}s generated"
-
-
-# ---------------------------------------------------------------------------
-# Voice Library helpers (called from UI)
-# ---------------------------------------------------------------------------
-def library_names_with_placeholder() -> list[str]:
-    lib = get_library()
-    names = lib.names()
-    return ["— select saved voice —"] + names
-
-def save_voice_to_library(name: str, audio_path, transcription: str):
-    """Save a (audio, transcription) pair to the library. Returns (new_dropdown, status)."""
-    name = (name or "").strip()
-    if not name:
-        return gr.update(), "Enter a name for this voice."
-    if audio_path is None:
-        return gr.update(), "No reference audio to save."
-    if not transcription or not transcription.strip():
-        return gr.update(), "Transcription is empty — auto-transcribe first."
-    try:
-        get_library().add(name, str(audio_path), transcription)
-    except Exception as e:
-        return gr.update(), f"Save failed: {e}"
-    choices = library_names_with_placeholder()
-    return gr.update(choices=choices, value=name), f"Saved '{name}' to voice library."
-
-def load_voice_from_library(name: str):
-    """Load a saved voice. Returns (audio_path, transcription, status)."""
-    if not name or name.startswith("—"):
-        return None, "", ""
-    entry = get_library().get(name)
-    if entry is None:
-        return None, "", f"Voice '{name}' not found."
-    audio = entry["audio_path"]
-    if not Path(audio).exists():
-        return None, "", f"Audio file missing: {audio}"
-    return audio, entry["transcription"], f"Loaded '{name}'"
-
-def delete_voice_from_library(name: str):
-    """Delete a voice. Returns (new_dropdown_update, status)."""
-    if not name or name.startswith("—"):
-        return gr.update(), "Select a voice to delete."
-    ok = get_library().remove(name)
-    choices = library_names_with_placeholder()
-    msg = f"Deleted '{name}'." if ok else f"Voice '{name}' not found."
-    return gr.update(choices=choices, value=choices[0]), msg
-
-def refresh_library_dropdown():
-    choices = library_names_with_placeholder()
-    return gr.update(choices=choices)
-
-def library_summary():
-    return get_library().summary_text()
-
-
-# ---------------------------------------------------------------------------
-# Status / unload
-# ---------------------------------------------------------------------------
-def get_status(memory_mode: str) -> str:
-    return get_manager(memory_mode).status_str()
-
-def unload_all(memory_mode: str) -> str:
-    mgr = get_manager(memory_mode)
-    mgr.release_all()
-    return "All models unloaded.\n" + mgr.status_str()
-
-
-# ---------------------------------------------------------------------------
-# Download helpers
-# ---------------------------------------------------------------------------
-def _model_inventory() -> str:
-    lines = ["AudioDiT TTS models:"]
-    for k, (repo, hint) in AUDIODIT_MODELS.items():
-        st = "[downloaded]" if _audiodit_present(k) else "not downloaded"
-        lines.append(f"  AudioDiT-{k:<6}  {hint:<8}  {st}")
-    lines.append("")
-    lines.append("Whisper STT models:")
-    for k, (repo, hint) in WHISPER_MODELS.items():
-        st = "[downloaded]" if _whisper_present(k) else "not downloaded"
-        lines.append(f"  Whisper-{k:<10}  {hint:<8}  {st}")
-    return "\n".join(lines)
-
-def download_with_progress(selected_models: list):
-    if not selected_models:
-        yield "Nothing selected."
-        return
-    log = []
-    def emit(msg):
-        log.append(msg)
-    for label in selected_models:
-        if label.startswith("AudioDiT-"):
-            size = label.replace("AudioDiT-", "")
-            _, hint = AUDIODIT_MODELS.get(size, ("", "?"))
-            log.append(f"AudioDiT-{size} ({hint}): {'already downloaded' if _audiodit_present(size) else 'downloading...'}"); yield "\n".join(log)
-            download_audiodit(size, callback=emit); yield "\n".join(log)
-        elif label.startswith("Whisper-"):
-            size = label.replace("Whisper-", "")
-            _, hint = WHISPER_MODELS.get(size, ("", "?"))
-            log.append(f"Whisper-{size} ({hint}): {'already downloaded' if _whisper_present(size) else 'downloading...'}"); yield "\n".join(log)
-            download_whisper(size, callback=emit); yield "\n".join(log)
-    log.extend(["", _model_inventory()])
-    yield "\n".join(log)
-
-
-# ---------------------------------------------------------------------------
-# Gradio UI
-# ---------------------------------------------------------------------------
-def build_ui(default_device: str = "cuda"):
-
-    AUDIODIT_CHOICES = ["1B", "3.5B"]
-    WHISPER_CHOICES  = ["turbo", "large-v3", "medium", "small"]
-    MEMORY_MODES     = ["auto", "simultaneous", "sequential"]
-    GUIDANCE_METHODS = ["cfg", "apg"]
-    LANGUAGE_CHOICES = [
-        "auto", "en", "zh", "ja", "ko", "de", "fr", "es", "pt", "ru",
-        "ar", "hi", "it", "nl", "pl", "tr", "uk", "vi", "id", "th",
-    ]
-
-    with gr.Blocks(title="LongCat-AudioDiT — Voice Cloning") as demo:
-
-        gr.Markdown(
-            "# LongCat-AudioDiT — Voice Cloning Studio\n"
-            "State-of-the-art voice cloning: give it a reference audio, type your text, get the result."
-        )
-
-        # ── Global settings row ──────────────────────────────────────────
-        with gr.Row():
-            memory_mode_dd = gr.Dropdown(MEMORY_MODES, value="auto", label="Memory Mode", scale=1)
-            device_dd      = gr.Dropdown(["cuda", "cpu"], value=default_device, label="Device", scale=1)
-            status_box     = gr.Textbox(label="Model Status", lines=3, interactive=False, scale=3)
-            with gr.Column(scale=1, min_width=160):
-                btn_status = gr.Button("Refresh Status", size="sm")
-                btn_unload = gr.Button("Unload All", size="sm", variant="stop")
-
-        gr.Markdown("---")
-
-        with gr.Tabs():
-
-            # ================================================================
-            # TAB 1 — Voice Cloning (primary workflow)
-            # ================================================================
-            with gr.Tab("Voice Cloning"):
-
-                with gr.Row():
-
-                    # ── Left: reference voice ────────────────────────────
-                    with gr.Column(scale=2):
-                        gr.Markdown("### Reference Voice")
-
-                        with gr.Row():
-                            voice_dd = gr.Dropdown(
-                                choices=library_names_with_placeholder(),
-                                value="— select saved voice —",
-                                label="Saved Voices",
-                                scale=3,
-                            )
-                            btn_load_voice   = gr.Button("Load", size="sm", scale=1)
-                            btn_refresh_lib  = gr.Button("Refresh", size="sm", scale=1)
-
-                        ref_audio = gr.Audio(
-                            label="Reference Audio  (upload or record)",
-                            type="filepath",
-                        )
-
-                        whisper_dd = gr.Dropdown(
-                            WHISPER_CHOICES, value="turbo",
-                            label="Whisper Model for Auto-Transcribe",
-                        )
-                        lang_dd = gr.Dropdown(
-                            LANGUAGE_CHOICES, value="auto", label="Language (auto=detect)"
-                        )
-                        btn_transcribe = gr.Button("Auto-Transcribe Reference", variant="secondary")
-
-                        ref_transcription = gr.Textbox(
-                            label="Reference Transcription  (auto-filled or type manually)",
-                            lines=3,
-                            placeholder="What is being said in the reference audio?",
-                        )
-
-                        gr.Markdown("**Save this voice to library**")
-                        with gr.Row():
-                            voice_name_input = gr.Textbox(
-                                label="Voice Name", placeholder="e.g. Alice", scale=3
-                            )
-                            btn_save_voice  = gr.Button("Save Voice", size="sm", scale=1, variant="primary")
-                            btn_delete_voice = gr.Button("Delete", size="sm", scale=1, variant="stop")
-
-                        lib_status = gr.Textbox(
-                            label="Library", lines=4, interactive=False,
-                            value=library_summary(),
-                        )
-
-                    # ── Right: synthesis ─────────────────────────────────
-                    with gr.Column(scale=3):
-                        gr.Markdown("### Text to Synthesise")
-
-                        synth_text = gr.Textbox(
-                            label="Text",
-                            lines=6,
-                            placeholder="Type what you want spoken in the reference voice…",
-                        )
-
-                        with gr.Row():
-                            audiodit_dd  = gr.Dropdown(AUDIODIT_CHOICES, value="1B", label="AudioDiT Model")
-                            guidance_dd  = gr.Dropdown(GUIDANCE_METHODS, value="cfg", label="Guidance")
-
-                        with gr.Accordion("Advanced", open=False):
-                            with gr.Row():
-                                nfe_sl       = gr.Slider(4, 64, value=16, step=1, label="ODE Steps")
-                                strength_sl  = gr.Slider(1.0, 10.0, value=4.0, step=0.5, label="Guidance Strength")
-                            seed_nb = gr.Number(value=1024, label="Seed", precision=0)
-
-                        btn_clone = gr.Button(
-                            "Generate  —  Clone Voice", variant="primary", size="lg"
-                        )
-
-                        clone_audio_out = gr.Audio(label="Output", type="filepath")
-                        clone_status    = gr.Textbox(label="Status", lines=2, interactive=False)
-
-                # ── Wire up Tab 1 ────────────────────────────────────────
-
-                btn_transcribe.click(
-                    fn=transcribe_reference,
-                    inputs=[ref_audio, whisper_dd, lang_dd, memory_mode_dd, device_dd],
-                    outputs=[ref_transcription, clone_status],
-                    api_name="transcribe_reference",
-                )
-
-                btn_clone.click(
-                    fn=clone_voice,
-                    inputs=[
-                        synth_text, ref_audio, ref_transcription,
-                        audiodit_dd, nfe_sl, strength_sl, guidance_dd,
-                        seed_nb, memory_mode_dd, device_dd,
-                    ],
-                    outputs=[clone_audio_out, clone_status],
-                    api_name="clone_voice",
-                )
-
-                btn_save_voice.click(
-                    fn=save_voice_to_library,
-                    inputs=[voice_name_input, ref_audio, ref_transcription],
-                    outputs=[voice_dd, lib_status],
-                    api_name="save_voice",
-                )
-
-                btn_load_voice.click(
-                    fn=load_voice_from_library,
-                    inputs=[voice_dd],
-                    outputs=[ref_audio, ref_transcription, clone_status],
-                    api_name="load_voice",
-                )
-
-                btn_delete_voice.click(
-                    fn=delete_voice_from_library,
-                    inputs=[voice_dd],
-                    outputs=[voice_dd, lib_status],
-                    api_name="delete_voice",
-                )
-
-                btn_refresh_lib.click(
-                    fn=lambda: (refresh_library_dropdown(), library_summary()),
-                    inputs=[],
-                    outputs=[voice_dd, lib_status],
-                    api_name="list_voices",
-                )
-
-            # ================================================================
-            # TAB 2 — Plain TTS (no reference voice)
-            # ================================================================
-            with gr.Tab("Plain TTS"):
-                gr.Markdown(
-                    "Synthesise speech without a reference voice. "
-                    "The model picks a random voice — useful for testing or when you just need audio."
-                )
-                with gr.Row():
-                    with gr.Column(scale=3):
-                        tts_text = gr.Textbox(label="Text", lines=6, placeholder="Enter text here…")
-                        with gr.Row():
-                            tts_model_dd   = gr.Dropdown(AUDIODIT_CHOICES, value="1B", label="Model")
-                            tts_guidance_dd = gr.Dropdown(GUIDANCE_METHODS, value="cfg", label="Guidance")
-                        with gr.Accordion("Advanced", open=False):
-                            with gr.Row():
-                                tts_nfe      = gr.Slider(4, 64, value=16, step=1, label="ODE Steps")
-                                tts_guidance = gr.Slider(1.0, 10.0, value=4.0, step=0.5, label="Guidance Strength")
-                            tts_seed = gr.Number(value=1024, label="Seed", precision=0)
-                        tts_btn = gr.Button("Generate Speech", variant="primary", size="lg")
-                    with gr.Column(scale=2):
-                        tts_audio_out = gr.Audio(label="Output", type="filepath")
-                        tts_status    = gr.Textbox(label="Status", lines=2, interactive=False)
-
-                tts_btn.click(
-                    fn=plain_tts,
-                    inputs=[
-                        tts_text, tts_model_dd, tts_nfe, tts_guidance,
-                        tts_guidance_dd, tts_seed, memory_mode_dd, device_dd,
-                    ],
-                    outputs=[tts_audio_out, tts_status],
-                    api_name="plain_tts",
-                )
-
-            # ================================================================
-            # TAB 3 — Transcribe Only
-            # ================================================================
-            with gr.Tab("Transcribe Audio"):
-                gr.Markdown("Transcribe any audio file with Whisper — output is plain text.")
-                with gr.Row():
-                    with gr.Column():
-                        stt_audio_in  = gr.Audio(label="Audio", type="filepath")
-                        stt_model_dd  = gr.Dropdown(WHISPER_CHOICES, value="turbo", label="Whisper Model")
-                        stt_lang_dd   = gr.Dropdown(LANGUAGE_CHOICES, value="auto", label="Language")
-                        stt_btn       = gr.Button("Transcribe", variant="primary", size="lg")
-                    with gr.Column():
-                        stt_text_out  = gr.Textbox(label="Transcription", lines=10)
-                        stt_lang_out  = gr.Textbox(label="Detected Language", scale=1)
-                        stt_status    = gr.Textbox(label="Status", lines=2, interactive=False)
-
-                stt_btn.click(
-                    fn=_stt_flat,
-                    inputs=[stt_audio_in, stt_model_dd, stt_lang_dd, memory_mode_dd, device_dd],
-                    outputs=[stt_text_out, stt_lang_out, stt_status],
-                    api_name="transcribe",
-                )
-
-            # ================================================================
-            # TAB 4 — Download Models
-            # ================================================================
-            with gr.Tab("Download Models"):
-                gr.Markdown(
-                    "**Download models before using them.** "
-                    "Select what you need, hit Download, watch the live log. "
-                    "Already-downloaded models are skipped automatically."
-                )
-
-                _dl_choices = (
-                    [f"AudioDiT-{k}  ({hint})" for k, (_, hint) in AUDIODIT_MODELS.items()]
-                    + [f"Whisper-{k}  ({hint})" for k, (_, hint) in WHISPER_MODELS.items()]
-                )
-                _dl_values = (
-                    [f"AudioDiT-{k}" for k in AUDIODIT_MODELS]
-                    + [f"Whisper-{k}" for k in WHISPER_MODELS]
-                )
-                _label_to_value = dict(zip(_dl_choices, _dl_values))
-
-                dl_checkboxes = gr.CheckboxGroup(
-                    choices=_dl_choices,
-                    value=[_dl_choices[0], _dl_choices[2]],
-                    label="Models to Download",
-                )
-                with gr.Row():
-                    dl_btn     = gr.Button("Download Selected", variant="primary", size="lg")
-                    dl_refresh = gr.Button("Refresh Status", size="lg")
-
-                dl_log = gr.Textbox(
-                    label="Download Log", lines=16, interactive=False,
-                    value=_model_inventory(),
-                )
-
-                def _run_download(selected_labels):
-                    keys = [_label_to_value.get(lbl, lbl.split("  ")[0]) for lbl in selected_labels]
-                    yield from download_with_progress(keys)
-
-                dl_btn.click(fn=_run_download, inputs=[dl_checkboxes], outputs=[dl_log])
-                dl_refresh.click(fn=lambda: _model_inventory(), inputs=[], outputs=[dl_log])
-
-            # ================================================================
-            # TAB 5 — About
-            # ================================================================
-            with gr.Tab("About"):
-                gr.Markdown("""
-## LongCat-AudioDiT Enhanced
-
-Enhanced fork of [LongCat-AudioDiT](https://github.com/meituan-longcat/LongCat-AudioDiT) (Meituan) — Apache-2.0.
-
-### API Endpoints (Gradio REST API)
-All actions are available as REST endpoints at `/api/`:
-
-| Endpoint | Description |
-|---|---|
-| `POST /api/clone_voice` | Clone a voice: text + reference audio + transcription → audio |
-| `POST /api/transcribe_reference` | Transcribe reference audio with Whisper |
-| `POST /api/plain_tts` | Generate speech without a reference voice |
-| `POST /api/transcribe` | Transcribe any audio file |
-| `POST /api/save_voice` | Save a voice to the library |
-| `POST /api/load_voice` | Load a voice from the library by name |
-| `POST /api/delete_voice` | Delete a voice from the library |
-| `POST /api/list_voices` | List all saved voices |
-
-### Models
-| Model | VRAM | Notes |
-|---|---|---|
-| AudioDiT-1B | ~4 GB | Fast, great quality |
-| AudioDiT-3.5B | ~10 GB | SOTA quality |
-| Whisper Turbo | ~1.6 GB | Fast transcription |
-| Whisper large-v3 | ~3 GB | Most accurate |
-
-### Voice Library
-Voices are stored in `./voices/library.json` with audio files in `./voices/`.
-                """)
-
-        # ── Global callbacks ─────────────────────────────────────────────
-        btn_status.click(fn=get_status, inputs=[memory_mode_dd], outputs=[status_box])
-        btn_unload.click(fn=unload_all, inputs=[memory_mode_dd], outputs=[status_box])
-        memory_mode_dd.change(fn=get_status, inputs=[memory_mode_dd], outputs=[status_box])
-
-    return demo
-
-
-# ---------------------------------------------------------------------------
-# STT flat helper (avoids walrus-operator gymnastics in the lambda above)
-# ---------------------------------------------------------------------------
-def _stt_flat(audio_path, whisper_size, language, memory_mode, device):
-    """Returns (transcription, detected_language, status_msg) — three separate values."""
-    from memory_manager import ModelMemoryManager
-    mgr = get_manager(memory_mode)
-    try:
-        whisper = mgr.get_whisper(whisper_size=whisper_size)
-    except Exception as e:
-        return "", "", f"Failed to load Whisper: {e}"
-    if audio_path is None:
-        return "", "", "Upload an audio file."
-    lang_arg = language if language and language != "auto" else None
-    try:
-        text, detected = whisper.transcribe(str(audio_path), language=lang_arg)
-    except Exception as e:
-        return "", "", f"Transcription failed: {e}"
-    return text, detected, f"Transcribed [{detected}] — {len(text)} chars"
-
-
-# ---------------------------------------------------------------------------
-# Entry point
-# ---------------------------------------------------------------------------
-def main():
-    parser = argparse.ArgumentParser(description="LongCat-AudioDiT Voice Cloning Studio")
-    parser.add_argument("--port",   type=int,  default=0)
-    parser.add_argument("--host",   type=str,  default="0.0.0.0")
-    parser.add_argument("--share",  action="store_true")
-    parser.add_argument("--device", type=str,  default="auto")
-    parser.add_argument("--mode",   type=str,  default="auto",
-                        choices=["auto", "simultaneous", "sequential"])
-    args = parser.parse_args()
-
-    device = "cuda" if (args.device == "auto" and torch.cuda.is_available()) else args.device
-
-    if args.port == 0:
-        port = find_free_port(7860, 7960)
-    elif not _port_free(args.port):
-        logger.warning("Port %d busy, searching…", args.port)
-        port = find_free_port(args.port + 1, args.port + 100)
-    else:
-        port = args.port
-
-    logger.info("Starting on %s:%d (device=%s, mode=%s)", args.host, port, device, args.mode)
-    get_manager(args.mode)
-
-    demo = build_ui(default_device=device)
-    demo.launch(
-        server_name=args.host,
-        server_port=port,
-        share=args.share,
-        show_error=True,
-        theme=gr.themes.Soft(),
-    )
-
-
-if __name__ == "__main__":
-    main()
+"""
+LongCat-AudioDiT Enhanced – Gradio Web UI
+
+Primary workflow: Voice Cloning
+  1. Upload reference audio → auto-transcribe with Whisper
+  2. Type text to synthesise in the cloned voice
+  3. Generate → save to Voice Library with a name
+  4. Reuse any saved voice from the dropdown
+
+All actions are exposed as Gradio REST API endpoints.
+
+Usage:
+    python app.py
+    python app.py --port 7860 --share
+    python app.py --device cpu
+"""
+
+import argparse
+import logging
+import os
+import socket
+import time
+from pathlib import Path
+
+import gradio as gr
+import numpy as np
+import soundfile as sf
+import torch
+import torch.nn.functional as F
+
+from utils import normalize_text, load_audio, approx_duration_from_text
+from memory_manager import ModelMemoryManager
+from voice_library import get_library
+from download_models import (
+    download_audiodit, download_whisper,
+    _audiodit_present, _whisper_present,
+    AUDIODIT_MODELS, WHISPER_MODELS,
+    AUDIODIT_DIR, WHISPER_DIR,
+)
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
+logger = logging.getLogger(__name__)
+
+OUTPUT_DIR = Path(__file__).parent / "outputs"
+OUTPUT_DIR.mkdir(exist_ok=True)
+
+# ---------------------------------------------------------------------------
+# Memory manager
+# ---------------------------------------------------------------------------
+_mgr: ModelMemoryManager = None
+
+def get_manager(mode: str = "auto") -> ModelMemoryManager:
+    global _mgr
+    if _mgr is None or _mgr.mode.value != mode:
+        if _mgr is not None:
+            _mgr.release_all()
+        _mgr = ModelMemoryManager(mode=mode)
+    return _mgr
+
+# ---------------------------------------------------------------------------
+# Port helpers
+# ---------------------------------------------------------------------------
+def _port_free(port: int) -> bool:
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.settimeout(1)
+        return s.connect_ex(("127.0.0.1", port)) != 0
+
+def find_free_port(start: int = 7860, end: int = 7960) -> int:
+    for p in range(start, end):
+        if _port_free(p):
+            return p
+    raise RuntimeError(f"No free port found in {start}-{end}")
+
+# ---------------------------------------------------------------------------
+# Core: transcribe reference audio
+# ---------------------------------------------------------------------------
+def transcribe_reference(audio_path, whisper_size: str, language: str, memory_mode: str, device: str):
+    """
+    Transcribe a reference audio file with Whisper.
+    Returns (transcription_text, status_msg).
+    """
+    if audio_path is None:
+        return "", "Upload a reference audio file first."
+
+    mgr = get_manager(memory_mode)
+    try:
+        whisper = mgr.get_whisper(whisper_size=whisper_size)
+    except Exception as e:
+        return "", f"Failed to load Whisper: {e}"
+
+    lang_arg = language if language and language != "auto" else None
+    try:
+        text, detected = whisper.transcribe(str(audio_path), language=lang_arg)
+    except Exception as e:
+        return "", f"Transcription failed: {e}"
+
+    return text, f"Transcribed [{detected}] — {len(text)} characters"
+
+
+# ---------------------------------------------------------------------------
+# Core: clone voice (reference audio + transcription → new speech)
+# ---------------------------------------------------------------------------
+def clone_voice(
+    text: str,
+    ref_audio_path,
+    ref_transcription: str,
+    audiodit_size: str,
+    nfe: int,
+    guidance_strength: float,
+    guidance_method: str,
+    seed: int,
+    memory_mode: str,
+    device: str,
+):
+    """
+    Synthesise `text` in the voice captured from `ref_audio_path`.
+    Returns (output_audio_path, status_msg).
+    """
+    if not text or not text.strip():
+        return None, "Enter text to synthesise."
+    if ref_audio_path is None:
+        return None, "Upload a reference audio file."
+    if not ref_transcription or not ref_transcription.strip():
+        return None, "Reference transcription is empty. Use 'Auto-Transcribe' first."
+
+    mgr = get_manager(memory_mode)
+    try:
+        model, tokenizer = mgr.get_tts(audiodit_size=audiodit_size, device=device)
+    except Exception as e:
+        return None, f"Failed to load TTS model: {e}"
+
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(seed)
+
+    sr        = model.config.sampling_rate
+    full_hop  = model.config.latent_hop
+    max_dur   = model.config.max_wav_duration
+
+    synth_text = normalize_text(text)
+    ref_text   = normalize_text(ref_transcription)
+    full_text  = f"{ref_text} {synth_text}"
+
+    inputs = tokenizer([full_text], padding="longest", return_tensors="pt")
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+
+    # Encode reference audio to get prompt duration
+    try:
+        off = 3
+        pw = load_audio(str(ref_audio_path), sr)
+        if pw.shape[-1] % full_hop != 0:
+            pw = F.pad(pw, (0, full_hop - pw.shape[-1] % full_hop))
+        pw_padded = F.pad(pw, (0, full_hop * off))
+        with torch.no_grad():
+            plt = model.vae.encode(pw_padded.unsqueeze(0).to(device))
+        if off:
+            plt = plt[..., :-off]
+        prompt_dur = plt.shape[-1]
+        prompt_wav = load_audio(str(ref_audio_path), sr).unsqueeze(0)
+    except Exception as e:
+        return None, f"Failed to process reference audio: {e}"
+
+    prompt_time = prompt_dur * full_hop / sr
+    dur_sec = approx_duration_from_text(synth_text, max_duration=max_dur - prompt_time)
+    try:
+        approx_pd = approx_duration_from_text(ref_text, max_duration=max_dur)
+        ratio = np.clip(prompt_time / approx_pd, 1.0, 1.5)
+        dur_sec = dur_sec * ratio
+    except Exception:
+        pass
+
+    duration = int(dur_sec * sr // full_hop)
+    duration = min(duration + prompt_dur, int(max_dur * sr // full_hop))
+
+    try:
+        with torch.no_grad():
+            output = model(
+                input_ids=inputs["input_ids"],
+                attention_mask=inputs["attention_mask"],
+                prompt_audio=prompt_wav,
+                duration=duration,
+                steps=nfe,
+                cfg_strength=guidance_strength,
+                guidance_method=guidance_method,
+            )
+    except Exception as e:
+        return None, f"Generation failed: {e}"
+
+    wav = output.waveform.squeeze().detach().cpu().numpy()
+    out_path = OUTPUT_DIR / f"clone_{int(time.time())}.wav"
+    sf.write(str(out_path), wav, sr)
+
+    return str(out_path), f"Done — {len(wav)/sr:.2f}s generated"
+
+
+# ---------------------------------------------------------------------------
+# Core: plain TTS (no reference voice)
+# ---------------------------------------------------------------------------
+def plain_tts(
+    text: str,
+    audiodit_size: str,
+    nfe: int,
+    guidance_strength: float,
+    guidance_method: str,
+    seed: int,
+    memory_mode: str,
+    device: str,
+):
+    """Synthesise text with no voice reference (random voice)."""
+    if not text or not text.strip():
+        return None, "Enter text to synthesise."
+
+    mgr = get_manager(memory_mode)
+    try:
+        model, tokenizer = mgr.get_tts(audiodit_size=audiodit_size, device=device)
+    except Exception as e:
+        return None, f"Failed to load TTS model: {e}"
+
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(seed)
+
+    sr       = model.config.sampling_rate
+    full_hop = model.config.latent_hop
+    max_dur  = model.config.max_wav_duration
+
+    t = normalize_text(text)
+    inputs = tokenizer([t], padding="longest", return_tensors="pt")
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+
+    dur_sec  = approx_duration_from_text(t, max_duration=max_dur)
+    duration = int(dur_sec * sr // full_hop)
+    duration = min(duration, int(max_dur * sr // full_hop))
+
+    try:
+        with torch.no_grad():
+            output = model(
+                input_ids=inputs["input_ids"],
+                attention_mask=inputs["attention_mask"],
+                prompt_audio=None,
+                duration=duration,
+                steps=nfe,
+                cfg_strength=guidance_strength,
+                guidance_method=guidance_method,
+            )
+    except Exception as e:
+        return None, f"Generation failed: {e}"
+
+    wav = output.waveform.squeeze().detach().cpu().numpy()
+    out_path = OUTPUT_DIR / f"tts_{int(time.time())}.wav"
+    sf.write(str(out_path), wav, sr)
+    return str(out_path), f"Done — {len(wav)/sr:.2f}s generated"
+
+
+# ---------------------------------------------------------------------------
+# Voice Library helpers (called from UI)
+# ---------------------------------------------------------------------------
+def library_names_with_placeholder() -> list[str]:
+    lib = get_library()
+    names = lib.names()
+    return ["— select saved voice —"] + names
+
+def save_voice_to_library(name: str, audio_path, transcription: str):
+    """Save a (audio, transcription) pair to the library. Returns (new_dropdown, status)."""
+    name = (name or "").strip()
+    if not name:
+        return gr.update(), "Enter a name for this voice."
+    if audio_path is None:
+        return gr.update(), "No reference audio to save."
+    if not transcription or not transcription.strip():
+        return gr.update(), "Transcription is empty — auto-transcribe first."
+    try:
+        get_library().add(name, str(audio_path), transcription)
+    except Exception as e:
+        return gr.update(), f"Save failed: {e}"
+    choices = library_names_with_placeholder()
+    return gr.update(choices=choices, value=name), f"Saved '{name}' to voice library."
+
+def load_voice_from_library(name: str):
+    """Load a saved voice. Returns (audio_path, transcription, status)."""
+    if not name or name.startswith("—"):
+        return None, "", ""
+    entry = get_library().get(name)
+    if entry is None:
+        return None, "", f"Voice '{name}' not found."
+    audio = entry["audio_path"]
+    if not Path(audio).exists():
+        return None, "", f"Audio file missing: {audio}"
+    return audio, entry["transcription"], f"Loaded '{name}'"
+
+def delete_voice_from_library(name: str):
+    """Delete a voice. Returns (new_dropdown_update, status)."""
+    if not name or name.startswith("—"):
+        return gr.update(), "Select a voice to delete."
+    ok = get_library().remove(name)
+    choices = library_names_with_placeholder()
+    msg = f"Deleted '{name}'." if ok else f"Voice '{name}' not found."
+    return gr.update(choices=choices, value=choices[0]), msg
+
+def refresh_library_dropdown():
+    choices = library_names_with_placeholder()
+    return gr.update(choices=choices)
+
+def library_summary():
+    return get_library().summary_text()
+
+
+# ---------------------------------------------------------------------------
+# Status / unload
+# ---------------------------------------------------------------------------
+def get_status(memory_mode: str) -> str:
+    return get_manager(memory_mode).status_str()
+
+def unload_all(memory_mode: str) -> str:
+    mgr = get_manager(memory_mode)
+    mgr.release_all()
+    return "All models unloaded.\n" + mgr.status_str()
+
+
+# ---------------------------------------------------------------------------
+# Download helpers
+# ---------------------------------------------------------------------------
+def _model_inventory() -> str:
+    lines = ["AudioDiT TTS models:"]
+    for k, (repo, hint) in AUDIODIT_MODELS.items():
+        st = "[downloaded]" if _audiodit_present(k) else "not downloaded"
+        lines.append(f"  AudioDiT-{k:<6}  {hint:<8}  {st}")
+    lines.append("")
+    lines.append("Whisper STT models:")
+    for k, (repo, hint) in WHISPER_MODELS.items():
+        st = "[downloaded]" if _whisper_present(k) else "not downloaded"
+        lines.append(f"  Whisper-{k:<10}  {hint:<8}  {st}")
+    return "\n".join(lines)
+
+def download_with_progress(selected_models: list):
+    if not selected_models:
+        yield "Nothing selected."
+        return
+    log = []
+    def emit(msg):
+        log.append(msg)
+    for label in selected_models:
+        if label.startswith("AudioDiT-"):
+            size = label.replace("AudioDiT-", "")
+            _, hint = AUDIODIT_MODELS.get(size, ("", "?"))
+            log.append(f"AudioDiT-{size} ({hint}): {'already downloaded' if _audiodit_present(size) else 'downloading...'}"); yield "\n".join(log)
+            download_audiodit(size, callback=emit); yield "\n".join(log)
+        elif label.startswith("Whisper-"):
+            size = label.replace("Whisper-", "")
+            _, hint = WHISPER_MODELS.get(size, ("", "?"))
+            log.append(f"Whisper-{size} ({hint}): {'already downloaded' if _whisper_present(size) else 'downloading...'}"); yield "\n".join(log)
+            download_whisper(size, callback=emit); yield "\n".join(log)
+    log.extend(["", _model_inventory()])
+    yield "\n".join(log)
+
+
+# ---------------------------------------------------------------------------
+# Gradio UI
+# ---------------------------------------------------------------------------
+def build_ui(default_device: str = "cuda"):
+
+    AUDIODIT_CHOICES = ["1B", "3.5B"]
+    WHISPER_CHOICES  = ["turbo", "large-v3", "medium", "small"]
+    MEMORY_MODES     = ["auto", "simultaneous", "sequential"]
+    GUIDANCE_METHODS = ["cfg", "apg"]
+    LANGUAGE_CHOICES = [
+        "auto", "en", "zh", "ja", "ko", "de", "fr", "es", "pt", "ru",
+        "ar", "hi", "it", "nl", "pl", "tr", "uk", "vi", "id", "th",
+    ]
+
+    with gr.Blocks(title="LongCat-AudioDiT — Voice Cloning") as demo:
+
+        gr.Markdown(
+            "# LongCat-AudioDiT — Voice Cloning Studio\n"
+            "State-of-the-art voice cloning based on [LongCat-AudioDiT](https://github.com/meituan-longcat/LongCat-AudioDiT) by the Meituan LongCat Team. "
+            "Give it a reference audio, type your text, get the result.\n\n"
+            "> **Research & Testing Only.** This tool is provided strictly for research, educational, and personal experimentation purposes. "
+            "It is **not** intended for generating deceptive, misleading, or harmful content. "
+            "Do not use it to impersonate real individuals without their explicit consent, to create non-consensual deepfakes, "
+            "or for any activity that violates applicable laws. By using this tool you accept full responsibility for your use."
+        )
+
+        # ── Global settings row ──────────────────────────────────────────
+        with gr.Row():
+            memory_mode_dd = gr.Dropdown(MEMORY_MODES, value="auto", label="Memory Mode", scale=1)
+            device_dd      = gr.Dropdown(["cuda", "cpu"], value=default_device, label="Device", scale=1)
+            status_box     = gr.Textbox(label="Model Status", lines=3, interactive=False, scale=3)
+            with gr.Column(scale=1, min_width=160):
+                btn_status = gr.Button("Refresh Status", size="sm")
+                btn_unload = gr.Button("Unload All", size="sm", variant="stop")
+
+        gr.Markdown("---")
+
+        with gr.Tabs():
+
+            # ================================================================
+            # TAB 1 — Voice Cloning (primary workflow)
+            # ================================================================
+            with gr.Tab("Voice Cloning"):
+
+                with gr.Row():
+
+                    # ── Left: reference voice ────────────────────────────
+                    with gr.Column(scale=2):
+                        gr.Markdown("### Reference Voice")
+
+                        with gr.Row():
+                            voice_dd = gr.Dropdown(
+                                choices=library_names_with_placeholder(),
+                                value="— select saved voice —",
+                                label="Saved Voices",
+                                scale=3,
+                            )
+                            btn_load_voice   = gr.Button("Load", size="sm", scale=1)
+                            btn_refresh_lib  = gr.Button("Refresh", size="sm", scale=1)
+
+                        ref_audio = gr.Audio(
+                            label="Reference Audio  (upload or record)",
+                            type="filepath",
+                        )
+
+                        whisper_dd = gr.Dropdown(
+                            WHISPER_CHOICES, value="turbo",
+                            label="Whisper Model for Auto-Transcribe",
+                        )
+                        lang_dd = gr.Dropdown(
+                            LANGUAGE_CHOICES, value="auto", label="Language (auto=detect)"
+                        )
+                        btn_transcribe = gr.Button("Auto-Transcribe Reference", variant="secondary")
+
+                        ref_transcription = gr.Textbox(
+                            label="Reference Transcription  (auto-filled or type manually)",
+                            lines=3,
+                            placeholder="What is being said in the reference audio?",
+                        )
+
+                        gr.Markdown("**Save this voice to library**")
+                        with gr.Row():
+                            voice_name_input = gr.Textbox(
+                                label="Voice Name", placeholder="e.g. Alice", scale=3
+                            )
+                            btn_save_voice  = gr.Button("Save Voice", size="sm", scale=1, variant="primary")
+                            btn_delete_voice = gr.Button("Delete", size="sm", scale=1, variant="stop")
+
+                        lib_status = gr.Textbox(
+                            label="Library", lines=4, interactive=False,
+                            value=library_summary(),
+                        )
+
+                    # ── Right: synthesis ─────────────────────────────────
+                    with gr.Column(scale=3):
+                        gr.Markdown("### Text to Synthesise")
+
+                        synth_text = gr.Textbox(
+                            label="Text",
+                            lines=6,
+                            placeholder="Type what you want spoken in the reference voice…",
+                        )
+
+                        with gr.Row():
+                            audiodit_dd  = gr.Dropdown(AUDIODIT_CHOICES, value="1B", label="AudioDiT Model")
+                            guidance_dd  = gr.Dropdown(GUIDANCE_METHODS, value="cfg", label="Guidance")
+
+                        with gr.Accordion("Advanced", open=False):
+                            with gr.Row():
+                                nfe_sl       = gr.Slider(4, 64, value=16, step=1, label="ODE Steps")
+                                strength_sl  = gr.Slider(1.0, 10.0, value=4.0, step=0.5, label="Guidance Strength")
+                            seed_nb = gr.Number(value=1024, label="Seed", precision=0)
+
+                        btn_clone = gr.Button(
+                            "Generate  —  Clone Voice", variant="primary", size="lg"
+                        )
+
+                        clone_audio_out = gr.Audio(label="Output", type="filepath")
+                        clone_status    = gr.Textbox(label="Status", lines=2, interactive=False)
+
+                # ── Wire up Tab 1 ────────────────────────────────────────
+
+                btn_transcribe.click(
+                    fn=transcribe_reference,
+                    inputs=[ref_audio, whisper_dd, lang_dd, memory_mode_dd, device_dd],
+                    outputs=[ref_transcription, clone_status],
+                    api_name="transcribe_reference",
+                )
+
+                btn_clone.click(
+                    fn=clone_voice,
+                    inputs=[
+                        synth_text, ref_audio, ref_transcription,
+                        audiodit_dd, nfe_sl, strength_sl, guidance_dd,
+                        seed_nb, memory_mode_dd, device_dd,
+                    ],
+                    outputs=[clone_audio_out, clone_status],
+                    api_name="clone_voice",
+                )
+
+                btn_save_voice.click(
+                    fn=save_voice_to_library,
+                    inputs=[voice_name_input, ref_audio, ref_transcription],
+                    outputs=[voice_dd, lib_status],
+                    api_name="save_voice",
+                )
+
+                btn_load_voice.click(
+                    fn=load_voice_from_library,
+                    inputs=[voice_dd],
+                    outputs=[ref_audio, ref_transcription, clone_status],
+                    api_name="load_voice",
+                )
+
+                btn_delete_voice.click(
+                    fn=delete_voice_from_library,
+                    inputs=[voice_dd],
+                    outputs=[voice_dd, lib_status],
+                    api_name="delete_voice",
+                )
+
+                btn_refresh_lib.click(
+                    fn=lambda: (refresh_library_dropdown(), library_summary()),
+                    inputs=[],
+                    outputs=[voice_dd, lib_status],
+                    api_name="list_voices",
+                )
+
+            # ================================================================
+            # TAB 2 — Plain TTS (no reference voice)
+            # ================================================================
+            with gr.Tab("Plain TTS"):
+                gr.Markdown(
+                    "Synthesise speech without a reference voice. "
+                    "The model picks a random voice — useful for testing or when you just need audio."
+                )
+                with gr.Row():
+                    with gr.Column(scale=3):
+                        tts_text = gr.Textbox(label="Text", lines=6, placeholder="Enter text here…")
+                        with gr.Row():
+                            tts_model_dd   = gr.Dropdown(AUDIODIT_CHOICES, value="1B", label="Model")
+                            tts_guidance_dd = gr.Dropdown(GUIDANCE_METHODS, value="cfg", label="Guidance")
+                        with gr.Accordion("Advanced", open=False):
+                            with gr.Row():
+                                tts_nfe      = gr.Slider(4, 64, value=16, step=1, label="ODE Steps")
+                                tts_guidance = gr.Slider(1.0, 10.0, value=4.0, step=0.5, label="Guidance Strength")
+                            tts_seed = gr.Number(value=1024, label="Seed", precision=0)
+                        tts_btn = gr.Button("Generate Speech", variant="primary", size="lg")
+                    with gr.Column(scale=2):
+                        tts_audio_out = gr.Audio(label="Output", type="filepath")
+                        tts_status    = gr.Textbox(label="Status", lines=2, interactive=False)
+
+                tts_btn.click(
+                    fn=plain_tts,
+                    inputs=[
+                        tts_text, tts_model_dd, tts_nfe, tts_guidance,
+                        tts_guidance_dd, tts_seed, memory_mode_dd, device_dd,
+                    ],
+                    outputs=[tts_audio_out, tts_status],
+                    api_name="plain_tts",
+                )
+
+            # ================================================================
+            # TAB 3 — Transcribe Only
+            # ================================================================
+            with gr.Tab("Transcribe Audio"):
+                gr.Markdown("Transcribe any audio file with Whisper — output is plain text.")
+                with gr.Row():
+                    with gr.Column():
+                        stt_audio_in  = gr.Audio(label="Audio", type="filepath")
+                        stt_model_dd  = gr.Dropdown(WHISPER_CHOICES, value="turbo", label="Whisper Model")
+                        stt_lang_dd   = gr.Dropdown(LANGUAGE_CHOICES, value="auto", label="Language")
+                        stt_btn       = gr.Button("Transcribe", variant="primary", size="lg")
+                    with gr.Column():
+                        stt_text_out  = gr.Textbox(label="Transcription", lines=10)
+                        stt_lang_out  = gr.Textbox(label="Detected Language", scale=1)
+                        stt_status    = gr.Textbox(label="Status", lines=2, interactive=False)
+
+                stt_btn.click(
+                    fn=_stt_flat,
+                    inputs=[stt_audio_in, stt_model_dd, stt_lang_dd, memory_mode_dd, device_dd],
+                    outputs=[stt_text_out, stt_lang_out, stt_status],
+                    api_name="transcribe",
+                )
+
+            # ================================================================
+            # TAB 4 — Download Models
+            # ================================================================
+            with gr.Tab("Download Models"):
+                gr.Markdown(
+                    "**Download models before using them.** "
+                    "Select what you need, hit Download, watch the live log. "
+                    "Already-downloaded models are skipped automatically."
+                )
+
+                _dl_choices = (
+                    [f"AudioDiT-{k}  ({hint})" for k, (_, hint) in AUDIODIT_MODELS.items()]
+                    + [f"Whisper-{k}  ({hint})" for k, (_, hint) in WHISPER_MODELS.items()]
+                )
+                _dl_values = (
+                    [f"AudioDiT-{k}" for k in AUDIODIT_MODELS]
+                    + [f"Whisper-{k}" for k in WHISPER_MODELS]
+                )
+                _label_to_value = dict(zip(_dl_choices, _dl_values))
+
+                dl_checkboxes = gr.CheckboxGroup(
+                    choices=_dl_choices,
+                    value=[_dl_choices[0], _dl_choices[2]],
+                    label="Models to Download",
+                )
+                with gr.Row():
+                    dl_btn     = gr.Button("Download Selected", variant="primary", size="lg")
+                    dl_refresh = gr.Button("Refresh Status", size="lg")
+
+                dl_log = gr.Textbox(
+                    label="Download Log", lines=16, interactive=False,
+                    value=_model_inventory(),
+                )
+
+                def _run_download(selected_labels):
+                    keys = [_label_to_value.get(lbl, lbl.split("  ")[0]) for lbl in selected_labels]
+                    yield from download_with_progress(keys)
+
+                dl_btn.click(fn=_run_download, inputs=[dl_checkboxes], outputs=[dl_log])
+                dl_refresh.click(fn=lambda: _model_inventory(), inputs=[], outputs=[dl_log])
+
+            # ================================================================
+            # TAB 5 — About
+            # ================================================================
+            with gr.Tab("About"):
+                gr.Markdown("""
+## LongCat-AudioDiT Enhanced
+
+Enhanced fork of [LongCat-AudioDiT](https://github.com/meituan-longcat/LongCat-AudioDiT) (Meituan) — Apache-2.0.
+
+### API Endpoints (Gradio REST API)
+All actions are available as REST endpoints at `/api/`:
+
+| Endpoint | Description |
+|---|---|
+| `POST /api/clone_voice` | Clone a voice: text + reference audio + transcription → audio |
+| `POST /api/transcribe_reference` | Transcribe reference audio with Whisper |
+| `POST /api/plain_tts` | Generate speech without a reference voice |
+| `POST /api/transcribe` | Transcribe any audio file |
+| `POST /api/save_voice` | Save a voice to the library |
+| `POST /api/load_voice` | Load a voice from the library by name |
+| `POST /api/delete_voice` | Delete a voice from the library |
+| `POST /api/list_voices` | List all saved voices |
+
+### Models
+| Model | VRAM | Notes |
+|---|---|---|
+| AudioDiT-1B | ~4 GB | Fast, great quality |
+| AudioDiT-3.5B | ~10 GB | SOTA quality |
+| Whisper Turbo | ~1.6 GB | Fast transcription |
+| Whisper large-v3 | ~3 GB | Most accurate |
+
+### Voice Library
+Voices are stored in `./voices/library.json` with audio files in `./voices/`.
+                """)
+
+        # ── Global callbacks ─────────────────────────────────────────────
+        btn_status.click(fn=get_status, inputs=[memory_mode_dd], outputs=[status_box])
+        btn_unload.click(fn=unload_all, inputs=[memory_mode_dd], outputs=[status_box])
+        memory_mode_dd.change(fn=get_status, inputs=[memory_mode_dd], outputs=[status_box])
+
+    return demo
+
+
+# ---------------------------------------------------------------------------
+# STT flat helper (avoids walrus-operator gymnastics in the lambda above)
+# ---------------------------------------------------------------------------
+def _stt_flat(audio_path, whisper_size, language, memory_mode, device):
+    """Returns (transcription, detected_language, status_msg) — three separate values."""
+    from memory_manager import ModelMemoryManager
+    mgr = get_manager(memory_mode)
+    try:
+        whisper = mgr.get_whisper(whisper_size=whisper_size)
+    except Exception as e:
+        return "", "", f"Failed to load Whisper: {e}"
+    if audio_path is None:
+        return "", "", "Upload an audio file."
+    lang_arg = language if language and language != "auto" else None
+    try:
+        text, detected = whisper.transcribe(str(audio_path), language=lang_arg)
+    except Exception as e:
+        return "", "", f"Transcription failed: {e}"
+    return text, detected, f"Transcribed [{detected}] — {len(text)} chars"
+
+
+# ---------------------------------------------------------------------------
+# Entry point
+# ---------------------------------------------------------------------------
+def main():
+    parser = argparse.ArgumentParser(description="LongCat-AudioDiT Voice Cloning Studio")
+    parser.add_argument("--port",   type=int,  default=0)
+    parser.add_argument("--host",   type=str,  default="0.0.0.0")
+    parser.add_argument("--share",  action="store_true")
+    parser.add_argument("--device", type=str,  default="auto")
+    parser.add_argument("--mode",   type=str,  default="auto",
+                        choices=["auto", "simultaneous", "sequential"])
+    args = parser.parse_args()
+
+    device = "cuda" if (args.device == "auto" and torch.cuda.is_available()) else args.device
+
+    if args.port == 0:
+        port = find_free_port(7860, 7960)
+    elif not _port_free(args.port):
+        logger.warning("Port %d busy, searching…", args.port)
+        port = find_free_port(args.port + 1, args.port + 100)
+    else:
+        port = args.port
+
+    logger.info("Starting on %s:%d (device=%s, mode=%s)", args.host, port, device, args.mode)
+    get_manager(args.mode)
+
+    demo = build_ui(default_device=device)
+    demo.launch(
+        server_name=args.host,
+        server_port=port,
+        share=args.share,
+        show_error=True,
+        theme=gr.themes.Soft(),
+    )
+
+
+if __name__ == "__main__":
+    main()