Update app.py

app.py

@@ -1,96 +1,636 @@
-# app.py  ── Zonos TTS (transformer only, minimal UI)
+# # app.py  ── Zonos TTS (transformer only, minimal UI)
+
+# import os, tempfile, torch, numpy as np, gradio as gr, torchaudio, soundfile as sf, spaces
+# from zonos.model import Zonos
+# from zonos.conditioning import make_cond_dict, supported_language_codes
+
+# # disable Torch-Inductor (keeps Spaces happy)
+# os.environ["TORCH_COMPILE_DISABLE"] = os.environ["TORCHINDUCTOR_DISABLE"] = "1"
+# torch._dynamo.disable()
+# torch.compile = lambda f,*a,**k: f            # no-op
+
+# device = "cuda"
+# model  = Zonos.from_pretrained("Zyphra/Zonos-v0.1-transformer", device=device).eval()
+
+# # ─────────────────── helpers ────────────────────
+# def _speaker_embed(aud):
+#     if aud is None:
+#         return None
+#     sr, wav = aud                                         # gradio returns (sr, np.ndarray)
+#     if wav.dtype.kind in "iu":                            # int → float
+#         wav = wav.astype(np.float32) / np.iinfo(wav.dtype).max
+#     wav_t = torch.from_numpy(wav).unsqueeze(0)            # (1,C,N)
+#     return model.make_speaker_embedding(wav_t, sr)
+
+# # ─────────────────── inference ───────────────────
+# @spaces.GPU
+# def tts(text, lang, speaker,
+#         e1,e2,e3,e4,e5,e6,e7,e8,
+#         vq, fmax, pitch, rate,
+#         cfg, minp, tokens):
+
+#     emotion = torch.tensor([float(x) for x in [e1,e2,e3,e4,e5,e6,e7,e8]],
+#                            device=device, dtype=torch.float32)
+
+#     cond = make_cond_dict(
+#         text=text,
+#         language=lang,
+#         speaker=_speaker_embed(speaker),
+#         emotion=emotion,
+#         vqscore_8=torch.tensor([vq]*8, device=device).unsqueeze(0),
+#         fmax=float(fmax),
+#         pitch_std=float(pitch),
+#         speaking_rate=float(rate),
+#         device=device
+#     )
+
+#     with torch.no_grad():
+#         codes = model.generate(model.prepare_conditioning(cond),
+#                                max_new_tokens=int(tokens),
+#                                cfg_scale=float(cfg),
+#                                sampling_params=dict(min_p=float(minp)))
+#         wav = model.autoencoder.decode(codes)[0]          # (C,N) torch
+#         wav = wav.cpu().clamp_(-1,1).numpy()              # → numpy
+
+#     # >>> FIX <<<  ensure (N,) or (N, C) for libsndfile
+#     wav = np.squeeze(wav)
+#     if wav.ndim == 2:             # currently (C,N)
+#         wav = wav.T               # → (N,C)
+
+#     with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
+#         sf.write(tmp.name, wav,
+#                  model.autoencoder.sampling_rate,
+#                  format="WAV", subtype="PCM_16")
+#     return tmp.name
+
+# # ─────────────────── UI ──────────────────────────
+# langs = supported_language_codes
+# with gr.Blocks() as demo:
+#     text = gr.Textbox(label="Text")
+#     lang = gr.Dropdown(langs, value="en-us", label="Language")
+#     speaker = gr.Audio(type="numpy", label="Speaker ref (optional)")
+
+#     # emotion sliders (all default 0)
+#     emotions = []
+#     for label in ["happiness","sadness","disgust","fear",
+#                   "surprise","anger","other","neutral"]:
+#         emotions.append(gr.Slider(0,1,0.0,0.05,label=label))
+
+#     vq   = gr.Slider(0.5,0.9,0.78,0.01,label="clarity (vq)")
+#     fmax = gr.Slider(0,24000,24000,100,label="fmax (Hz)")
+#     pitch= gr.Slider(0,300,45,1,label="pitch variation")
+#     rate = gr.Slider(5,30,15,0.5,label="speaking rate")
+#     cfg  = gr.Slider(1.1,5,2,0.1,label="guidance scale")
+#     minp = gr.Slider(0,1,0.15,0.01,label="min-p")
+#     tokens = gr.Slider(0,3000,300,1,label="tokens (≈steps)")
+
+#     out = gr.Audio(type="filepath", label="Output")
+#     gr.Button("Generate").click(
+#         tts,
+#         inputs=[text, lang, speaker, *emotions,
+#                 vq, fmax, pitch, rate, cfg, minp, tokens],
+#         outputs=out
+#     )
+
+# if __name__ == "__main__":
+#     demo.launch()
+
+import os
+import shlex
+import subprocess
+
+subprocess.run(
+    shlex.split("pip install flash-attn --no-build-isolation"),
+    env=os.environ | {"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
+    check=True,
+)
+subprocess.run(
+    shlex.split("pip install https://github.com/state-spaces/mamba/releases/download/v2.2.4/mamba_ssm-2.2.4+cu12torch2.4cxx11abiFALSE-cp310-cp310-linux_x86_64.whl"),
+    check=True,
+)
+subprocess.run(
+    shlex.split("pip install https://github.com/Dao-AILab/causal-conv1d/releases/download/v1.5.0.post8/causal_conv1d-1.5.0.post8+cu12torch2.4cxx11abiFALSE-cp310-cp310-linux_x86_64.whl"),
+    check=True,
+)
+
+import spaces
+import torch
+import torchaudio
+import gradio as gr
+from os import getenv
 
-import os, tempfile, torch, numpy as np, gradio as gr, torchaudio, soundfile as sf, spaces
 from zonos.model import Zonos
 from zonos.conditioning import make_cond_dict, supported_language_codes
 
-# disable Torch-Inductor (keeps Spaces happy)
-os.environ["TORCH_COMPILE_DISABLE"] = os.environ["TORCHINDUCTOR_DISABLE"] = "1"
-torch._dynamo.disable()
-torch.compile = lambda f,*a,**k: f            # no-op
-
 device = "cuda"
-model  = Zonos.from_pretrained("Zyphra/Zonos-v0.1-transformer", device=device).eval()
-
-# ─────────────────── helpers ────────────────────
-def _speaker_embed(aud):
-    if aud is None:
-        return None
-    sr, wav = aud                                         # gradio returns (sr, np.ndarray)
-    if wav.dtype.kind in "iu":                            # int → float
-        wav = wav.astype(np.float32) / np.iinfo(wav.dtype).max
-    wav_t = torch.from_numpy(wav).unsqueeze(0)            # (1,C,N)
-    return model.make_speaker_embedding(wav_t, sr)
-
-# ─────────────────── inference ───────────────────
-@spaces.GPU
-def tts(text, lang, speaker,
-        e1,e2,e3,e4,e5,e6,e7,e8,
-        vq, fmax, pitch, rate,
-        cfg, minp, tokens):
-
-    emotion = torch.tensor([float(x) for x in [e1,e2,e3,e4,e5,e6,e7,e8]],
-                           device=device, dtype=torch.float32)
-
-    cond = make_cond_dict(
+MODEL_NAMES = ["Zyphra/Zonos-v0.1-transformer", "Zyphra/Zonos-v0.1-hybrid"]
+MODELS = {name: Zonos.from_pretrained(name, device=device) for name in MODEL_NAMES}
+for model in MODELS.values():
+    model.requires_grad_(False).eval()
+
+
+def update_ui(model_choice):
+    """
+    Dynamically show/hide UI elements based on the model's conditioners.
+    We do NOT display 'language_id' or 'ctc_loss' even if they exist in the model.
+    """
+    model = MODELS[model_choice]
+    cond_names = [c.name for c in model.prefix_conditioner.conditioners]
+    print("Conditioners in this model:", cond_names)
+
+    text_update = gr.update(visible=("espeak" in cond_names))
+    language_update = gr.update(visible=("espeak" in cond_names))
+    speaker_audio_update = gr.update(visible=("speaker" in cond_names))
+    prefix_audio_update = gr.update(visible=True)
+    emotion1_update = gr.update(visible=("emotion" in cond_names))
+    emotion2_update = gr.update(visible=("emotion" in cond_names))
+    emotion3_update = gr.update(visible=("emotion" in cond_names))
+    emotion4_update = gr.update(visible=("emotion" in cond_names))
+    emotion5_update = gr.update(visible=("emotion" in cond_names))
+    emotion6_update = gr.update(visible=("emotion" in cond_names))
+    emotion7_update = gr.update(visible=("emotion" in cond_names))
+    emotion8_update = gr.update(visible=("emotion" in cond_names))
+    vq_single_slider_update = gr.update(visible=("vqscore_8" in cond_names))
+    fmax_slider_update = gr.update(visible=("fmax" in cond_names))
+    pitch_std_slider_update = gr.update(visible=("pitch_std" in cond_names))
+    speaking_rate_slider_update = gr.update(visible=("speaking_rate" in cond_names))
+    dnsmos_slider_update = gr.update(visible=("dnsmos_ovrl" in cond_names))
+    speaker_noised_checkbox_update = gr.update(visible=("speaker_noised" in cond_names))
+    unconditional_keys_update = gr.update(
+        choices=[name for name in cond_names if name not in ("espeak", "language_id")]
+    )
+
+    return (
+        text_update,
+        language_update,
+        speaker_audio_update,
+        prefix_audio_update,
+        emotion1_update,
+        emotion2_update,
+        emotion3_update,
+        emotion4_update,
+        emotion5_update,
+        emotion6_update,
+        emotion7_update,
+        emotion8_update,
+        vq_single_slider_update,
+        fmax_slider_update,
+        pitch_std_slider_update,
+        speaking_rate_slider_update,
+        dnsmos_slider_update,
+        speaker_noised_checkbox_update,
+        unconditional_keys_update,
+    )
+
+
+@spaces.GPU(duration=120)
+def generate_audio(
+    model_choice,
+    text,
+    language,
+    speaker_audio,
+    prefix_audio,
+    e1,
+    e2,
+    e3,
+    e4,
+    e5,
+    e6,
+    e7,
+    e8,
+    vq_single,
+    fmax,
+    pitch_std,
+    speaking_rate,
+    dnsmos_ovrl,
+    speaker_noised,
+    cfg_scale,
+    min_p,
+    seed,
+    randomize_seed,
+    unconditional_keys,
+    progress=gr.Progress(),
+):
+    """
+    Generates audio based on the provided UI parameters.
+    We do NOT use language_id or ctc_loss even if the model has them.
+    """
+    selected_model = MODELS[model_choice]
+
+    speaker_noised_bool = bool(speaker_noised)
+    fmax = float(fmax)
+    pitch_std = float(pitch_std)
+    speaking_rate = float(speaking_rate)
+    dnsmos_ovrl = float(dnsmos_ovrl)
+    cfg_scale = float(cfg_scale)
+    min_p = float(min_p)
+    seed = int(seed)
+    max_new_tokens = 86 * 30
+
+    if randomize_seed:
+        seed = torch.randint(0, 2**32 - 1, (1,)).item()
+    torch.manual_seed(seed)
+
+    speaker_embedding = None
+    if speaker_audio is not None and "speaker" not in unconditional_keys:
+        wav, sr = torchaudio.load(speaker_audio)
+        speaker_embedding = selected_model.make_speaker_embedding(wav, sr)
+        speaker_embedding = speaker_embedding.to(device, dtype=torch.bfloat16)
+
+    audio_prefix_codes = None
+    if prefix_audio is not None:
+        wav_prefix, sr_prefix = torchaudio.load(prefix_audio)
+        wav_prefix = wav_prefix.mean(0, keepdim=True)
+        wav_prefix = torchaudio.functional.resample(wav_prefix, sr_prefix, selected_model.autoencoder.sampling_rate)
+        wav_prefix = wav_prefix.to(device, dtype=torch.float32)
+        with torch.autocast(device, dtype=torch.float32):
+            audio_prefix_codes = selected_model.autoencoder.encode(wav_prefix.unsqueeze(0))
+
+    emotion_tensor = torch.tensor(list(map(float, [e1, e2, e3, e4, e5, e6, e7, e8])), device=device)
+
+    vq_val = float(vq_single)
+    vq_tensor = torch.tensor([vq_val] * 8, device=device).unsqueeze(0)
+
+    cond_dict = make_cond_dict(
         text=text,
-        language=lang,
-        speaker=_speaker_embed(speaker),
-        emotion=emotion,
-        vqscore_8=torch.tensor([vq]*8, device=device).unsqueeze(0),
-        fmax=float(fmax),
-        pitch_std=float(pitch),
-        speaking_rate=float(rate),
-        device=device
+        language=language,
+        speaker=speaker_embedding,
+        emotion=emotion_tensor,
+        vqscore_8=vq_tensor,
+        fmax=fmax,
+        pitch_std=pitch_std,
+        speaking_rate=speaking_rate,
+        dnsmos_ovrl=dnsmos_ovrl,
+        speaker_noised=speaker_noised_bool,
+        device=device,
+        unconditional_keys=unconditional_keys,
     )
+    conditioning = selected_model.prepare_conditioning(cond_dict)
+
+    estimated_generation_duration = 30 * len(text) / 400
+    estimated_total_steps = int(estimated_generation_duration * 86)
 
-    with torch.no_grad():
-        codes = model.generate(model.prepare_conditioning(cond),
-                               max_new_tokens=int(tokens),
-                               cfg_scale=float(cfg),
-                               sampling_params=dict(min_p=float(minp)))
-        wav = model.autoencoder.decode(codes)[0]          # (C,N) torch
-        wav = wav.cpu().clamp_(-1,1).numpy()              # → numpy
-
-    # >>> FIX <<<  ensure (N,) or (N, C) for libsndfile
-    wav = np.squeeze(wav)
-    if wav.ndim == 2:             # currently (C,N)
-        wav = wav.T               # → (N,C)
-
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
-        sf.write(tmp.name, wav,
-                 model.autoencoder.sampling_rate,
-                 format="WAV", subtype="PCM_16")
-    return tmp.name
-
-# ─────────────────── UI ──────────────────────────
-langs = supported_language_codes
-with gr.Blocks() as demo:
-    text = gr.Textbox(label="Text")
-    lang = gr.Dropdown(langs, value="en-us", label="Language")
-    speaker = gr.Audio(type="numpy", label="Speaker ref (optional)")
-
-    # emotion sliders (all default 0)
-    emotions = []
-    for label in ["happiness","sadness","disgust","fear",
-                  "surprise","anger","other","neutral"]:
-        emotions.append(gr.Slider(0,1,0.0,0.05,label=label))
-
-    vq   = gr.Slider(0.5,0.9,0.78,0.01,label="clarity (vq)")
-    fmax = gr.Slider(0,24000,24000,100,label="fmax (Hz)")
-    pitch= gr.Slider(0,300,45,1,label="pitch variation")
-    rate = gr.Slider(5,30,15,0.5,label="speaking rate")
-    cfg  = gr.Slider(1.1,5,2,0.1,label="guidance scale")
-    minp = gr.Slider(0,1,0.15,0.01,label="min-p")
-    tokens = gr.Slider(0,3000,300,1,label="tokens (≈steps)")
-
-    out = gr.Audio(type="filepath", label="Output")
-    gr.Button("Generate").click(
-        tts,
-        inputs=[text, lang, speaker, *emotions,
-                vq, fmax, pitch, rate, cfg, minp, tokens],
-        outputs=out
+    def update_progress(_frame: torch.Tensor, step: int, _total_steps: int) -> bool:
+        progress((step, estimated_total_steps))
+        return True
+
+    codes = selected_model.generate(
+        prefix_conditioning=conditioning,
+        audio_prefix_codes=audio_prefix_codes,
+        max_new_tokens=max_new_tokens,
+        cfg_scale=cfg_scale,
+        batch_size=1,
+        sampling_params=dict(min_p=min_p),
+        callback=update_progress,
     )
 
+    wav_out = selected_model.autoencoder.decode(codes).cpu().detach()
+    sr_out = selected_model.autoencoder.sampling_rate
+    if wav_out.dim() == 2 and wav_out.size(0) > 1:
+        wav_out = wav_out[0:1, :]
+    return (sr_out, wav_out.squeeze().numpy()), seed
+
+
+# Custom CSS for pastel gradient background and enhanced UI
+custom_css = """
+.gradio-container {
+    background: linear-gradient(135deg, #f3e7ff, #e6f0ff, #ffe6f2, #e6fff9);
+    background-size: 400% 400%;
+    animation: gradient 15s ease infinite;
+}
+@keyframes gradient {
+    0% {
+        background-position: 0% 50%;
+    }
+    50% {
+        background-position: 100% 50%;
+    }
+    100% {
+        background-position: 0% 50%;
+    }
+}
+.container {
+    max-width: 1200px;
+    margin: 0 auto;
+    padding: 20px;
+}
+.panel {
+    background-color: rgba(255, 255, 255, 0.7);
+    border-radius: 16px;
+    padding: 20px;
+    box-shadow: 0 4px 12px rgba(0, 0, 0, 0.08);
+    margin-bottom: 16px;
+    backdrop-filter: blur(5px);
+    transition: all 0.3s ease;
+}
+.panel:hover {
+    box-shadow: 0 6px 16px rgba(0, 0, 0, 0.12);
+    transform: translateY(-2px);
+}
+.title {
+    font-size: 1.2em;
+    font-weight: 600;
+    margin-bottom: 12px;
+    color: #6a3ea1;
+    border-bottom: 2px solid #f0e6ff;
+    padding-bottom: 8px;
+}
+.slider-container {
+    background-color: rgba(255, 255, 255, 0.5);
+    border-radius: 10px;
+    padding: 10px;
+    margin: 5px 0;
+}
+/* Make sliders more appealing */
+input[type=range] {
+    height: 5px;
+    appearance: none;
+    width: 100%;
+    border-radius: 3px;
+    background: linear-gradient(90deg, #9c83e0, #83b1e0);
+}
+.generate-button {
+    background: linear-gradient(90deg, #a673ff, #7c4dff);
+    color: white;
+    border: none;
+    border-radius: 8px;
+    padding: 12px 24px;
+    font-size: 16px;
+    font-weight: 500;
+    cursor: pointer;
+    transition: all 0.3s ease;
+    box-shadow: 0 4px 10px rgba(124, 77, 255, 0.2);
+    display: block;
+    width: 100%;
+    margin: 20px 0;
+}
+.generate-button:hover {
+    background: linear-gradient(90deg, #9c5eff, #6a3aff);
+    box-shadow: 0 6px 15px rgba(124, 77, 255, 0.3);
+    transform: translateY(-2px);
+}
+/* Tabs styling */
+.tabs {
+    display: flex;
+    border-bottom: 1px solid #e0e0e0;
+    margin-bottom: 20px;
+}
+.tab {
+    padding: 10px 20px;
+    cursor: pointer;
+    transition: all 0.3s ease;
+    background-color: transparent;
+    border: none;
+    color: #666;
+}
+.tab.active {
+    color: #7c4dff;
+    border-bottom: 3px solid #7c4dff;
+    font-weight: 600;
+}
+/* Emotion sliders container */
+.emotion-grid {
+    display: grid;
+    grid-template-columns: repeat(4, 1fr);
+    gap: 12px;
+}
+/* Header styling */
+.app-header {
+    text-align: center;
+    margin-bottom: 25px;
+}
+.app-header h1 {
+    font-size: 2.5em;
+    color: #6a3ea1;
+    margin-bottom: 8px;
+    font-weight: 700;
+}
+.app-header p {
+    font-size: 1.1em;
+    color: #666;
+    margin-bottom: 20px;
+}
+/* Audio player styling */
+.audio-output {
+    margin-top: 20px;
+}
+/* Make output area more prominent */
+.output-container {
+    background-color: rgba(255, 255, 255, 0.85);
+    border-radius: 16px;
+    padding: 24px;
+    box-shadow: 0 8px 18px rgba(0, 0, 0, 0.1);
+    margin-top: 20px;
+}
+"""
+
+
+def build_interface():
+    # Build interface with enhanced visual elements and layout
+    with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
+        # Header section
+        with gr.Column(elem_classes="app-header"):
+            gr.Markdown("# ✨ Zonos Text-to-Speech Generator ✨")
+            gr.Markdown("Create natural-sounding speech with customizable voice characteristics")
+        
+        # Main content container 
+        with gr.Column(elem_classes="container"):
+            # First panel - Text & Model Selection
+            with gr.Column(elem_classes="panel"):
+                gr.Markdown('<div class="title">💬 Text & Model Configuration</div>')
+                with gr.Row():
+                    with gr.Column(scale=2):
+                        model_choice = gr.Dropdown(
+                            choices=MODEL_NAMES,
+                            value="Zyphra/Zonos-v0.1-transformer",
+                            label="Zonos Model Type",
+                            info="Select the model variant to use.",
+                        )
+                        text = gr.Textbox(
+                            label="Text to Synthesize",
+                            value="Zonos uses eSpeak for text to phoneme conversion!",
+                            lines=4,
+                            max_length=500,
+                        )
+                        language = gr.Dropdown(
+                            choices=supported_language_codes,
+                            value="en-us",
+                            label="Language Code",
+                            info="Select a language code.",
+                        )
+                    with gr.Column(scale=1):
+                        prefix_audio = gr.Audio(
+                            value="assets/silence_100ms.wav",
+                            label="Optional Prefix Audio (continue from this audio)",
+                            type="filepath",
+                        )
+            
+            # Second panel - Voice Characteristics
+            with gr.Column(elem_classes="panel"):
+                gr.Markdown('<div class="title">🎤 Voice Characteristics</div>')
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        speaker_audio = gr.Audio(
+                            label="Optional Speaker Audio (for voice cloning)",
+                            type="filepath",
+                        )
+                        speaker_noised_checkbox = gr.Checkbox(label="Denoise Speaker?", value=False)
+                    
+                    with gr.Column(scale=2):
+                        with gr.Row():
+                            with gr.Column():
+                                dnsmos_slider = gr.Slider(1.0, 5.0, value=4.0, step=0.1, label="Voice Quality", elem_classes="slider-container")
+                                fmax_slider = gr.Slider(0, 24000, value=24000, step=1, label="Frequency Max (Hz)", elem_classes="slider-container")
+                                vq_single_slider = gr.Slider(0.5, 0.8, 0.78, 0.01, label="Voice Clarity", elem_classes="slider-container")
+                            with gr.Column():
+                                pitch_std_slider = gr.Slider(0.0, 300.0, value=45.0, step=1, label="Pitch Variation", elem_classes="slider-container")
+                                speaking_rate_slider = gr.Slider(5.0, 30.0, value=15.0, step=0.5, label="Speaking Rate", elem_classes="slider-container")
+            
+            # Third panel - Generation Parameters
+            with gr.Column(elem_classes="panel"):
+                gr.Markdown('<div class="title">⚙️ Generation Parameters</div>')
+                with gr.Row():
+                    with gr.Column():
+                        cfg_scale_slider = gr.Slider(1.0, 5.0, 2.0, 0.1, label="Guidance Scale", elem_classes="slider-container")
+                        min_p_slider = gr.Slider(0.0, 1.0, 0.15, 0.01, label="Min P (Randomness)", elem_classes="slider-container")
+                    with gr.Column():
+                        seed_number = gr.Number(label="Seed", value=420, precision=0)
+                        randomize_seed_toggle = gr.Checkbox(label="Randomize Seed (before generation)", value=True)
+            
+            # Emotion Panel with Tabbed Interface
+            with gr.Accordion("🎭 Emotion Settings", open=False, elem_classes="panel"):
+                gr.Markdown(
+                    "Adjust these sliders to control the emotional tone of the generated speech.\n"
+                    "For a neutral voice, keep 'Neutral' high and other emotions low."
+                )
+                with gr.Row(elem_classes="emotion-grid"):
+                    emotion1 = gr.Slider(0.0, 1.0, 1.0, 0.05, label="Happiness", elem_classes="slider-container")
+                    emotion2 = gr.Slider(0.0, 1.0, 0.05, 0.05, label="Sadness", elem_classes="slider-container")
+                    emotion3 = gr.Slider(0.0, 1.0, 0.05, 0.05, label="Disgust", elem_classes="slider-container")
+                    emotion4 = gr.Slider(0.0, 1.0, 0.05, 0.05, label="Fear", elem_classes="slider-container")
+                with gr.Row(elem_classes="emotion-grid"):
+                    emotion5 = gr.Slider(0.0, 1.0, 0.05, 0.05, label="Surprise", elem_classes="slider-container")
+                    emotion6 = gr.Slider(0.0, 1.0, 0.05, 0.05, label="Anger", elem_classes="slider-container")
+                    emotion7 = gr.Slider(0.0, 1.0, 0.1, 0.05, label="Other", elem_classes="slider-container")
+                    emotion8 = gr.Slider(0.0, 1.0, 0.2, 0.05, label="Neutral", elem_classes="slider-container")
+            
+            # Advanced Settings Panel
+            with gr.Accordion("⚡ Advanced Settings", open=False, elem_classes="panel"):
+                gr.Markdown(
+                    "### Unconditional Toggles\n"
+                    "Checking a box will make the model ignore the corresponding conditioning value and make it unconditional.\n"
+                    'Practically this means the given conditioning feature will be unconstrained and "filled in automatically".'
+                )
+                unconditional_keys = gr.CheckboxGroup(
+                    [
+                        "speaker",
+                        "emotion",
+                        "vqscore_8",
+                        "fmax",
+                        "pitch_std",
+                        "speaking_rate",
+                        "dnsmos_ovrl",
+                        "speaker_noised",
+                    ],
+                    value=["emotion"],
+                    label="Unconditional Keys",
+                )
+            
+            # Generate Button and Output Area
+            with gr.Column(elem_classes="panel output-container"):
+                gr.Markdown('<div class="title">🔊 Generate & Output</div>')
+                generate_button = gr.Button("Generate Audio", elem_classes="generate-button")
+                output_audio = gr.Audio(label="Generated Audio", type="numpy", autoplay=True, elem_classes="audio-output")
+
+        model_choice.change(
+            fn=update_ui,
+            inputs=[model_choice],
+            outputs=[
+                text,
+                language,
+                speaker_audio,
+                prefix_audio,
+                emotion1,
+                emotion2,
+                emotion3,
+                emotion4,
+                emotion5,
+                emotion6,
+                emotion7,
+                emotion8,
+                vq_single_slider,
+                fmax_slider,
+                pitch_std_slider,
+                speaking_rate_slider,
+                dnsmos_slider,
+                speaker_noised_checkbox,
+                unconditional_keys,
+            ],
+        )
+
+        # On page load, trigger the same UI refresh
+        demo.load(
+            fn=update_ui,
+            inputs=[model_choice],
+            outputs=[
+                text,
+                language,
+                speaker_audio,
+                prefix_audio,
+                emotion1,
+                emotion2,
+                emotion3,
+                emotion4,
+                emotion5,
+                emotion6,
+                emotion7,
+                emotion8,
+                vq_single_slider,
+                fmax_slider,
+                pitch_std_slider,
+                speaking_rate_slider,
+                dnsmos_slider,
+                speaker_noised_checkbox,
+                unconditional_keys,
+            ],
+        )
+
+        # Generate audio on button click
+        generate_button.click(
+            fn=generate_audio,
+            inputs=[
+                model_choice,
+                text,
+                language,
+                speaker_audio,
+                prefix_audio,
+                emotion1,
+                emotion2,
+                emotion3,
+                emotion4,
+                emotion5,
+                emotion6,
+                emotion7,
+                emotion8,
+                vq_single_slider,
+                fmax_slider,
+                pitch_std_slider,
+                speaking_rate_slider,
+                dnsmos_slider,
+                speaker_noised_checkbox,
+                cfg_scale_slider,
+                min_p_slider,
+                seed_number,
+                randomize_seed_toggle,
+                unconditional_keys,
+            ],
+            outputs=[output_audio, seed_number],
+        )
+
+    return demo
+
+
 if __name__ == "__main__":
-    demo.launch()
+    demo = build_interface()
+    share = getenv("GRADIO_SHARE", "False").lower() in ("true", "1", "t")
+    demo.launch(server_name="0.0.0.0", server_port=7860, share=share, mcp_server=True)