Delete app.py

app.py

@@ -1,561 +0,0 @@
-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
-
-from qhash.model import Zonos
-from qhash.conditioning import make_cond_dict, supported_language_codes
-
-device = "cuda"
-MODEL_NAMES = ["Quantumhash/Qhash-v0.1-transformer", "Quantumhash/Qhash-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=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)
-
-    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: #0C101B;
-    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(159, 153, 96, 0.9);
-    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 p {
-    font-size: 1.1em;
-    color: black;
-}
-.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: #6a3ea1;
-    margin-bottom: 20px;
-}
-
-/* Audio player styling */
-.audio-output {
-    margin-top: 20px;
-}
-
-/* Make output area more prominent */
-.output-container {
-    background-color: rgba(24, 82, 79, 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("# ✨ Qhash Text-to-Speech Clone ✨")
-            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("💬 Text & Model Configuration")
-                with gr.Row():
-                    with gr.Column(scale=2):
-                        model_choice = gr.Dropdown(
-                            choices=MODEL_NAMES,
-                            value="Quantumhash/Qhash-v0.1-transformer",
-                            label="Qhash Model Type",
-                            info="Select the model variant to use.",
-                        )
-                        text = gr.Textbox(
-                            label="Text to Synthesize",
-                            value="Qhash 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("🎤 Voice Characteristics")
-                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("⚙️ Generation Parameters")
-                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("🔊 Generate & Output")
-                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 = 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)