app.py

# -*- coding: utf-8 -*-
"""Copy of VoxCPM2_Gradio_Colab_AIQUEST.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1Qv6hRNMMm0Afyxyy85UyfUb4WsWKN9cJ

<div style='text-align: center; padding: 30px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); border-radius: 15px; margin: 10px 0; box-shadow: 0 10px 30px rgba(0,0,0,0.2);'>
  <h1 style='color: white; margin: 0 0 8px 0; font-size: 2.5em;'>🎙️ VoxCPM2 — Multilingual TTS</h1>
  <h3 style='color: #f0f0f0; margin: 0 0 5px 0; font-weight: 400;'>Google Colab Edition — Created by <strong>AIQUEST</strong></h3>
  <p style='color: #ddd; margin: 0 0 0 0;'>T4 GPU (16GB VRAM) | 2B Parameters · 30 Languages · 48kHz Output · Voice Design & Cloning</p>
</div>

---
---

### ⚠️ Quick Start
1. Go to **Runtime → Change runtime type → T4 GPU**
2. Run all cells in order
3. The Gradio UI link will appear at the bottom
"""

#@title 📦 Install Dependencies

#@title ✅ Verify GPU & VRAM
import torch
assert torch.cuda.is_available(), "❌ No GPU detected! Go to Runtime → Change runtime type → T4 GPU"
gpu_name = torch.cuda.get_device_name(0)
vram_gb = torch.cuda.get_device_properties(0).total_memory / 1e9
print(f"✅ GPU: {gpu_name}  |  VRAM: {vram_gb:.1f} GB")

#@title 🚀 Load VoxCPM2 Model
import torch
import torch._dynamo

# Disable torch.compile globally BEFORE model loads
torch._dynamo.config.suppress_errors = True
torch._dynamo.config.disable = True

from voxcpm import VoxCPM

print("Loading VoxCPM2 with denoiser (quality mode)...")
print(f"GPU: {torch.cuda.get_device_name(0)}")
print(f"VRAM: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB")

model = VoxCPM.from_pretrained(
    "openbmb/VoxCPM2",
    load_denoiser=True,     # ← denoiser ON for better quality
    optimize=False,          # ← eager mode (avoids torch.compile threading bugs)
)

SAMPLE_RATE = model.tts_model.sample_rate

# Warm-up
print("Warming up...")
warmup_chunks = []
for chunk in model.generate(
    text="Hello, warm up test.",
    inference_timesteps=5,
):
    warmup_chunks.append(chunk)

used = torch.cuda.memory_allocated() / 1e9
total = torch.cuda.get_device_properties(0).total_memory / 1e9
print(f"✅ Model loaded! VRAM: {used:.1f} / {total:.1f} GB")
print(f"Sample rate: {SAMPLE_RATE} Hz")

#@title 🎛️ Launch Gradio Interface
import gradio as gr
import soundfile as sf
import numpy as np
import tempfile
import os
import random

CSS = """
@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700&display=swap');
* { font-family: 'Inter', sans-serif !important; }
.gradio-container { max-width: 1000px !important; margin: auto !important; }
.brand-header { text-align: center; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 28px; border-radius: 15px; margin-bottom: 20px; box-shadow: 0 10px 25px rgba(102,126,234,0.3); }
.brand-title { color: white; font-size: 2em; font-weight: 700; margin: 0 0 6px 0; }
.brand-subtitle { color: rgba(255,255,255,0.88); font-size: 1em; margin-bottom: 16px; }
.btn-row { display: flex; justify-content: center; gap: 10px; flex-wrap: wrap; }
.social-btn { display: inline-flex; align-items: center; justify-content: center; min-width: 150px; padding: 10px 18px; border-radius: 10px; font-weight: 700; font-size: 13px; text-decoration: none; color: white; white-space: nowrap; }
.yt-btn  { background: #FF0000; box-shadow: 0 4px 12px rgba(255,0,0,0.3); }
.x-btn   { background: #000000; box-shadow: 0 4px 12px rgba(0,0,0,0.25); }
.sup-btn { background: linear-gradient(135deg,#f6d365,#fda085); box-shadow: 0 4px 12px rgba(253,160,133,0.35); }
button.primary { background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important; color: white !important; font-weight: 600 !important; border-radius: 12px !important; }
"""

BRAND_HTML = """
<div class="brand-header">
  <div class="brand-title">🎙️ VoxCPM2 — Multilingual TTS</div>
  <div class="brand-subtitle">Created by <strong>AIQuest Academy</strong> &nbsp;|&nbsp; 2B Parameters · 30 Languages · 48kHz Output · Voice Design &amp; Cloning</div>
  <div class="btn-row">
    <a href="https://www.youtube.com/@aiquestacademy?sub_confirmation=1" target="_blank" class="social-btn yt-btn">▶ Subscribe</a>
    <a href="https://x.com/aiquestacademy" target="_blank" class="social-btn x-btn">𝕏 Follow on X</a>
    <a href="https://aiquest.site" target="_blank" class="social-btn sup-btn">❤️ Support My Work</a>
  </div>
</div>
"""

# ── Helper ────
def save_wav(wav_array: np.ndarray) -> str:
    tmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
    sf.write(tmp.name, wav_array, SAMPLE_RATE)
    return tmp.name


def collect_audio(generator) -> np.ndarray:
    chunks = []
    for chunk in generator:
        arr = np.asarray(chunk)
        if arr.ndim == 0:
            arr = arr.reshape(1)
        chunks.append(arr)
    if not chunks:
        raise gr.Error("Model returned no audio.")
    return np.concatenate(chunks)


def resolve_seed(seed, locked):
    if locked and seed is not None and seed >= 0:
        s = int(seed)
    else:
        s = random.randint(0, 2**31 - 1)
    torch.manual_seed(s)
    torch.cuda.manual_seed_all(s)
    return s


# ── Tab 1: Text-to-Speech ────
def tts_generate(text, cfg, steps, seed, locked):
    if not text.strip():
        raise gr.Error("Please enter some text.")
    used_seed = resolve_seed(seed, locked)
    gen = model.generate(text=text, cfg_value=cfg, inference_timesteps=int(steps))
    wav = collect_audio(gen)
    return save_wav(wav), used_seed


# ── Tab 2: Voice Design ────
def voice_design(description, text, cfg, steps, seed, locked):
    if not text.strip():
        raise gr.Error("Please enter text content.")
    if not description.strip():
        raise gr.Error("Please enter a voice description.")
    combined = f"({description.strip()}){text.strip()}"
    used_seed = resolve_seed(seed, locked)
    gen = model.generate(text=combined, cfg_value=cfg, inference_timesteps=int(steps))
    wav = collect_audio(gen)
    return save_wav(wav), used_seed


# ── Tab 3: Voice Cloning ────
def voice_clone(text, ref_audio, style, cfg, steps, seed, locked):
    if not text.strip():
        raise gr.Error("Please enter text.")
    if ref_audio is None:
        raise gr.Error("Please upload reference audio.")
    if style and style.strip():
        text = f"({style.strip()}){text.strip()}"
    used_seed = resolve_seed(seed, locked)
    gen = model.generate(
        text=text,
        reference_wav_path=ref_audio,
        cfg_value=cfg,
        inference_timesteps=int(steps),
    )
    wav = collect_audio(gen)
    return save_wav(wav), used_seed


# ── Tab 4: Ultimate Cloning ────
def ultimate_clone(text, ref_audio, transcript, cfg, steps, seed, locked):
    if not text.strip():
        raise gr.Error("Please enter text.")
    if ref_audio is None:
        raise gr.Error("Please upload reference audio.")
    if not transcript.strip():
        raise gr.Error("Please enter the reference audio transcript.")
    used_seed = resolve_seed(seed, locked)
    gen = model.generate(
        text=text,
        prompt_wav_path=ref_audio,
        prompt_text=transcript,
        reference_wav_path=ref_audio,
        cfg_value=cfg,
        inference_timesteps=int(steps),
    )
    wav = collect_audio(gen)
    return save_wav(wav), used_seed


# ── Seed UI ────
def seed_row():
    with gr.Row():
        seed = gr.Number(value=-1, label="Seed", precision=0, scale=3)
        locked = gr.Checkbox(value=False, label="🔒 Lock Seed", scale=1)
    return seed, locked


# ── Build UI ────
with gr.Blocks(theme=gr.themes.Soft(), css=CSS) as demo:
    gr.HTML(BRAND_HTML)

    with gr.Tab("🗣️ Text-to-Speech"):
        with gr.Accordion("📖 Instructions", open=False):
            gr.Markdown(
                "Enter any text and the model will synthesize speech with natural prosody.\n\n"
                "- **CFG Scale**: Higher = more adherence to text, lower = more natural/relaxed\n"
                "- **Inference Steps**: Higher = better quality but slower (try 6-10 for speed)\n"
                "- **Seed**: Shows the seed used. Check **🔒 Lock Seed** to reuse it for reproducible results.\n"
                "- **Supported Languages (30):** Arabic, Burmese, Chinese, Danish, Dutch, English, "
                "Finnish, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Khmer, "
                "Korean, Lao, Malay, Norwegian, Polish, Portuguese, Russian, Spanish, Swahili, Swedish, "
                "Tagalog, Thai, Turkish, Vietnamese\n"
                "- **Chinese Dialects:** 四川话, 粤语, 吴语, 东北话, 河南话, 陕西话, 山东话, 天津话, 闽南话"
            )
        with gr.Row():
            with gr.Column():
                tts_text = gr.Textbox(label="Text", lines=4, placeholder="Enter text in any of the 30 supported languages...")
                with gr.Row():
                    tts_cfg = gr.Slider(0.5, 5.0, value=2.8, step=0.1, label="CFG Scale")
                    tts_steps = gr.Slider(5, 30, value=15, step=1, label="Inference Steps")
                tts_seed, tts_locked = seed_row()
                tts_btn = gr.Button("🗣️ Generate Speech", variant="primary", size="lg")
            with gr.Column():
                tts_out = gr.Audio(label="Output", type="filepath")
        tts_btn.click(tts_generate, [tts_text, tts_cfg, tts_steps, tts_seed, tts_locked], [tts_out, tts_seed])

    with gr.Tab("🎨 Voice Design"):
        with gr.Accordion("📖 Instructions", open=False):
            gr.Markdown(
                "Create a brand-new voice from a natural-language description — no reference audio needed!\n\n"
                "**How it works:** Describe the voice you want (gender, age, tone, emotion, pace, accent) "
                "and the model will create it.\n\n"
                "**Examples:**\n"
                "- `A young woman, gentle and sweet voice`\n"
                "- `An elderly British man, deep and authoritative`\n"
                "- `A cheerful child, energetic and playful`\n"
                "- `A calm female narrator with a warm tone`\n\n"
                "💡 *Results may vary between runs — try 1-3 times to get the desired voice.*\n"
                "🔒 *Lock the seed to reproduce a voice you liked.*"
            )
        with gr.Row():
            with gr.Column():
                vd_desc = gr.Textbox(label="Voice Description", lines=2, placeholder="A young woman, gentle and sweet voice")
                vd_text = gr.Textbox(label="Text Content", lines=3, placeholder="Hello, welcome to VoxCPM2!")
                with gr.Row():
                    vd_cfg = gr.Slider(0.5, 5.0, value=2.8, step=0.1, label="CFG Scale")
                    vd_steps = gr.Slider(5, 30, value=15, step=1, label="Inference Steps")
                vd_seed, vd_locked = seed_row()
                vd_btn = gr.Button("🎨 Design Voice", variant="primary", size="lg")
            with gr.Column():
                vd_out = gr.Audio(label="Output", type="filepath")
        vd_btn.click(voice_design, [vd_desc, vd_text, vd_cfg, vd_steps, vd_seed, vd_locked], [vd_out, vd_seed])

    with gr.Tab("🎛️ Voice Cloning"):
        with gr.Accordion("📖 Instructions", open=False):
            gr.Markdown(
                "Upload a short reference audio clip to clone the voice.\n\n"
                "- The model clones **timbre, accent, and speaking style** from the reference\n"
                "- Optionally add a **style description** to control emotion/pace while preserving timbre\n"
                "- Reference audio should be **clear and 5-30 seconds** for best results\n"
                "- The model accepts **16kHz input** and outputs **48kHz audio**\n\n"
                "**Style control examples:**\n"
                "- `slightly faster, cheerful tone`\n"
                "- `slow and dramatic`\n"
                "- `whispering, intimate`"
            )
        with gr.Row():
            with gr.Column():
                vc_ref = gr.Audio(label="Reference Audio", type="filepath")
                vc_text = gr.Textbox(label="Text to Synthesize", lines=3, placeholder="This is a cloned voice generated by VoxCPM2.")
                vc_style = gr.Textbox(label="Style Description (optional)", lines=1, placeholder="slightly faster, cheerful tone")
                with gr.Row():
                    vc_cfg = gr.Slider(0.5, 5.0, value=2.8, step=0.1, label="CFG Scale")
                    vc_steps = gr.Slider(5, 30, value=15, step=1, label="Inference Steps")
                vc_seed, vc_locked = seed_row()
                vc_btn = gr.Button("🎛️ Clone Voice", variant="primary", size="lg")
            with gr.Column():
                vc_out = gr.Audio(label="Output", type="filepath")
        vc_btn.click(voice_clone, [vc_text, vc_ref, vc_style, vc_cfg, vc_steps, vc_seed, vc_locked], [vc_out, vc_seed])

    with gr.Tab("🎙️ Ultimate Cloning"):
        with gr.Accordion("📖 Instructions", open=False):
            gr.Markdown(
                "Provide reference audio **and** its exact transcript for maximum fidelity cloning.\n\n"
                "- This mode reproduces **every vocal nuance**: timbre, rhythm, emotion, and style\n"
                "- The model continues seamlessly from the reference audio\n"
                "- **Both** the reference audio and its transcript are required\n"
                "- For best results, use the **same audio file** as both reference and prompt\n\n"
                "💡 *This is the highest quality cloning mode — use it when you need perfect voice reproduction.*"
            )
        with gr.Row():
            with gr.Column():
                uc_ref = gr.Audio(label="Reference Audio", type="filepath")
                uc_transcript = gr.Textbox(label="Reference Audio Transcript", lines=2, placeholder="The exact transcript of the reference audio.")
                uc_text = gr.Textbox(label="Text to Synthesize", lines=3, placeholder="This is an ultimate cloning demonstration.")
                with gr.Row():
                    uc_cfg = gr.Slider(0.5, 5.0, value=2.8, step=0.1, label="CFG Scale")
                    uc_steps = gr.Slider(5, 30, value=15, step=1, label="Inference Steps")
                uc_seed, uc_locked = seed_row()
                uc_btn = gr.Button("🎙️ Ultimate Clone", variant="primary", size="lg")
            with gr.Column():
                uc_out = gr.Audio(label="Output", type="filepath")
        uc_btn.click(ultimate_clone, [uc_text, uc_ref, uc_transcript, uc_cfg, uc_steps, uc_seed, uc_locked], [uc_out, uc_seed])

# Launch with share=True for a public Colab link
demo.queue().launch(share=True, debug=True)

"""---

<div align="center">

  <a href="https://www.youtube.com/@aiquestacademy?sub_confirmation=1">
    <img src="https://img.shields.io/badge/Subscribe%20on%20YouTube-FF0000?style=for-the-badge&logo=youtube&logoColor=white" />
  </a>
  <a href="https://x.com/aiquestacademy">
    <img src="https://img.shields.io/badge/Follow%20on%20X-000000?style=for-the-badge&logo=x&logoColor=white" />
  </a>
  <a href="https://aiquest.site">
    <img src="https://img.shields.io/badge/Support%20My%20Work-f59e0b?style=for-the-badge&logoColor=white" />
  </a>

</div>

<p align="center" style="color:#6b7280; font-size:12px; margin-top:8px;">
  ⚡ Made with ❤️ by <strong>AIQUEST Academy</strong> · aiquest.site · © All rights reserved
</p>

---
"""