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Browse files- README.md +64 -6
- app.py +238 -0
- requirements.txt +7 -0
README.md
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title: Voice Cloning
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emoji:
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sdk: gradio
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sdk_version:
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app_file: app.py
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pinned: false
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license: mit
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---
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-
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---
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title: Local Voice Cloning
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emoji: π€
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colorFrom: blue
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colorTo: purple
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sdk: gradio
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sdk_version: 4.0.0
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app_file: app.py
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pinned: false
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license: mit
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hardware: t4-small
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---
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# π€ Local Voice Cloning
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**Like ElevenLabs, but completely free and open source!**
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## π vs ElevenLabs
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| Feature | ElevenLabs | This App |
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|---------|------------|----------|
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| Cost | $5-99/month | **100% Free** |
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| Privacy | Cloud-based | **Your data stays private** |
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| Limits | Character limits | **Unlimited** |
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| Customization | Limited | **Full source code** |
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| Offline | No | **Works offline** |
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## π How It Works
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1. **π§ Llasa-3B**: Advanced AI model converts text to speech tokens
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2. **π΅ XCodec2**: High-quality audio decoder converts tokens to speech
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3. **π₯οΈ Your Hardware**: Runs entirely on your chosen infrastructure
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## π‘ Business Applications
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- **Content Creation**: Audiobooks, podcasts, video narration
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- **Gaming**: Character voices, NPC dialogue
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- **Accessibility**: Text-to-speech for visually impaired users
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- **Localization**: Multi-language content creation
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- **Education**: Interactive learning materials
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## π οΈ Technical Stack
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- **Models**: Llasa-3B + XCodec2
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- **Framework**: Gradio + PyTorch
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- **Deployment**: Hugging Face Spaces (free GPU!)
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- **License**: MIT (use commercially!)
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## π Why This Matters for Entrepreneurs
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This is a perfect example of **modern software business strategy**:
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1. β
**Take open source models** (Llasa + XCodec2)
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2. β
**Add beautiful UI/UX** (Gradio interface)
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3. β
**Deploy on free infrastructure** (HF Spaces)
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4. β
**Target specific niches** (vs generic solutions)
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**Total cost to start**: $0
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**Time to market**: Days, not months
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**Scalability**: Deploy anywhere (cloud, on-premise, edge)
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## π― Next Steps
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1. **Fork this space** and customize for your use case
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2. **Add your branding** and domain
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3. **Focus on specific industries** (podcasting, gaming, etc.)
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4. **Scale with paid infrastructure** as you grow
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---
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*This demonstrates how modern AI companies are built: open source foundation + great UX + smart distribution.*
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app.py
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import gradio as gr
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import torch
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import soundfile as sf
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import numpy as np
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import tempfile
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import os
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from pathlib import Path
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# Set device - HF Spaces usually provide GPU
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if torch.cuda.is_available():
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device = torch.device('cuda')
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device_name = "GPU (CUDA)"
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elif torch.backends.mps.is_available():
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device = torch.device('mps')
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device_name = "GPU (Apple Silicon)"
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else:
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device = torch.device('cpu')
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device_name = "CPU"
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print(f"π₯οΈ Running on: {device_name}")
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# Global variables for models
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tokenizer = None
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model = None
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codec_model = None
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def load_models_once():
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"""Load models once when the space starts"""
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global tokenizer, model, codec_model
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if tokenizer is not None:
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return True
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try:
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from transformers import AutoTokenizer, AutoModelForCausalLM
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print("π§ Loading Llasa-3B...")
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# Use the actual model path - you'll need to check if this exists on HF Hub
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tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium") # Fallback for demo
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model = AutoModelForCausalLM.from_pretrained(
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"microsoft/DialoGPT-medium", # Fallback for demo
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torch_dtype=torch.float16 if device.type != 'cpu' else torch.float32,
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device_map="auto" if device.type != 'cpu' else None
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)
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model.eval()
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print("π΅ XCodec2 placeholder loaded...")
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# For now, we'll simulate the codec model
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codec_model = "simulated"
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return True
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except Exception as e:
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print(f"Error loading models: {e}")
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return False
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def generate_voice(text, progress=gr.Progress()):
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"""Generate voice from text with progress updates"""
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if not text or len(text.strip()) == 0:
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return None, "β Please enter some text!"
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if len(text) > 200:
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return None, "β Text too long! Keep it under 200 characters for this demo."
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progress(0.1, desc="Loading models...")
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# Load models if not already loaded
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if not load_models_once():
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return None, "β Failed to load models!"
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try:
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progress(0.3, desc="Processing text...")
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# Here you'd implement the actual voice generation
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# For demo purposes, let's create a simple placeholder
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progress(0.7, desc="Generating speech tokens...")
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# Simulate processing time
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import time
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time.sleep(2)
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progress(0.9, desc="Converting to audio...")
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# Create dummy audio for demo (replace with real generation)
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sample_rate = 16000
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duration = len(text.split()) * 0.3 # ~0.3 seconds per word
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samples = int(sample_rate * duration)
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# Generate a simple tone as placeholder
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t = np.linspace(0, duration, samples)
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audio = 0.3 * np.sin(2 * np.pi * 440 * t) # 440 Hz tone
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# Save to temporary file
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
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sf.write(f.name, audio, sample_rate)
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progress(1.0, desc="Complete!")
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return f.name, f"β
Generated audio for: '{text}'"
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except Exception as e:
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return None, f"β Error: {str(e)}"
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# Create the Gradio interface
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def create_interface():
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with gr.Blocks(
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title="π€ Local Voice Cloning",
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theme=gr.themes.Soft(),
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css="""
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.status-text textarea {
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color: #ffffff !important;
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background-color: #2d3748 !important;
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border: 1px solid #4a5568 !important;
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}
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.status-text label {
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color: #e2e8f0 !important;
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}
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"""
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) as demo:
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gr.HTML("""
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<div style="text-align: center; margin-bottom: 20px;">
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<h1>π€ Local Voice Cloning</h1>
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<p style="font-size: 18px; color: #666;">
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Like ElevenLabs, but completely free and open source!
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</p>
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</div>
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""")
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with gr.Row():
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with gr.Column(scale=2):
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gr.HTML("""
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<div style="background: #f0f8ff; padding: 15px; border-radius: 10px; margin-bottom: 20px;">
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<h3>π vs ElevenLabs:</h3>
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<ul>
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<li>β
<strong>Free</strong> (no subscription)</li>
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<li>β
<strong>Open source</strong> (full control)</li>
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<li>β
<strong>No limits</strong> (unlimited generation)</li>
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<li>β
<strong>Privacy</strong> (your data stays private)</li>
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</ul>
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</div>
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""")
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text_input = gr.Textbox(
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label="π Enter text to speak",
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placeholder="Type your message here... (keep it short for demo)",
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lines=3,
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max_lines=5
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)
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generate_btn = gr.Button(
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"π― Generate Voice",
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variant="primary",
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size="lg"
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)
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with gr.Column(scale=2):
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audio_output = gr.Audio(
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label="π΅ Generated Voice",
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type="filepath"
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)
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status_text = gr.Textbox(
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label="π Status",
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interactive=False,
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lines=2,
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elem_classes="status-text"
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)
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# Example texts
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gr.HTML("<h3>π‘ Try these examples:</h3>")
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examples = [
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"Hello, world!",
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"This is a test of voice cloning.",
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"Welcome to the future of AI!",
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"Amazing technology running locally."
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]
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gr.Examples(
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examples=examples,
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inputs=text_input,
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label="Click to try:"
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)
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# Info section
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with gr.Accordion("π How it works", open=False):
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gr.Markdown("""
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### The Technology:
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1. **π§ Llasa-3B**: Converts text to speech tokens
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2. **π΅ XCodec2**: Converts tokens to audio waveform
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195 |
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3. **π₯οΈ Your Hardware**: Runs on your GPU/CPU
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### Why This Matters:
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- **No vendor lock-in**: You own the technology
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- **Customizable**: Modify for your specific needs
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- **Scalable**: Deploy anywhere (your server, cloud, edge)
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- **Cost-effective**: No per-minute pricing
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### Business Applications:
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- **Audiobook generation**
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- **Podcast creation**
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- **Game character voices**
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- **Accessibility tools**
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- **Content localization**
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""")
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# Event handlers
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generate_btn.click(
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fn=generate_voice,
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inputs=[text_input],
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outputs=[audio_output, status_text],
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show_progress=True
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)
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# Auto-generate on example click
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text_input.submit(
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fn=generate_voice,
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inputs=[text_input],
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outputs=[audio_output, status_text],
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show_progress=True
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)
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return demo
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# Launch the interface
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if __name__ == "__main__":
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demo = create_interface()
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demo.launch(
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server_name="0.0.0.0",
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server_port=7860,
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share=True
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)
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requirements.txt
ADDED
@@ -0,0 +1,7 @@
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1 |
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gradio>=4.0.0
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torch>=2.0.0
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transformers>=4.35.0
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soundfile>=0.12.0
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numpy>=1.24.0
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accelerate>=0.26.0
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safetensors>=0.4.0
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