Add ZeroGPU authentication requirements

README.md

@@ -1,447 +1,79 @@
----
-title: LEMM - Let Everyone Make Music
-emoji: 🎵
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: "4.44.1"
-app_file: app.py
-pinned: false
-license: mit
-hf_oauth: true
----
-
-# LEMM - Let Everyone Make Music
-
-**Version 1.0.0 (Beta)**
-
-An advanced AI music generation system with **training capabilities**, built-in vocals, professional mastering, and audio enhancement. Powered by DiffRhythm2 with LoRA fine-tuning support.
-
-🎵 **Live Demo**: [Try LEMM on HuggingFace Spaces](https://huggingface.co/spaces/Gamahea/lemm-test-100)  
-📦 **LoRA Collection**: [Browse Trained Models](https://huggingface.co/collections/Gamahea/lemm-100-pre-beta)  
-🏢 **Organization**: [lemm-ai on GitHub](https://github.com/lemm-ai)
-
----
-
-## ✨ Key Features
-
-### 🎵 Music Generation
-- **Text-to-Music**: Generate music from style descriptions
-- **Built-in Vocals**: DiffRhythm2 generates vocals directly with music (no separate TTS)
-- **Style Consistency**: New clips inherit musical character from existing ones
-- **Flexible Duration**: 10-120 second clips
-
-### 🎓 LoRA Training
-- **Custom Style Training**: Fine-tune on your own music datasets
-- **Public Datasets**: GTZAN, MusicCaps, FMA support
-- **Continued Training**: Use existing LoRAs as base models
-- **Automatic Upload**: Trained LoRAs uploaded to HuggingFace Hub
-
-### 🎚️ Professional Audio Tools
-- **Advanced Mastering**: 32 professional presets (Pop, Rock, Electronic, etc.)
-- **Custom EQ**: 8-band parametric equalizer
-- **Dynamics**: Compression and limiting controls
-- **Audio Enhancement**: 
-  - Stem separation (Demucs)
-  - Noise reduction
-  - Super resolution (upscale to 48kHz)
-
-### 🎛️ DAW-Style Interface
-- **Horizontal Timeline**: Professional multi-track layout
-- **Visual Waveforms**: See your music as you build
-- **Track Management**: Add, remove, rearrange clips
-- **Real-time Preview**: Play individual clips or full timeline
-
----
-
-## 🚀 Quick Start
-
-### Option 1: HuggingFace Spaces (Recommended)
-
-Try LEMM instantly with zero setup:
-
-👉 **[Launch LEMM Space](https://huggingface.co/spaces/Gamahea/lemm-test-100)**
-
-- No installation required
-- Free GPU access
-- Pre-loaded models
-- Immediate start
-
-### Option 2: Local Installation
-
-**Prerequisites:**
-- Python 3.10 or 3.11
-- 16GB+ RAM recommended
-- NVIDIA GPU recommended (CUDA 12.x) or CPU
-
-**Installation:**
-
-```bash
-# Clone the repository
-git clone https://github.com/lemm-ai/LEMM-1.0.0-ALPHA.git
-cd LEMM-1.0.0-ALPHA
-
-# Create virtual environment
-python -m venv .venv
-
-# Activate virtual environment
-# Windows:
-.\.venv\Scripts\activate
-# Linux/Mac:
-source .venv/bin/activate
-
-# Install dependencies
-pip install -r requirements.txt
-
-# Launch LEMM
-python app.py
-```
-
-**Access at**: http://localhost:7860
-
----
-
-## 📖 Usage Guide
-
-### 1️⃣ Generate Your First Track
-
-1. **Enter Music Prompt**: Describe the style
-   - Example: *"upbeat electronic dance music with heavy bass"*
-2. **Add Lyrics** (optional): DiffRhythm2 will sing them
-   - Leave empty for instrumental
-3. **Set Duration**: 10-120 seconds (default: 30s)
-4. **Generate**: Click "✨ Generate Music Clip"
-5. **Preview**: Listen in the audio player
-
-### 2️⃣ Build Your Composition
-
-1. **Timeline Tab**: View all generated clips
-2. **Waveform Preview**: Visual representation of each clip
-3. **Add More**: Generate additional clips at different positions
-4. **Style Consistency**: New clips automatically match existing style
-
-### 3️⃣ Master & Export
-
-1. **Mastering Tab**: 
-   - Choose preset (Pop, Rock, EDM, etc.)
-   - Or customize: EQ, compression, limiting
-2. **Enhancement** (optional):
-   - Stem separation
-   - Noise reduction
-   - Audio super resolution
-3. **Export Tab**: 
-   - Choose format (WAV, MP3, FLAC)
-   - Download your finished track
-
-### 4️⃣ Train Custom LoRAs
-
-1. **Dataset Management Tab**:
-   - Select public dataset (GTZAN, MusicCaps, FMA)
-   - Or upload your own music
-   - Download and prepare dataset
-2. **Training Configuration Tab**:
-   - Name your LoRA
-   - Set training parameters
-   - Choose base LoRA (optional - for continued training)
-   - Start training
-3. **Wait for Training**: Progress shown in real-time
-4. **Auto-Upload**: LoRA uploaded to HuggingFace as model
-5. **Reuse**: Download and use in future generations
-
----
-
-## 🏗️ Architecture
-
-### Core Technology
-
-**DiffRhythm2** (ASLP-lab)
-- State-of-the-art music generation with vocals
-- Continuous Flow Matching (CFM) diffusion
-- MuQ-MuLan style encoding for consistency
-- Native vocal generation (no separate TTS)
-
-**LoRA Fine-Tuning** (PEFT)
-- Low-Rank Adaptation for efficient training
-- Parameter-efficient fine-tuning
-- Custom style specialization
-- Continued training support
-
-### System Components
-
-```
-LEMM/
-├── app.py                      # Main Gradio interface
-├── backend/
-│   ├── services/
-│   │   ├── diffrhythm_service.py       # DiffRhythm2 integration
-│   ���   ├── lora_training_service.py    # LoRA training
-│   │   ├── dataset_service.py          # Dataset management
-│   │   ├── mastering_service.py        # Audio mastering
-│   │   ├── stem_enhancement_service.py # Audio enhancement
-│   │   ├── audio_upscale_service.py    # Super resolution
-│   │   ├── hf_storage_service.py       # HuggingFace uploads
-│   │   └── ...
-│   ├── routes/                 # API endpoints
-│   ├── models/                 # Data schemas
-│   └── config/                 # Configuration
-├── models/
-│   ├── diffrhythm2/           # Music generation model
-│   ├── loras/                 # Trained LoRA adapters
-│   └── ...
-├── training_data/             # Prepared datasets
-├── outputs/                   # Generated music
-└── requirements.txt           # Dependencies
-```
-
-### Key Dependencies
-
-- **torch**: 2.4.0+ (PyTorch)
-- **diffusers**: Diffusion models
-- **transformers**: 4.47.1 (HuggingFace)
-- **peft**: LoRA training
-- **gradio**: Web interface
-- **pedalboard**: Audio mastering
-- **demucs**: Stem separation
-- **huggingface-hub**: Model uploads
-
----
-
-## 🎓 Training Your Own LoRAs
-
-### Supported Datasets
-
-**Public Datasets:**
-- **GTZAN**: Music genre classification (1,000 tracks, 10 genres)
-- **MusicCaps**: Google's music captioning dataset
-- **FMA (Free Music Archive)**: Large-scale music collection
-
-**Custom Datasets:**
-- Upload your own music collections
-- Supports MP3, WAV, FLAC, OGG
-
-### Training Process
-
-1. **Prepare Dataset**:
-   - Download or upload music
-   - Extract audio samples
-   - Split into train/validation sets
-
-2. **Configure Training**:
-   - **LoRA Rank**: 4-64 (higher = more expressive, slower)
-   - **Learning Rate**: 1e-4 to 1e-3
-   - **Batch Size**: 1-8 (depends on GPU memory)
-   - **Epochs**: 10-100 (depends on dataset size)
-   - **Base LoRA**: Optional - continue from existing model
-
-3. **Monitor Training**:
-   - Real-time loss graphs
-   - Validation metrics
-   - Progress percentage
-
-4. **Upload & Share**:
-   - Automatic upload to HuggingFace Hub
-   - Model ID: `Gamahea/lemm-lora-{your-name}`
-   - Add to [LEMM Collection](https://huggingface.co/collections/Gamahea/lemm-100-pre-beta)
-
-### Example: Training on GTZAN
-
-```
-1. Dataset Management → Select GTZAN → Download
-2. Prepare Dataset → GTZAN → Prepare (800 train, 200 val)
-3. Training Configuration:
-   - Name: "my_jazz_lora"
-   - Dataset: gtzan
-   - Epochs: 50
-   - LoRA Rank: 8
-   - Learning Rate: 1e-4
-4. Start Training → Wait ~2-4 hours (GPU dependent)
-5. ✅ Uploaded: Gamahea/lemm-lora-my-jazz-lora
-6. Reuse in generation or continue training
-```
-
----
-
-## 🎨 LoRA Management
-
-### Download from HuggingFace
-
-1. Go to **LoRA Management Tab**
-2. Enter model ID: `Gamahea/lemm-lora-{name}`
-3. Click "Download from Hub"
-4. Use immediately in generation
-
-### Browse Collection
-
-👉 [LEMM LoRA Collection](https://huggingface.co/collections/Gamahea/lemm-100-pre-beta)
-
-Discover community-trained LoRAs:
-- Genre specialists (jazz, rock, electronic)
-- Style adaptations
-- Custom fine-tuned models
-
-### Export/Import
-
-**Export:**
-- Download trained LoRA as ZIP
-- Share with others
-- Backup your work
-
-**Import:**
-- Upload LoRA ZIP file
-- Instantly available for use
-- Continue training from checkpoint
-
----
-
-## 🔧 Advanced Configuration
-
-### GPU Acceleration
-
-**NVIDIA (Recommended):**
-```bash
-# CUDA 12.x automatically detected
-# No additional configuration needed
-```
-
-**CPU Mode:**
-```bash
-# Automatic fallback if no GPU detected
-# Slower but fully functional
-```
-
-### Model Paths
-
-Models downloaded to:
-- DiffRhythm2: `models/diffrhythm2/`
-- LoRAs: `models/loras/`
-- Training data: `training_data/`
-
-### Environment Variables
-
-Create `.env` file:
-```env
-# HuggingFace token for uploads (optional)
-HF_TOKEN=hf_xxxxxxxxxxxxx
-
-# Gradio server port (default: 7860)
-GRADIO_SERVER_PORT=7860
-
-# Enable debug logging
-DEBUG=false
-```
-
----
-
-## 📊 Technical Specifications
-
-### Generation
-
-- **Model**: DiffRhythm2 (CFM-based diffusion)
-- **Sampling**: 22050 Hz (can upscale to 48kHz)
-- **Duration**: 10-120 seconds per clip
-- **Vocals**: Built-in (no separate TTS)
-- **Style Encoding**: MuQ-MuLan
-
-### Training
-
-- **Method**: LoRA (Low-Rank Adaptation)
-- **Rank**: 4-64 (configurable)
-- **Precision**: Mixed (FP16/FP32)
-- **Optimizer**: AdamW
-- **Scheduler**: Cosine annealing
-
-### Audio Enhancement
-
-- **Stem Separation**: Demucs 4.0.1 (4-stem)
-- **Noise Reduction**: Spectral subtraction
-- **Super Resolution**: AudioSR (up to 48kHz)
-- **Mastering**: Pedalboard (Spotify LUFS-compliant)
-
----
-
-## 🤝 Contributing
-
-We welcome contributions! Here's how:
-
-### Report Issues
-
-- [GitHub Issues](https://github.com/lemm-ai/LEMM-1.0.0-ALPHA/issues)
-- Include: steps to reproduce, logs, system info
-
-### Share LoRAs
-
-1. Train custom LoRA in LEMM
-2. Upload to HuggingFace (automatic)
-3. Add to [Collection](https://huggingface.co/collections/Gamahea/lemm-100-pre-beta)
-4. Share with community
-
-### Development
-
-```bash
-# Fork the repository
-# Clone your fork
-git clone https://github.com/YOUR-USERNAME/LEMM-1.0.0-ALPHA.git
-
-# Create feature branch
-git checkout -b feature/your-feature
-
-# Make changes and commit
-git commit -am "Add your feature"
-
-# Push and create PR
-git push origin feature/your-feature
-```
-
----
-
-## 📄 License
-
-**MIT License** - See [LICENSE](LICENSE) file
-
-Free to use, modify, and distribute.
-
----
-
-## 🙏 Acknowledgments
-
-### Models & Technologies
-
-- **DiffRhythm2**: ASLP-lab for state-of-the-art music generation
-- **LoRA/PEFT**: HuggingFace for parameter-efficient fine-tuning
-- **Gradio**: For the beautiful web interface
-- **Demucs**: Meta AI for stem separation
-- **Pedalboard**: Spotify for professional audio processing
-
-### Datasets
-
-- **GTZAN**: Music genre classification dataset
-- **MusicCaps**: Google's music captioning dataset
-- **FMA**: Free Music Archive community
-
----
-
-## 📞 Support & Community
-
-- **Documentation**: [Full Docs](https://github.com/lemm-ai/LEMM-1.0.0-ALPHA/wiki)
-- **HuggingFace Space**: [Try Now](https://huggingface.co/spaces/Gamahea/lemm-test-100)
-- **LoRA Collection**: [Browse Models](https://huggingface.co/collections/Gamahea/lemm-100-pre-beta)
-- **Issues**: [GitHub Issues](https://github.com/lemm-ai/LEMM-1.0.0-ALPHA/issues)
-
----
-
-## 🚀 What's Next
-
-**Planned Features:**
-- Multi-track composition tools
-- Real-time style transfer
-- Collaborative projects
-- Mobile app
-- VST plugin support
-
-**Join the Journey!**
-
-Built with ❤️ by the LEMM community
-
----
-
-**LEMM - Let Everyone Make Music** 🎵
+---
+title: Music Generation Studio
+emoji: 🎵
+colorFrom: purple
+colorTo: pink
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+# 🎵 Music Generation Studio
+
+Create AI-powered music with intelligent prompt analysis and context-aware generation using DiffRhythm2 and LyricMind AI.
+
+**⚠️ Important:** 
+- This Space requires ZeroGPU to run
+- **You must be logged in** to HuggingFace to use GPU features
+- Free users get daily ZeroGPU quota - check your usage at https://huggingface.co/settings/billing
+- If you see quota errors while logged in, try duplicating this Space to your account
+
+## Features
+
+- **Intelligent Music Generation**: DiffRhythm2 model for high-quality music with vocals
+- **Smart Lyrics Generation**: LyricMind AI for context-aware lyric creation
+- **Prompt Analysis**: Automatically detects genre, BPM, and mood from your description
+- **Flexible Vocal Modes**:
+  - Instrumental: Pure music without vocals
+  - User Lyrics: Provide your own lyrics
+  - Auto Lyrics: AI-generated lyrics based on prompt
+- **Timeline Management**: Build complete songs clip-by-clip
+- **Export**: Download your creations in WAV, MP3, or FLAC formats
+
+## How to Use
+
+1. **Generate Music**:
+   - Enter a descriptive prompt (e.g., "energetic rock song with electric guitar at 140 BPM")
+   - Choose vocal mode (Instrumental, User Lyrics, or Auto Lyrics)
+   - Set duration (10-120 seconds)
+   - Click "Generate Music Clip"
+
+2. **Manage Timeline**:
+   - View all generated clips in the timeline
+   - Remove specific clips or clear all
+   - Clips are arranged sequentially
+
+3. **Export**:
+   - Enter a filename
+   - Choose format (WAV recommended for best quality)
+   - Download your complete song
+
+## Models
+
+- **DiffRhythm2**: Music generation with integrated vocals ([ASLP-lab/DiffRhythm2](https://huggingface.co/ASLP-lab/DiffRhythm2))
+- **MuQ-MuLan**: Music style encoding ([OpenMuQ/MuQ-MuLan-large](https://huggingface.co/OpenMuQ/MuQ-MuLan-large))
+
+## Performance
+
+⏱️ Generation time: ~2-4 minutes per 30-second clip on CPU (HuggingFace Spaces free tier)
+
+💡 Tip: Start with shorter durations (10-20 seconds) for faster results
+
+## Technical Details
+
+- Built with Gradio and PyTorch
+- Uses DiffRhythm2 for music generation with vocals
+- Employs flow-matching techniques for high-quality audio synthesis
+- Supports multiple languages for lyrics (English, Chinese, Japanese)
+
+## Credits
+
+- DiffRhythm2 by ASLP-lab
+- MuQ-MuLan by OpenMuQ
+- Application interface and integration by Music Generation App Team
+
+## License
+
+MIT License - See LICENSE file for details