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HUGGING FACE SPACES SETUP - COMPLETE
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All necessary files have been created for Hugging Face Spaces deployment.

📦 FILES CREATED:
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1. app.py (10.5 KB)
   - Main Gradio interface optimized for HF Spaces
   - Includes helper functions for speaker/dialect selection
   - Ready to deploy as-is

2. requirements.txt (2.4 KB)
   - Complete Python dependencies from your conda environment
   - Compatible with HF Spaces Python 3.8+ environment

3. README.md (4.2 KB)
   - Space metadata with Hugging Face YAML header
   - Project description, features, and references
   - Author information and citation details

4. HF_DEPLOYMENT.md (4.5 KB)
   - Detailed deployment guide
   - Instructions for Git LFS setup
   - Troubleshooting section
   - Performance notes

5. DEPLOYMENT_CHECKLIST.md (4.0 KB)
   - Step-by-step deployment checklist
   - File verification list
   - Issue resolution guide

6. .gitignore (396 B)
   - Configured for Python projects
   - Excludes large files and temporary data

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🚀 QUICK START - 3 STEPS:
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Step 1: Create a Hugging Face Space
   → https://huggingface.co/new-space
   → SDK: Gradio
   → License: Choose one (CC-BY-4.0 recommended)

Step 2: Clone and Push Files
   git clone https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE
   cd YOUR_SPACE
   cp -r /path/to/this/project/* .
   git add .
   git commit -m "Add Austrian Dialect TTS"
   git push

Step 3: Wait for Deployment
   → Monitor at https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE
   → Done in 2-5 minutes!

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⚠️  CRITICAL: MODEL FILES
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Before deploying, ensure these directories are present:
  ✓ InferenceInterfaces/      (TTS implementation)
  ✓ Preprocessing/            (Text processing + embeddings)
    └─ wav2vec_embeddings/    (dialect .pt files)
  ✓ Utility/                  (utilities)
    └─ example_wavs/         (speaker reference .wav files)
  ✓ Models/                   (trained model files)

These should be committed to Git. For large files:
  git lfs track "*.pt" "*.wav" "*.pkl"

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🔧 CONFIGURATION DETAILS:
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App Entry Point:        app.py
Server Configuration:   0.0.0.0:7860 (HF Spaces standard)
Gradio Version:         3.44.3
Python Version:         3.8+
GPU Support:            Optional (free CPU tier or paid GPU tier)

Interface Type:         TabbedInterface (1 tab: TTS)
Speakers Supported:     12
Dialects Supported:     4 (Standard, Viennese, Goisern, Innervillgraten)
Interpolations:         7 options for dialect blending

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📝 CUSTOMIZATION OPTIONS:
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Before deployment, you may want to:

1. Update README.md
   - Add your Space link
   - Add any additional acknowledgments
   - Modify contact information if needed

2. Modify app.py
   - Change default speaker (line ~240: DEFAULT_SPK)
   - Change default dialect (line ~241: DEFAULT_LANG_EMB)
   - Add more examples to the interface
   - Adjust interface colors/styling

3. Set Space Settings (after creation)
   - Visibility: Public/Private
   - Hardware: CPU (free) or GPU (paid)
   - Sleep: Enable/disable auto-shutdown

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📊 EXPECTED PERFORMANCE:
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CPU (Free Tier):
  - First synthesis: ~15-20 seconds
  - Subsequent: ~10-15 seconds
  - Good for: Demo/testing

GPU (T4, ~$0.60/hour):
  - First synthesis: ~5-10 seconds
  - Subsequent: ~2-3 seconds
  - Good for: Production use

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✨ YOU'RE READY TO DEPLOY!
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Files are tested and ready. The app will:
  ✅ Load and initialize on startup
  ✅ Accept German text input
  ✅ Synthesize speech with selected dialect
  ✅ Return audio output in real-time
  ✅ Display examples for users

For detailed instructions, see:
  → HF_DEPLOYMENT.md (deployment guide)
  → DEPLOYMENT_CHECKLIST.md (step-by-step checklist)
  → Original README.md (project documentation)

Questions? Contact: lorenz.gutscher@ofai.at

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