What's New: Training Infrastructure Release

Version 1.0 - Training Capabilities

Release Date: November 17, 2025

🎉 Major Update: Train Your Own Luna AI

You can now train production-ready speech-to-speech models from scratch, completely independent of external APIs!

✨ New Features

1. Trainable Speech Tokenizer

File: src/models/speech_tokenizer_trainable.py

✓ Residual Vector Quantization (RVQ) with 8 quantizers
✓ Convolutional encoder/decoder for high-quality audio
✓ HiFiGAN vocoder integration (MIT licensed)
✓ Checkpoint management - save/resume training anytime
✓ LibriSpeech-ready - works out-of-box with free datasets

Architecture:

Audio → Mel → CNN Encoder → RVQ (8x1024) → CNN Decoder → Mel → HiFiGAN → Audio
         └────────── Discrete Tokens ────────────┘

Key Methods:

tokenize(audio) - Convert audio to discrete tokens
detokenize(tokens) - Convert tokens back to audio
forward(audio) - Full training forward pass with losses
save_checkpoint() / load_checkpoint() - Checkpoint management

2. Complete Training Pipeline

File: training/train_tokenizer.py

✓ LibriSpeech dataset loader with automatic preprocessing
✓ Multi-GPU support with PyTorch DataLoader
✓ WandB integration for real-time monitoring
✓ Automatic checkpointing every N epochs
✓ Best model tracking based on validation loss
✓ Gradient clipping and mixed precision support

Training Loop:

Load LibriSpeech dataset (auto-download)
Create train/val split (95/5)
Train with reconstruction + commitment losses
Validate every epoch
Save checkpoints
Log to WandB/TensorBoard

Usage:

python training/train_tokenizer.py --config training/configs/tokenizer_config.yaml

3. Configuration System

File: training/configs/tokenizer_config.yaml

✓ YAML-based configuration for easy experimentation
✓ Model hyperparameters (hidden_dim, codebook_size, etc.)
✓ Training settings (batch_size, learning_rate, epochs)
✓ Data configuration (dataset paths, splits)
✓ Logging options (WandB, TensorBoard)

Example Config:

model:
  sample_rate: 24000
  codebook_size: 1024
  hidden_dim: 512
  num_quantizers: 8

training:
  epochs: 100
  batch_size: 16
  learning_rate: 1e-4

4. Quality Testing Tools

File: scripts/test_tokenizer.py

✓ Audio reconstruction test - Compare original vs reconstructed
✓ Codebook utilization analysis - Check if quantizers are used efficiently
✓ Latency measurement - Real-time factor (RTF) calculation
✓ Quality metrics - MSE, MAE, peak amplitude

Usage:

# Test reconstruction quality
python scripts/test_tokenizer.py \
    --checkpoint checkpoints/tokenizer_best.pt \
    --input test.wav \
    --output reconstructed.wav

# Analyze codebook usage
python scripts/test_tokenizer.py \
    --checkpoint checkpoints/tokenizer_best.pt \
    --analyze \
    --test_dir /workspace/data/LibriSpeech/test-clean

5. Automated Setup Script

File: setup_training.sh

✓ One-command setup for entire training environment
✓ Python/CUDA verification - Check system requirements
✓ Dependency installation - PyTorch, torchaudio, all packages
✓ Dataset download - Interactive LibriSpeech downloader
✓ WandB configuration - Optional monitoring setup

Usage:

bash setup_training.sh

6. Comprehensive Documentation

File: TRAINING_GUIDE.md (12KB, 400+ lines)

✓ Quick start guide - Get training in 30 minutes
✓ Cost breakdowns - Detailed GPU hour estimates
✓ Phase-by-phase roadmap - Tokenizer → S2S → Emotion
✓ Troubleshooting - Common issues and solutions
✓ Dataset preparation - LibriSpeech, Common Voice, synthetic

📊 Training Cost Estimates

Dataset	Size	Training Time	Cost (RunPod A100)	Quality
train-clean-100	100h	8-12 hours	$10-15	Testing
train-clean-360	360h	30-40 hours	$36-48	Production
train-other-500	500h	40-50 hours	$48-60	Excellent
All combined	960h	80-120 hours	$95-143	Best

Full Training Pipeline (All 3 Phases):

Phase 1 (Tokenizer): $95-143
Phase 2 (S2S Model): $119-476 (coming soon)
Phase 3 (Emotions): $60-179 (coming soon)
Total: $274-798

Compare to:

OpenAI GPT-4o Realtime: $5-10/hr usage, no ownership
Luna AI: API-only, pricing TBD
Your model: One-time cost, full ownership, unlimited use

🛠️ What Changed

Files Added

✓ src/models/speech_tokenizer_trainable.py  (13KB)
✓ training/train_tokenizer.py              (13KB)
✓ training/configs/tokenizer_config.yaml    (1.5KB)
✓ scripts/test_tokenizer.py                 (7KB)
✓ requirements-training.txt                 (700 bytes)
✓ setup_training.sh                         (7KB)
✓ TRAINING_GUIDE.md                         (12KB)
✓ WHATS_NEW.md                              (this file)

Files Updated

✓ README.md - Added training sections, cost breakdowns, Luna AI comparison

Directories Created

Testing-S2S/
├── training/              # Training scripts
│   ├── train_tokenizer.py
│   └── configs/
│       └── tokenizer_config.yaml
├── scripts/               # Utility scripts
│   └── test_tokenizer.py
└── checkpoints/          # Model checkpoints (gitignored)
    ├── tokenizer/
    └── s2s/

🚀 Quick Start (New Users)

1. Clone Repository

git clone https://github.com/devasphn/Testing-S2S.git
cd Testing-S2S

2. Run Automated Setup

bash setup_training.sh

This will:

Check system requirements (Python 3.10+, CUDA)
Create virtual environment
Install PyTorch with CUDA 12.1
Install all dependencies
Download LibriSpeech dataset (optional)
Configure WandB (optional)

3. Start Training

source venv/bin/activate
python training/train_tokenizer.py

4. Monitor Progress

# Terminal 1: Training logs
python training/train_tokenizer.py

# Terminal 2: GPU monitoring
watch -n 1 nvidia-smi

# Terminal 3: WandB dashboard
# Visit https://wandb.ai/your-username/luna-speech-tokenizer

🎯 Next Steps

Immediate Actions

Set up RunPod environment
- Launch A100 80GB pod
- Run setup_training.sh
- Verify GPU with nvidia-smi
Download dataset
- Start with train-clean-100 (6GB, $10 training)
- Verify with ls /workspace/data/LibriSpeech/
Start tokenizer training
- Edit config if needed
- Run training script
- Monitor first 10 epochs
Test quality
- Use test_tokenizer.py on checkpoint
- Listen to reconstructed audio
- Check if MAE < 0.1

Short-term (2 Weeks)

Complete tokenizer training (100 epochs)
Evaluate reconstruction quality
Scale up to larger dataset if quality is good
Begin Phase 2 planning (S2S model)

Long-term (1-2 Months)

Train Hybrid S2S Model (Phase 2)
Add emotional control (Phase 3)
Deploy to production
Fine-tune on Indian languages

👥 For Existing Users

If you've been using Testing-S2S for inference only:

What Still Works

✓ Existing inference server (src/server.py) ✓ WebSocket streaming API ✓ Turn-based and stream modes ✓ HiFiGAN vocoder ✓ All API endpoints

New Capabilities

✓ Train your own models instead of using random weights ✓ Replace SpeechTokenizer with trained version ✓ Full control over model architecture and data ✓ No dependency on external APIs

Migration Path

Keep using existing setup - Nothing breaks
Train tokenizer - Follow TRAINING_GUIDE.md

Update server - Load trained checkpoint:

tokenizer = TrainableSpeechTokenizer(
    checkpoint_path="checkpoints/tokenizer_best.pt"
).to(device)

Test quality - Compare audio before/after

❓ FAQ

Q: Do I need to retrain everything?

A: No! Existing inference code still works. Training is optional for those who want:

Full model ownership
Custom datasets
Specialized use cases
Independence from APIs

Q: How long does training take?

A: Depends on dataset:

100h dataset: 8-12 GPU hours ($10-15)
360h dataset: 30-40 GPU hours ($36-48)
960h dataset: 80-120 GPU hours ($95-143)

Q: Can I pause and resume training?

A: Yes! Checkpoints saved every N epochs:

# Resume from checkpoint
python training/train_tokenizer.py \
    --config my_config.yaml \
    --resume checkpoints/tokenizer_epoch_50.pt

Q: What GPU do I need?

Training: A100 80GB (RunPod: $0.89-1.19/hr)
Testing: A40 48GB or RTX 4090
Development: Any GPU with 8GB+ VRAM

Q: Is this production-ready?

A: Phase 1 (Tokenizer) is production-ready:

✓ Tested architecture (based on Encodec/SpeechTokenizer)
✓ Works with LibriSpeech out-of-box
✓ Checkpoint management
✓ Quality testing tools

Phases 2-3 coming soon (S2S model, emotions).

📝 Technical Details

Speech Tokenizer Architecture

Encoder:

4 convolutional layers with GroupNorm
Downsampling by 8x (24kHz → 3kHz frame rate)
Output: 512-dim latent vectors

Quantizer:

8 residual vector quantizers (RVQ)
1024 codebook entries per quantizer
Total vocabulary: 1024^8 possible combinations

Decoder:

4 transposed convolutional layers
Upsampling by 8x (3kHz → 24kHz)
Output: 80-dim mel spectrogram

Vocoder:

HiFiGAN Universal (pretrained, frozen)
Mel → 24kHz audio waveform

Loss Function:

Total Loss = Mel_L1 + 0.5 * Mel_MSE + 0.25 * Commitment

Training Hyperparameters

Optimizer: AdamW
Learning Rate: 1e-4
Scheduler: CosineAnnealing
Batch Size: 16 (adjustable)
Gradient Clip: 1.0
Weight Decay: 0.01
Epochs: 100-200

🔗 Resources

Training Guide: TRAINING_GUIDE.md
README: README.md
GitHub: https://github.com/devasphn/Testing-S2S
RunPod: https://runpod.io
LibriSpeech: http://www.openslr.org/12/
WandB: https://wandb.ai

🆗 Support

Need help?

Check documentation: TRAINING_GUIDE.md has troubleshooting
Open GitHub issue: Include logs and error messages
Share checkpoints: Use RunPod Network Storage for team collaboration

Version: 1.0.0

Release Date: November 17, 2025

Status: ✓ Tokenizer training ready, S2S & Emotion coming soon

License: MIT - Full commercial use allowed

🎉 Acknowledgments

This training infrastructure was inspired by:

Luna AI (Pixa) - First Indian speech-to-speech model
Sparsh Agrawal - Proof that world-class AI can be built with limited resources
Moshi (Kyutai Labs) - Real-time duplex architecture
GLM-4-Voice (Tsinghua) - Chinese+English speech model
LibriSpeech - High-quality free speech dataset

Built with ❤️ by developers who believe in open, independent AI.

⭐ Star the repo to follow Phase 2 (S2S Model) and Phase 3 (Emotions) development!

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What's New: Training Infrastructure Release

🎉 Major Update: Train Your Own Luna AI

✨ New Features

1. Trainable Speech Tokenizer

2. Complete Training Pipeline

3. Configuration System

4. Quality Testing Tools

5. Automated Setup Script

6. Comprehensive Documentation

📊 Training Cost Estimates

🛠️ What Changed

Files Added

Files Updated

Directories Created

🚀 Quick Start (New Users)

1. Clone Repository

2. Run Automated Setup

3. Start Training

4. Monitor Progress

🎯 Next Steps

Immediate Actions

Short-term (2 Weeks)

Long-term (1-2 Months)

👥 For Existing Users

What Still Works

New Capabilities

Migration Path

❓ FAQ

Q: Do I need to retrain everything?

Q: How long does training take?

Q: Can I pause and resume training?

Q: What GPU do I need?

Q: Is this production-ready?

📝 Technical Details

Speech Tokenizer Architecture

Training Hyperparameters

🔗 Resources

🆗 Support

🎉 Acknowledgments