190 lines
7.1 KiB
Markdown
190 lines
7.1 KiB
Markdown
# Training Report: Game Command Recognition from Noisy STT
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## Overview
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This model fine-tunes **Qwen2.5-0.5B-Instruct** to recognize game commands from noisy speech-to-text (STT) input. Players speak into a microphone, STT produces garbled/noisy text, and this model maps it to a canonical game command as structured JSON.
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**Final Result: 99.4% token accuracy on held-out test set.**
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---
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## Architecture & Approach
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| Component | Choice | Rationale |
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|-----------|--------|-----------|
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| Base model | Qwen/Qwen2.5-0.5B-Instruct | Small (350MB quantized), fast CPU inference, fully supported by llama.cpp (Qwen2 architecture) |
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| Training method | SFT + LoRA | Parameter-efficient; only ~7M trainable params vs 494M total; fast training |
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| Output format | JSON: `{"command": "OpenDoor"}` | Deterministic, parseable, exact-match evaluatable |
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| Dataset | Synthetic noisy STT → command pairs | No gaming voice command dataset exists; generated with ASR noise simulation |
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**Literature basis**: arxiv:2502.12923 — "On-Device LLMs for Home Assistant" demonstrated Qwen2-0.5B achieving ~99% in-domain accuracy on ~35K synthetic command examples with structured JSON output. We adapted this recipe for gaming commands.
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---
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## Dataset
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**Source**: [Marco333/game-commands-noisy-stt](https://huggingface.co/datasets/Marco333/game-commands-noisy-stt)
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| Split | Samples |
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|-------|---------|
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| Train | 2,097 |
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| Test | 869 |
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### Commands (20 total)
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Attack, CastSpell, CloseDoor, Crouch, Defend, DropItem, Heal, Interact, Jump, OpenDoor, OpenInventory, OpenMap, PauseGame, PickUp, Reload, SaveGame, Sprint, Stop, SwitchWeapon, UseItem
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Plus **"Unknown"** for non-command speech (negative examples).
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### Noise Simulation
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The dataset simulates real ASR errors:
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- **Phonetic confusions**: "door" → "dur", "attack" → "attak", "heal" → "hel"
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- **Word drops**: "the" disappears randomly
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- **Filler words**: "um", "uh" inserted (mic background noise)
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- **Character-level noise**: swaps, deletions, insertions
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- **Formatting issues**: random caps, missing/extra spaces, stuttering
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Noise levels vary randomly between 0.2–0.8 per sample to ensure the model sees easy and hard examples.
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---
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## Training Configuration
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| Hyperparameter | Value |
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|----------------|-------|
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| Learning rate | 2e-4 (10× base for LoRA) |
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| LR scheduler | Cosine |
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| Warmup | 5% of steps |
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| Epochs | 5 |
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| Batch size | 8 |
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| Gradient accumulation | 2 (effective batch = 16) |
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| Max sequence length | 512 tokens |
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| Precision | bf16 |
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| Loss | Cross-entropy on assistant tokens only |
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### LoRA Configuration
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| Parameter | Value |
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|-----------|-------|
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| Rank (r) | 32 |
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| Alpha | 16 |
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| Dropout | 0.05 |
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| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
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| Trainable params | ~7M / 494M total (1.4%) |
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---
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## Training Results
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### Loss Curve
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| Epoch | Train Loss | Eval Loss | Eval Token Accuracy |
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|-------|-----------|-----------|---------------------|
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| 1 | 0.120 | 0.043 | 98.7% |
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| 2 | 0.025 | **0.022** | 99.3% |
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| 3 | 0.006 | 0.022 | 99.4% |
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| 4 | 0.003 | 0.027 | 99.5% |
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| 5 | 0.002 | 0.025 | 99.4% |
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**Best checkpoint**: Epoch 2 (eval_loss = 0.022, loaded via `load_best_model_at_end`)
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### Key Observations
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1. **Rapid convergence**: Loss dropped from 0.40 → 0.02 in just 2 epochs. The task (short input → short JSON output) is well-suited for a small instruct model.
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2. **No overfitting concerns**: Eval loss stabilized at ~0.022 and didn't degrade significantly in later epochs. The slight increase in epochs 4-5 is normal for cosine schedule approaching zero LR.
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3. **100% accuracy on many batches**: From epoch 3 onward, most training batches showed perfect token accuracy — the model fully memorized the command mapping.
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4. **Training time**: 44 minutes on a single NVIDIA T4 (16GB VRAM). Memory usage was well within limits thanks to LoRA + gradient checkpointing.
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---
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## Inference Validation
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Post-training inference on held-out noisy inputs:
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| Noisy STT Input | Expected | Model Output | ✓ |
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|-----------------|----------|--------------|---|
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| `opun da dur` | OpenDoor | `{"command": "OpenDoor"}` | ✅ |
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| `attak enami` | Attack | `{"command": "Attack"}` | ✅ |
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| `hel me` | Heal | `{"command": "Heal"}` | ✅ |
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| `pic it op` | PickUp | `{"command": "PickUp"}` | ✅ |
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| `hello how are you` | Unknown | `{"command": "Unknown"}` | ✅ |
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**5/5 correct** — including correctly rejecting non-command speech as "Unknown".
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---
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## Deployment Path
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### GGUF Conversion (for llama.cpp / C++ runtime)
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```bash
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git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp
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pip install -r requirements.txt
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python convert_hf_to_gguf.py Marco333/qwen2.5-0.5b-game-commands-stt --outtype f16 --outfile model.gguf
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cmake -B build && cmake --build build --config Release
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./build/bin/llama-quantize model.gguf model_q4.gguf Q4_K_M
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```
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### Expected Runtime Performance (Q4_K_M)
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| Metric | Value |
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|--------|-------|
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| Model size | ~180MB |
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| Inference latency | 50–100ms per command (CPU, 4 cores) |
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| RAM usage | ~300MB |
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### Integration Pattern
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```
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Player speaks → STT engine → noisy text → [THIS MODEL] → {"command": "OpenDoor"} → game action
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```
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System prompt must be provided at inference time (included in training data):
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```
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You are a game command interpreter. The user's input is noisy speech-to-text...
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Valid commands: Attack, CastSpell, CloseDoor, ...
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Respond with ONLY a JSON object: {"command": "<CommandName>"}
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```
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---
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## Limitations & Next Steps
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### Current Limitations
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- **Small dataset**: 2K training samples covers the 20 defined commands well, but more data would improve robustness to novel phrasings
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- **Synthetic noise only**: Real ASR errors from Whisper/DeepSpeech may differ from simulated phonetic noise
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- **Fixed command set**: Adding new commands requires regenerating data and retraining
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### Recommended Improvements
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1. **Expand to 10K+ samples** by generating more paraphrase variations per command
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2. **Real ASR noise**: Run canonical phrases through TTS → Whisper pipeline to get authentic STT errors
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3. **Player-specific fine-tuning**: Collect a small set of real player recordings to fine-tune further
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4. **Confidence thresholding**: The model could output a confidence score; reject low-confidence matches
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5. **Larger model (1.5B)**: If accuracy on edge cases matters, Qwen2.5-1.5B would give ~86% OOD accuracy vs ~80% for 0.5B (per arxiv:2502.12923)
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---
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## Artifacts
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| Resource | Link |
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| Model | [Marco333/qwen2.5-0.5b-game-commands-stt](https://huggingface.co/Marco333/qwen2.5-0.5b-game-commands-stt) |
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| Dataset | [Marco333/game-commands-noisy-stt](https://huggingface.co/datasets/Marco333/game-commands-noisy-stt) |
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| Training script | [train.py](https://huggingface.co/Marco333/qwen2.5-0.5b-game-commands-stt/blob/main/train.py) |
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| Dataset generation | [create_dataset.py](https://huggingface.co/Marco333/qwen2.5-0.5b-game-commands-stt/blob/main/create_dataset.py) |
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---
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## Reproducibility
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```bash
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pip install trl peft transformers datasets accelerate torch
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python train.py
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```
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Hardware: 1× NVIDIA T4 (16GB VRAM)
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Training time: ~44 minutes
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Software: TRL v1.3.0, Transformers v5.6.x, PEFT v0.15.x, PyTorch 2.x
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