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+# Models and Model Paths
+
+## Defaults
+
+**Default Whisper Model**: `base`  
+When no model is specified, WhisperLiveKit uses the `base` model, which provides a good balance of speed and accuracy for most use cases.
+
+**Default Model Cache Directory**: `~/.cache/whisper`  
+Models are automatically downloaded from OpenAI's model hub and cached in this directory. You can override this with `--model_cache_dir`.
+
+**Default Translation Model**: `600M` (NLLB-200-distilled)  
+When translation is enabled, the 600M distilled NLLB model is used by default. This provides good quality with minimal resource usage.
+
+**Default Translation Backend**: `transformers`  
+The translation backend defaults to Transformers. On Apple Silicon, this automatically uses MPS acceleration for better performance.
+
+---
+
+
+## Available Whisper model sizes:
+
+| Available Model    | Speed    | Accuracy  | Multilingual | Translation | Hardware Requirements | Best Use Case                   |
+|--------------------|----------|-----------|--------------|-------------|----------------------|----------------------------------|
+| tiny(.en)          | Fastest  | Basic     | Yes/No       | Yes/No      | ~1GB VRAM            | Real-time, low resources         |
+| base(.en)          | Fast     | Good      | Yes/No       | Yes/No      | ~1GB VRAM            | Balanced performance             |
+| small(.en)         | Medium   | Better    | Yes/No       | Yes/No      | ~2GB VRAM            | Quality on limited hardware      |
+| medium(.en)        | Slow     | High      | Yes/No       | Yes/No      | ~5GB VRAM            | High quality, moderate resources |
+| large-v2           | Slowest  | Excellent | Yes          | Yes         | ~10GB VRAM           | Good overall accuracy & language support          |
+| large-v3           | Slowest  | Excellent | Yes          | Yes         | ~10GB VRAM           | Best overall accuracy & language support                |
+| large-v3-turbo     | Fast     | Excellent | Yes          | No          | ~6GB VRAM            | Fast, high-quality transcription |
+
+
+### How to choose?
+
+#### Language Support
+- **English only**: Use `.en` (ex: `base.en`) models for better accuracy and faster processing when you only need English transcription
+- **Multilingual**: Do not use `.en` models.
+      
+#### Special Cases
+- **No translation needed**: Use `large-v3-turbo`
+  - Same transcription quality as `large-v2` but significantly faster
+  - **Important**: Does not translate correctly, only transcribes
+
+### Additional Considerations
+
+**Model Performance**:
+- Accuracy improves significantly from tiny to large models
+- English-only models are ~10-15% more accurate for English audio
+- Newer versions (v2, v3) have better punctuation and formatting
+
+**Audio Quality Impact**:
+- Clean, clear audio: smaller models may suffice
+- Noisy, accented, or technical audio: larger models recommended
+- Phone/low-quality audio: use at least `small` model
+
+_______________________
+
+
+# Custom Models:
+
+The `--model-path` parameter accepts:
+
+## File Path
+- **`.pt` / `.bin` / `.safetensor` formats** Should be openable by pytorch/safetensor.
+
+## Directory Path (recommended)
+Must contain:
+- **`.pt` / `.bin` / `.safetensor` file** (required for decoder)
+
+May optionally contain:
+- **`.bin` file** - faster-whisper model for encoder (requires faster-whisper)
+- **`weights.npz`** or **`weights.safetensors`** - for encoder (requires whisper-mlx)
+
+## Hugging Face Repo ID
+- Provide the repo ID (e.g. `openai/whisper-large-v3`) and WhisperLiveKit will download and cache the snapshot automatically. For gated repos, authenticate via `huggingface-cli login` first.
+
+To improve speed/reduce hallucinations, you may want to use `scripts/determine_alignment_heads.py` to determine the alignment heads to use for your model, and use the `--custom-alignment-heads` to pass them to WLK. If not, alignment heads are set to be all the heads of the last half layer of decoder.
+
+
+_______________________
+
+# Translation Models and Backend
+
+**Language Support**: ~200 languages
+
+## Distilled Model Sizes Available
+
+| Model | Size | Parameters | VRAM (FP16) | VRAM (INT8) | Quality |
+|-------|------|------------|-------------|-------------|---------|
+| 600M | 2.46 GB | 600M | ~1.5GB | ~800MB | Good, understandable |
+| 1.3B | 5.48 GB | 1.3B | ~3GB | ~1.5GB | Better accuracy, context |
+
+**Quality Impact**: 1.3B has ~15-25% better BLEU scores vs 600M across language pairs.
+
+## Backend Performance
+
+| Backend | Speed vs Base | Memory Usage | Quality Loss |
+|---------|---------------|--------------|--------------|
+| CTranslate2 | 6-10x faster | 40-60% less | ~5% BLEU drop |
+| Transformers | Baseline | High | None |
+| Transformers + MPS (on Apple Silicon) | 2x faster | Medium | None |
+
+**Metrics**:
+- CTranslate2: 50-100+ tokens/sec
+- Transformers: 10-30 tokens/sec
+- Apple Silicon with MPS: Up to 2x faster than CTranslate2