README.md

---
license: mit
language:
  - ar
  - en
tags:
  - speech
  - asr
  - automatic-speech-recognition
  - ctc
  - conformer
  - egyptian-arabic
  - code-switching
  - arabic
  - audio
  - pytorch
library_name: metro-asr
pipeline_tag: automatic-speech-recognition
datasets:
  - AlaaSamir/custom-egy-tts
  - OmarAhmedSobhy/egyption-with-emotion-dataset
  - MightyStudent/Egyptian-ASR-MGB-3
  - MAdel121/arabic-egy-cleaned
  - MAdel121/Continuation-egy-for-ultravox-v1
  - Raniahossam33/Egyptian_TTS3RS
  - ahmedbasemdev/egyptain-tts-dataset
  - MohamedRashad/arabic-english-code-switching
  - librispeech_asr
metrics:
  - wer
  - cer
model-index:
  - name: Metro-ASR Small
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          type: custom
          name: Egyptian Arabic + Code-Switching Test Set
          config: all
          split: test
        metrics:
          - type: wer
            value: 46.85
            name: WER (All)
          - type: cer
            value: 28.41
            name: CER (All)
          - type: wer
            value: 37.24
            name: WER (Arabic)
          - type: cer
            value: 17.45
            name: CER (Arabic)
          - type: wer
            value: 36.32
            name: WER (Code-Switching)
          - type: cer
            value: 17.44
            name: CER (Code-Switching)
---

<h1 align="center">Metro-ASR Small (61M)</h1>

<p align="center">
  <strong>Non-Autoregressive CTC Speech Recognition for Egyptian Arabic + Code-Switching</strong>
</p>

<p align="center">
  <a href="https://github.com/mohammedaly22/Metro-ASR"><img src="https://img.shields.io/badge/GitHub-Repository-blue?style=for-the-badge&logo=github" alt="GitHub"></a>
  <a href="https://pypi.org/project/metro-asr/"><img src="https://img.shields.io/pypi/v/metro-asr?style=for-the-badge&logo=pypi&logoColor=white&color=blue" alt="PyPI"></a>
  <a href="https://huggingface.co/spaces/mohammedaly22/metro-asr"><img src="https://img.shields.io/badge/%F0%9F%A4%97_Space-Demo-orange?style=for-the-badge" alt="Space"></a>
</p>

## Model Description

**Metro-ASR Small** is a 61M parameter non-autoregressive CTC-based ASR model built on a modern Conformer encoder. It is specifically designed for **Egyptian Arabic** (العامية المصرية) with native **Arabic-English code-switching** support.

### Architecture

| Component | Details |
|-----------|---------|
| Encoder | Conformer (12 layers, d_model=384, 6 heads) |
| Position Encoding | RoPE (Rotary Position Embeddings) |
| Feed-Forward | SwiGLU (Macaron-style dual FFN) |
| Normalization | RMSNorm (Pre-norm) |
| Convolution | SE-Gated Depthwise Separable (kernel=31) |
| Regularization | Stochastic Depth (rate=0.05) |
| Auxiliary Loss | Intermediate CTC at layer 6 |
| Tokenizer | BPE (SentencePiece, vocab=5000) |
| Decoding | CTC Greedy / Beam Search + KenLM |
| Parameters | 61.6M |

### Performance

| Split | WER (%) | CER (%) |
|-------|---------|---------|
| All | 46.85 | 28.41 |
| Arabic Only | 37.24 | 17.45 |
| Code-Switching | 36.32 | 17.44 |

**Speed:** RTF ~0.002 on CPU (500x faster than real-time)

## Usage

### Install

```bash
pip install metro-asr
```

### Quick Start

```python
from metro_asr import MetroASREngine

engine = MetroASREngine.from_pretrained("small")  # Auto-downloads this model
result = engine.transcribe("audio.wav")
print(result.text)
```

### With Language Model (Beam Search)

```python
engine = MetroASREngine.from_pretrained(
    "small",
    lm_path="lm_5gram.bin",     # Download from this repo
    beam_width=100,
    lm_alpha=0.5,
    lm_beta=5.0,
)

result = engine.transcribe("audio.wav", beam_search=True)
print(result.text)
```

### Batch Transcription

```python
results = engine.transcribe_batch(["audio1.wav", "audio2.wav", "audio3.wav"])
for r in results:
    print(f"{r.text}  (RTF={r.rtf:.4f})")
```

## Files in this Repository

| File | Description | Size |
|------|-------------|------|
| `model.pt` | Model checkpoint (weights + optimizer state) | ~706MB |
| `config.yaml` | Model architecture configuration | <1KB |
| `bpe.model` | SentencePiece BPE tokenizer model | ~200KB |
| `bpe.vocab` | BPE vocabulary file | ~100KB |
| `lm_5gram.bin` | KenLM 5-gram language model (optional) | ~6GB |

## Training

Trained on a combination of open-source Egyptian Arabic datasets and custom YouTube data:

- **Audio data:** 130K+ samples from 8 datasets + YouTube Egyptian Arabic content
- **Text data:** 1.9M Egyptian sentences for LM training
- **Training:** 440K steps, batch size 32, 4x gradient accumulation, AdamW + Cosine LR
- **Hardware:** Single GPU training

## Limitations

- Optimized for Egyptian Arabic dialect; MSA and other dialects may have higher error rates
- Code-switching support is Arabic-English only
- Best performance on audio 0.5s-30s in duration
- The language model significantly improves accuracy but adds ~6GB to download

## Citation

```bibtex
@software{metro-asr-2025,
  title  = {Metro-ASR: Non-Autoregressive CTC-based ASR for Egyptian Arabic and Code-Switching},
  author = {Mohammed Aly},
  year   = {2025},
  url    = {https://github.com/mohammedaly22/Metro-ASR}
}
```

## License

MIT