README.md

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# 🫢 NextInnoMind / next\_bemba\_ai\_medium

**Multilingual Whisper ASR (Automatic Speech Recognition)**
Fine-tuned Whisper model for Bemba and English using language tokens.
Developed and maintained by **NextInnoMind**, led by **Chalwe Silas**.

---

### 🧪 Model Type

`WhisperForConditionalGeneration` — fine-tuned using [openai/whisper-medium](https://huggingface.co/openai/whisper-medium)
Framework: `Transformers`
Checkpoint Format: `Safetensors`
Languages: `Bemba`, `English` (with `<|bem|>` language token support)

---

## 📜 Model Description

This model is a Whisper Medium variant fine-tuned for **Bemba** and **English**, enabling robust multilingual transcription. It supports the use of language tokens (e.g., `<|bem|>`) to help guide decoding, particularly for low-resource languages like Bemba.

---

## 📚 Training Details

* **Base Model**: [`openai/whisper-medium`](https://huggingface.co/openai/whisper-medium)
* **Dataset**:

  * BembaSpeech (curated dataset of Bemba audio + transcripts)
  * English subset of [Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0)
* **Training Time**: 8 epochs (\~55 hours on A100 GPU)
* **Learning Rate**: 1e-5
* **Batch Size**: 16
* **Framework**: Transformers + Accelerate
* **Tokenizer**: WhisperProcessor with `language="<|bem|>"` and `task="transcribe"`

---

## 🚀 Usage

```python
from transformers import pipeline

pipe = pipeline(
    "automatic-speech-recognition",
    model="NextInnoMind/next_bemba_ai_medium",
    chunk_length_s=30,
    return_timestamps=True
)

# Example
result = pipe("path_to_audio.wav")
print(result["text"])
```

> 📌 Tip: For Bemba, use the language token `<|bem|>` to improve transcription accuracy.

---

## 🔍 Applications

* **Multilingual Education**: Bemba-English subtitles and transcription
* **Broadcast & Media**: Transcribe bilingual radio or TV content
* **Research**: Language preservation and Bantu-English linguistic studies
* **Voice Accessibility**: Multilingual ASR tools and captioning

---

## ⚠️ Limitations & Biases

* Slight performance drop with highly noisy or code-switched audio
* Trained on formal and clean speech; informal speech may lower accuracy
* `<|bem|>` is required for optimal Bemba decoding

---

## 📊 Evaluation

| Language | WER (Word Error Rate) | Dataset              |
| -------- | --------------------- | -------------------- |
| Bemba    | \~15.2%               | BembaSpeech Eval Set |
| English  | \~10.5%               | Common Voice EN      |

---

## 🌱 Environmental Impact

* **Hardware**: A100 40GB x1
* **Training Time**: \~55 hours
* **Carbon Emissions**: Estimated \~25.8 kg CO₂
  *(via [ML CO2 Impact](https://mlco2.github.io/impact))*

---

## 📄 Citation

```bibtex
@misc{nextbembaai2025,
  title={NextInnoMind next_bemba_ai_medium: Multilingual Whisper ASR model for Bemba and English},
  author={Silas Chalwe and NextInnoMind},
  year={2025},
  howpublished={\url{https://huggingface.co/NextInnoMind/next_bemba_ai_medium}},
}
```

---

## 🧑‍💻 Maintainers

* **Chalwe Silas** (Lead Developer & Dataset Curator)
* Team **NextInnoMind**

📬 Contact:

* [silaschalwe@outlook.com](mailto:silaschalwe@outlook.com)
* [mchalwesilas@gmail.com](mailto:mchalwesilas@gmail.com)

🔗 GitHub: [SilasChalwe](https://github.com/SilasChalwe)

---

## 📌 Related Resources

* [BembaSpeech Dataset](https://huggingface.co/datasets/NextInnoMind/BembaSpeech)
* [NextInnoMind on GitHub](https://github.com/SilasChalwe)

---

Fine tuned in Zambia.