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Silas Chalwe commited on Jul 23, 2025

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----
-library_name: transformers
-tags: []
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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+<p align="center">
+  <img src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" height="80" />
+</p>
+# 🫢 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.