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next_bemba_ai

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----
-library_name: transformers
-tags: []
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+---
+library_name: transformers
+pipeline_tag: text-to-audio
+---
+<p align="center">
+  <img src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" height="80" />
+</p>
+# 🫢 NextInnoMind / next\_bemba\_ai
+**Bemba Whisper ASR (Automatic Speech Recognition)**
+Fine-tuned Whisper model for the Bemba language only.
+Developed and maintained by **NextInnoMind**, led by **Chalwe Silas**.
+---
+### 🧪 Model Type
+`WhisperForConditionalGeneration` — fine-tuned using [openai/whisper-small](https://huggingface.co/openai/whisper-small)
+Framework: `Transformers`
+Checkpoint Format: `Safetensors`
+Languages: `Bemba`
+---
+## 📜 Model Description
+This model is a Whisper Small variant fine-tuned exclusively for **Bemba**, a major Zambian language. It is designed to enhance local language ASR performance and promote indigenous language technology.
+---
+## 📚 Training Details
+* **Base Model**: [`openai/whisper-small`](https://huggingface.co/openai/whisper-small)
+* **Dataset**:
+  * BembaSpeech (curated dataset of Bemba audio + transcripts)
+* **Training Time**: 8 epochs (\~45 hours on A100 GPU)
+* **Learning Rate**: 1e-5
+* **Batch Size**: 16
+* **Framework**: Transformers + Accelerate
+* **Tokenizer**: WhisperProcessor with `task="transcribe"` (no language token used)
+---
+## 🚀 Usage
+```python
+from transformers import pipeline
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model="NextInnoMind/next_bemba_ai",
+    chunk_length_s=30,
+    return_timestamps=True
+)
+# Example
+result = pipe("path_to_audio.wav")
+print(result["text"])
+```
+> 📌 Tip: No language token is required. The model is fine-tuned for Bemba only.
+---
+## 🔍 Applications
+* **Education**: Local language transcriptions and learning tools
+* **Broadcast & Media**: Transcribe Bemba radio and TV shows
+* **Research**: Bantu language documentation and analysis
+* **Accessibility**: Voice-to-text systems in local apps and platforms
+---
+## ⚠️ Limitations & Biases
+* Trained only on Bemba: does not support English or other languages.
+* Accuracy may drop with heavy background noise or strong dialectal variation.
+* Not optimized for code-switching or informal speech styles.
+---
+## 📊 Evaluation
+| Language | WER (Word Error Rate) | Dataset              |
+| -------- | --------------------- | -------------------- |
+| Bemba    | \~16.7%               | BembaSpeech Eval Set |
+---
+## 🌱 Environmental Impact
+* **Hardware**: A100 40GB x1
+* **Training Time**: \~45 hours
+* **Carbon Emissions**: Estimated \~20.4 kg CO₂
+  *(via [ML CO2 Impact](https://mlco2.github.io/impact))*
+---
+## 📄 Citation
+```bibtex
+@misc{nextbembaai2025,
+  title={NextInnoMind next_bemba_ai: Whisper-based ASR model for Bemba},
+  author={Silas Chalwe and NextInnoMind},
+  year={2025},
+  howpublished={\url{https://huggingface.co/NextInnoMind/next_bemba_ai}},
+}
+```
+---
+## 🧑‍💻 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.