🫢 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.

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🧪 Model Type

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

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📜 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.

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📚 Training Details

• Base Model: openai/whisper-medium

• Dataset:

  • BembaSpeech (curated dataset of Bemba audio + transcripts)
  • English subset of Common Voice

• 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"

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🚀 Usage

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.

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🔍 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

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⚠️ 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

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📊 Evaluation

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

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🌱 Environmental Impact

• Hardware: A100 40GB x1
• Training Time: ~55 hours
• Carbon Emissions: Estimated ~25.8 kg CO₂ (via ML CO2 Impact)

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📄 Citation

@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}},
}

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🧑‍💻 Maintainers

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

📬 Contact:

• [email protected]
• [email protected]

🔗 GitHub: SilasChalwe

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📌 Related Resources

• BembaSpeech Dataset
• NextInnoMind on GitHub

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Fine tuned in Zambia.