From French to Fulfulde: How Well Does Whisper Handle Low-Resource Languages?
In this video, we dive into the challenges and opportunities of using Whisper, OpenAI’s speech recognition model, to transcribe low-resource languages—specifically African languages like Fulfulde, Hausa, Wolof, Mandinka, and Kiswahili. How does Whisper perform when moving beyond European languages like French or English? Let’s find out!
This exploration is part of a broader project on AI-Augmented Journalism, where we focus on practical implementations of AI tools for multilingual transcription, semantic clustering, and automation. The goal? To bridge the gap between AI capabilities and real-world applications in journalism and beyond.
Key topics covered:
– Testing Whisper’s performance on Brazilian Portuguese, Chinese, Vietnamese, and African languages/
– The importance of fine-tuning and training environments for low-resource languages.
– Tools and scripts for data curation, LoRA/finetune experiments, and evaluation metrics (WER, CER).
– How these experiments fit into a larger framework of AI-driven journalism, multilingual NLP, and WordPress automation.
Full Details:
You can read the article on my blog: AI-Augmented Journalism: Practical Implementation of Semantic Clustering, Multilingual Transcription, and WordPress Automation
hhttps://wp.me/p3Vuhl-3q4
The code is available on my github account:
https://shorturl.at/peoy2
️Dive Deeper:
You can listen to the “podcast” extracted from this Blog Post Audio made with NotebookLM on this post:
https://on.soundcloud.com/AqNjInTzRbPoD7Hyv9
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