Update README.md

README.md

@@ -36,15 +36,15 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-This model is fine-tuned for general swahili speech recognition tasks.
+This model is fine-tuned for general swahili speech recognition tasks. You can watch our hour long [webinar](https://drive.google.com/file/d/1OkLx3d9xivdyxH8yYsZtwObhEX5Ptn5y/view?usp=drive_link) and see the [slides](https://docs.google.com/presentation/d/1sExJLwZLMNMKGnpuxy-ttF5KqDXJyKK2jNNTUabo5_Q/edit?usp=sharing) on this work.
 
 ## Intended uses & limitations
 
-The intention is to transcribe general swahili speeches. With further development, we'll further fine-tune the model for domain-specific (we are focused on hospital tasks) swahili conversations.
+The intention is to transcribe general swahili speeches. With further development, we'll fine-tune the model for domain-specific (we are focused on hospital tasks) swahili conversations.
 
 ## Training and evaluation data
 
-We used swahili data provided by the common voice on hugging face. To appreciate the transformation we did on the data, you can read our [blog on data preparation](https://medium.com/@gitau_am/from-raw-data-to-accurate-speech-recognition-asr-my-journey-of-data-preparation-df3a1b0dee3a).
+To appreciate the transformation we did on the data, you can read our [blog on data preparation](https://medium.com/@gitau_am/from-raw-data-to-accurate-speech-recognition-asr-my-journey-of-data-preparation-df3a1b0dee3a).
 
 ## Training procedure
 We also [documented](https://medium.com/@gitau_am/exploring-asr-model-development-fine-tuning-xls-r-wav2vec2-model-with-swahili-data-b95134d116b8) some lessons from the fine-tuning exercise.