Update README.md

README.md

@@ -21,13 +21,13 @@ It achieves the following results on the evaluation set:
 ## Model description
 Wav2vec2 Automatic speech recognition for Indian English accent using the language model.
 ## Intended uses & limitations
-This model is intended for my personal use only. Intentionally, the data set has absolutely no speech variance. It is fine-tuned only on my own data and I am using it for live speech dictation with Pyaudio non-blocking streaming microphone data (https://gist.github.com/KenoLeon/13dfb803a21a08cf224b2e6df0feed80). Before inference, train further on your own data. The training data has a lot of quantitative finance-related terminologies and a lot of modern reddit slangs. Note that it doesn't hash out F words. 
+This model is intended for my personal use only. Intentionally, the data set has absolutely no speech variance. It is fine-tuned only on my own data and I am using it for live speech dictation with Pyaudio non-blocking streaming microphone data (https://gist.github.com/KenoLeon/13dfb803a21a08cf224b2e6df0feed80). Before inference, train further on your own data. The training data has a lot of quantitative finance-related jargon and a lot of urban slang. Note that it doesn't hash out F words, so NSFW. 
 
 ## Training and evaluation data
  Facebook base large dataset further fine-tuned on thirty-two hours of personal recordings. It has a male voice with an Indian English accent. The recording is done on the omnidirectional microphone with a lot of background noise. 
 
 ## Training procedure
- I downloaded my Reddit and Twitter data and started recording with each clip not exceeding 13 seconds. When I got enough sample size of 6 hrs I fine-tuned the model which had approximately 19% WER. Afterwards, I kept adding the data and kept fine-tuning it. It is now trained on thirty hours of data.
+ I downloaded my Reddit and Twitter data and started recording each clip not exceeding 13 seconds. When I got enough sample size of 6 hrs I fine-tuned the model with approximately 19% WER. Afterwards, I kept adding the data and kept fine-tuning it. It is now trained on thirty hours of data.
 (Now the idea is to fine-tune every two-three months only on unrecognized words)
 
 ### Training hyperparameters