Update README.md

README.md

@@ -67,7 +67,7 @@ teacher whisper model, and a decoder with two layers initialized from the first
 As the initial version, we release ***kotoba-whisper-v1.0*** trained on the `large` subset of [ReazonSpeech](https://huggingface.co/datasets/reazon-research/reazonspeech), 
 which amounts 1,253 hours of audio with 16,861,235 characters of transcriptions (5 sec audio with 18 text tokens in average) after 
 those transcriptions more than 10 WER are removed (see [WER Filter](https://huggingface.co/distil-whisper/distil-large-v3#wer-filter) for detail).
-The model was trained for 8 epochs with batch size 256 with sampling rate of 16kHz, and the raining and evaluation code to reproduce kotoba-whisper is available at [https://github.com/kotoba-tech/kotoba-whisper](https://github.com/kotoba-tech/kotoba-whisper).
+The model was trained for 8 epochs with batch size 256 with sampling rate of 16kHz, and the training and evaluation code to reproduce kotoba-whisper is available at [https://github.com/kotoba-tech/kotoba-whisper](https://github.com/kotoba-tech/kotoba-whisper).
 
 Kotoba-whisper-v1.0 achieves better CER and WER than the [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) in the in-domain held-out test set
 from ReazonSpeech, and achieves competitive CER and WER on the out-of-domain test sets including [JSUT basic 5000](https://sites.google.com/site/shinnosuketakamichi/publication/jsut) and