Update README.md

README.md

@@ -16,6 +16,9 @@ It achieves the following WER results on the evaluation set:
 - Normalised WER: 6.324
 - Orthographic WER: 8.233
 
+Full tensorboard logs can be found under the tab [Training Metrics](https://huggingface.co/sanchit-gandhi/distil-whisper-large-v3-de-kd/tensorboard?params=scalars#frame),
+and steps to reproduce [here](https://huggingface.co/sanchit-gandhi/distil-whisper-large-v3-de-kd#training-procedure).
+
 ## Model description
 
 We copy the entire encoder module and freeze it during training. We copy only two decoder layers, which are initialised from the first and last decoder layers from Whisper. All other decoder layers from Whisper are discarded. 
@@ -28,7 +31,7 @@ The model was trained and evaluated on the German subset of the [Common Voice 15
 
 ## Training procedure
 
-To reproduce this training run, first clone and install Distil-Whisper according to the instructions [here]. 
+To reproduce this training run, first clone and install Distil-Whisper according to the instructions [here](https://github.com/huggingface/distil-whisper/tree/main/training#requirements). 
 
 Next, we can pick a name for our distilled model, e.g. `distil-whisper-large-v3-de-kd`. We can then run the following command to create a repository under this name:
 
@@ -45,7 +48,7 @@ git lfs install
 git clone https://huggingface.co/sanchit-gandhi/distil-whisper-large-v3-de-kd
 ```
 
-> **Note:** Be sure to change the repo address to `https://huggingface.co/<your-user-name>/<your-repo-name>`
+**Note:** Be sure to change the repo address to `https://huggingface.co/<your-user-name>/<your-repo-name>`
 
 Next, copy the relevant training scrips from Distil-Whisper to the repository: