cli instructions

README.md

@@ -17,7 +17,7 @@ with the OpenAI Whisper long-form transcription algorithm. In our benchmark over
 outperformed distil-large-v2 by 5% WER average. Thus, you can expect significant performance gains by switching to this 
 latest checkpoint.
 
-## Usage
+## Python Usage
 
 To use the model in the original Whisper format, first ensure you have the [`openai-whisper`](https://pypi.org/project/openai-whisper/) package installed.
 For this example, we'll also install 🤗 Datasets to load a toy audio dataset from the Hugging Face Hub:
@@ -55,6 +55,27 @@ To transcribe a local audio file, simply pass the path to the audio file as the
 pred_out = transcribe(model, audio="audio.mp3")
 ```
 
+## CLI Usage
+
+The Distil-Whisper model can also be used with the OpenAI Whisper CLI. First, pip install the Hugging Face Hub package:
+
+```bash
+pip install --upgrade huggingface_hub
+```
+
+Next, download the weights for distil-large-v3 locally:
+
+```bash
+huggingface-cli download distil-whisper/distil-large-v3-openai model.bin --local-dir distil-large-v3
+```
+
+Finally, use the OpenAI Whisper CLI to transcribe:
+
+```bash
+whisper audio.mp3 --model distil-large-v3/model.bin
+```
+
+
 ## Model Details
 
 For more information about the distil-large-v3 model, refer to the original [model card](https://huggingface.co/distil-whisper/distil-large-v3).