Update README.md

README.md

@@ -40,10 +40,30 @@ Other arguments can be specified to control audio loading:
 - `sampling_rate` and `mono=True` to control the sampling rate and number of channels.
 - `decode_audio=False` to skip decoding the audio and just get the MP3 file paths.
 
-## Running evaluation
+## Running the benchmark
 
-Use the [`alt-eval`](https://github.com/audioshake/alt-eval) package for evaluation:
+The evaluation is implemented in our [`alt-eval` package](https://github.com/audioshake/alt-eval):
 ```python
+from datasets import load_dataset
 from alt_eval import compute_metrics
+
+dataset = load_dataset("audioshake/jam-alt", revision="v1.0.0")["test"]
+# transcriptions: list[str]
+compute_metrics(dataset["text"], transcriptions, languages=dataset["language"])
+```
+
+By default, the dataset includes the audio, allowing you to run transcription directly.
+For example, the following code can be used to evaluate Whisper:
+```python
+dataset = load_dataset("audioshake/jam-alt", revision="v1.0.0", decode_audio=False)["test"]
+model = whisper.load_model("tiny")
+transcriptions = [
+  "\n".join(s["text"].strip() for s in model.transcribe(a["path"])["segments"])
+  for a in dataset["audio"]
+]
 compute_metrics(dataset["text"], transcriptions, languages=dataset["language"])
+```
+Alternatively, if you already have transcriptions, you might prefer to skip loading the audio:
+```python
+dataset = load_dataset("audioshake/jam-alt", revision="v1.0.0", with_audio=False)["test"]
 ```