Update README.md

README.md

@@ -155,17 +155,18 @@ See the [project website](https://audioshake.github.io/jam-alt/) for details.
 
 ```python
 from datasets import load_dataset
-dataset = load_dataset("audioshake/jam-alt")["test"]
+dataset = load_dataset("audioshake/jam-alt", split="test")
 ```
 
 A subset is defined for each language (`en`, `fr`, `de`, `es`);
 for example, use `load_dataset("audioshake/jam-alt", "es")` to load only the Spanish songs.
 
-By default, the dataset comes with audio. To skip loading the audio, use `with_audio=False`.
 To control how the audio is decoded, cast the `audio` column using `dataset.cast_column("audio", datasets.Audio(...))`.
 Useful arguments to `datasets.Audio()` are:
 - `sampling_rate` and `mono=True` to control the sampling rate and number of channels.
-- `decode=False` to skip decoding the audio and just get the MP3 file paths.
+- `decode=False` to skip decoding the audio and just get the MP3 file paths and contents.
+
+The `load_dataset` function also accepts a `columns` parameter, which can be useful for example if you want to skip downloading the audio (see the example below).
 
 ## Running the benchmark
 
@@ -174,14 +175,14 @@ The evaluation is implemented in our [`alt-eval` package](https://github.com/aud
 from datasets import load_dataset
 from alt_eval import compute_metrics
 
-dataset = load_dataset("audioshake/jam-alt", revision="v1.0.0")["test"]
+dataset = load_dataset("audioshake/jam-alt", revision="v1.0.0", split="test")
 # transcriptions: list[str]
 compute_metrics(dataset["text"], transcriptions, languages=dataset["language"])
 ```
 
 For example, the following code can be used to evaluate Whisper:
 ```python
-dataset = load_dataset("audioshake/jam-alt", revision="v1.0.0")["test"]
+dataset = load_dataset("audioshake/jam-alt", revision="v1.0.0", split="test")
 dataset = dataset.cast_column("audio", datasets.Audio(decode=False))  # Get the raw audio file, let Whisper decode it
 
 model = whisper.load_model("tiny")
@@ -191,9 +192,9 @@ transcriptions = [
 ]
 compute_metrics(dataset["text"], transcriptions, languages=dataset["language"])
 ```
-Alternatively, if you already have transcriptions, you might prefer to skip loading the audio:
+Alternatively, if you already have transcriptions, you might prefer to skip loading the `audio` column:
 ```python
-dataset = load_dataset("audioshake/jam-alt", revision="v1.0.0", with_audio=False)["test"]
+dataset = load_dataset("audioshake/jam-alt", revision="v1.0.0", split="test", columns=["name", "text", "language", "license_type"])
 ```
 
 ## Citation
@@ -221,4 +222,4 @@ When using the benchmark, please cite [our paper](https://www.arxiv.org/abs/2408
   address={Rhodes Island, Greece},
   doi={10.1109/ICASSP49357.2023.10096725}
 }
-```
+```