Datasets:

jamendolyrics
/

jam-alt

Tasks:

Automatic Speech Recognition

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cifkao commited on Feb 28

Commit

ae12804

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verified ·

1 Parent(s): 3cac019

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@@ -80,11 +80,11 @@ See the [project website](https://audioshake.github.io/jam-alt/) for details and
 ```python
 from datasets import load_dataset
-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.
 To control how the audio is decoded, cast the `audio` column using `dataset.cast_column("audio", datasets.Audio(...))`.
 Useful arguments to `datasets.Audio()` are:
@@ -100,14 +100,14 @@ 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="v1.2.0", 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="v1.2.0", 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")
@@ -119,7 +119,7 @@ compute_metrics(dataset["text"], transcriptions, languages=dataset["language"])
 ```
 Alternatively, if you already have transcriptions, you might prefer to skip loading the `audio` column:
 ```python
-dataset = load_dataset("audioshake/jam-alt", revision="v1.2.0", split="test", columns=["name", "text", "language", "license_type"])
 ```
 ## Citation

 ```python
 from datasets import load_dataset
+dataset = load_dataset("jamendolyrics/jam-alt", split="test")
 ```
 A subset is defined for each language (`en`, `fr`, `de`, `es`);
+for example, use `load_dataset("jamendolyrics/jam-alt", "es")` to load only the Spanish songs.
 To control how the audio is decoded, cast the `audio` column using `dataset.cast_column("audio", datasets.Audio(...))`.
 Useful arguments to `datasets.Audio()` are:
 from datasets import load_dataset
 from alt_eval import compute_metrics
+dataset = load_dataset("jamendolyrics/jam-alt", revision="v1.2.0", 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("jamendolyrics/jam-alt", revision="v1.2.0", 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")
 ```
 Alternatively, if you already have transcriptions, you might prefer to skip loading the `audio` column:
 ```python
+dataset = load_dataset("jamendolyrics/jam-alt", revision="v1.2.0", split="test", columns=["name", "text", "language", "license_type"])
 ```
 ## Citation