v1.2.0

README.md

@@ -94,14 +94,14 @@ 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="v1.1.0", split="test")
+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="v1.1.0", split="test")
+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")
@@ -113,7 +113,7 @@ 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="v1.1.0", split="test", columns=["name", "text", "language", "license_type"])
+dataset = load_dataset("audioshake/jam-alt", revision="v1.2.0", split="test", columns=["name", "text", "language", "license_type"])
 ```
 
 ## Citation

loader.py

@@ -3,15 +3,13 @@
 
 import csv
 from dataclasses import dataclass
-import json
 import os
-from pathlib import Path
 from typing import Optional
 
 import datasets
 
 
-_VERSION = "1.1.0"
+_VERSION = "1.2.0"
 
 
 _CITATION = """\