Update files from the datasets library (from 1.13.3)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.13.3

README.md

@@ -80,6 +80,10 @@ A typical data point comprises the path to the audio file, usually called `file`
 ```
 {'chapter_id': 141231,
  'file': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac',
+  'audio': {'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac',
+  'array': array([-0.00048828, -0.00018311, -0.00137329, ...,  0.00079346,
+          0.00091553,  0.00085449], dtype=float32),
+  'sampling_rate': 16000},
  'id': '1272-141231-0000',
  'speaker_id': 1272,
  'text': 'A MAN SAID TO THE UNIVERSE SIR I EXIST'}
@@ -90,6 +94,8 @@ A typical data point comprises the path to the audio file, usually called `file`
 
 - file: A path to the downloaded audio file in .flac format.
 
+- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
+
 - text: the transcription of the audio file.
 
 - id: unique id of the data sample.
@@ -195,4 +201,4 @@ CC BY 4.0
 
 ### Contributions
 
-Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
+Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

librispeech_asr.py

@@ -104,6 +104,7 @@ class LibrispeechASR(datasets.GeneratorBasedBuilder):
             features=datasets.Features(
                 {
                     "file": datasets.Value("string"),
+                    "audio": datasets.features.Audio(sampling_rate=16_000),
                     "text": datasets.Value("string"),
                     "speaker_id": datasets.Value("int64"),
                     "chapter_id": datasets.Value("int64"),
@@ -151,6 +152,7 @@ class LibrispeechASR(datasets.GeneratorBasedBuilder):
                         "speaker_id": speaker_id,
                         "chapter_id": chapter_id,
                         "file": os.path.join(transcript_dir_path, audio_file),
+                        "audio": os.path.join(transcript_dir_path, audio_file),
                         "text": transcript,
                     }
                     key += 1