Update files from the datasets library (from 1.14.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.14.0

README.md

@@ -73,7 +73,7 @@ SUPERB is a leaderboard to benchmark the performance of a shared model across a
 
 ### Supported Tasks and Leaderboards
 
-The SUPERB leaderboard can be found here **ADD LINK WHEN LIVE** and consists of the following tasks:
+The SUPERB leaderboard can be found here https://superbbenchmark.org/leaderboard and consists of the following tasks:
 
 #### pr
 
@@ -205,6 +205,9 @@ An example from each split looks like:
 ```python
 {'chapter_id': 1240,
  'file': 'path/to/file.flac',
+ 'audio': {'path': 'path/to/file.flac',
+		   'array': array([-0.00048828, -0.00018311, -0.00137329, ...,  0.00079346, 0.00091553,  0.00085449], dtype=float32),
+		   'sampling_rate': 16000},
  'id': '103-1240-0000',
  'speaker_id': 103,
  'text': 'CHAPTER ONE MISSUS RACHEL LYNDE IS SURPRISED MISSUS RACHEL LYNDE '
@@ -220,6 +223,9 @@ An example from each split looks like:
 ```python
 {
   'file': '/path/yes/af7a8296_nohash_1.wav', 
+  'audio': {'path': '/path/yes/af7a8296_nohash_1.wav',
+		    'array': array([-0.00048828, -0.00018311, -0.00137329, ...,  0.00079346, 0.00091553,  0.00085449], dtype=float32),
+		    'sampling_rate': 16000},
   'label': 0  # 'yes'
 }
 ```
@@ -234,6 +240,9 @@ An example from each split looks like:
 ```python
 {
   'file': "/path/wavs/speakers/2BqVo8kVB2Skwgyb/063aa8f0-4479-11e9-a9a5-5dbec3b8816a.wav",
+  'audio': {'path': '/path/wavs/speakers/2BqVo8kVB2Skwgyb/063aa8f0-4479-11e9-a9a5-5dbec3b8816a.wav',
+		    'array': array([-0.00048828, -0.00018311, -0.00137329, ...,  0.00079346, 0.00091553,  0.00085449], dtype=float32),
+		    'sampling_rate': 16000},
   'speaker_id': '2BqVo8kVB2Skwgyb',
   'text': 'Turn the bedroom lights off',
   'action': 3,  # 'deactivate'
@@ -252,6 +261,9 @@ An example from each split looks like:
 ```python
 {
   'file': '/path/wav/id10003/na8-QEFmj44/00003.wav', 
+  'audio': {'path': '/path/wav/id10003/na8-QEFmj44/00003.wav',
+		    'array': array([-0.00048828, -0.00018311, -0.00137329, ...,  0.00079346, 0.00091553,  0.00085449], dtype=float32),
+		    'sampling_rate': 16000},
   'label': 2  # 'id10003'
 }
 ```
@@ -268,6 +280,9 @@ An example from each split looks like:
 {
   'record_id': '1578-6379-0038_6415-111615-0009',
   'file': 'path/to/file.wav',
+  'audio': {'path': 'path/to/file.wav',
+		    'array': array([-0.00048828, -0.00018311, -0.00137329, ...,  0.00079346, 0.00091553,  0.00085449], dtype=float32),
+		    'sampling_rate': 16000},
   'start': 0,
   'end': 1590,
   'speakers': [
@@ -287,6 +302,10 @@ An example from each split looks like:
 
 ### Data Fields
 
+####Note abouth the `audio` fields
+
+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]`.
+
 #### pr
 
 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
@@ -294,15 +313,17 @@ An example from each split looks like:
 
 #### asr
 
-- `file`: a `string` feature.
-- `text`: a `string` feature.
-- `speaker_id`: a `int64` feature.
-- `chapter_id`: a `int64` feature.
-- `id`: a `string` feature.
+- `file` (`string`): Path to the WAV audio file.
+- `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
+- `text` (`string`): The transcription of the audio file.
+- `speaker_id` (`integer`): A unique ID of the speaker. The same speaker id can be found for multiple data samples.
+- `chapter_id` (`integer`): ID of the audiobook chapter which includes the transcription.
+- `id` (`string`): A unique ID of the data sample.
 
 #### ks
 
 - `file` (`string`): Path to the WAV audio file.
+- `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
 - `label` (`ClassLabel`): Label of the spoken command. Possible values:
     - `0: "yes", 1: "no", 2: "up", 3: "down", 4: "left", 5: "right", 6: "on", 7: "off", 8: "stop", 9: "go", 10: "_silence_", 11: "_unknown_"`
 
@@ -313,6 +334,7 @@ An example from each split looks like:
 #### ic
 
 - `file` (`string`): Path to the WAV audio file.
+- `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
 - `speaker_id` (`string`): ID of the speaker.
 - `text` (`string`): Transcription of the spoken command.
 - `action` (`ClassLabel`): Label of the command's action. Possible values:
@@ -330,6 +352,7 @@ An example from each split looks like:
 #### si
 
 - `file` (`string`): Path to the WAV audio file.
+- `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
 - `label` (`ClassLabel`): Label (ID) of the speaker. Possible values: 
     - `0: "id10001", 1: "id10002", 2: "id10003", ..., 1250: "id11251"`
 
@@ -343,6 +366,7 @@ An example from each split looks like:
 The data fields in all splits are:
 - `record_id` (`string`): ID of the record.
 - `file` (`string`): Path to the WAV audio file.
+- `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
 - `start` (`integer`): Start frame of the audio.
 - `end` (`integer`): End frame of the audio.
 - `speakers` (`list` of `dict`): List of speakers in the audio. Each item contains the fields:
@@ -353,6 +377,7 @@ The data fields in all splits are:
 #### er
 
 - `file` (`string`): Path to the WAV audio file.
+- `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
 - `label` (`ClassLabel`): Label of the speech emotion. Possible values:
     - `0: "neu", 1: "hap", 2: "ang", 3: "sad"`
 

superb.py

@@ -137,6 +137,7 @@ class Superb(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"),
@@ -161,6 +162,7 @@ class Superb(datasets.GeneratorBasedBuilder):
             features=datasets.Features(
                 {
                     "file": datasets.Value("string"),
+                    "audio": datasets.features.Audio(sampling_rate=16_000),
                     "label": datasets.ClassLabel(
                         names=[
                             "yes",
@@ -194,6 +196,7 @@ class Superb(datasets.GeneratorBasedBuilder):
             features=datasets.Features(
                 {
                     "file": datasets.Value("string"),
+                    "audio": datasets.features.Audio(sampling_rate=16_000),
                     "speaker_id": datasets.Value("string"),
                     "text": datasets.Value("string"),
                     "action": datasets.ClassLabel(
@@ -235,6 +238,7 @@ class Superb(datasets.GeneratorBasedBuilder):
             features=datasets.Features(
                 {
                     "file": datasets.Value("string"),
+                    "audio": datasets.features.Audio(sampling_rate=16_000),
                     # VoxCeleb1 contains 1251 speaker IDs in range ["id10001",..."id11251"]
                     "label": datasets.ClassLabel(names=[f"id{i + 10001}" for i in range(1251)]),
                 }
@@ -257,6 +261,7 @@ class Superb(datasets.GeneratorBasedBuilder):
                 {
                     "record_id": datasets.Value("string"),
                     "file": datasets.Value("string"),
+                    "audio": datasets.features.Audio(sampling_rate=16_000),
                     "start": datasets.Value("int64"),
                     "end": datasets.Value("int64"),
                     "speakers": [
@@ -284,6 +289,7 @@ class Superb(datasets.GeneratorBasedBuilder):
             features=datasets.Features(
                 {
                     "file": datasets.Value("string"),
+                    "audio": datasets.features.Audio(sampling_rate=16_000),
                     "label": datasets.ClassLabel(names=["neu", "hap", "ang", "sad"]),
                 }
             ),
@@ -440,11 +446,13 @@ class Superb(datasets.GeneratorBasedBuilder):
                         id_, transcript = line.split(" ", 1)
                         audio_file = f"{id_}.flac"
                         speaker_id, chapter_id = [int(el) for el in id_.split("-")[:2]]
+                        audio_path = os.path.join(transcript_dir_path, audio_file)
                         yield key, {
                             "id": id_,
                             "speaker_id": speaker_id,
                             "chapter_id": chapter_id,
-                            "file": os.path.join(transcript_dir_path, audio_file),
+                            "file": audio_path,
+                            "audio": audio_path,
                             "text": transcript,
                         }
                         key += 1
@@ -460,7 +468,7 @@ class Superb(datasets.GeneratorBasedBuilder):
                     label = "_silence_"
                 else:
                     label = "_unknown_"
-                yield key, {"file": audio_file, "label": label}
+                yield key, {"file": audio_file, "audio": audio_file, "label": label}
         elif self.config.name == "ic":
             root_path = os.path.join(archive_path, "fluent_speech_commands_dataset")
             csv_path = os.path.join(root_path, "data", f"{split}_data.csv")
@@ -469,8 +477,10 @@ class Superb(datasets.GeneratorBasedBuilder):
                 next(csv_reader)
                 for row in csv_reader:
                     key, file_path, speaker_id, text, action, object_, location = row
+                    audio_path = os.path.join(root_path, file_path)
                     yield key, {
-                        "file": os.path.join(root_path, file_path),
+                        "file": audio_path,
+                        "audio": audio_path,
                         "speaker_id": speaker_id,
                         "text": text,
                         "action": action,
@@ -486,8 +496,10 @@ class Superb(datasets.GeneratorBasedBuilder):
                     if int(split_id) != split:
                         continue
                     speaker_id = file_path.split("/")[0]
+                    audio_path = os.path.join(wav_path, file_path)
                     yield key, {
-                        "file": os.path.join(wav_path, file_path),
+                        "file": audio_path,
+                        "audio": audio_path,
                         "label": speaker_id,
                     }
         elif self.config.name == "sd":
@@ -500,6 +512,7 @@ class Superb(datasets.GeneratorBasedBuilder):
                     yield key, {
                         "record_id": rec,
                         "file": data.wavs[rec],
+                        "audio": data.wavs[rec],
                         "start": st,
                         "end": ed,
                         "speakers": speakers,
@@ -512,6 +525,7 @@ class Superb(datasets.GeneratorBasedBuilder):
                         yield key, {
                             "record_id": rec,
                             "file": data.wavs[rec],
+                            "audio": data.wavs[rec],
                             "start": st,
                             "end": ed,
                             "speakers": speakers,
@@ -533,8 +547,10 @@ class Superb(datasets.GeneratorBasedBuilder):
                             continue
                         wav_subdir = filename.rsplit("_", 1)[0]
                         filename = f"{filename}.wav"
+                        audio_path = os.path.join(wav_path, wav_subdir, filename)
                         yield key, {
-                            "file": os.path.join(wav_path, wav_subdir, filename),
+                            "file": audio_path,
+                            "audio": audio_path,
                             "label": emo.replace("exc", "hap"),
                         }
                         key += 1