add configs for smaller datasets

README.md

@@ -50,6 +50,7 @@ dataset_info:
   - [Table of Contents](#table-of-contents)
   - [Dataset Description](#dataset-description)
     - [Dataset Summary](#dataset-summary)
+    - [How to use](#how-to-use)
     - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
     - [Languages](#languages)
   - [Dataset Structure](#dataset-structure)
@@ -88,6 +89,72 @@ dataset_info:
 AFRISPEECH-200 is a 200hr Pan-African speech corpus for clinical and general domain English accented ASR; a dataset with 120 African accents from 13 countries and 2,463 unique African speakers. 
 Our goal is to raise awareness for and advance Pan-African English ASR research, especially for the clinical domain. 
 
+## How to use
+
+The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. 
+
+For example, to download the isizulu config, simply specify the corresponding language config name, list of supported accents provided in accent list section below:
+```python
+from datasets import load_dataset
+
+afrispeech = load_dataset("tobiolatunji/afrispeech-200", "isizulu", split="train")
+```
+
+Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
+```python
+from datasets import load_dataset
+
+afrispeech = load_dataset("tobiolatunji/afrispeech-200", "isizulu", split="train", streaming=True)
+
+print(next(iter(afrispeech)))
+```
+
+### Local
+
+```python
+from datasets import load_dataset
+from torch.utils.data.sampler import BatchSampler, RandomSampler
+
+afrispeech = load_dataset("tobiolatunji/afrispeech-200", "isizulu", split="train")
+batch_sampler = BatchSampler(RandomSampler(afrispeech), batch_size=32, drop_last=False)
+dataloader = DataLoader(afrispeech, batch_sampler=batch_sampler)
+```
+
+### Streaming
+
+```python
+from datasets import load_dataset
+from torch.utils.data import DataLoader
+
+afrispeech = load_dataset("tobiolatunji/afrispeech-200", "isizulu", split="train")
+dataloader = DataLoader(afrispeech, batch_size=32)
+```
+
+To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets).
+
+### Example scripts
+
+Train your own CTC or Seq2Seq Automatic Speech Recognition models on AfriSpeech-200 with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).
+
+AfriSpeech-200 can be downloaded and used as follows:
+
+```py
+from datasets import load_dataset
+
+afrispeech = load_dataset("tobiolatunji/afrispeech-200", "isizulu") # for isizulu, 
+# to download all data for multi-accent fine-tuning uncomment following line
+# afrispeech = load_dataset("tobiolatunji/afrispeech-200", "all")
+
+# see structure
+print(afrispeech)
+
+# load audio sample on the fly
+audio_input = afrispeech["train"][0]["audio"]  # audio bytes
+transcript = afrispeech["train"][0]["transcript"]  # transcript
+
+# use audio_input and text transcript to fine-tune your model for audio classification
+```
+
 ### Supported Tasks and Leaderboards
 
 - Automatic Speech Recognition
@@ -112,6 +179,8 @@ A typical data point comprises the path to the audio file, called `path` and its
  'transcirpt': 'The patient took the correct medication'}
 ```
 
+
+
 ### Data Fields
 
 - speaker_id: An id for which speaker (voice) made the recording

afrispeech-200.py

@@ -35,6 +35,30 @@ Our goal is to raise awareness for and advance Pan-African English ASR research,
 especially for the clinical domain. 
 """
 
+_ALL_CONFIGS = [
+       'yoruba', 'igbo', 'swahili', 'ijaw', 'xhosa', 'twi', 'luhya',
+       'igala', 'urhobo', 'hausa', 'kiswahili', 'zulu', 'isizulu',
+       'venda and xitsonga', 'borana', 'afrikaans', 'setswana', 'idoma',
+       'izon', 'chichewa', 'ebira', 'tshivenda', 'isixhosa',
+       'kinyarwanda', 'tswana', 'luganda', 'luo', 'venda', 'dholuo',
+       'akan (fante)', 'sepedi', 'kikuyu', 'isindebele',
+       'luganda and kiswahili', 'akan', 'sotho', 'south african english',
+       'sesotho', 'swahili ,luganda ,arabic', 'shona', 'damara',
+       'southern sotho', 'luo, swahili', 'ateso', 'meru', 'siswati',
+       'portuguese', 'esan', 'nasarawa eggon', 'ibibio', 'isoko',
+       'pidgin', 'alago', 'nembe', 'ngas', 'kagoma', 'ikwere', 'fulani',
+       'bette', 'efik', 'edo', 'hausa/fulani', 'bekwarra', 'epie',
+       'afemai', 'benin', 'nupe', 'tiv', 'okrika', 'etsako', 'ogoni',
+       'kubi', 'gbagyi', 'brass', 'oklo', 'ekene', 'ika', 'berom', 'jaba',
+       'itsekiri', 'ukwuani', 'yala mbembe', 'afo', 'english', 'ebiobo',
+       'igbo and yoruba', 'okirika', 'kalabari', 'ijaw(nembe)', 'anaang',
+       'eggon', 'bini', 'yoruba, hausa', 'ekpeye', 'bajju', 'kanuri',
+       'delta', 'khana', 'ogbia', 'mada', 'mwaghavul', 'angas', 'ikulu',
+       'eleme', 'igarra', 'etche', 'agatu', 'bassa', 'jukun', 'urobo',
+       'ibani', 'obolo', 'idah', 'eket', 'nyandang', 'estako', 'ishan',
+       'bassa-nge/nupe', 'bagi', 'gerawa'
+    ]
+
 _HOMEPAGE = "https://github.com/intron-innovation/AfriSpeech-Dataset-Paper"
 
 _LICENSE = "http://creativecommons.org/licenses/by-nc-sa/4.0/"
@@ -51,10 +75,35 @@ _SHARDS = {
     'test': 4
 }
 
+class AfriSpeechConfig(datasets.BuilderConfig):
+    """BuilderConfig for afrispeech"""
+
+    def __init__(
+        self, name, description, homepage, data_url
+    ):
+        super(AfriSpeechConfig, self).__init__(
+            name=self.name,
+            version=datasets.Version("1.0.0", ""),
+            description=self.description,
+        )
+        self.name = name
+        self.description = description
+        self.homepage = homepage
+        self.data_url = data_url
+
+
+def _build_config(name):
+    return AfriSpeechConfig(
+        name=name,
+        description=_DESCRIPTION,
+        homepage=_HOMEPAGE_URL,
+        data_url=_DATA_URL,
+    )
 
 class AfriSpeech(datasets.GeneratorBasedBuilder):
     DEFAULT_WRITER_BATCH_SIZE = 1000
-    VERSION = datasets.Version("1.1.0")
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [_build_config(name) for name in _ALL_CONFIGS + ["all"]]
 
     def _info(self):
         description = _DESCRIPTION
@@ -93,7 +142,13 @@ class AfriSpeech(datasets.GeneratorBasedBuilder):
         # with the url replaced with path to local files.
         # By default the archives will be extracted and a path to a cached folder
         # where they are extracted is returned instead of the archive
-
+        
+        langs = (
+            _ALL_CONFIGS
+            if self.config.name == "all"
+            else [self.config.name]
+        )
+        
         n_shards = _SHARDS
 
         audio_urls = {}
@@ -139,14 +194,16 @@ class AfriSpeech(datasets.GeneratorBasedBuilder):
         with open(meta_path, "r", encoding="utf-8") as f:
             reader = csv.DictReader(f)
             for row in tqdm(reader, desc="Reading metadata..."):
-                row["speaker_id"] = row["user_ids"]
-                audio_id = row["audio_paths"].split("/")[-1]
-                # if data is incomplete, fill with empty values
-                metadata[audio_id] = {field: row.get(field, "") for field in data_fields}
+                if (row['accent'] == self.config.name) or (self.config.name == 'all'):
+                    row["speaker_id"] = row["user_ids"]
+                    audio_id = "/".join(row["audio_paths"].split("/")[-2:])
+                    # if data is incomplete, fill with empty values
+                    metadata[audio_id] = {field: row.get(field, "") for field in data_fields}
 
         for i, audio_archive in enumerate(archives):
             for filename, file in audio_archive:
-                _, filename = os.path.split(filename)
+                # _, filename = os.path.split(filename)
+                filename = "/".join(filename.split("/")[-2:])
                 if filename in metadata:
                     result = dict(metadata[filename])
                     # set the audio feature and the path to the extracted file