| """ALFFAAmharic automatic speech recognition dataset.""" |
|
|
|
|
| import os |
| from pathlib import Path |
|
|
| import datasets |
|
|
|
|
| _CITATION = """\ |
| @inproceedings{ |
| title={ALFFAAmharic Acoustic-Phonetic Continuous Speech Corpus}, |
| author={Samuael et al}, |
| } |
| """ |
|
|
| _DESCRIPTION = """\ |
| The ALFFAAmharic corpus of reading speech has been developed to provide speech data for acoustic-phonetic research studies |
| and for the evaluation of automatic speech recognition systems. |
| """ |
|
|
|
|
| class ALFFAAmharicASRConfig(datasets.BuilderConfig): |
| """BuilderConfig for ALFFAAmharicASR.""" |
|
|
| def __init__(self, **kwargs): |
| """ |
| Args: |
| data_dir: `string`, the path to the folder containing the files in the |
| downloaded .tar |
| citation: `string`, citation for the data set |
| url: `string`, url for information about the data set |
| **kwargs: keyword arguments forwarded to super. |
| """ |
| super(ALFFAAmharicASRConfig, self).__init__(version=datasets.Version("2.0.1", ""), **kwargs) |
|
|
|
|
| class ALFFAAmharic(datasets.GeneratorBasedBuilder): |
| """ALFFAAmharicASR dataset.""" |
|
|
| BUILDER_CONFIGS = [ALFFAAmharicASRConfig(name="clean", description="'Clean' speech.")] |
|
|
| @property |
| def manual_download_instructions(self): |
| return ( |
| "To use ALFFAAmharic you have to download it manually. " |
| "`datasets.load_dataset('ALFFAAmharic_asr', data_dir='path/to/folder/folder_name')`" |
| ) |
|
|
| def _info(self): |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=datasets.Features( |
| { |
| "file": datasets.Value("string"), |
| "audio": datasets.Audio(sampling_rate=16_000), |
| "text": datasets.Value("string"), |
| } |
| ), |
| supervised_keys=("file", "text"), |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
|
|
| data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir)) |
|
|
| if not os.path.exists(data_dir): |
| raise FileNotFoundError( |
| f"{data_dir} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('ALFFAAmharic_asr', data_dir=...)` that includes files unzipped from the ALFFAAmharic zip. Manual download instructions: {self.manual_download_instructions}" |
| ) |
|
|
| return [ |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"split": "train", "data_dir": data_dir}), |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"split": "test", "data_dir": data_dir}), |
| ] |
|
|
| def _generate_examples(self, split, data_dir): |
| """Generate examples from ALFFAAmharic archive_path based on the test/train csv information.""" |
| file = open(f"{data_dir}/{split}/text.txt", "r", encoding="utf-8") |
| lines = file.readlines() |
| file.close() |
| |
| |
| for i in range(len(lines)): |
| splited = lines[i].strip("\n").split(" ") |
| if len(splited)==0: |
| continue |
| wav_path = f"{data_dir}/{split}/wav/{splited[0]}.wav" |
| transcript = " ".join(splited[1:]) |
| |
| yield i, { |
| "file": str(wav_path), |
| "audio": str(wav_path), |
| "text": transcript, |
| } |
|
|
|
|
| def with_case_insensitive_suffix(path: Path, suffix: str): |
| path = path.with_suffix(suffix.lower()) |
| path = path if path.exists() else path.with_suffix(suffix.upper()) |
| return path |
|
|
