| """LibriAdapt Dataset""" |
|
|
| import os |
| from pathlib import Path |
|
|
| import datasets |
| import csv |
|
|
| _CITATION = """\ |
| @inproceedings{mathur20, |
| doi = {10.1109/icassp40776.2020.9053074}, |
| url = {https://doi.org/10.1109\%2Ficassp40776.2020.9053074}, |
| year = 2020, |
| month = {may}, |
| publisher = {{IEEE}}, |
| author = {Akhil Mathur and Fahim Kawsar and Nadia Berthouze and Nicholas D. Lane}, |
| title = {Libri-Adapt: a New Speech Dataset for Unsupervised Domain Adaptation}, |
| booktitle = {{ICASSP} 2020 - 2020 {IEEE} International Conference on Acoustics, Speech and Signal Processing ({ICASSP})} |
| } |
| """ |
|
|
| _DESCRIPTION = """\ |
| LibriAdapt (For more information refer to the original paper at https://doi.org/10.1109%2Ficassp40776.2020.9053074) |
| """ |
|
|
| _HOMEPAGE = "https://github.com/akhilmathurs/libriadapt" |
|
|
|
|
| class LRL_TranserConfig(datasets.BuilderConfig): |
| """BuilderConfig for TimitASR.""" |
|
|
| 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(LRL_TranserConfig, self).__init__( |
| version=datasets.Version("1.0.0", ""), **kwargs) |
|
|
|
|
| class LRL_Transfer(datasets.GeneratorBasedBuilder): |
| """LRL Transfer dataset.""" |
|
|
| BUILDER_CONFIGS = [ |
| LRL_TranserConfig(name="clean", description="'Clean' speech.")] |
|
|
| @property |
| def manual_download_instructions(self): |
| return ( |
| "If you want to use noise, you can download the dataset and script here: https://github.com/akhilmathurs/libriadapt" |
| ) |
|
|
| 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"), |
| "accent": datasets.Value("string"), |
| "microphone": datasets.Value("string"), |
| "id": datasets.Value("string"), |
| } |
| ), |
| supervised_keys=("file", "text"), |
| homepage=_HOMEPAGE, |
| 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('timit_asr', data_dir=...)` that includes files unzipped from the TIMIT 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 dataset based on the test/train csv information.""" |
| |
| wav_paths = sorted(Path(data_dir).glob(f"**/{split}/**/*.wav")) |
| wav_paths = wav_paths if wav_paths else sorted( |
| Path(data_dir).glob(f"**/{split.upper()}/**/*.WAV")) |
| for key, wav_path in enumerate(wav_paths): |
| |
| mic = str(wav_path).split("clean")[-1].split("/")[1] |
|
|
| |
| accent = str(wav_path).split("en-")[-1][0:2] |
|
|
| |
| num = str(wav_path).split("/")[-1].split(".")[0] |
| csv_path = str(wav_path).split("clean")[ |
| 0] + "clean/" + split + "_files_" + mic + ".csv" |
| with open(csv_path) as csvfile: |
| reader = csv.reader(csvfile, delimiter=',') |
| for row in reader: |
| if row[0].split("/")[-1].split(".")[0] == num: |
| transcript = row[2] |
|
|
| id_ = wav_path.stem |
|
|
| example = { |
| "file": str(wav_path), |
| "audio": str(wav_path), |
| "text": transcript, |
| "accent": accent, |
| "microphone": mic, |
| "id": id_, |
| } |
|
|
| yield key, example |
|
|
|
|
| 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 |
|
|
