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| import json |
| import logging |
| import re |
| import os |
|
|
| import datasets |
|
|
| from glob import glob |
|
|
| logger = logging.getLogger(__name__) |
|
|
| _DESCRIPTION = """\ |
| AudioMNIST, a research baseline dataset |
| """ |
|
|
| _BASE_URL = "https://huggingface.co/datasets/flexthink/audiomnist/resolve/main" |
| _HOMEPAGE_URL = "https://huggingface.co/datasets/flexthink/audiomnist" |
| _SPLITS = ["train", "valid", "test"] |
| _GENDERS = ["female", "male"] |
| _ACCENTS = [ |
| "Arabic", |
| "Brasilian", |
| "Chinese", |
| "Danish", |
| "English", |
| "French", |
| "German", |
| "Italian", |
| "Levant", |
| "Madras", |
| "South African", |
| "South Korean", |
| "Spanish", |
| "Tamil", |
| ] |
| _SAMPLING_RATE = 48000 |
|
|
| _ACCENT_MAP = { |
| "german": "German", |
| "Egyptian_American?": "Arabic", |
| "German/Spanish": "German", |
| } |
|
|
| _META_FILE = "audioMNIST_meta.json" |
| _RE_FILE_NAME = re.compile("(?P<digit>\d+)_(?P<speaker_id>\d+)_(?P<sample_idx>\d+).wav") |
|
|
|
|
| class GraphemeToPhoneme(datasets.GeneratorBasedBuilder): |
| def __init__(self, base_url=None, splits=None, *args, **kwargs): |
| super().__init__(*args, **kwargs) |
| self.base_url = base_url or _BASE_URL |
| self.splits = splits or _SPLITS |
|
|
| def _info(self): |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=datasets.Features( |
| { |
| "file_name": datasets.Value("string"), |
| "audio": datasets.features.Audio(sampling_rate=_SAMPLING_RATE), |
| "speaker_id": datasets.Value("string"), |
| "age": datasets.Value("int8"), |
| "gender": datasets.ClassLabel(names=_GENDERS), |
| "accent": datasets.ClassLabel(names=_ACCENTS), |
| "native_speaker": datasets.Value("bool"), |
| "origin": datasets.Value("string"), |
| "digit": datasets.Value("int8"), |
| }, |
| ), |
| supervised_keys=None, |
| homepage=_HOMEPAGE_URL, |
| ) |
|
|
| def _get_url(self, split): |
| return f"{self.base_url}/dataset/{split}.tar.gz" |
|
|
| def _get_meta_url(self): |
| return f"{self.base_url}/meta/{_META_FILE}" |
|
|
| def _split_generator(self, dl_manager, split): |
| archive_url = self._get_url(split) |
| archive_path = dl_manager.download_and_extract(archive_url) |
| meta_url = self._get_meta_url() |
| meta_file = dl_manager.download(meta_url) |
| speaker_map = self._get_speaker_map(meta_file) |
| return datasets.SplitGenerator( |
| name=split, |
| gen_kwargs={ |
| "archive_path": archive_path, |
| "speaker_map": speaker_map, |
| }, |
| ) |
|
|
| def _get_speaker_map(self, file_name): |
| with open(file_name) as speaker_file: |
| result = json.load(speaker_file) |
| for entry in result.values(): |
| entry["accent"] = _ACCENT_MAP.get( |
| entry["accent"], entry["accent"]) |
| return result |
|
|
| def _split_generators(self, dl_manager): |
| return [self._split_generator(dl_manager, split) for split in self.splits] |
|
|
| def _map_speaker_info(self, speaker_info): |
| result = dict(speaker_info) |
| result["native_speaker"] = speaker_info["native speaker"] == "yes" |
| del result["native speaker"] |
| del result["recordingdate"] |
| del result["recordingroom"] |
| return result |
|
|
| def _generate_examples(self, archive_path, speaker_map): |
| wav_files = glob(os.path.join(archive_path, 'dataset', '**', '*.wav')) |
| for path in wav_files: |
| match = _RE_FILE_NAME.search(path) |
| if not match: |
| logger.warn( |
| f"File {path} does not match the naming convention" |
| ) |
| continue |
| digit, speaker_id = [ |
| match.group(key) for key in ["digit", "speaker_id"] |
| ] |
| with open(path, 'rb') as wav_file: |
| sample = { |
| "digit": digit, |
| "speaker_id": speaker_id, |
| "file_name": os.path.join(archive_path, path), |
| "audio": {"path": path, "bytes": wav_file.read()}, |
| } |
| if speaker_id not in speaker_map: |
| logger.warn(f"Speaker {speaker_id} not found") |
| speaker_info = speaker_map[speaker_id] |
| sample.update(self._map_speaker_info(speaker_info)) |
| yield path, sample |
|
|
