|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 |
|
|
