File size: 6,502 Bytes
f69bf6a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 |
import csv
import os
import json
import datasets
from datasets.utils.py_utils import size_str
from tqdm import tqdm
_CITATION = """\
@inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
}
"""
_HOMEPAGE = "https://commonvoice.mozilla.org/en/datasets"
_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"
# TODO: change "streaming" to "main" after merge!
_BASE_URL = "https://huggingface.co/datasets/reach-vb/common_voice_16_0/resolve/main/"
_AUDIO_URL = _BASE_URL + "audio/{lang}/{split}/{lang}_{split}_{shard_idx}.tar"
_TRANSCRIPT_URL = _BASE_URL + "transcript/{lang}/{split}.tsv"
_N_SHARDS_URL = _BASE_URL + "n_shards.json"
class CommonVoiceConfig(datasets.BuilderConfig):
"""BuilderConfig for CommonVoice."""
def __init__(self, name, version, **kwargs):
self.language = kwargs.pop("language", None)
self.release_date = kwargs.pop("release_date", None)
self.num_clips = kwargs.pop("num_clips", None)
self.num_speakers = kwargs.pop("num_speakers", None)
self.validated_hr = kwargs.pop("validated_hr", None)
self.total_hr = kwargs.pop("total_hr", None)
self.size_bytes = kwargs.pop("size_bytes", None)
self.size_human = size_str(self.size_bytes)
description = (
f"Common Voice speech to text dataset in {self.language} released on {self.release_date}. "
f"The dataset comprises {self.validated_hr} hours of validated transcribed speech data "
f"out of {self.total_hr} hours in total from {self.num_speakers} speakers. "
f"The dataset contains {self.num_clips} audio clips and has a size of {self.size_human}."
)
super(CommonVoiceConfig, self).__init__(
name=name,
version=datasets.Version(version),
description=description,
**kwargs,
)
class CommonVoice(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1000
def _info(self):
description = (
"Common Voice is Mozilla's initiative to help teach machines how real people speak. "
"The dataset currently consists of validated hours of speech in one language, "
"but more voices and languages are always added."
)
features = datasets.Features(
{
"client_id": datasets.Value("string"),
"path": datasets.Value("string"),
"audio": datasets.features.Audio(sampling_rate=48_000),
"sentence": datasets.Value("string"),
"up_votes": datasets.Value("int64"),
"down_votes": datasets.Value("int64"),
"age": datasets.Value("string"),
"gender": datasets.Value("string"),
"accent": datasets.Value("string"),
"locale": datasets.Value("string"),
"segment": datasets.Value("string"),
"variant": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=description,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
version=datasets.Version("1.0.0"),
)
def _split_generators(self, dl_manager):
lang = "ha" # Assuming Hausa language
n_shards_path = dl_manager.download_and_extract(_N_SHARDS_URL)
with open(n_shards_path, encoding="utf-8") as f:
n_shards = json.load(f)
audio_urls = {}
splits = ("train", "dev", "test", "other", "invalidated")
for split in splits:
audio_urls[split] = [
_AUDIO_URL.format(lang=lang, split=split, shard_idx=i) for i in range(n_shards[lang][split])
]
archive_paths = dl_manager.download(audio_urls)
local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
meta_urls = {split: _TRANSCRIPT_URL.format(lang=lang, split=split) for split in splits}
meta_paths = dl_manager.download_and_extract(meta_urls)
split_generators = []
split_names = {
"train": datasets.Split.TRAIN,
"dev": datasets.Split.VALIDATION,
"test": datasets.Split.TEST,
}
for split in splits:
split_generators.append(
datasets.SplitGenerator(
name=split_names.get(split, split),
gen_kwargs={
"local_extracted_archive_paths": local_extracted_archive_paths.get(split),
"archives": [dl_manager.iter_archive(path) for path in archive_paths.get(split)],
"meta_path": meta_paths[split],
},
),
)
return split_generators
def _generate_examples(self, local_extracted_archive_paths, archives, meta_path):
data_fields = list(self._info().features.keys())
metadata = {}
with open(meta_path, encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
for row in tqdm(reader, desc="Reading metadata..."):
if not row["path"].endswith(".mp3"):
row["path"] += ".mp3"
if "accents" in row:
row["accent"] = row["accents"]
del row["accents"]
for field in data_fields:
if field not in row:
row[field] = ""
metadata[row["path"]] = row
for i, audio_archive in enumerate(archives):
for path, file in audio_archive:
_, filename = os.path.split(path)
if filename in metadata:
result = dict(metadata[filename])
path = os.path.join(local_extracted_archive_paths[i], path) if local_extracted_archive_paths else path
result["audio"] = {"path": path, "bytes": file.read()}
result["path"] = path
yield path, result
|