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