Datasets:

File size: 5,314 Bytes
7525156
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
# coding=utf-8
# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Lint as: python3
"""VCTK dataset."""


import os
import re

import datasets
from datasets.tasks import AutomaticSpeechRecognition


_CITATION = """\
@inproceedings{Veaux2017CSTRVC,
    title        = {CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit},
    author       = {Christophe Veaux and Junichi Yamagishi and Kirsten MacDonald},
    year         = 2017
}
"""

_DESCRIPTION = """\
The CSTR VCTK Corpus includes speech data uttered by 110 English speakers with various accents.
"""

_URL = "https://datashare.ed.ac.uk/handle/10283/3443"
_DL_URL = "https://datashare.is.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip"


class VCTK(datasets.GeneratorBasedBuilder):
    """VCTK dataset."""

    VERSION = datasets.Version("0.9.2")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="main", version=VERSION, description="VCTK dataset"),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "speaker_id": datasets.Value("string"),
                    "audio": datasets.features.Audio(sampling_rate=48_000),
                    "file": datasets.Value("string"),
                    "text": datasets.Value("string"),
                    "text_id": datasets.Value("string"),
                    "age": datasets.Value("string"),
                    "gender": datasets.Value("string"),
                    "accent": datasets.Value("string"),
                    "region": datasets.Value("string"),
                    "comment": datasets.Value("string"),
                }
            ),
            supervised_keys=("file", "text"),
            homepage=_URL,
            citation=_CITATION,
            task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")],
        )

    def _split_generators(self, dl_manager):
        root_path = dl_manager.download_and_extract(_DL_URL)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"root_path": root_path}),
        ]

    def _generate_examples(self, root_path):
        """Generate examples from the VCTK corpus root path."""

        meta_path = os.path.join(root_path, "speaker-info.txt")
        txt_root = os.path.join(root_path, "txt")
        wav_root = os.path.join(root_path, "wav48_silence_trimmed")
        # NOTE: "comment" is handled separately in logic below
        fields = ["speaker_id", "age", "gender", "accent", "region"]

        key = 0
        with open(meta_path, encoding="utf-8") as meta_file:
            _ = next(iter(meta_file))
            for line in meta_file:
                data = {}
                line = line.strip()
                search = re.search(r"\(.*\)", line)
                if search is None:
                    data["comment"] = ""
                else:
                    start, _ = search.span()
                    data["comment"] = line[start:]
                    line = line[:start]
                values = line.split()
                for i, field in enumerate(fields):
                    if field == "region":
                        data[field] = " ".join(values[i:])
                    else:
                        data[field] = values[i] if i < len(values) else ""
                speaker_id = data["speaker_id"]
                speaker_txt_path = os.path.join(txt_root, speaker_id)
                speaker_wav_path = os.path.join(wav_root, speaker_id)
                # NOTE: p315 does not have text
                if not os.path.exists(speaker_txt_path):
                    continue
                for txt_file in sorted(os.listdir(speaker_txt_path)):
                    filename, _ = os.path.splitext(txt_file)
                    _, text_id = filename.split("_")
                    for i in [1, 2]:
                        wav_file = os.path.join(speaker_wav_path, f"{filename}_mic{i}.flac")
                        # NOTE: p280 does not have mic2 files
                        if not os.path.exists(wav_file):
                            continue
                        with open(os.path.join(speaker_txt_path, txt_file), encoding="utf-8") as text_file:
                            text = text_file.readline().strip()
                            more_data = {
                                "file": wav_file,
                                "audio": wav_file,
                                "text": text,
                                "text_id": text_id,
                            }
                            yield key, {**data, **more_data}
                        key += 1