Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
crowdsourced
Annotations Creators:
expert-generated
Source Datasets:
original
License:
| # 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 | |