Delete common_voice_16_0 .py
Browse files- common_voice_16_0 .py +0 -198
common_voice_16_0 .py
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# coding=utf-8
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# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Common Voice Dataset"""
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import csv
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import os
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import json
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import datasets
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from datasets.utils.py_utils import size_str
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from tqdm import tqdm
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from .languages import LANGUAGES
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from .release_stats import STATS
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_CITATION = """\
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@inproceedings{commonvoice:2020,
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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.},
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title = {Common Voice: A Massively-Multilingual Speech Corpus},
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booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
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pages = {4211--4215},
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year = 2020
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}
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"""
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_HOMEPAGE = "https://commonvoice.mozilla.org/en/datasets"
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_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"
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# TODO: change "streaming" to "main" after merge!
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_BASE_URL = "https://huggingface.co/datasets/reach-vb/common_voice_16_0/resolve/main/"
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_AUDIO_URL = _BASE_URL + "audio/{lang}/{split}/{lang}_{split}_{shard_idx}.tar"
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_TRANSCRIPT_URL = _BASE_URL + "transcript/{lang}/{split}.tsv"
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_N_SHARDS_URL = _BASE_URL + "n_shards.json"
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class CommonVoiceConfig(datasets.BuilderConfig):
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"""BuilderConfig for CommonVoice."""
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def __init__(self, name, version, **kwargs):
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self.language = kwargs.pop("language", None)
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self.release_date = kwargs.pop("release_date", None)
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self.num_clips = kwargs.pop("num_clips", None)
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self.num_speakers = kwargs.pop("num_speakers", None)
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self.validated_hr = kwargs.pop("validated_hr", None)
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self.total_hr = kwargs.pop("total_hr", None)
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self.size_bytes = kwargs.pop("size_bytes", None)
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self.size_human = size_str(self.size_bytes)
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description = (
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f"Common Voice speech to text dataset in {self.language} released on {self.release_date}. "
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f"The dataset comprises {self.validated_hr} hours of validated transcribed speech data "
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f"out of {self.total_hr} hours in total from {self.num_speakers} speakers. "
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f"The dataset contains {self.num_clips} audio clips and has a size of {self.size_human}."
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)
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super(CommonVoiceConfig, self).__init__(
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name=name,
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version=datasets.Version(version),
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description=description,
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**kwargs,
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)
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class CommonVoice(datasets.GeneratorBasedBuilder):
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DEFAULT_WRITER_BATCH_SIZE = 1000
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BUILDER_CONFIGS = [
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CommonVoiceConfig(
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name=lang,
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version=STATS["version"],
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language=LANGUAGES[lang],
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release_date=STATS["date"],
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num_clips=lang_stats["clips"],
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num_speakers=lang_stats["users"],
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validated_hr=float(lang_stats["validHrs"]) if lang_stats["validHrs"] else None,
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total_hr=float(lang_stats["totalHrs"]) if lang_stats["totalHrs"] else None,
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size_bytes=int(lang_stats["size"]) if lang_stats["size"] else None,
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)
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for lang, lang_stats in STATS["locales"].items()
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]
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def _info(self):
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total_languages = len(STATS["locales"])
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total_valid_hours = STATS["totalValidHrs"]
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description = (
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"Common Voice is Mozilla's initiative to help teach machines how real people speak. "
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f"The dataset currently consists of {total_valid_hours} validated hours of speech "
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f" in {total_languages} languages, but more voices and languages are always added."
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)
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features = datasets.Features(
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{
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"client_id": datasets.Value("string"),
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"path": datasets.Value("string"),
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"audio": datasets.features.Audio(sampling_rate=48_000),
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"sentence": datasets.Value("string"),
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"up_votes": datasets.Value("int64"),
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"down_votes": datasets.Value("int64"),
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"age": datasets.Value("string"),
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"gender": datasets.Value("string"),
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"accent": datasets.Value("string"),
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"locale": datasets.Value("string"),
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"segment": datasets.Value("string"),
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"variant": datasets.Value("string"),
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}
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)
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return datasets.DatasetInfo(
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description=description,
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features=features,
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supervised_keys=None,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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version=self.config.version,
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)
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def _split_generators(self, dl_manager):
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lang = self.config.name
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n_shards_path = dl_manager.download_and_extract(_N_SHARDS_URL)
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with open(n_shards_path, encoding="utf-8") as f:
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n_shards = json.load(f)
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audio_urls = {}
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splits = ("train", "dev", "test", "other", "invalidated")
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for split in splits:
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audio_urls[split] = [
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_AUDIO_URL.format(lang=lang, split=split, shard_idx=i) for i in range(n_shards[lang][split])
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]
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archive_paths = dl_manager.download(audio_urls)
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local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
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meta_urls = {split: _TRANSCRIPT_URL.format(lang=lang, split=split) for split in splits}
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meta_paths = dl_manager.download_and_extract(meta_urls)
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split_generators = []
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split_names = {
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"train": datasets.Split.TRAIN,
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"dev": datasets.Split.VALIDATION,
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"test": datasets.Split.TEST,
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}
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for split in splits:
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split_generators.append(
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datasets.SplitGenerator(
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name=split_names.get(split, split),
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gen_kwargs={
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"local_extracted_archive_paths": local_extracted_archive_paths.get(split),
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"archives": [dl_manager.iter_archive(path) for path in archive_paths.get(split)],
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"meta_path": meta_paths[split],
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},
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),
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)
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return split_generators
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def _generate_examples(self, local_extracted_archive_paths, archives, meta_path):
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data_fields = list(self._info().features.keys())
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metadata = {}
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with open(meta_path, encoding="utf-8") as f:
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reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
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for row in tqdm(reader, desc="Reading metadata..."):
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if not row["path"].endswith(".mp3"):
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row["path"] += ".mp3"
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# accent -> accents in CV 8.0
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if "accents" in row:
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row["accent"] = row["accents"]
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del row["accents"]
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# if data is incomplete, fill with empty values
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for field in data_fields:
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if field not in row:
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row[field] = ""
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metadata[row["path"]] = row
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for i, audio_archive in enumerate(archives):
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for path, file in audio_archive:
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_, filename = os.path.split(path)
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if filename in metadata:
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result = dict(metadata[filename])
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# set the audio feature and the path to the extracted file
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path = os.path.join(local_extracted_archive_paths[i], path) if local_extracted_archive_paths else path
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result["audio"] = {"path": path, "bytes": file.read()}
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result["path"] = path
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yield path, result
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