Datasets:
Tasks:
Automatic Speech Recognition
Multilinguality:
multilingual
Size Categories:
100K<n<1M
Annotations Creators:
expert-generated
Source Datasets:
extended|other-common-voice
ArXiv:
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 | |
import csv | |
import os | |
import pandas as pd | |
import datasets | |
_VERSION = "1.0.0" | |
_CITATION = """ | |
@misc{wang2020covost, | |
title={CoVoST 2: A Massively Multilingual Speech-to-Text Translation Corpus}, | |
author={Changhan Wang and Anne Wu and Juan Pino}, | |
year={2020}, | |
eprint={2007.10310}, | |
archivePrefix={arXiv}, | |
primaryClass={cs.CL} | |
""" | |
_DESCRIPTION = """ | |
CoVoST 2, a large-scale multilingual speech translation corpus covering translations from 21 languages into English \ | |
and from English into 15 languages. The dataset is created using Mozilla’s open source Common Voice database of \ | |
crowdsourced voice recordings. | |
Note that in order to limit the required storage for preparing this dataset, the audio | |
is stored in the .mp3 format and is not converted to a float32 array. To convert, the audio | |
file to a float32 array, please make use of the `.map()` function as follows: | |
```python | |
import torchaudio | |
def map_to_array(batch): | |
speech_array, _ = torchaudio.load(batch["file"]) | |
batch["speech"] = speech_array.numpy() | |
return batch | |
dataset = dataset.map(map_to_array, remove_columns=["file"]) | |
``` | |
""" | |
_HOMEPAGE = "https://github.com/facebookresearch/covost" | |
# fmt: off | |
XX_EN_LANGUAGES = ["fr", "de", "es", "ca", "it", "ru", "zh-CN", "pt", "fa", "et", "mn", "nl", "tr", "ar", "sv-SE", "lv", "sl", "ta", "ja", "id", "cy"] | |
EN_XX_LANGUAGES = ["de", "tr", "fa", "sv-SE", "mn", "zh-CN", "cy", "ca", "sl", "et", "id", "ar", "ta", "lv", "ja"] | |
# fmt: on | |
COVOST_URL_TEMPLATE = "https://dl.fbaipublicfiles.com/covost/covost_v2.{src_lang}_{tgt_lang}.tsv.tar.gz" | |
def _get_builder_configs(): | |
builder_configs = [ | |
datasets.BuilderConfig(name=f"en_{lang}", version=datasets.Version(_VERSION)) for lang in EN_XX_LANGUAGES | |
] | |
builder_configs += [ | |
datasets.BuilderConfig(name=f"{lang}_en", version=datasets.Version(_VERSION)) for lang in XX_EN_LANGUAGES | |
] | |
return builder_configs | |
class Covost2(datasets.GeneratorBasedBuilder): | |
"""CoVOST2 Dataset.""" | |
VERSION = datasets.Version(_VERSION) | |
BUILDER_CONFIGS = _get_builder_configs() | |
def manual_download_instructions(self): | |
return f"""Please download the Common Voice Corpus 4 in {self.config.name.split('_')[0]} from https://commonvoice.mozilla.org/en/datasets and unpack it with `tar xvzf {self.config.name.split('_')[0]}.tar`. Make sure to pass the path to the directory in which you unpacked the downloaded file as `data_dir`: `datasets.load_dataset('covost2', data_dir="path/to/dir")` | |
""" | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
client_id=datasets.Value("string"), | |
file=datasets.Value("string"), | |
audio=datasets.Audio(sampling_rate=16_000), | |
sentence=datasets.Value("string"), | |
translation=datasets.Value("string"), | |
id=datasets.Value("string"), | |
), | |
supervised_keys=("file", "translation"), | |
homepage=_HOMEPAGE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
data_root = os.path.abspath(os.path.expanduser(dl_manager.manual_dir)) | |
source_lang, target_lang = self.config.name.split("_") | |
if not os.path.exists(data_root): | |
raise FileNotFoundError( | |
f"You are trying to load the {self.config.name} speech translation dataset. " | |
f"It is required that you manually download the input speech data {source_lang}. " | |
f"Manual download instructions: {self.manual_download_instructions}" | |
) | |
covost_url = COVOST_URL_TEMPLATE.format(src_lang=source_lang, tgt_lang=target_lang) | |
extracted_path = dl_manager.download_and_extract(covost_url) | |
covost_tsv_path = os.path.join(extracted_path, f"covost_v2.{source_lang}_{target_lang}.tsv") | |
cv_tsv_path = os.path.join(data_root, "validated.tsv") | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"source_path": data_root, | |
"covost_tsv_path": covost_tsv_path, | |
"cv_tsv_path": cv_tsv_path, | |
"split": "train", | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={ | |
"source_path": data_root, | |
"covost_tsv_path": covost_tsv_path, | |
"cv_tsv_path": cv_tsv_path, | |
"split": "dev", | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
"source_path": data_root, | |
"covost_tsv_path": covost_tsv_path, | |
"cv_tsv_path": cv_tsv_path, | |
"split": "test", | |
}, | |
), | |
] | |
def _generate_examples(self, source_path, covost_tsv_path, cv_tsv_path, split): | |
covost_tsv = self._load_df_from_tsv(covost_tsv_path) | |
cv_tsv = self._load_df_from_tsv(cv_tsv_path) | |
df = pd.merge( | |
left=cv_tsv[["path", "sentence", "client_id"]], | |
right=covost_tsv[["path", "translation", "split"]], | |
how="inner", | |
on="path", | |
) | |
if split == "train": | |
df = df[(df["split"] == "train") | (df["split"] == "train_covost")] | |
else: | |
df = df[df["split"] == split] | |
for i, row in df.iterrows(): | |
yield i, { | |
"id": row["path"].replace(".mp3", ""), | |
"client_id": row["client_id"], | |
"sentence": row["sentence"], | |
"translation": row["translation"], | |
"file": os.path.join(source_path, "clips", row["path"]), | |
"audio": os.path.join(source_path, "clips", row["path"]), | |
} | |
def _load_df_from_tsv(self, path): | |
return pd.read_csv( | |
path, | |
sep="\t", | |
header=0, | |
encoding="utf-8", | |
escapechar="\\", | |
quoting=csv.QUOTE_NONE, | |
na_filter=False, | |
) | |