# 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() @property 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, )