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covost2.py

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+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+
+import csv
+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from nusacrowd.utils import schemas
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import DEFAULT_NUSANTARA_VIEW_NAME, DEFAULT_SOURCE_VIEW_NAME, Tasks
+
+_LANGUAGES = ["ind", "eng"]
+_CITATION = """\
+
+@article{wang2020covost,
+  title={Covost 2 and massively multilingual speech-to-text translation},
+  author={Wang, Changhan and Wu, Anne and Pino, Juan},
+  journal={arXiv preprint arXiv:2007.10310},
+  year={2020}
+}
+
+@inproceedings{wang21s_interspeech,
+  author={Wang, Changhan and Wu, Anne and Pino, Juan},
+  title={{CoVoST 2 and Massively Multilingual Speech Translation}},
+  year=2021,
+  booktitle={Proc. Interspeech 2021},
+  pages={2247--2251},
+  url={https://www.isca-speech.org/archive/interspeech_2021/wang21s_interspeech}
+  doi={10.21437/Interspeech.2021-2027}
+}
+"""
+
+_DATASETNAME = "covost2"
+_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
+_UNIFIED_VIEW_NAME = DEFAULT_NUSANTARA_VIEW_NAME
+
+_DESCRIPTION = """\
+CoVoST2 is a large-scale multilingual speech translation corpus covering translations from 21 languages to English
+and from English into 15 languages. The dataset is created using Mozilla's open-source Common Voice database of
+crowdsourced voice recordings. There are 2,900 hours of speech represented in the corpus.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/covost2"
+
+_LOCAL = False
+_LICENSE = "CC BY-NC 4.0"
+
+COMMONVOICE_URL_TEMPLATE = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/{lang}.tar.gz"
+LANG_CODE = {"eng": "en", "ind": "id"}
+LANG_COMBINATION_CODE = [("ind", "eng"), ("eng", "ind")]
+_URLS = {_DATASETNAME: {"ind": COMMONVOICE_URL_TEMPLATE.format(lang=LANG_CODE["ind"]), "eng": COMMONVOICE_URL_TEMPLATE.format(lang=LANG_CODE["eng"])}}
+
+_SUPPORTED_TASKS = [Tasks.SPEECH_TO_TEXT_TRANSLATION, Tasks.MACHINE_TRANSLATION]
+_SOURCE_VERSION = "1.0.0"
+_NUSANTARA_VERSION = "1.0.0"
+
+
+def nusantara_config_constructor(src_lang, tgt_lang, schema, version):
+    if src_lang == "" or tgt_lang == "":
+        raise ValueError(f"Invalid src_lang {src_lang} or tgt_lang {tgt_lang}")
+
+    if schema not in ["source", "nusantara_sptext", "nusantara_t2t"]:
+        raise ValueError(f"Invalid schema: {schema}")
+
+    return NusantaraConfig(
+        name="covost2_{src}_{tgt}_{schema}".format(src=src_lang, tgt=tgt_lang, schema=schema),
+        version=datasets.Version(version),
+        description="covost2 source schema for {schema} from {src} to {tgt}".format(schema=schema, src=src_lang, tgt=tgt_lang),
+        schema=schema,
+        subset_id="co_vo_st2_{src}_{tgt}".format(src=src_lang, tgt=tgt_lang),
+    )
+
+
+class Covost2(datasets.GeneratorBasedBuilder):
+    """CoVoST2 dataset is a dataset mainly for speech to text translation task. The data was taken from Mozilla Common
+    Voices dataset. In the implementation of the source schema, the audio and transcriptions of the source language,
+    as well as the translated transcriptions are provided. In the implementation of the nusantara schema, only the audio of the source language and transcriptions of the
+    target language are provided. The source and target languages available are eng->ind and ind -> eng respectively.
+    In addition to the speech to text translation, this dataset (text only) can be used as a machine translation for
+    eng->ind and ind->eng.
+
+    Side note: the amount of data takes about 40GB for the English source data and 1GB for the Indonesian source data.
+    """
+
+    COVOST_URL_TEMPLATE = "https://dl.fbaipublicfiles.com/covost/covost_v2.{src_lang}_{tgt_lang}.tsv.tar.gz"
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+
+    BUILDER_CONFIGS = (
+        [nusantara_config_constructor(src, tgt, "source", _SOURCE_VERSION) for (src, tgt) in LANG_COMBINATION_CODE]
+        + [nusantara_config_constructor(src, tgt, "nusantara_sptext", _NUSANTARA_VERSION) for (src, tgt) in LANG_COMBINATION_CODE]
+        + [nusantara_config_constructor(src, tgt, "nusantara_t2t", _NUSANTARA_VERSION) for (src, tgt) in LANG_COMBINATION_CODE]
+    )
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_eng_ind_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            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")}
+            )
+        elif self.config.schema == "nusantara_sptext":
+            features = schemas.speech_text_features
+        elif self.config.schema == "nusantara_t2t":
+            features = schemas.text2text_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+            task_templates=[datasets.AutomaticSpeechRecognition(audio_column="audio", transcription_column="sentences")] if (self.config.schema == "nusantara_sptext" or self.config.schema == "source") else None,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        name_split = self.config.name.split("_")
+        src_lang, tgt_lang = name_split[1], name_split[2]
+
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls[src_lang])
+
+        src_lang = LANG_CODE[src_lang]
+        tgt_lang = LANG_CODE[tgt_lang]
+
+        data_dir = data_dir + "/" + "/".join(["cv-corpus-6.1-2020-12-11", src_lang])
+
+        covost_tsv_path = self.COVOST_URL_TEMPLATE.format(src_lang=src_lang, tgt_lang=tgt_lang)
+        extracted_dir = dl_manager.download_and_extract(covost_tsv_path)
+
+        covost_tsv_filename = "covost_v2.{src_lang}_{tgt_lang}.tsv"
+        covost_tsv_dir = os.path.join(extracted_dir, covost_tsv_filename.format(src_lang=src_lang, tgt_lang=tgt_lang))
+        cv_tsv_dir = os.path.join(data_dir, "validated.tsv")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir,
+                    "covost_tsv_path": covost_tsv_dir,
+                    "cv_tsv_path": cv_tsv_dir,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_dir,
+                    "covost_tsv_path": covost_tsv_dir,
+                    "cv_tsv_path": cv_tsv_dir,
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": data_dir,
+                    "covost_tsv_path": covost_tsv_dir,
+                    "cv_tsv_path": cv_tsv_dir,
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, covost_tsv_path: Path, cv_tsv_path: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        name_split = self.config.name.split("_")
+        src_lang, tgt_lang = name_split[1], name_split[2]
+
+        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 id, row in df.iterrows():
+            if self.config.schema == "source":
+                yield id, {
+                    "id": row["path"].replace(".mp3", ""),
+                    "client_id": row["client_id"],
+                    "sentence": row["sentence"],
+                    "translation": row["translation"],
+                    "file": os.path.join(filepath, "clips", row["path"]),
+                    "audio": os.path.join(filepath, "clips", row["path"]),
+                }
+            elif self.config.schema == "nusantara_sptext":
+                yield id, {
+                    "id": row["path"].replace(".mp3", ""),
+                    "speaker_id": row["client_id"],
+                    "text": row["translation"],
+                    "path": os.path.join(filepath, "clips", row["path"]),
+                    "audio": os.path.join(filepath, "clips", row["path"]),
+                    "metadata": {
+                        "speaker_age": None,
+                        "speaker_gender": None,
+                    },
+                }
+            elif self.config.schema == "nusantara_t2t":
+                yield id, {"id": row["path"].replace(".mp3", ""), "text_1": row["sentence"], "text_2": row["translation"], "text_1_name": src_lang, "text_2_name": tgt_lang}
+            else:
+                raise NotImplementedError(f"Schema '{self.config.schema}' is not defined.")
+
+    @staticmethod
+    def _load_df_from_tsv(path):
+        return pd.read_csv(
+            path,
+            sep="\t",
+            header=0,
+            encoding="utf-8",
+            escapechar="\\",
+            quoting=csv.QUOTE_NONE,
+            na_filter=False,
+        )