Delete multilingual_librispeech.py

multilingual_librispeech.py

@@ -1,226 +0,0 @@
-# coding=utf-8
-# Copyright 2022 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
-"""Multilingual Librispeech automatic speech recognition dataset."""
-
-import os
-
-import datasets
-
-
-_CITATION = """\
-@article{Pratap2020MLSAL,
-  title={MLS: A Large-Scale Multilingual Dataset for Speech Research},
-  author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert},
-  journal={ArXiv},
-  year={2020},
-  volume={abs/2012.03411}
-}
-"""
-
-_DESCRIPTION = """\
-This is a streamable version of the Multilingual LibriSpeech (MLS) dataset. 
-The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94) 
-to make it easier to stream. 
-
-MLS dataset is a large multilingual corpus suitable for speech research. 
-The dataset is derived from read audiobooks from LibriVox and consists of 8 languages: 
-English, German, Dutch, Spanish, French, Italian, Portuguese, Polish.
-"""
-
-_URL = "http://www.openslr.org/94"
-
-_DL_URL_FORMAT = "data/mls_{name}"
-
-
-class MultilingualLibrispeechConfig(datasets.BuilderConfig):
-    """BuilderConfig for MultilingualLibrispeech."""
-
-    def __init__(self, name, **kwargs):
-        """
-        Args:
-          name: `string`, name of dataset config (=language)
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(MultilingualLibrispeechConfig, self).__init__(
-            version=datasets.Version("2.1.0", ""), name=name, **kwargs
-        )
-        # relative path to full data inside a repo (for example `data/mls_german`)
-        self.data_root_url = _DL_URL_FORMAT.format(name=name)
-
-
-class MultilingualLibrispeech(datasets.GeneratorBasedBuilder):
-    """Multilingual Librispeech dataset."""
-
-    BUILDER_CONFIGS = [
-        MultilingualLibrispeechConfig(name="german", description="German LibriSpeech dataset"),
-        MultilingualLibrispeechConfig(name="dutch", description="Dutch LibriSpeech dataset"),
-        MultilingualLibrispeechConfig(name="french", description="French LibriSpeech dataset"),
-        MultilingualLibrispeechConfig(name="spanish", description="Spanish LibriSpeech dataset"),
-        MultilingualLibrispeechConfig(name="italian", description="Italian LibriSpeech dataset"),
-        MultilingualLibrispeechConfig(name="portuguese", description="Portuguese LibriSpeech dataset"),
-        MultilingualLibrispeechConfig(name="polish", description="Polish LibriSpeech dataset"),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "file": datasets.Value("string"),
-                    "audio": datasets.features.Audio(sampling_rate=16_000),
-                    "text": datasets.Value("string"),
-                    "speaker_id": datasets.Value("int64"),
-                    "chapter_id": datasets.Value("int64"),
-                    "id": datasets.Value("string"),
-                }
-            ),
-            supervised_keys=("file", "text"),
-            homepage=_URL,
-            citation=_CITATION,
-            task_templates=None,
-        )
-
-    def _split_generators(self, dl_manager):
-        
-        transcripts = dl_manager.download({
-            "train": self.config.data_root_url + "/train/transcripts.txt",
-            "dev": self.config.data_root_url + "/dev/transcripts.txt",
-            "test": self.config.data_root_url + "/test/transcripts.txt",
-        })
-
-        # Download handles.txt files containing ids for limited supervision train sets
-        limited_supervision_9h = dl_manager.download(
-            [self.config.data_root_url + "/train/limited_supervision/9hr/handles.txt"],
-        )
-        # in our case of 1 hour limited supervision ("train.1h") there are always 6 subfolders like:
-        # "limited_supervision/1h/0/handles.txt", "limited_supervision/1h/1/handles.txt", ...
-        limited_supervision_1h = dl_manager.download([
-            self.config.data_root_url + f"/train/limited_supervision/1hr/{i}/handles.txt" for i in range(6)
-        ])
-        
-        # each split contains many .tar.gz archives with its audio files
-        # audio_filenames.txt contains the names of these archives
-        audio_filenames_paths = dl_manager.download({
-            "train": self.config.data_root_url + "/train/audio_filenames.txt",
-            "dev": self.config.data_root_url + "/dev/audio_filenames.txt",
-            "test": self.config.data_root_url + "/test/audio_filenames.txt",
-        })
-
-        audio_archives = {}
-        for split in audio_filenames_paths:
-            with open(audio_filenames_paths[split], encoding="utf-8") as f:
-                audio_filenames = [line.strip() for line in f.readlines()]
-                audio_archives[split] = dl_manager.download([
-                    self.config.data_root_url + "/" + split + "/audio/" + filename
-                    for filename in audio_filenames
-                ])
-
-        # (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
-        local_extracted_archives = dl_manager.extract(audio_archives) if not dl_manager.is_streaming else {}
-
-        train_splits = [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "transcript_path": transcripts["train"],
-                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["train"]],
-                    "local_extracted_archive": local_extracted_archives.get("train"),
-                }
-            ),
-            datasets.SplitGenerator(
-                name="train.9h",
-                gen_kwargs={
-                    "transcript_path": transcripts["train"],
-                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["train"]],
-                    "local_extracted_archive": local_extracted_archives.get("train"),
-                    "limited_ids_paths": tuple(limited_supervision_9h),
-                },
-            ),
-            datasets.SplitGenerator(
-                name="train.1h",
-                gen_kwargs={
-                    "transcript_path": transcripts["train"],
-                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["train"]],
-                    "local_extracted_archive": local_extracted_archives.get("train"),
-                    "limited_ids_paths": tuple(limited_supervision_1h),
-                },
-            ),
-        ]
-
-        return train_splits + [
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION, gen_kwargs={
-                    "transcript_path": transcripts["dev"],
-                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["dev"]],
-                    "local_extracted_archive": local_extracted_archives.get("dev"),
-                }
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={
-                    "transcript_path": transcripts["test"],
-                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["test"]],
-                    "local_extracted_archive": local_extracted_archives.get("test"),
-                }
-            ),
-        ]
-
-    def _generate_examples(self, transcript_path, audio_archives, local_extracted_archive, limited_ids_paths=None):
-        """Generate examples from a Multilingual LibriSpeech data dir."""
-        transcripts = dict()
-        with open(transcript_path, "r", encoding="utf-8") as file:
-            for line in file:
-                audio_id, transcript = line.strip().split("\t")
-                transcripts[audio_id] = transcript
-
-        limited_ids, limited_ids_archives_names = [], []
-        if limited_ids_paths:
-            for path in limited_ids_paths:
-                with open(path, "r", encoding="utf-8") as file:
-                    limited_ids.extend([line.strip() for line in file.readlines()])
-
-            limited_ids = set(limited_ids)
-
-        for archive_idx, audio_archive in enumerate(audio_archives):
-            #  TODO: check that archive doesn't contain needed ids
-            # if limited_ids and audio_archive not in limited_ids_archives_names:
-            #     continue
-
-            for audio_filename, file in audio_archive:
-                speaker_id, chapter_id = audio_filename.split("_")[:2]
-                speaker_id, chapter_id = int(speaker_id), int(chapter_id)
-                audio_id = audio_filename.split(".flac")[0]
-                audio_transcript = transcripts[audio_id]
-
-                if limited_ids and audio_id not in limited_ids:
-                    # this only can be true in limited supervision sets ("train.9h" and "train.1h")
-                    continue
-
-                local_audio_file_path = os.path.join(
-                    local_extracted_archive[archive_idx], audio_filename
-                ) if local_extracted_archive else None
-
-                yield audio_filename, {
-                    "file": local_audio_file_path,
-                    "audio": {
-                        "path": local_audio_file_path if local_audio_file_path else audio_filename,
-                        "bytes": file.read()
-                    },
-                    "text": audio_transcript,
-                    "speaker_id": speaker_id,
-                    "chapter_id": chapter_id,
-                    "id": audio_id
-                }