diff --git a/README.md b/README.md index 80dd60022b2096b35a9e700cd4db6a53ee9af234..da9ddf91571bc1ccaf14aa355f985ff76c2eeca9 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,4 @@ --- -pretty_name: MultiLingual LibriSpeech annotations_creators: - expert-generated language_creators: @@ -13,17 +12,387 @@ language: - es - pt - pl +- en license: - cc-by-4.0 multilinguality: - multilingual -paperswithcode_id: multilingual-librispeech size_categories: - 100K 10k hours + + csv_dict = {} + for split in splits: + segment_path = os.path.join(main_folder_path, split, "segments.txt") + transcript_path = os.path.join(main_folder_path, split, "transcripts.txt") + + segments = pd.read_csv(segment_path, sep='\t', names=["audio", "original_path", "begin_time", "end_time"], + index_col="audio") + transcripts = pd.read_csv(transcript_path, sep='\t', names=["audio", "transcript"], index_col="audio") + + df = pd.concat([segments, transcripts], axis=1, join="inner") + print( + f"Segments and transcripts of {split} has been joined: new length {len(df)}, old lengths {(len(segments), len(transcripts))}") + + # add audio duration + df["audio_duration"] = df["end_time"] - df["begin_time"] + df["split"] = split + + print(f"len df {len(df)}") + + df.to_csv(os.path.join(csv_folder_path, f"{split}.csv")) + csv_dict[split] = os.path.join(csv_folder_path, f"{split}.csv") + + # take care of /limited_supervision + if split == "train": + nine_hours_segment_path = os.path.join(main_folder_path, "train/limited_supervision/9hr/handles.txt") + nine_hours_segment = pd.read_csv(nine_hours_segment_path, sep='\t', names=["audio"], index_col="audio").index + nine_hours_df = df.filter(items=nine_hours_segment, axis=0) + nine_hours_df.to_csv(os.path.join(csv_folder_path, f"9_hours.csv")) + csv_dict["9_hours"] = os.path.join(csv_folder_path, f"9_hours.csv") + + one_hours_segments = [ os.path.join(f.path, "handles.txt") for f in os.scandir( os.path.join(main_folder_path, "train/limited_supervision/1hr")) if f.is_dir()] + one_hours_segments = pd.concat([pd.read_csv(one, sep='\t', names=["audio"], index_col="audio") for one in one_hours_segments], axis=0).index + one_hours_df = df.filter(items=one_hours_segments, axis=0) + one_hours_df.to_csv(os.path.join(csv_folder_path, f"1_hours.csv")) + csv_dict["1_hours"] = os.path.join(csv_folder_path, f"1_hours.csv") + + + + + dataset = DatasetDict.from_csv(csv_dict) + + def extract_speaker_id_and_format_path(audio, split): + speaker_id = audio.split("_")[0] + chapter_id = audio.split("_")[1] + file = f"{audio}.opus" + + path = os.path.join(main_folder_path, split, "audio", speaker_id, chapter_id, file) + return {"audio": path, "speaker_id": speaker_id, "chapter_id": chapter_id, "file": file, "id": audio} + + # correct audio path + dataset = dataset.map(extract_speaker_id_and_format_path, input_columns=["audio", "split"], num_proc=args.cpu_num_workers, remove_columns=["split"]) + dataset = dataset.cast_column("audio", Audio()) + + print(dataset) + print(dataset["dev"][0]) + + print("Embed table storage") + + # load_dataset(...) + format = dataset["train"].format + dataset = dataset.with_format("arrow") + dataset = dataset.map(embed_table_storage, batched=True, num_proc=args.cpu_num_workers) + dataset = dataset.with_format(**format) + + + dataset.save_to_disk(args.output_dir, num_proc=args.cpu_num_workers) + + if args.repo_id: + pushed = False + while not pushed: + try: + dataset.push_to_hub(args.repo_id, args.configuration, revision="refs/pr/15") + pushed = True + except: + pass diff --git a/dutch/1_hours-00000-of-00001.parquet b/dutch/1_hours-00000-of-00001.parquet new file mode 100644 index 0000000000000000000000000000000000000000..c0496bcaee6f86e15313852c2243879b4a939cf8 --- /dev/null +++ b/dutch/1_hours-00000-of-00001.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1ba5b95170c0452a71580c730104d42fce130ef291922fa5bec8c94abd4b24bb +size 15398863 diff --git a/dutch/9_hours-00000-of-00001.parquet 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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 - 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