Datasets:
sanchit-gandhi
HF staff
[LibriSpeech] Fix dev split local_extracted_archive for 'all' config (#4904)
8cb4c64
| # 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 | |
| """Librispeech automatic speech recognition dataset.""" | |
| import os | |
| import datasets | |
| from datasets.tasks import AutomaticSpeechRecognition | |
| _CITATION = """\ | |
| @inproceedings{panayotov2015librispeech, | |
| title={Librispeech: an ASR corpus based on public domain audio books}, | |
| author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev}, | |
| booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on}, | |
| pages={5206--5210}, | |
| year={2015}, | |
| organization={IEEE} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz, | |
| prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read | |
| audiobooks from the LibriVox project, and has been carefully segmented and aligned.87 | |
| """ | |
| _URL = "http://www.openslr.org/12" | |
| _DL_URL = "http://www.openslr.org/resources/12/" | |
| _DL_URLS = { | |
| "clean": { | |
| "dev": _DL_URL + "dev-clean.tar.gz", | |
| "test": _DL_URL + "test-clean.tar.gz", | |
| "train.100": _DL_URL + "train-clean-100.tar.gz", | |
| "train.360": _DL_URL + "train-clean-360.tar.gz", | |
| }, | |
| "other": { | |
| "test": _DL_URL + "test-other.tar.gz", | |
| "dev": _DL_URL + "dev-other.tar.gz", | |
| "train.500": _DL_URL + "train-other-500.tar.gz", | |
| }, | |
| "all": { | |
| "dev.clean": _DL_URL + "dev-clean.tar.gz", | |
| "dev.other": _DL_URL + "dev-other.tar.gz", | |
| "test.clean": _DL_URL + "test-clean.tar.gz", | |
| "test.other": _DL_URL + "test-other.tar.gz", | |
| "train.clean.100": _DL_URL + "train-clean-100.tar.gz", | |
| "train.clean.360": _DL_URL + "train-clean-360.tar.gz", | |
| "train.other.500": _DL_URL + "train-other-500.tar.gz", | |
| }, | |
| } | |
| class LibrispeechASRConfig(datasets.BuilderConfig): | |
| """BuilderConfig for LibriSpeechASR.""" | |
| def __init__(self, **kwargs): | |
| """ | |
| Args: | |
| data_dir: `string`, the path to the folder containing the files in the | |
| downloaded .tar | |
| citation: `string`, citation for the data set | |
| url: `string`, url for information about the data set | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(LibrispeechASRConfig, self).__init__(version=datasets.Version("2.1.0", ""), **kwargs) | |
| class LibrispeechASR(datasets.GeneratorBasedBuilder): | |
| """Librispeech dataset.""" | |
| DEFAULT_WRITER_BATCH_SIZE = 256 | |
| DEFAULT_CONFIG_NAME = "all" | |
| BUILDER_CONFIGS = [ | |
| LibrispeechASRConfig(name="clean", description="'Clean' speech."), | |
| LibrispeechASRConfig(name="other", description="'Other', more challenging, speech."), | |
| LibrispeechASRConfig(name="all", description="Combined clean and other dataset."), | |
| ] | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "file": datasets.Value("string"), | |
| "audio": datasets.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=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")], | |
| ) | |
| def _split_generators(self, dl_manager): | |
| archive_path = dl_manager.download(_DL_URLS[self.config.name]) | |
| # (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files: | |
| local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {} | |
| if self.config.name == "clean": | |
| train_splits = [ | |
| datasets.SplitGenerator( | |
| name="train.100", | |
| gen_kwargs={ | |
| "local_extracted_archive": local_extracted_archive.get("train.100"), | |
| "files": dl_manager.iter_archive(archive_path["train.100"]), | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name="train.360", | |
| gen_kwargs={ | |
| "local_extracted_archive": local_extracted_archive.get("train.360"), | |
| "files": dl_manager.iter_archive(archive_path["train.360"]), | |
| }, | |
| ), | |
| ] | |
| dev_splits = [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "local_extracted_archive": local_extracted_archive.get("dev"), | |
| "files": dl_manager.iter_archive(archive_path["dev"]), | |
| }, | |
| ) | |
| ] | |
| test_splits = [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "local_extracted_archive": local_extracted_archive.get("test"), | |
| "files": dl_manager.iter_archive(archive_path["test"]), | |
| }, | |
| ) | |
| ] | |
| elif self.config.name == "other": | |
| train_splits = [ | |
| datasets.SplitGenerator( | |
| name="train.500", | |
| gen_kwargs={ | |
| "local_extracted_archive": local_extracted_archive.get("train.500"), | |
| "files": dl_manager.iter_archive(archive_path["train.500"]), | |
| }, | |
| ) | |
| ] | |
| dev_splits = [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "local_extracted_archive": local_extracted_archive.get("dev"), | |
| "files": dl_manager.iter_archive(archive_path["dev"]), | |
| }, | |
| ) | |
| ] | |
| test_splits = [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "local_extracted_archive": local_extracted_archive.get("test"), | |
| "files": dl_manager.iter_archive(archive_path["test"]), | |
| }, | |
| ) | |
| ] | |
| elif self.config.name == "all": | |
| train_splits = [ | |
| datasets.SplitGenerator( | |
| name="train.clean.100", | |
| gen_kwargs={ | |
| "local_extracted_archive": local_extracted_archive.get("train.clean.100"), | |
| "files": dl_manager.iter_archive(archive_path["train.clean.100"]), | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name="train.clean.360", | |
| gen_kwargs={ | |
| "local_extracted_archive": local_extracted_archive.get("train.clean.360"), | |
| "files": dl_manager.iter_archive(archive_path["train.clean.360"]), | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name="train.other.500", | |
| gen_kwargs={ | |
| "local_extracted_archive": local_extracted_archive.get("train.other.500"), | |
| "files": dl_manager.iter_archive(archive_path["train.other.500"]), | |
| }, | |
| ), | |
| ] | |
| dev_splits = [ | |
| datasets.SplitGenerator( | |
| name="validation.clean", | |
| gen_kwargs={ | |
| "local_extracted_archive": local_extracted_archive.get("dev.clean"), | |
| "files": dl_manager.iter_archive(archive_path["dev.clean"]), | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name="validation.other", | |
| gen_kwargs={ | |
| "local_extracted_archive": local_extracted_archive.get("dev.other"), | |
| "files": dl_manager.iter_archive(archive_path["dev.other"]), | |
| }, | |
| ), | |
| ] | |
| test_splits = [ | |
| datasets.SplitGenerator( | |
| name="test.clean", | |
| gen_kwargs={ | |
| "local_extracted_archive": local_extracted_archive.get("test.clean"), | |
| "files": dl_manager.iter_archive(archive_path["test.clean"]), | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name="test.other", | |
| gen_kwargs={ | |
| "local_extracted_archive": local_extracted_archive.get("test.other"), | |
| "files": dl_manager.iter_archive(archive_path["test.other"]), | |
| }, | |
| ), | |
| ] | |
| return train_splits + dev_splits + test_splits | |
| def _generate_examples(self, files, local_extracted_archive): | |
| """Generate examples from a LibriSpeech archive_path.""" | |
| key = 0 | |
| audio_data = {} | |
| transcripts = [] | |
| for path, f in files: | |
| if path.endswith(".flac"): | |
| id_ = path.split("/")[-1][: -len(".flac")] | |
| audio_data[id_] = f.read() | |
| elif path.endswith(".trans.txt"): | |
| for line in f: | |
| if line: | |
| line = line.decode("utf-8").strip() | |
| id_, transcript = line.split(" ", 1) | |
| audio_file = f"{id_}.flac" | |
| speaker_id, chapter_id = [int(el) for el in id_.split("-")[:2]] | |
| audio_file = ( | |
| os.path.join(local_extracted_archive, audio_file) | |
| if local_extracted_archive | |
| else audio_file | |
| ) | |
| transcripts.append( | |
| { | |
| "id": id_, | |
| "speaker_id": speaker_id, | |
| "chapter_id": chapter_id, | |
| "file": audio_file, | |
| "text": transcript, | |
| } | |
| ) | |
| if audio_data and len(audio_data) == len(transcripts): | |
| for transcript in transcripts: | |
| audio = {"path": transcript["file"], "bytes": audio_data[transcript["id"]]} | |
| yield key, {"audio": audio, **transcript} | |
| key += 1 | |
| audio_data = {} | |
| transcripts = [] | |