reborn-uasr_multilingual-librispeech-no-silence-100hr
/
reborn-uasr_multilingual-librispeech-no-silence-100hr.py
| # 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 | |
| """ | |
| Librispeech automatic speech recognition dataset for reproducing Reborn UASR results. | |
| Note that the silence in each audio has been removed by performing unsupervised VAD (https://github.com/zhenghuatan/rVADfast). | |
| We only process the 100-hour split from LibriSpeech 'train-clean-100' as the training split. | |
| """ | |
| 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} | |
| } | |
| @article{tan2020rvad, | |
| title={rVAD: An unsupervised segment-based robust voice activity detection method}, | |
| author={Tan, Zheng-Hua and Dehak, Najim and others}, | |
| journal={Computer speech \& language}, | |
| volume={59}, | |
| pages={1--21}, | |
| year={2020}, | |
| publisher={Elsevier} | |
| } | |
| @article{tseng2024reborn, | |
| title={REBORN: Reinforcement-Learned Boundary Segmentation with Iterative Training for Unsupervised ASR}, | |
| author={Tseng, Liang-Hsuan and Hu, En-Pei and Chiang, Cheng-Han and Tseng, Yuan and Lee, Hung-yi and Lee, Lin-shan and Sun, Shao-Hua}, | |
| journal={arXiv preprint arXiv:2402.03988}, | |
| year={2024} | |
| } | |
| """ | |
| _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 | |
| This dataset is the 100-hour subset of LibriSpeech 'train-clean-100' split, with silence removed. | |
| Additionally, all the dev and test sets are included for fair comparison and evaluation if needed. | |
| The dataset is prepared by the Reborn UASR team. | |
| Arxiv paper link: https://arxiv.org/abs/2402.03988 | |
| """ | |
| _URL = "http://www.openslr.org/12" | |
| _DL_URL_FORMAT = "data/{name}" | |
| class RebornLibrispeechConfig(datasets.BuilderConfig): | |
| """BuilderConfig for Reborn-Librispeech.""" | |
| def __init__(self, name, **kwargs): | |
| """ | |
| Args: | |
| name: `string`, name of dataset config (=language) | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(RebornLibrispeechConfig, self).__init__( | |
| version=datasets.Version("2.12.0", ""), name=name, **kwargs | |
| ) | |
| # relative path to full data inside a repo (for example `data/train-clean-100`) | |
| self.data_root_url = _DL_URL_FORMAT.format(name=name) | |
| self.metadata_root_url = self.data_root_url.replace("data", "metadata") | |
| class RebornLibrispeech(datasets.GeneratorBasedBuilder): | |
| """Multilingual Librispeech dataset.""" | |
| BUILDER_CONFIGS = [ | |
| RebornLibrispeechConfig(name="german", description="MLS 100hr German dataset without silence"), | |
| RebornLibrispeechConfig(name="french", description="MLS 100hr French dataset without silence"), | |
| RebornLibrispeechConfig(name="dutch", description="MLS 100hr Dutch dataset without silence"), | |
| RebornLibrispeechConfig(name="spanish", description="MLS 100hr Spanish dataset without silence"), | |
| RebornLibrispeechConfig(name="italian", description="MLS 100hr Italian dataset without silence"), | |
| RebornLibrispeechConfig(name="portuguese", description="MLS 100hr Portuguese dataset without silence"), | |
| ] | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "file": datasets.Value("string"), | |
| "audio": datasets.features.Audio(sampling_rate=16_000), | |
| "word": datasets.Value("string"), | |
| "phoneme": datasets.Value("string"), | |
| "speaker_id": datasets.Value("int64"), | |
| "chapter_id": datasets.Value("int64"), | |
| "id": datasets.Value("string"), | |
| } | |
| ), | |
| supervised_keys=("file", "phone"), | |
| homepage=_URL, | |
| citation=_CITATION, | |
| task_templates=None, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| metadata = dl_manager.download({ | |
| "train_100hr": f"{self.config.metadata_root_url}/train_100hr.tsv", | |
| "dev": f"{self.config.metadata_root_url}/dev.tsv", | |
| "test": f"{self.config.metadata_root_url}/test.tsv", | |
| "dev_small": f"{self.config.metadata_root_url}/dev_small.tsv", | |
| }) | |
| all_splits = [ | |
| "train_100hr", | |
| "dev", | |
| "test", | |
| ] | |
| audio_archives = {} | |
| for split in all_splits: | |
| audio_archives[split] = dl_manager.download( | |
| os.path.join(self.config.data_root_url, f"{split}.tar.gz") | |
| ) | |
| # (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={ | |
| "metadata_fpaths": [metadata["train_100hr"]], | |
| "audio_archives": [dl_manager.iter_archive(audio_archives["train_100hr"])], | |
| "local_extracted_archives": [local_extracted_archives.get("train_100hr")], | |
| } | |
| ), | |
| datasets.SplitGenerator( | |
| name="train.100hr", | |
| gen_kwargs={ | |
| "metadata_fpaths": [metadata["train_100hr"]], | |
| "audio_archives": [dl_manager.iter_archive(audio_archives["train_100hr"])], | |
| "local_extracted_archives": [local_extracted_archives.get("train_100hr")], | |
| } | |
| ), | |
| ] | |
| dev_splits = [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "metadata_fpath": [metadata["dev"]], | |
| "audio_archives": [dl_manager.iter_archive(audio_archives["dev"])], | |
| "local_extracted_archives": [local_extracted_archives.get("dev")], | |
| } | |
| ), | |
| datasets.SplitGenerator( | |
| name="dev", | |
| gen_kwargs={ | |
| "metadata_fpath": [metadata["dev"]], | |
| "audio_archives": [dl_manager.iter_archive(audio_archives["dev"])], | |
| "local_extracted_archives": [local_extracted_archives.get("dev")], | |
| } | |
| ), | |
| datasets.SplitGenerator( | |
| name="valid", | |
| gen_kwargs={ | |
| "metadata_fpath": [metadata["dev"]], | |
| "audio_archives": [dl_manager.iter_archive(audio_archives["dev"])], | |
| "local_extracted_archives": [local_extracted_archives.get("dev")], | |
| } | |
| ), | |
| datasets.SplitGenerator( | |
| name="dev.small", | |
| gen_kwargs={ | |
| "metadata_fpaths": [metadata["dev_small"]], | |
| "audio_archives": [dl_manager.iter_archive(audio_archives["dev"])], | |
| "local_extracted_archives": [local_extracted_archives.get("dev")], | |
| }, | |
| ), | |
| ] | |
| test_splits = [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "metadata_fpaths": [metadata["test"]], | |
| "audio_archives": [dl_manager.iter_archive(audio_archives["test"])], | |
| "local_extracted_archives": [local_extracted_archives.get("test")], | |
| } | |
| ), | |
| ] | |
| return train_splits + dev_splits + test_splits | |
| def _generate_examples(self, metadata_fpaths, audio_archives, local_extracted_archives): | |
| """Generate examples from a Multilingual LibriSpeech data dir.""" | |
| words, phones = dict(), dict() | |
| for metadata_fpath in metadata_fpaths: | |
| with open(metadata_fpath, "r", encoding="utf-8") as file: | |
| for line in file: | |
| audio_fpath, word, phone = line.strip().split("\t") | |
| audio_id = audio_fpath.split('/')[-1].split(".flac")[0] | |
| words[audio_id] = word | |
| phones[audio_id] = phone | |
| for archive_idx, audio_archive in enumerate(audio_archives): | |
| for audio_filename, file in audio_archive: | |
| audio_id = audio_filename.split('/')[-1].split(".flac")[0] | |
| speaker_id, chapter_id = (int(item) for item in audio_id.split("_")[:2]) | |
| word = words.get(audio_id, None) | |
| if word == None: | |
| continue | |
| local_audio_file_path = os.path.join( | |
| local_extracted_archives[archive_idx], audio_filename | |
| ) if local_extracted_archives[archive_idx] 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() | |
| }, | |
| "word": word, | |
| "phoneme": phones.get(audio_id, None), | |
| "speaker_id": speaker_id, | |
| "chapter_id": chapter_id, | |
| "id": audio_id | |
| } |