# 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-PC dataset module refered from LibriSpeech dataset module.""" import os import datasets import json _CITATION = { "librispeech": """\ @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} }""", "librispeech_pc": """\ @article{meister2023librispeechpc, title={LibriSpeech-PC: Benchmark for Evaluation of Punctuation and Capitalization Capabilities of end-to-end ASR Models}, author={A. Meister and M. Novikov and N. Karpov and E. Bakhturina and V. Lavrukhin and B. Ginsburg}, journal={arXiv preprint arXiv:2310.02943}, year={2023}, } """ } _DESCRIPTION = """\ Merge Librispeech audio files with punctuation and captalization restored transcripts from LibriSpeech-PC. I refered to the original LibriSpeech dataset module script from HuggingFace Datasets (https://huggingface.co/datasets/openslr/librispeech_asr). If you already have downloaded the LibriSpeech dataset via `load_dataset('openslr/librispeech_asr')`, the script will use the extracted audio files from the local directory and not download them twice. (only tested in my local environment though) """ _URL = "http://www.openslr.org/12" _DL_URL = "http://www.openslr.org/resources/12/" _URL_PC = "https://www.openslr.org/145" _DL_URL_PC = "https://www.openslr.org/resources/145/" _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", "transcript_pc": _DL_URL_PC + "manifests.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", "transcript_pc": _DL_URL_PC + "manifests.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", "transcript_pc": _DL_URL_PC + "manifests.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"), "text_raw": datasets.Value("string"), "text_normalized": datasets.Value("string"), "speaker_id": datasets.Value("int64"), "chapter_id": datasets.Value("int64"), "id": datasets.Value("string"), "duration": datasets.Value("float"), } ), supervised_keys=("file", "text"), homepage=_URL, citation=_CITATION, ) 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 {} # print(local_extracted_archive) # print(list(dl_manager.iter_archive(archive_path["transcript_pc"]))) transcript_pc_dir = local_extracted_archive.get("transcript_pc") 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"]), "transcript_pc_fname": os.path.join(transcript_pc_dir, "train-clean-100.json"), }, ), 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"]), "transcript_pc_fname": os.path.join(transcript_pc_dir, "train-clean-360.json"), }, ), ] 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"]), "transcript_pc_fname": os.path.join(transcript_pc_dir, "dev-clean.json"), }, ) ] 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"]), "transcript_pc_fname": os.path.join(transcript_pc_dir, "test-clean.json"), }, ) ] 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"]), "transcript_pc_fname": os.path.join(transcript_pc_dir, "train-other-500.json"), }, ) ] 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"]), "transcript_pc_fname": os.path.join(transcript_pc_dir, "dev-other.json"), }, ) ] 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"]), "transcript_pc_fname": os.path.join(transcript_pc_dir, "test-other.json"), }, ) ] 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"]), "transcript_pc_fname": os.path.join(transcript_pc_dir, "train-clean-100.json"), }, ), 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"]), "transcript_pc_fname": os.path.join(transcript_pc_dir, "train-clean-360.json"), }, ), 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"]), "transcript_pc_fname": os.path.join(transcript_pc_dir, "train-other-500.json"), }, ), ] 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"]), "transcript_pc_fname": os.path.join(transcript_pc_dir, "dev-clean.json"), }, ), 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"]), "transcript_pc_fname": os.path.join(transcript_pc_dir, "dev-other.json"), }, ), ] 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"]), "transcript_pc_fname": os.path.join(transcript_pc_dir, "test-clean.json"), }, ), 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"]), "transcript_pc_fname": os.path.join(transcript_pc_dir, "test-other.json"), }, ), ] return train_splits + dev_splits + test_splits def _generate_examples(self, files, local_extracted_archive, transcript_pc_fname): # original """Generate examples from a LibriSpeech archive_path.""" key, unseen = 0, 0 audio_data = {} transcripts = [] # Load transcripts from LibriSpeech-PC transcripts_pc = dict() with open(transcript_pc_fname, mode='r') as f: data = (f.read().splitlines()) data = [json.loads(d) for d in data] for d in data: _id = d['audio_filepath'].split("/")[-1][: -len(".flac")] del d['audio_filepath'] transcripts_pc.update( {_id: d} # keys in d : duration, text, text_raw ) os.makedirs("./unexisting_transcripts_id", exist_ok=True) try: os.remove(f"./unexisting_transcripts_id/{os.path.basename(transcript_pc_fname)[:-5]}.txt") except FileNotFoundError: pass 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_normalized": transcript, } ) if audio_data and len(audio_data) == len(transcripts): for transcript in transcripts: audio = {"path": transcript["file"], "bytes": audio_data[transcript["id"]]} transcript_pc = transcripts_pc.pop(transcript["id"], {}) if transcript_pc: yield key, {"audio": audio, **transcript, **transcript_pc} key += 1 else: with open(f"./unexisting_transcripts_id/{os.path.basename(transcript_pc_fname)[:-5]}.txt", mode='a') as log: log.write(f"{transcript['id']}\n") unseen += 1 audio_data = {} transcripts = [] print(f"{unseen} transcripts are dropped in LibriSpeech-PC dataset {os.path.basename(transcript_pc_fname)[:-5]} compared to LibriSpeech dataset.")