# 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 """TIMIT automatic speech recognition dataset.""" import os from pathlib import Path import datasets from datasets.tasks import AutomaticSpeechRecognition _CITATION = """\ @inproceedings{ title={TIMIT Acoustic-Phonetic Continuous Speech Corpus}, author={Garofolo, John S., et al}, ldc_catalog_no={LDC93S1}, DOI={https://doi.org/10.35111/17gk-bn40}, journal={Linguistic Data Consortium, Philadelphia}, year={1983} } """ _DESCRIPTION = """\ The TIMIT corpus of reading speech has been developed to provide speech data for acoustic-phonetic research studies and for the evaluation of automatic speech recognition systems. TIMIT contains high quality recordings of 630 individuals/speakers with 8 different American English dialects, with each individual reading upto 10 phonetically rich sentences. More info on TIMIT dataset can be understood from the "README" which can be found here: https://catalog.ldc.upenn.edu/docs/LDC93S1/readme.txt """ _HOMEPAGE = "https://catalog.ldc.upenn.edu/LDC93S1" class TimitASRConfig(datasets.BuilderConfig): """BuilderConfig for TimitASR.""" 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(TimitASRConfig, self).__init__(version=datasets.Version("2.0.1", ""), **kwargs) class TimitASR(datasets.GeneratorBasedBuilder): """TimitASR dataset.""" BUILDER_CONFIGS = [TimitASRConfig(name="clean", description="'Clean' speech.")] @property def manual_download_instructions(self): return ( "To use TIMIT you have to download it manually. " "Please create an account and download the dataset from https://catalog.ldc.upenn.edu/LDC93S1 \n" "Then extract all files in one folder and load the dataset with: " "`datasets.load_dataset('timit_asr', data_dir='path/to/folder/folder_name')`" ) 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"), "phonetic_detail": datasets.Sequence( { "start": datasets.Value("int64"), "stop": datasets.Value("int64"), "utterance": datasets.Value("string"), } ), "word_detail": datasets.Sequence( { "start": datasets.Value("int64"), "stop": datasets.Value("int64"), "utterance": datasets.Value("string"), } ), "dialect_region": datasets.Value("string"), "sentence_type": datasets.Value("string"), "speaker_id": datasets.Value("string"), "id": datasets.Value("string"), } ), supervised_keys=("file", "text"), homepage=_HOMEPAGE, citation=_CITATION, task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")], ) def _split_generators(self, dl_manager): data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir)) if not os.path.exists(data_dir): raise FileNotFoundError( f"{data_dir} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('timit_asr', data_dir=...)` that includes files unzipped from the TIMIT zip. Manual download instructions: {self.manual_download_instructions}" ) return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"split": "train", "data_dir": data_dir}), datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"split": "test", "data_dir": data_dir}), ] def _generate_examples(self, split, data_dir): """Generate examples from TIMIT archive_path based on the test/train csv information.""" # Iterating the contents of the data to extract the relevant information wav_paths = sorted(Path(data_dir).glob(f"**/{split}/**/*.wav")) wav_paths = wav_paths if wav_paths else sorted(Path(data_dir).glob(f"**/{split.upper()}/**/*.WAV")) for key, wav_path in enumerate(wav_paths): # extract transcript txt_path = with_case_insensitive_suffix(wav_path, ".txt") with txt_path.open(encoding="utf-8") as op: transcript = " ".join(op.readlines()[0].split()[2:]) # first two items are sample number # extract phonemes phn_path = with_case_insensitive_suffix(wav_path, ".phn") with phn_path.open(encoding="utf-8") as op: phonemes = [ { "start": i.split(" ")[0], "stop": i.split(" ")[1], "utterance": " ".join(i.split(" ")[2:]).strip(), } for i in op.readlines() ] # extract words wrd_path = with_case_insensitive_suffix(wav_path, ".wrd") with wrd_path.open(encoding="utf-8") as op: words = [ { "start": i.split(" ")[0], "stop": i.split(" ")[1], "utterance": " ".join(i.split(" ")[2:]).strip(), } for i in op.readlines() ] dialect_region = wav_path.parents[1].name sentence_type = wav_path.name[0:2] speaker_id = wav_path.parents[0].name[1:] id_ = wav_path.stem example = { "file": str(wav_path), "audio": str(wav_path), "text": transcript, "phonetic_detail": phonemes, "word_detail": words, "dialect_region": dialect_region, "sentence_type": sentence_type, "speaker_id": speaker_id, "id": id_, } yield key, example def with_case_insensitive_suffix(path: Path, suffix: str): path = path.with_suffix(suffix.lower()) path = path if path.exists() else path.with_suffix(suffix.upper()) return path