Datasets:
timit_asr

	# 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