Datasets:

timit-asr
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timit_asr

Tasks:

Automatic Speech Recognition

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English

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timit_asr / timit_asr.py

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	# 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."""

	from __future__ import absolute_import, division, print_function

	import os

	import pandas as pd

	import datasets


	_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
	"""

	_URL = "https://data.deepai.org/timit.zip"
	_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.")]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"file": datasets.Value("string"),
	"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,
	)

	def _split_generators(self, dl_manager):
	archive_path = dl_manager.download_and_extract(_URL)

	train_csv_path = os.path.join(archive_path, "train_data.csv")
	test_csv_path = os.path.join(archive_path, "test_data.csv")

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"data_info_csv": train_csv_path}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"data_info_csv": test_csv_path}),
	]

	def _generate_examples(self, data_info_csv):
	"""Generate examples from TIMIT archive_path based on the test/train csv information."""
	# Extract the archive path
	data_path = os.path.join(os.path.dirname(data_info_csv).strip(), "data")

	# Read the data info to extract rows mentioning about non-converted audio only
	data_info = pd.read_csv(data_info_csv, encoding="utf8")
	# making sure that the columns having no information about the file paths are removed
	data_info.dropna(subset=["path_from_data_dir"], inplace=True)

	# filter out only the required information for data preparation
	data_info = data_info.loc[(data_info["is_audio"]) & (~data_info["is_converted_audio"])]

	# Iterating the contents of the data to extract the relevant information
	for audio_idx in range(data_info.shape[0]):
	audio_data = data_info.iloc[0]

	# extract the path to audio
	wav_path = os.path.join(data_path, *(audio_data["path_from_data_dir"].split("/")))

	# extract transcript
	with open(wav_path.replace(".WAV", ".TXT"), "r", encoding="utf-8") as op:
	transcript = " ".join(op.readlines()[0].split()[2:]) # first two items are sample number

	# extract phonemes
	with open(wav_path.replace(".WAV", ".PHN"), "r", 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
	with open(wav_path.replace(".WAV", ".WRD"), "r", 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()
	]

	example = {
	"file": wav_path,
	"text": transcript,
	"phonetic_detail": phonemes,
	"word_detail": words,
	"dialect_region": audio_data["dialect_region"],
	"sentence_type": audio_data["filename"][0:2],
	"speaker_id": audio_data["speaker_id"],
	"id": audio_data["filename"].replace(".WAV", ""),
	}

	yield audio_idx, example