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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
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