Datasets:
Tasks:
Automatic Speech Recognition
Languages:
Arabic
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
crowdsourced
Annotations Creators:
expert-generated
Source Datasets:
original
Tags:
License:
# 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 | |
"""Arabic Speech Corpus""" | |
import os | |
import datasets | |
from datasets.tasks import AutomaticSpeechRecognition | |
_CITATION = """\ | |
@phdthesis{halabi2016modern, | |
title={Modern standard Arabic phonetics for speech synthesis}, | |
author={Halabi, Nawar}, | |
year={2016}, | |
school={University of Southampton} | |
} | |
""" | |
_DESCRIPTION = """\ | |
This Speech corpus has been developed as part of PhD work carried out by Nawar Halabi at the University of Southampton. | |
The corpus was recorded in south Levantine Arabic | |
(Damascian accent) using a professional studio. Synthesized speech as an output using this corpus has produced a high quality, natural voice. | |
Note that in order to limit the required storage for preparing this dataset, the audio | |
is stored in the .flac format and is not converted to a float32 array. To convert, the audio | |
file to a float32 array, please make use of the `.map()` function as follows: | |
```python | |
import soundfile as sf | |
def map_to_array(batch): | |
speech_array, _ = sf.read(batch["file"]) | |
batch["speech"] = speech_array | |
return batch | |
dataset = dataset.map(map_to_array, remove_columns=["file"]) | |
``` | |
""" | |
_URL = "http://en.arabicspeechcorpus.com/arabic-speech-corpus.zip" | |
class ArabicSpeechCorpusConfig(datasets.BuilderConfig): | |
"""BuilderConfig for ArabicSpeechCorpu.""" | |
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(ArabicSpeechCorpusConfig, self).__init__(version=datasets.Version("2.1.0", ""), **kwargs) | |
class ArabicSpeechCorpus(datasets.GeneratorBasedBuilder): | |
"""ArabicSpeechCorpus dataset.""" | |
BUILDER_CONFIGS = [ | |
ArabicSpeechCorpusConfig(name="clean", description="'Clean' speech."), | |
] | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"file": datasets.Value("string"), | |
"text": datasets.Value("string"), | |
"audio": datasets.Audio(sampling_rate=48_000), | |
"phonetic": datasets.Value("string"), | |
"orthographic": datasets.Value("string"), | |
} | |
), | |
supervised_keys=("file", "text"), | |
homepage=_URL, | |
citation=_CITATION, | |
task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")], | |
) | |
def _split_generators(self, dl_manager): | |
archive_path = dl_manager.download_and_extract(_URL) | |
archive_path = os.path.join(archive_path, "arabic-speech-corpus") | |
return [ | |
datasets.SplitGenerator(name="train", gen_kwargs={"archive_path": archive_path}), | |
datasets.SplitGenerator(name="test", gen_kwargs={"archive_path": os.path.join(archive_path, "test set")}), | |
] | |
def _generate_examples(self, archive_path): | |
"""Generate examples from a Librispeech archive_path.""" | |
lab_dir = os.path.join(archive_path, "lab") | |
wav_dir = os.path.join(archive_path, "wav") | |
if "test set" in archive_path: | |
phonetic_path = os.path.join(archive_path, "phonetic-transcript.txt") | |
else: | |
phonetic_path = os.path.join(archive_path, "phonetic-transcipt.txt") | |
orthographic_path = os.path.join(archive_path, "orthographic-transcript.txt") | |
phonetics = {} | |
orthographics = {} | |
with open(phonetic_path, "r", encoding="utf-8") as f: | |
for line in f: | |
wav_file, phonetic = line.split('"')[1::2] | |
phonetics[wav_file] = phonetic | |
with open(orthographic_path, "r", encoding="utf-8") as f: | |
for line in f: | |
wav_file, orthographic = line.split('"')[1::2] | |
orthographics[wav_file] = orthographic | |
for _id, lab_name in enumerate(sorted(os.listdir(lab_dir))): | |
lab_path = os.path.join(lab_dir, lab_name) | |
lab_text = open(lab_path, "r", encoding="utf-8").read() | |
wav_name = lab_name[:-4] + ".wav" | |
wav_path = os.path.join(wav_dir, wav_name) | |
example = { | |
"file": wav_path, | |
"audio": wav_path, | |
"text": lab_text, | |
"phonetic": phonetics[wav_name], | |
"orthographic": orthographics[wav_name], | |
} | |
yield str(_id), example | |