Datasets:
Tasks:
Automatic Speech Recognition
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
License:
updating the repo with the loader script
Browse files- atc_data_loader.py +274 -0
atc_data_loader.py
ADDED
@@ -0,0 +1,274 @@
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1 |
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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#
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# SPDX-FileCopyrightText: Copyright © <2022> Idiap Research Institute <[email protected]>
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#
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# SPDX-FileContributor: Juan Zuluaga-Gomez <[email protected]>
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#
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# SPDX-License-Identifier: MIT-License
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"""\
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Script for loading air traffic control (ATC) speech datasets for automatic speech recognition (ASR).
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This script has been designed for ATC datasets that are in Kaldi format
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Required files: text, wav.scp and segments files
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- Databases
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- Training:
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- ATCOSIM, LDC-ATCC and, UWB-ATCC corpora.
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- Testing:
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- ATCO2-test-set, ATCOSIM, LDC-ATCC and, UWB-ATCC corpora.
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"""
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import os
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import re
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import datasets
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import numpy as np
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import soundfile as sf
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from datasets.tasks import AutomaticSpeechRecognition
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+
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_CITATION = """\
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@article{zuluaga2022does,
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title={How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications},
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author={Zuluaga-Gomez, Juan and Prasad, Amrutha and Nigmatulina, Iuliia and Sarfjoo, Saeed and Motlicek, Petr and Kleinert, Matthias and Helmke, Hartmut and Ohneiser, Oliver and Zhan, Qingran},
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journal={2022 IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar},
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year={2022}
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}
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@article{zuluagabertraffic,
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title={BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications (submitted to @ SLT-2022)},
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author={Zuluaga-Gomez, Juan and Sarfjoo, Seyyed Saeed and Prasad, Amrutha and Nigmatulina, Iuliia and Motlicek, Petr and Ohneiser, Oliver and Helmke, Hartmut},
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journal={2022 IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar},
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year={2022}
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}
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"""
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_DESCRIPTION = """\
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ATC speech DATASET. This DataLoader works with data in Kaldi format.
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- We use the following files: text, segments and wav.scp
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- text --> utt_id transcript
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- segments --> utt_id recording_id t_begin t_end
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- wav.scp --> recording_id /path/to/wav/
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The default dataset is from ATCO2 project, a 1-hour sample: https://www.replaywell.com/atco2/download/ATCO2-ASRdataset-v1_beta.tgz
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"""
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_DATA_URL = "http://catalog.elra.info/en-us/repository/browse/ELRA-S0484/"
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_HOMEPAGE = "https://github.com/idiap/w2v2-air-traffic"
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logger = datasets.logging.get_logger(__name__)
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# Our models work with audio data at 16kHZ,
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_SAMPLING_RATE = int(16000)
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class ATCDataASRConfig(datasets.BuilderConfig):
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"""BuilderConfig for air traffic control datasets."""
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def __init__(self, **kwargs):
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"""
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Args:
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data_dir: `string`, the path to the folder containing the files required to read: json or wav.scp
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**kwargs: keyword arguments forwarded to super.
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"""
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super(ATCDataASRConfig, self).__init__(**kwargs)
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+
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+
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class ATCDataASR(datasets.GeneratorBasedBuilder):
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DEFAULT_WRITER_BATCH_SIZE = 256
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DEFAULT_CONFIG_NAME = "all"
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BUILDER_CONFIGS = [
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# TRAIN, DEV AND TEST DATASETS
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ATCDataASRConfig(name="train", description="ATC train dataset."),
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ATCDataASRConfig(name="dev", description="ATC dev dataset."),
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ATCDataASRConfig(name="test", description="ATC test dataset."),
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# UNSUPERVISED DATASETS
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ATCDataASRConfig(name="unsupervised", description="ATC unsupervised dataset."),
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]
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# provide some information about the Dataset we just gathered
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"id": datasets.Value("string"),
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"file": datasets.Value("string"),
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"audio": datasets.features.Audio(sampling_rate=_SAMPLING_RATE),
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"text": datasets.Value("string"),
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"segment_start_time": datasets.Value("float"),
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"segment_end_time": datasets.Value("float"),
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"duration": datasets.Value("float"),
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}
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),
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supervised_keys=("audio", "text"),
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homepage=_HOMEPAGE,
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citation=_CITATION,
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task_templates=[
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AutomaticSpeechRecognition(
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audio_column="audio", transcription_column="text"
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)
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],
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)
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+
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def _split_generators(self, dlmanager):
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"""Returns SplitGenerators."""
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+
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split = self.config.name
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+
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# UNSUPERVISED set (used only for decoding)
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+
if "unsupervised" in split:
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split_name = datasets.Split.TEST
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elif "test" in split or "dev" in split or "dummy" in split:
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split_name = datasets.Split.TEST
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# The last option left is: Train set
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else:
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split_name = datasets.Split.TRAIN
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# you need to pass a data directory where the Kaldi folder is stored
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filepath = self.config.data_dir
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return [
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datasets.SplitGenerator(
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name=split_name,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": filepath,
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"split": split,
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},
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)
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]
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def _generate_examples(self, filepath, split):
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"""You need to pass a path with the kaldi data, the folder should have
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audio: wav.scp,
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transcripts: text,
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timing information: segments
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"""
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logger.info("Generating examples located in: %s", filepath)
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text_file = os.path.join(filepath, "text")
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wavscp = os.path.join(filepath, "wav.scp")
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segments = os.path.join(filepath, "segments")
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id_ = ""
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text_dict, wav_dict = {}, {}
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segments_dict, utt2wav_id = {}, {}
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line = 0
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# get the text file
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with open(text_file) as text_f:
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for line in text_f:
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if len(line.split(" ")) > 1:
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id_, transcript = line.split(" ", maxsplit=1)
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transcript = _remove_special_characters(transcript)
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if len(transcript.split(" ")) == 0:
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continue
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if len(transcript) < 2:
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continue
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text_dict[id_] = transcript
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else: # line is empty
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# if unsupervised set, then it's normal. else, continue
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if not "test_unsup" in self.config.name:
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continue
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id_ = line.rstrip().split(" ")[0]
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text_dict[id_] = ""
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+
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# get wav.scp and load data into memory
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with open(wavscp) as text_f:
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for line in text_f:
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if line:
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if len(line.split()) < 2:
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continue
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id_, wavpath = line.split(" ", maxsplit=1)
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# only selects the part that ends of wav, flac or sph
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wavpath = [
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x
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for x in wavpath.split(" ")
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if ".wav" in x or ".WAV" in x or ".flac" in x or ".sph" in x
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][0].rstrip()
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+
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# make the output
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segment, sampling_rate = sf.read(wavpath, dtype=np.int16)
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wav_dict[id_] = [wavpath.rstrip(), segment, sampling_rate]
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+
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# get segments dictionary
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with open(segments) as text_f:
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for line in text_f:
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if line:
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if len(line.split()) < 4:
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continue
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id_, wavid_, start, end = line.rstrip().split(" ")
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segments_dict[id_] = start.rstrip(), end.rstrip()
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utt2wav_id[id_] = wavid_
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+
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for rec_id, text in text_dict.items():
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if rec_id in utt2wav_id and rec_id in segments_dict:
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+
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# get audio data from memory and the path of the file
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wavpath, segment, sampling_rate = wav_dict[utt2wav_id[rec_id]]
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# get timing information
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seg_start, seg_end = segments_dict[rec_id]
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seg_start, seg_end = float(seg_start), float(seg_end)
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duration = round((seg_end - seg_start), 3)
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# get the samples, bytes, already cropping by segment,
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samples = _extract_audio_segment(
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segment, sampling_rate, float(seg_start), float(seg_end)
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)
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# output data for given dataset
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example = {
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"audio": {
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"path": wavpath,
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"array": samples,
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"sampling_rate": sampling_rate,
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},
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"id": rec_id,
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"file": wavpath,
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"text": text,
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"segment_start_time": format(float(seg_start), ".3f"),
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"segment_end_time": format(float(seg_end), ".3f"),
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"duration": format(float(duration), ".3f"),
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}
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+
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yield rec_id, example
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+
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+
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def _remove_special_characters(text):
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241 |
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"""Function to remove some special chars/symbols from the given transcript"""
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+
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text = text.split(" ")
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# first remove words between [] and <>
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text = " ".join(
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+
[
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x for x in text
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if "[" not in x and "]" not in x and "<" not in x and ">" not in x
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+
]
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+
)
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+
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# regex with predifined symbols to ignore/remove,
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chars_to_ignore_regex2 = '[\{\[\]\<\>\/\,\?\.\!\u00AC\;\:"\\%\\\]|[0-9]'
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+
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text = re.sub(chars_to_ignore_regex2, "", text).lower()
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+
sentence = text.replace("\u2013", "-")
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+
sentence = sentence.replace("\u2014", "-")
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sentence = sentence.replace("\u2018", "'")
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sentence = sentence.replace("\u201C", "")
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sentence = sentence.replace("\u201D", "")
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sentence = sentence.replace("ñ", "n")
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sentence = sentence.replace(" - ", " ")
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sentence = sentence.replace("-", "")
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sentence = sentence.replace("'", " ")
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return sentence.lower().rstrip()
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+
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+
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def _extract_audio_segment(segment, sampling_rate, start_sec, end_sec):
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"""Extracts segment of audio samples (as an ndarray) from the given segment."""
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# The dataset only contains mono audio.
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start_sample = int(start_sec * sampling_rate)
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end_sample = min(int(end_sec * sampling_rate), segment.shape[0])
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samples = segment[start_sample:end_sample]
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+
return samples
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