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-*.bz2 filter=lfs diff=lfs merge=lfs -text
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-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
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-*tfevents* filter=lfs diff=lfs merge=lfs -text
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-*.pcm filter=lfs diff=lfs merge=lfs -text
-*.sam filter=lfs diff=lfs merge=lfs -text
-*.raw filter=lfs diff=lfs merge=lfs -text
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data/test-00000-of-00001-112f39d2f116a22b.parquet → Jzuluaga--atco2_corpus_1h/parquet-test.parquet

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README.md

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----
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: audio
-    dtype:
-      audio:
-        sampling_rate: 16000
-  - name: text
-    dtype: string
-  - name: segment_start_time
-    dtype: float32
-  - name: segment_end_time
-    dtype: float32
-  - name: duration
-    dtype: float32
-  splits:
-  - name: test
-    num_bytes: 113872168.0
-    num_examples: 871
-  download_size: 113467762
-  dataset_size: 113872168.0
-tags:
-- audio
-- automatic-speech-recognition
-- en-atc
-- en
-- noisy-speech-recognition
-- speech-recognition
-task_categories:
-- automatic-speech-recognition  
-language:
-- en
-multilinguality:
-- monolingual
----
-
-# Dataset Card for ATCO2 test set corpus (1hr set)
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages and Other Details](#languages-and-other-details)
-- [Dataset Structure](#dataset-structure)
-  - [Data Fields](#data-fields)
-- [Additional Information](#additional-information)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-
-
-## Dataset Description
-- **Homepage:** [ATCO2 project homepage](https://www.atco2.org/)
-- **Repository:** [ATCO2 corpus](https://github.com/idiap/atco2-corpus)
-- **Paper:** [ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications](https://arxiv.org/abs/2211.04054)
-
-### Dataset Summary
-
-ATCO2 project aims at developing a unique platform allowing to collect, organize and pre-process air-traffic control (voice communication) data from air space. This project has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreement No 864702. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Clean Sky 2 JU members other than the Union.
-
-The project collected the real-time voice communication between air-traffic controllers and pilots available either directly through publicly accessible radio frequency channels or indirectly from air-navigation service providers (ANSPs). In addition to the voice communication data, contextual information is available in a form of metadata (i.e. surveillance data). The dataset consists of two distinct packages:
-
-- A corpus of 5000+ hours (pseudo-transcribed) of air-traffic control speech collected across different airports (Sion, Bern, Zurich, etc.) in .wav format for speech recognition. Speaker distribution is 90/10% between males and females and the group contains native and non-native speakers of English.
-- A corpus of 4 hours (transcribed) of air-traffic control speech collected across different airports (Sion, Bern, Zurich, etc.) in .wav format for speech recognition. Speaker distribution is 90/10% between males and females and the group contains native and non-native speakers of English. This corpus has been transcribed with orthographic information in XML format with speaker noise information, SNR values and others. Read Less
-- A free sample of the 4 hours transcribed data is in [ATCO2 project homepage](https://www.atco2.org/data)
-
-### Supported Tasks and Leaderboards
-
-- `automatic-speech-recognition`. Already adapted/fine-tuned models are available here --> [Wav2Vec 2.0 LARGE mdel](https://huggingface.co/Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc-and-atcosim).
-
-### Languages and other details
-
-The text and the recordings are in English. For more information see Table 3 and Table 4 of [ATCO2 corpus paper](https://arxiv.org/abs/2211.04054)
-
-## Dataset Structure
-
-### Data Fields
-
-- `id (string)`: a string of recording identifier for each example, corresponding to its.
-- `audio (audio)`: audio data for the given ID
-- `text (string)`: transcript of the file already normalized. Follow these repositories for more details [w2v2-air-traffic](https://github.com/idiap/w2v2-air-traffic) and [bert-text-diarization-atc](https://github.com/idiap/bert-text-diarization-atc)
-- `segment_start_time (float32)`: segment start time (normally 0)
-- `segment_end_time (float32): segment end time
-- `duration (float32)`: duration of the recording, compute as segment_end_time - segment_start_time
-
-## Additional Information
-
-### Licensing Information
-
-The licensing status of the ATCO2-test-set-1h corpus is in the file **ATCO2-ASRdataset-v1_beta - End-User Data Agreement** in the data folder. Download the data in [ATCO2 project homepage](https://www.atco2.org/data)
-
-### Citation Information
-
-Contributors who prepared, processed, normalized and uploaded the dataset in HuggingFace:
-
-```
-@article{zuluaga2022how,
-    title={How Does Pre-trained Wav2Vec2. 0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications},
-    author={Zuluaga-Gomez, Juan and Prasad, Amrutha and Nigmatulina, Iuliia and Sarfjoo, Saeed and others},
-    journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar},
-    year={2022}
-  }
-@article{zuluaga2022bertraffic,
-  title={BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications},
-  author={Zuluaga-Gomez, Juan and Sarfjoo, Seyyed Saeed and Prasad, Amrutha and others},
-  journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar},
-  year={2022}
-  }
-@article{zuluaga2022atco2,
-  title={ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications},
-  author={Zuluaga-Gomez, Juan and Vesel{\`y}, Karel and Sz{\"o}ke, Igor and Motlicek, Petr and others},
-  journal={arXiv preprint arXiv:2211.04054},
-  year={2022}
-}
-```
-

atc_data_loader.py

@@ -1,282 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-#
-# SPDX-FileCopyrightText: Copyright © <2022> Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-FileContributor: Juan Zuluaga-Gomez <jzuluaga@idiap.ch>
-#
-# SPDX-License-Identifier: MIT-License
-
-"""\
-Script for loading air traffic control (ATC) speech datasets for automatic speech recognition (ASR).
-This script has been designed for ATC datasets that are in Kaldi format
-
-Required files: text, wav.scp and segments files
-
-- Databases
-    - Training: 
-        - ATCOSIM, LDC-ATCC or, UWB-ATCC corpora.
-    - Testing:
-        - ATCO2-test-set-1h or 4h, LDC-ATCC or, UWB-ATCC corpora.
-"""
-
-import os
-import re
-
-import datasets
-import numpy as np
-import soundfile as sf
-from datasets.tasks import AutomaticSpeechRecognition
-
-_CITATION = """\
-@article{zuluaga2022atco2,
-  title={ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications},
-  author={Zuluaga-Gomez, Juan and Vesel{\'y}, Karel and Sz{\"o}ke, Igor and Motlicek, Petr and others},
-  journal={arXiv preprint arXiv:2211.04054},
-  year={2022}
-}    
-@article{zuluaga2022does,
-  title={How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications},
-  author={Zuluaga-Gomez, Juan and Prasad, Amrutha and Nigmatulina, Iuliia and others},
-  journal={2022 IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar},
-  year={2022}
-}
-@article{zuluagabertraffic,
-  title={BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications (submitted to @ SLT-2022)},
-  author={Zuluaga-Gomez, Juan and Sarfjoo, Seyyed Saeed and Prasad, Amrutha and others},
-  journal={2022 IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar},
-  year={2022}
-}
-"""
-
-_DESCRIPTION = """\
-ATC speech DATASET. This DataLoader works with data in Kaldi format.
-    - We use the following files: text, segments and wav.scp 
-            - text --> utt_id transcript
-            - segments --> utt_id recording_id t_begin t_end
-            - wav.scp --> recording_id /path/to/wav/
-The default dataset is from ATCO2 project, a 1-hour sample: https://www.replaywell.com/atco2/download/ATCO2-ASRdataset-v1_beta.tgz
-"""
-
-_DATA_URL = "http://catalog.elra.info/en-us/repository/browse/ELRA-S0484/"
-
-_HOMEPAGE = "https://github.com/idiap/w2v2-air-traffic"
-
-logger = datasets.logging.get_logger(__name__)
-
-# Our models work with audio data at 16kHZ,
-_SAMPLING_RATE = int(16000)
-
-
-class ATCDataASRConfig(datasets.BuilderConfig):
-    """BuilderConfig for air traffic control datasets."""
-
-    def __init__(self, **kwargs):
-        """
-        Args:
-          data_dir: `string`, the path to the folder containing the files required to read: json or wav.scp
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(ATCDataASRConfig, self).__init__(**kwargs)
-
-
-class ATCDataASR(datasets.GeneratorBasedBuilder):
-
-    DEFAULT_WRITER_BATCH_SIZE = 256
-    DEFAULT_CONFIG_NAME = "all"
-    BUILDER_CONFIGS = [
-        # TRAIN, DEV AND TEST DATASETS
-        ATCDataASRConfig(name="train", description="ATC train dataset."),
-        ATCDataASRConfig(name="dev", description="ATC dev dataset."),
-        ATCDataASRConfig(name="test", description="ATC test dataset."),
-        # UNSUPERVISED DATASETS
-        ATCDataASRConfig(name="unsupervised", description="ATC unsupervised dataset."),
-    ]
-
-    # provide some information about the Dataset we just gathered
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "file": datasets.Value("string"),
-                    "audio": datasets.features.Audio(sampling_rate=_SAMPLING_RATE),
-                    "text": datasets.Value("string"),
-                    "segment_start_time": datasets.Value("float"),
-                    "segment_end_time": datasets.Value("float"),
-                    "duration": datasets.Value("float"),
-                }
-            ),
-            supervised_keys=("audio", "text"),
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-            task_templates=[
-                AutomaticSpeechRecognition(
-                    audio_column="audio", transcription_column="text"
-                )
-            ],
-        )
-
-    def _split_generators(self, dlmanager):
-        """Returns SplitGenerators."""
-
-        split = self.config.name
-
-        # UNSUPERVISED set (used only for decoding)
-        if "unsupervised" in split:
-            split_name = datasets.Split.TEST
-        elif "test" in split or "dev" in split or "dummy" in split:
-            split_name = datasets.Split.TEST
-        # The last option left is: Train set
-        else:
-            split_name = datasets.Split.TRAIN
-
-        # you need to pass a data directory where the Kaldi folder is stored
-        filepath = self.config.data_dir
-
-        return [
-            datasets.SplitGenerator(
-                name=split_name,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": filepath,
-                    "split": split,
-                },
-            )
-        ]
-
-    def _generate_examples(self, filepath, split):
-        """You need to pass a path with the kaldi data, the folder should have
-        audio: wav.scp,
-        transcripts: text,
-        timing information: segments
-        """
-
-        logger.info("Generating examples located in: %s", filepath)
-
-        text_file = os.path.join(filepath, "text")
-        wavscp = os.path.join(filepath, "wav.scp")
-        segments = os.path.join(filepath, "segments")
-
-        id_ = ""
-        text_dict, wav_dict = {}, {}
-        segments_dict, utt2wav_id = {}, {}
-
-        line = 0
-        # get the text file
-        with open(text_file) as text_f:
-            for line in text_f:
-                if len(line.split(" ")) > 1:
-                    id_, transcript = line.split(" ", maxsplit=1)
-                    transcript = _remove_special_characters(transcript)
-                    if len(transcript.split(" ")) == 0:
-                        continue
-                    if len(transcript) < 2:
-                        continue
-                    text_dict[id_] = transcript
-                else:  # line is empty
-                    # if unsupervised set, then it's normal. else, continue
-                    if not "test_unsup" in self.config.name:
-                        continue
-                    id_ = line.rstrip().split(" ")[0]
-                    text_dict[id_] = ""
-
-        # get wav.scp and load data into memory
-        with open(wavscp) as text_f:
-            for line in text_f:
-                if line:
-                    if len(line.split()) < 2:
-                        continue
-                    id_, wavpath = line.split(" ", maxsplit=1)
-                    # only selects the part that ends of wav, flac or sph
-                    wavpath = [
-                        x
-                        for x in wavpath.split(" ")
-                        if ".wav" in x or ".WAV" in x or ".flac" in x or ".sph" in x
-                    ][0].rstrip()
-
-                    # make the output
-                    segment, sampling_rate = sf.read(wavpath, dtype=np.int16)
-                    wav_dict[id_] = [wavpath.rstrip(), segment, sampling_rate]
-
-        # get segments dictionary
-        with open(segments) as text_f:
-            for line in text_f:
-                if line:
-                    if len(line.split()) < 4:
-                        continue
-                    id_, wavid_, start, end = line.rstrip().split(" ")
-                    segments_dict[id_] = start.rstrip(), end.rstrip()
-                    utt2wav_id[id_] = wavid_
-
-        for rec_id, text in text_dict.items():
-            if rec_id in utt2wav_id and rec_id in segments_dict:
-
-                # get audio data from memory and the path of the file
-                wavpath, segment, sampling_rate = wav_dict[utt2wav_id[rec_id]]
-                # get timing information
-                seg_start, seg_end = segments_dict[rec_id]
-                seg_start, seg_end = float(seg_start), float(seg_end)
-                duration = round((seg_end - seg_start), 3)
-
-                # get the samples, bytes, already cropping by segment,
-                samples = _extract_audio_segment(
-                    segment, sampling_rate, float(seg_start), float(seg_end)
-                )
-
-                # output data for given dataset
-                example = {
-                    "audio": {
-                        "path": wavpath,
-                        "array": samples,
-                        "sampling_rate": sampling_rate,
-                    },
-                    "id": rec_id,
-                    "file": wavpath,
-                    "text": text,
-                    "segment_start_time": format(float(seg_start), ".3f"),
-                    "segment_end_time": format(float(seg_end), ".3f"),
-                    "duration": format(float(duration), ".3f"),
-                }
-
-                yield rec_id, example
-
-
-def _remove_special_characters(text):
-    """Function to remove some special chars/symbols from the given transcript"""
-
-    text = text.split(" ")
-    # first remove words between [] and <>
-    text = " ".join(
-        [
-            x
-            for x in text
-            if "[" not in x and "]" not in x and "<" not in x and ">" not in x
-        ]
-    )
-
-    # regex with predifined symbols to ignore/remove,
-    chars_to_ignore_regex2 = '[\{\[\]\<\>\/\,\?\.\!\u00AC\;\:"\\%\\\]|[0-9]'
-
-    text = re.sub(chars_to_ignore_regex2, "", text).lower()
-    sentence = text.replace("\u2013", "-")
-    sentence = sentence.replace("\u2014", "-")
-    sentence = sentence.replace("\u2018", "'")
-    sentence = sentence.replace("\u201C", "")
-    sentence = sentence.replace("\u201D", "")
-    sentence = sentence.replace("ñ", "n")
-    sentence = sentence.replace(" - ", " ")
-    sentence = sentence.replace("-", "")
-    sentence = sentence.replace("'", " ")
-    
-    return sentence.lower().rstrip()
-
-
-def _extract_audio_segment(segment, sampling_rate, start_sec, end_sec):
-    """Extracts segment of audio samples (as an ndarray) from the given segment."""
-    # The dataset only contains mono audio.
-    start_sample = int(start_sec * sampling_rate)
-    end_sample = min(int(end_sec * sampling_rate), segment.shape[0])
-    samples = segment[start_sample:end_sample]
-    return samples