--- license: apache-2.0 language: en datasets: - Jzuluaga/atco2_corpus_1h tags: - text - token-classification - en-atc - en - generated_from_trainer - bert - ner-for-atc metrics: - Precision - Recall - Accuracy - F1 widget: - text: "csa two nine six startup approved mike current qnh one zero one eight time check one seven" - text: "swiss four eight seven november runway three one cleared for takeoff wind one three zero degrees seven knots" - text: "lufthansa five yankee victor runway one three clear to land wind zero seven zero degrees" - text: "austrian seven one zulu hello to you reduce one six zero knots" - text: "sky travel one nine two approaching holding point three one ready for departure" model-index: - name: bert-base-ner-atc-en-atco2-1h results: - task: type: token-classification name: ner dataset: type: Jzuluaga/atco2_corpus_1h name: ATCO2 corpus (Air Traffic Control Communications) config: test split: test metrics: - type: F1 value: 0.94 name: TEST F1 (callsign) verified: False - type: F1 value: 0.74 name: TEST F1 (command) verified: False - type: F1 value: 0.81 name: TEST F1 (value) verified: False --- # bert-base-ner-atc-en-atco2-1h This model allow to perform named-entity recognition (NER) on air traffic control communications data. We solve this challenge by performing token classification (NER) with a BERT model. We fine-tune a pretrained BERT model on the ner task. For instance, if you have the following transcripts/gold annotations: - **Utterance**: lufthansa three two five cleared to land runway three four left Could you tell what are the main entities in the communication? The desired output is shown below: - **Named-entity module output**: [call] lufthansa three two five [/call] [cmd] cleared to land [/cmd] [val] runway three four left [/val] This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the [atco2_corpus_1h](https://huggingface.co/datasets/Jzuluaga/atco2_corpus_1h). GitHub It achieves the following results on the development set: - Loss: 1.4282 - Precision: 0.6195 - Recall: 0.7071 - F1: 0.6604 - Accuracy: 0.8182 **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) Authors: Juan Zuluaga-Gomez, Karel Veselý, Igor Szöke, Petr Motlicek, Martin Kocour, Mickael Rigault, Khalid Choukri, Amrutha Prasad and others Abstract: Personal assistants, automatic speech recognizers and dialogue understanding systems are becoming more critical in our interconnected digital world. A clear example is air traffic control (ATC) communications. ATC aims at guiding aircraft and controlling the airspace in a safe and optimal manner. These voice-based dialogues are carried between an air traffic controller (ATCO) and pilots via very-high frequency radio channels. In order to incorporate these novel technologies into ATC (low-resource domain), large-scale annotated datasets are required to develop the data-driven AI systems. Two examples are automatic speech recognition (ASR) and natural language understanding (NLU). In this paper, we introduce the ATCO2 corpus, a dataset that aims at fostering research on the challenging ATC field, which has lagged behind due to lack of annotated data. The ATCO2 corpus covers 1) data collection and pre-processing, 2) pseudo-annotations of speech data, and 3) extraction of ATC-related named entities. The ATCO2 corpus is split into three subsets. 1) ATCO2-test-set corpus contains 4 hours of ATC speech with manual transcripts and a subset with gold annotations for named-entity recognition (callsign, command, value). 2) The ATCO2-PL-set corpus consists of 5281 hours of unlabeled ATC data enriched with automatic transcripts from an in-domain speech recognizer, contextual information, speaker turn information, signal-to-noise ratio estimate and English language detection score per sample. Both available for purchase through ELDA at this http URL. 3) The ATCO2-test-set-1h corpus is a one-hour subset from the original test set corpus, that we are offering for free at this url: https://www.atco2.org/data. We expect the ATCO2 corpus will foster research on robust ASR and NLU not only in the field of ATC communications but also in the general research community. Code — GitHub repository: https://github.com/idiap/atco2-corpus ## Intended uses & limitations This model was fine-tuned on air traffic control data. We don't expect that it keeps the same performance on some others datasets where BERT was pre-trained or fine-tuned. ## Training and evaluation data See Table 6 (page 18) in our 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). We described there the data used to fine-tune our NER model. - We use the ATCO2 corpus to fine-tune this model. You can download a free sample here: https://www.atco2.org/data - However, do not worry, we have prepared a script in our repository for preparing this databases: - Dataset preparation folder: https://github.com/idiap/atco2-corpus/tree/main/data/databases/atco2_test_set_1h/data_prepare_atco2_corpus_other.sh - Get the data in the format required by HuggingFace: speaker_role/data_preparation/prepare_spkid_atco2_corpus_test_set_1h.sh ## Writing your own inference script The snippet of code: ```python from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-ner-atc-en-atco2-1h") model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-ner-atc-en-atco2-1h") ##### Process text sample from transformers import pipeline nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="first") nlp("lufthansa three two five cleared to land runway three four left") # output: [{'entity_group': 'callsign', 'score': 0.8753265, 'word': 'lufthansa three two five', 'start': 0, 'end': 24}, {'entity_group': 'command', 'score': 0.99988264, 'word': 'cleared to land', 'start': 25, 'end': 40}, {'entity_group': 'value', 'score': 0.9999145, 'word': 'runway three four left', 'start': 41, 'end': 63}] ``` # Cite us If you use this code for your research, please cite our paper with: ``` @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} } ``` and, ``` @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} } ``` and, ``` @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} } ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 3000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 125.0 | 500 | 0.8692 | 0.6396 | 0.7172 | 0.6762 | 0.8307 | | 0.2158 | 250.0 | 1000 | 1.0074 | 0.5702 | 0.6970 | 0.6273 | 0.8245 | | 0.2158 | 375.0 | 1500 | 1.3560 | 0.6577 | 0.7374 | 0.6952 | 0.8119 | | 0.0184 | 500.0 | 2000 | 1.3393 | 0.6182 | 0.6869 | 0.6507 | 0.8056 | | 0.0184 | 625.0 | 2500 | 1.3528 | 0.6087 | 0.7071 | 0.6542 | 0.8213 | | 0.0175 | 750.0 | 3000 | 1.4282 | 0.6195 | 0.7071 | 0.6604 | 0.8182 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.0 - Tokenizers 0.13.2