--- license: apache-2.0 language: en datasets: - Jzuluaga/atcosim_corpus - Jzuluaga/uwb_atcc tags: - audio - automatic-speech-recognition - en-atc - en - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc-and-atcosim results: - task: type: automatic-speech-recognition name: Speech Recognition dataset: type: Jzuluaga/uwb_atcc name: UWB-ATCC dataset (Air Traffic Control Communications) config: test split: test metrics: - type: wer value: 17.48 name: TEST WER verified: False - type: wer value: 14.26 name: TEST WER (+LM) verified: False - task: type: automatic-speech-recognition name: Speech Recognition dataset: type: Jzuluaga/atcosim_corpus name: ATCOSIM corpus (Air Traffic Control Communications) config: test split: test metrics: - type: wer value: 1.85 name: TEST WER verified: False - type: wer value: 1.13 name: TEST WER (+LM) verified: False --- This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the EXPERIMENTS/DATA/ATCOSIM_UWB_ATCC/TRAIN - NA dataset. It achieves the following results on the evaluation set: # wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc-and-atcosim This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on two corpus: - [UWB-ATCC corpus](https://huggingface.co/datasets/Jzuluaga/uwb_atcc), and - [ATCOSIM corpus](https://huggingface.co/datasets/Jzuluaga/atcosim_corpus). GitHub GitHub It achieves the following results on the evaluation set (two tests sets joined together: UWB-ATCC and ATCOSIM): - Loss: 0.4042 - Wer: 0.1049 Paper: [How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications](https://arxiv.org/abs/2203.16822). Authors: Juan Zuluaga-Gomez, Amrutha Prasad, Iuliia Nigmatulina, Saeed Sarfjoo, Petr Motlicek, Matthias Kleinert, Hartmut Helmke, Oliver Ohneiser, Qingran Zhan Abstract: Recent work on self-supervised pre-training focus on leveraging large-scale unlabeled speech data to build robust end-to-end (E2E)acoustic models (AM) that can be later fine-tuned on downstream tasks e.g., automatic speech recognition (ASR). Yet, few works investigated the impact on performance when the data properties substantially differ between the pre-training and fine-tuning phases, termed domain shift. We target this scenario by analyzing the robustness of Wav2Vec 2.0 and XLS-R models on downstream ASR for a completely unseen domain, air traffic control (ATC) communications. We benchmark these two models on several open-source and challenging ATC databases with signal-to-noise ratio between 5 and 20 dB. Relative word error rate (WER) reductions between 20% to 40% are obtained in comparison to hybrid-based ASR baselines by only fine-tuning E2E acoustic models with a smaller fraction of labeled data. We analyze WERs on the low-resource scenario and gender bias carried by one ATC dataset. Code — GitHub repository: https://github.com/idiap/w2v2-air-traffic ## Usage You can use our Google Colab notebook to run and evaluate our model: https://github.com/idiap/w2v2-air-traffic/blob/master/src/eval_xlsr_atc_model.ipynb ## 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, e.g., LibriSpeech or CommonVoice. ## Training and evaluation data See Table 1 (page 3) in our paper: [How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications](https://arxiv.org/abs/2203.16822). We described there the partitions of how to use our model. - We use the UWB-ATCC + ATCOSIM corpus to fine-tune this model. You can download the raw data here: - https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0 and, - https://www.spsc.tugraz.at/databases-and-tools/atcosim-air-traffic-control-simulation-speech-corpus.html - However, do not worry, we have prepared the database in `Datasets format`: - Here, [UWB-ATCC corpus on HuggingFace](https://huggingface.co/datasets/Jzuluaga/uwb_atcc). - Here: [ATCOSIM CORPUS on HuggingFace](https://huggingface.co/datasets/Jzuluaga/atcosim_corpus). - If you want to prepare a database in HuggingFace format, you can follow the data loader script in: [data_loader_atc.py](https://huggingface.co/datasets/Jzuluaga/uwb_atcc/blob/main/atc_data_loader.py). ## Writing your own inference script If you use language model, you need to install the KenLM bindings with: ```bash conda activate your_environment pip install https://github.com/kpu/kenlm/archive/master.zip ``` The snippet of code: ```python from datasets import load_dataset, load_metric, Audio import torch from transformers import AutoModelForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM import torchaudio.functional as F USE_LM = False DATASET_ID = "Jzuluaga/uwb_atcc" MODEL_ID = "Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc-and-atcosim" # 1. Load the dataset # we only load the 'test' partition, however, if you want to load the 'train' partition, you can change it accordingly uwb_atcc_corpus_test = load_dataset(DATASET_ID, "test", split="test") # 2. Load the model model = AutoModelForCTC.from_pretrained(MODEL_ID) # 3. Load the processors, we offer support with LM, which should yield better resutls if USE_LM: processor = Wav2Vec2ProcessorWithLM.from_pretrained(MODEL_ID) else: processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) # 4. Format the test sample sample = next(iter(uwb_atcc_corpus_test)) file_sampling_rate = sample['audio']['sampling_rate'] # resample if neccessary if file_sampling_rate != 16000: resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), file_sampling_rate, 16000).numpy() else: resampled_audio = torch.tensor(sample["audio"]["array"]).numpy() input_values = processor(resampled_audio, return_tensors="pt").input_values # 5. Run the forward pass in the model with torch.no_grad(): logits = model(input_values).logits # get the transcription with processor if USE_LM: transcription = processor.batch_decode(logits.numpy()).text else: pred_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(pred_ids) # print the output print(transcription) ``` # Cite us If you use this code for your research, please cite our paper with: ``` @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{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{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: 0.0001 - train_batch_size: 24 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | No log | 0.63 | 500 | 2.2638 | 0.9359 | | 2.6089 | 1.27 | 1000 | 0.7277 | 0.2407 | | 2.6089 | 1.9 | 1500 | 0.5800 | 0.1745 | | 0.6019 | 2.53 | 2000 | 0.4887 | 0.1514 | | 0.6019 | 3.17 | 2500 | 0.4666 | 0.1421 | | 0.4722 | 3.8 | 3000 | 0.4426 | 0.1451 | | 0.4722 | 4.44 | 3500 | 0.4176 | 0.1248 | | 0.4278 | 5.07 | 4000 | 0.4365 | 0.1239 | | 0.4278 | 5.7 | 4500 | 0.3816 | 0.1177 | | 0.369 | 6.34 | 5000 | 0.4113 | 0.1172 | | 0.369 | 6.97 | 5500 | 0.3863 | 0.1230 | | 0.341 | 7.6 | 6000 | 0.3850 | 0.1116 | | 0.341 | 8.24 | 6500 | 0.4014 | 0.1141 | | 0.3119 | 8.87 | 7000 | 0.3953 | 0.1078 | | 0.3119 | 9.51 | 7500 | 0.4018 | 0.1080 | | 0.3008 | 10.14 | 8000 | 0.3964 | 0.1074 | | 0.3008 | 10.77 | 8500 | 0.3917 | 0.1078 | | 0.2741 | 11.41 | 9000 | 0.3961 | 0.1057 | | 0.2741 | 12.04 | 9500 | 0.3974 | 0.1053 | | 0.2531 | 12.67 | 10000 | 0.4042 | 0.1049 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.6.1 - Tokenizers 0.13.2