--- license: apache-2.0 tags: - automatic-speech-recognition - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-xls-r-300m-en-atc-atcosim results: - task: type: automatic-speech-recognition name: Speech Recognition dataset: type: Jzuluaga/atcosim_corpus name: ATCOSIM dataset (Air Traffic Control Communications) config: test split: test metrics: - type: wer value: 7.36 name: TEST WER verified: False --- # wav2vec2-xls-r-300m-en-atc-atcosim This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the [ATCOSIM corpus](https://huggingface.co/datasets/Jzuluaga/atcosim_corpus). GitHub GitHub It achieves the following results on the evaluation set: - Loss: 0.0988 - Wer: 0.0736 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 ATCOSIM dataset for fine-tuning this model. You can download the raw data here: 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, [ATCOSIM CORPUS on HuggingFace](https://huggingface.co/datasets/Jzuluaga/atcosim_corpus). You can scroll and check the train/test partitions, and even listen to some audios. - 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/atcosim_corpus/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/atcosim_corpus" MODEL_ID = "Jzuluaga/wav2vec2-xls-r-300m-en-atc-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 atcosim_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(atcosim_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{zuluaga2022bertraffic, 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 Motlicek, Petr and Kleinert, Matthias and Helmke, Hartmut and Ohneiser, Oliver and Zhan, Qingran}, 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 Nigmatulina, Iuliia and Motlicek, Petr and Ondre, Karel and Ohneiser, Oliver and Helmke, Hartmut}, journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar}, year={2022} } ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 96 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 20000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:------:| | 1.9105 | 6.41 | 500 | 0.1622 | 0.1531 | | 0.1119 | 12.82 | 1000 | 0.0971 | 0.0936 | | 0.0614 | 19.23 | 1500 | 0.1002 | 0.0983 | | 0.044 | 25.64 | 2000 | 0.1011 | 0.0929 | | 0.0366 | 32.05 | 2500 | 0.0932 | 0.0828 | | 0.0315 | 38.46 | 3000 | 0.0926 | 0.0880 | | 0.0297 | 44.87 | 3500 | 0.0972 | 0.0882 | | 0.0216 | 51.28 | 4000 | 0.0911 | 0.0774 | | 0.0211 | 57.69 | 4500 | 0.0982 | 0.0891 | | 0.0187 | 64.1 | 5000 | 0.1009 | 0.0863 | | 0.02 | 70.51 | 5500 | 0.0953 | 0.0852 | | 0.0163 | 76.92 | 6000 | 0.1028 | 0.0804 | | 0.0128 | 83.33 | 6500 | 0.0930 | 0.0856 | | 0.0127 | 89.74 | 7000 | 0.0892 | 0.0676 | | 0.0116 | 96.15 | 7500 | 0.0857 | 0.0753 | | 0.0139 | 102.56 | 8000 | 0.1078 | 0.0481 | | 0.0107 | 108.97 | 8500 | 0.0955 | 0.0683 | | 0.0096 | 115.38 | 9000 | 0.0846 | 0.0697 | | 0.0089 | 121.79 | 9500 | 0.0854 | 0.0675 | | 0.0084 | 128.21 | 10000 | 0.0875 | 0.0779 | | 0.0074 | 134.62 | 10500 | 0.0840 | 0.0770 | | 0.0061 | 141.03 | 11000 | 0.0903 | 0.0754 | | 0.0076 | 147.44 | 11500 | 0.0872 | 0.0769 | | 0.0069 | 153.85 | 12000 | 0.0891 | 0.0772 | | 0.0061 | 160.26 | 12500 | 0.0971 | 0.0774 | | 0.0049 | 166.67 | 13000 | 0.0984 | 0.0726 | | 0.0045 | 173.08 | 13500 | 0.0952 | 0.0765 | | 0.0039 | 179.49 | 14000 | 0.1015 | 0.0762 | | 0.0031 | 185.9 | 14500 | 0.0937 | 0.0712 | | 0.0032 | 192.31 | 15000 | 0.0982 | 0.0635 | | 0.0028 | 198.72 | 15500 | 0.0981 | 0.0743 | | 0.0024 | 205.13 | 16000 | 0.1019 | 0.0712 | | 0.0024 | 211.54 | 16500 | 0.0957 | 0.0732 | | 0.002 | 217.95 | 17000 | 0.0941 | 0.0732 | | 0.0015 | 224.36 | 17500 | 0.1009 | 0.0717 | | 0.0017 | 230.77 | 18000 | 0.0955 | 0.0730 | | 0.0013 | 237.18 | 18500 | 0.0989 | 0.0732 | | 0.0013 | 243.59 | 19000 | 0.0967 | 0.0738 | | 0.0011 | 250.0 | 19500 | 0.0980 | 0.0734 | | 0.0008 | 256.41 | 20000 | 0.0988 | 0.0736 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.6.1 - Tokenizers 0.13.2