readme: ---
  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). 
  
  <a href="https://colab.research.google.com/github/idiap/w2v2-air-traffic/blob/main/src/eval_xlsr_atc_model.ipynb">
      <img alt="GitHub" src="https://colab.research.google.com/assets/colab-badge.svg\">
  </a>
  <a href="https://github.com/idiap/w2v2-air-traffic">
      <img alt="GitHub" src="https://img.shields.io/badge/GitHub-Open%20source-green\">
  </a>
  
  
  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</b> 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 