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--- |
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license: apache-2.0 |
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language: en |
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datasets: |
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- Jzuluaga/atcosim_corpus |
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tags: |
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- audio |
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- automatic-speech-recognition |
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- en-atc |
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- en |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-large-960h-lv60-self-en-atc-atcosim |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Speech Recognition |
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dataset: |
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type: Jzuluaga/atcosim_corpus |
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name: ATCOSIM dataset (Air Traffic Control Communications) |
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config: test |
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split: test |
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metrics: |
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- type: wer |
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value: 1.67 |
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name: TEST WER |
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verified: False |
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--- |
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# wav2vec2-large-960h-lv60-self-en-atc-atcosim |
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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 [ATCOSIM corpus](https://huggingface.co/datasets/Jzuluaga/atcosim_corpus). |
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<a href="https://colab.research.google.com/github/idiap/w2v2-air-traffic/blob/main/src/eval_xlsr_atc_model.ipynb"> |
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<img alt="GitHub" src="https://colab.research.google.com/assets/colab-badge.svg\"> |
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</a> |
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<a href="https://github.com/idiap/w2v2-air-traffic"> |
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<img alt="GitHub" src="https://img.shields.io/badge/GitHub-Open%20source-green\"> |
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</a> |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0850 |
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- Wer: 0.0167 (1.67% WER) |
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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). |
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Authors: Juan Zuluaga-Gomez, Amrutha Prasad, Iuliia Nigmatulina, Saeed Sarfjoo, Petr Motlicek, Matthias Kleinert, Hartmut Helmke, Oliver Ohneiser, Qingran Zhan |
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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. |
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Code — GitHub repository: https://github.com/idiap/w2v2-air-traffic |
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## Usage |
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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 |
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(you need to change the `MODEL_ID` param to `MODEL_ID=Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-atcosim`) |
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## Intended uses & limitations |
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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. |
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## Training and evaluation data |
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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. |
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- 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 |
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- 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. |
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- 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). |
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## Writing your own inference script |
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If you use language model, you need to install the KenLM bindings with: |
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```bash |
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conda activate your_environment |
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pip install https://github.com/kpu/kenlm/archive/master.zip |
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``` |
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The snippet of code: |
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```python |
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from datasets import load_dataset, load_metric, Audio |
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import torch |
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from transformers import AutoModelForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM |
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import torchaudio.functional as F |
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USE_LM = False |
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DATASET_ID = "Jzuluaga/atcosim_corpus" |
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MODEL_ID = "Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-atcosim" |
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# 1. Load the dataset |
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# we only load the 'test' partition, however, if you want to load the 'train' partition, you can change it accordingly |
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atcosim_corpus_test = load_dataset(DATASET_ID, "test", split="test") |
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# 2. Load the model |
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model = AutoModelForCTC.from_pretrained(MODEL_ID) |
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# 3. Load the processors, we offer support with LM, which should yield better resutls |
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if USE_LM: |
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processor = Wav2Vec2ProcessorWithLM.from_pretrained(MODEL_ID) |
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else: |
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processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) |
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# 4. Format the test sample |
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sample = next(iter(atcosim_corpus_test)) |
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file_sampling_rate = sample['audio']['sampling_rate'] |
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# resample if neccessary |
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if file_sampling_rate != 16000: |
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resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), file_sampling_rate, 16000).numpy() |
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else: |
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resampled_audio = torch.tensor(sample["audio"]["array"]).numpy() |
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input_values = processor(resampled_audio, return_tensors="pt").input_values |
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# 5. Run the forward pass in the model |
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with torch.no_grad(): |
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logits = model(input_values).logits |
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# get the transcription with processor |
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if USE_LM: |
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transcription = processor.batch_decode(logits.numpy()).text |
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else: |
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pred_ids = torch.argmax(logits, dim=-1) |
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transcription = processor.batch_decode(pred_ids) |
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# print the output |
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print(transcription) |
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``` |
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# Cite us |
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If you use this code for your research, please cite our paper with: |
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``` |
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@article{zuluaga2022how, |
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title={How Does Pre-trained Wav2Vec2. 0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications}, |
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author={Zuluaga-Gomez, Juan and Prasad, Amrutha and Nigmatulina, Iuliia and Sarfjoo, Saeed and others}, |
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journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar}, |
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year={2022} |
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} |
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``` |
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and, |
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``` |
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@article{zuluaga2022bertraffic, |
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title={BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications}, |
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author={Zuluaga-Gomez, Juan and Sarfjoo, Seyyed Saeed and Prasad, Amrutha and others}, |
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journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar}, |
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year={2022} |
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} |
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``` |
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and, |
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``` |
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@article{zuluaga2022atco2, |
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title={ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications}, |
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author={Zuluaga-Gomez, Juan and Vesel{\`y}, Karel and Sz{\"o}ke, Igor and Motlicek, Petr and others}, |
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journal={arXiv preprint arXiv:2211.04054}, |
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year={2022} |
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} |
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``` |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0005 |
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- train_batch_size: 24 |
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- eval_batch_size: 24 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 96 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 20000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:------:|:-----:|:---------------:|:------:| |
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| 1.4757 | 6.41 | 500 | 0.0614 | 0.0347 | |
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| 0.0624 | 12.82 | 1000 | 0.0525 | 0.0277 | |
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| 0.0388 | 19.23 | 1500 | 0.0693 | 0.0241 | |
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| 0.03 | 25.64 | 2000 | 0.0666 | 0.0244 | |
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| 0.0235 | 32.05 | 2500 | 0.0604 | 0.0260 | |
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| 0.0226 | 38.46 | 3000 | 0.0625 | 0.0230 | |
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| 0.0163 | 44.87 | 3500 | 0.0603 | 0.0195 | |
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| 0.0157 | 51.28 | 4000 | 0.0628 | 0.0209 | |
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| 0.0152 | 57.69 | 4500 | 0.0692 | 0.0238 | |
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| 0.0122 | 64.1 | 5000 | 0.0607 | 0.0210 | |
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| 0.011 | 70.51 | 5500 | 0.0608 | 0.0213 | |
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| 0.0114 | 76.92 | 6000 | 0.0681 | 0.0211 | |
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| 0.0106 | 83.33 | 6500 | 0.0613 | 0.0210 | |
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| 0.0081 | 89.74 | 7000 | 0.0654 | 0.0196 | |
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| 0.0078 | 96.15 | 7500 | 0.0612 | 0.0191 | |
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| 0.0082 | 102.56 | 8000 | 0.0758 | 0.0237 | |
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| 0.0078 | 108.97 | 8500 | 0.0664 | 0.0206 | |
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| 0.0075 | 115.38 | 9000 | 0.0658 | 0.0197 | |
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| 0.0052 | 121.79 | 9500 | 0.0669 | 0.0218 | |
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| 0.0054 | 128.21 | 10000 | 0.0695 | 0.0211 | |
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| 0.0053 | 134.62 | 10500 | 0.0726 | 0.0227 | |
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| 0.0046 | 141.03 | 11000 | 0.0702 | 0.0212 | |
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| 0.0043 | 147.44 | 11500 | 0.0846 | 0.0200 | |
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| 0.0041 | 153.85 | 12000 | 0.0764 | 0.0200 | |
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| 0.0032 | 160.26 | 12500 | 0.0785 | 0.0201 | |
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| 0.0028 | 166.67 | 13000 | 0.0839 | 0.0197 | |
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| 0.0035 | 173.08 | 13500 | 0.0785 | 0.0210 | |
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| 0.0027 | 179.49 | 14000 | 0.0730 | 0.0188 | |
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| 0.002 | 185.9 | 14500 | 0.0794 | 0.0193 | |
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| 0.002 | 192.31 | 15000 | 0.0859 | 0.0211 | |
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| 0.0019 | 198.72 | 15500 | 0.0727 | 0.0183 | |
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| 0.0017 | 205.13 | 16000 | 0.0784 | 0.0187 | |
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| 0.0016 | 211.54 | 16500 | 0.0801 | 0.0196 | |
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| 0.0014 | 217.95 | 17000 | 0.0821 | 0.0185 | |
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| 0.0011 | 224.36 | 17500 | 0.0822 | 0.0176 | |
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| 0.001 | 230.77 | 18000 | 0.0856 | 0.0171 | |
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| 0.001 | 237.18 | 18500 | 0.0792 | 0.0176 | |
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| 0.001 | 243.59 | 19000 | 0.0826 | 0.0173 | |
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| 0.0006 | 250.0 | 19500 | 0.0854 | 0.0170 | |
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| 0.0007 | 256.41 | 20000 | 0.0850 | 0.0167 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.2 |
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