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--- |
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language: |
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- en |
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license: apache-2.0 |
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base_model: openai/whisper-small |
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tags: |
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- generated_from_trainer |
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datasets: |
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- Shiry/ATC_combined |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Small ATC - ATCText |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: ATC |
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type: Shiry/ATC_combined |
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args: 'split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 10.612930650580948 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Whisper Small ATC - ATCText |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the ATC dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2486 |
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- Wer: 10.6129 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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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: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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: 4000 |
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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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| 0.2533 | 0.42 | 1000 | 0.3465 | 16.2868 | |
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| 0.235 | 0.84 | 2000 | 0.2881 | 13.5237 | |
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| 0.0851 | 1.27 | 3000 | 0.2607 | 10.6048 | |
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| 0.1317 | 1.69 | 4000 | 0.2486 | 10.6129 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.2.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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### Additional Information |
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## Licensing Information |
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The licensing status of the dataset hinges on the legal status of the UWB-ATCC corpus creators. |
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They used Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) licensing. |
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## Citation Information |
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Contributors who prepared, processed, normalized and uploaded the dataset in HuggingFace: |
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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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@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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@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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## Authors of the dataset: |
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@article{vsmidl2019air, |
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title={Air traffic control communication (ATCC) speech corpora and their use for ASR and TTS development}, |
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author={{\v{S}}m{\'\i}dl, Lubo{\v{s}} and {\v{S}}vec, Jan and Tihelka, Daniel and Matou{\v{s}}ek, Jind{\v{r}}ich and Romportl, Jan and Ircing, Pavel}, |
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journal={Language Resources and Evaluation}, |
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volume={53}, |
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number={3}, |
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pages={449--464}, |
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year={2019}, |
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publisher={Springer} |
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} |
