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Model: Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc-and-atcosim Source: Original Platform
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README.md
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README.md
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---
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language: en
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license: apache-2.0
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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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datasets:
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- Jzuluaga/atcosim_corpus
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- Jzuluaga/uwb_atcc
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metrics:
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- wer
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base_model: facebook/wav2vec2-large-960h-lv60-self
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model-index:
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- name: wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc-and-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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name: UWB-ATCC dataset (Air Traffic Control Communications)
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type: Jzuluaga/uwb_atcc
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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: 17.48
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name: TEST WER
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verified: false
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- type: wer
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value: 14.26
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name: TEST WER (+LM)
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verified: false
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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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name: ATCOSIM corpus (Air Traffic Control Communications)
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type: Jzuluaga/atcosim_corpus
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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: 1.85
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name: TEST WER
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verified: false
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- type: wer
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value: 1.13
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name: TEST WER (+LM)
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verified: false
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---
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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 EXPERIMENTS/DATA/ATCOSIM_UWB_ATCC/TRAIN - NA dataset.
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It achieves the following results on the evaluation set:
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# wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc-and-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 two corpus:
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- [UWB-ATCC corpus](https://huggingface.co/datasets/Jzuluaga/uwb_atcc), and
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- [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 (two tests sets joined together: UWB-ATCC and ATCOSIM):
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- Loss: 0.4042
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- Wer: 0.1049
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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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## 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 UWB-ATCC + ATCOSIM corpus to fine-tune this model. You can download the raw data here:
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- https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0 and,
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- 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`:
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- Here, [UWB-ATCC corpus on HuggingFace](https://huggingface.co/datasets/Jzuluaga/uwb_atcc).
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- Here: [ATCOSIM CORPUS on HuggingFace](https://huggingface.co/datasets/Jzuluaga/atcosim_corpus).
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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/uwb_atcc/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/uwb_atcc"
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MODEL_ID = "Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc-and-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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uwb_atcc_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(uwb_atcc_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: 0.0001
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- train_batch_size: 24
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- eval_batch_size: 12
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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: 1000
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- training_steps: 10000
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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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| No log | 0.63 | 500 | 2.2638 | 0.9359 |
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| 2.6089 | 1.27 | 1000 | 0.7277 | 0.2407 |
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| 2.6089 | 1.9 | 1500 | 0.5800 | 0.1745 |
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| 0.6019 | 2.53 | 2000 | 0.4887 | 0.1514 |
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| 0.6019 | 3.17 | 2500 | 0.4666 | 0.1421 |
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| 0.4722 | 3.8 | 3000 | 0.4426 | 0.1451 |
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| 0.4722 | 4.44 | 3500 | 0.4176 | 0.1248 |
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| 0.4278 | 5.07 | 4000 | 0.4365 | 0.1239 |
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| 0.4278 | 5.7 | 4500 | 0.3816 | 0.1177 |
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| 0.369 | 6.34 | 5000 | 0.4113 | 0.1172 |
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| 0.369 | 6.97 | 5500 | 0.3863 | 0.1230 |
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| 0.341 | 7.6 | 6000 | 0.3850 | 0.1116 |
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| 0.341 | 8.24 | 6500 | 0.4014 | 0.1141 |
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| 0.3119 | 8.87 | 7000 | 0.3953 | 0.1078 |
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| 0.3119 | 9.51 | 7500 | 0.4018 | 0.1080 |
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| 0.3008 | 10.14 | 8000 | 0.3964 | 0.1074 |
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| 0.3008 | 10.77 | 8500 | 0.3917 | 0.1078 |
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| 0.2741 | 11.41 | 9000 | 0.3961 | 0.1057 |
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| 0.2741 | 12.04 | 9500 | 0.3974 | 0.1053 |
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| 0.2531 | 12.67 | 10000 | 0.4042 | 0.1049 |
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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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