|
--- |
|
license: apache-2.0 |
|
tags: |
|
- automatic-speech-recognition |
|
- generated_from_trainer |
|
metrics: |
|
- wer |
|
model-index: |
|
- name: wav2vec2-xls-r-300m-en-atc-uwb-atcc |
|
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: 0.36 |
|
name: TEST WER |
|
verified: False |
|
--- |
|
|
|
# wav2vec2-xls-r-300m-en-atc-uwb-atcc |
|
|
|
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the [UWB-ATCC corpus](https://huggingface.co/datasets/Jzuluaga/uwb_atcc). |
|
|
|
<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: |
|
- Loss: 0.8470 |
|
- Wer: 0.1898 |
|
|
|
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 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 |
|
- 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). 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/uwb_atcc/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/uwb_atcc" |
|
MODEL_ID = "Jzuluaga/wav2vec2-xls-r-300m-en-atc-uwb-atcc" |
|
|
|
# 1. Load the dataset |
|
# we only load the 'test' partition, however, if you want to load the 'train' partition, you can change it accordingly |
|
uwb_atcc_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(uwb_atcc_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{zuluaga2022how, |
|
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: 0.0001 |
|
- train_batch_size: 24 |
|
- eval_batch_size: 12 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 1000 |
|
- training_steps: 10000 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | |
|
|:-------------:|:-----:|:-----:|:---------------:|:------:| |
|
| No log | 1.06 | 500 | 3.1697 | 1.0 | |
|
| 3.1489 | 2.12 | 1000 | 1.4184 | 0.5678 | |
|
| 3.1489 | 3.18 | 1500 | 0.8498 | 0.3366 | |
|
| 0.8499 | 4.25 | 2000 | 0.8089 | 0.2755 | |
|
| 0.8499 | 5.31 | 2500 | 0.7339 | 0.2963 | |
|
| 0.5901 | 6.37 | 3000 | 0.6376 | 0.2402 | |
|
| 0.5901 | 7.43 | 3500 | 0.6890 | 0.2336 | |
|
| 0.4724 | 8.49 | 4000 | 0.6844 | 0.2240 | |
|
| 0.4724 | 9.55 | 4500 | 0.6900 | 0.2222 | |
|
| 0.3981 | 10.62 | 5000 | 0.7051 | 0.2123 | |
|
| 0.3981 | 11.68 | 5500 | 0.6671 | 0.2095 | |
|
| 0.3436 | 12.74 | 6000 | 0.7425 | 0.2049 | |
|
| 0.3436 | 13.8 | 6500 | 0.7135 | 0.1994 | |
|
| 0.2925 | 14.86 | 7000 | 0.7350 | 0.2012 | |
|
| 0.2925 | 15.92 | 7500 | 0.7855 | 0.1945 | |
|
| 0.2525 | 16.99 | 8000 | 0.7933 | 0.1946 | |
|
| 0.2525 | 18.05 | 8500 | 0.8016 | 0.1915 | |
|
| 0.2285 | 19.11 | 9000 | 0.8284 | 0.1907 | |
|
| 0.2285 | 20.17 | 9500 | 0.8275 | 0.1902 | |
|
| 0.2025 | 21.23 | 10000 | 0.8470 | 0.1898 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.24.0 |
|
- Pytorch 1.13.0+cu117 |
|
- Datasets 2.6.1 |
|
- Tokenizers 0.13.2 |
|
|