---
library_name: transformers
language:
- en
license: apache-2.0
base_model: Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc-and-atcosim
tags:
- generated_from_trainer
datasets:
- lmejias/YBBN-YSSY
metrics:
- wer
model-index:
- name: lmejias/w2v2-baseline
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: LiveATC recent data
      type: lmejias/YBBN-YSSY
      config: default
      split: None
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 104.41176470588236
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# lmejias/w2v2-baseline

This model is a fine-tuned version of [Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc-and-atcosim](https://huggingface.co/Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc-and-atcosim) on the LiveATC recent data dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3462
- Wer: 104.4118

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch    | Step | Validation Loss | Wer      |
|:-------------:|:--------:|:----:|:---------------:|:--------:|
| 5.5429        | 7.1429   | 50   | 6.3329          | 102.3396 |
| 3.6092        | 14.2857  | 100  | 3.6703          | 103.8770 |
| 2.9412        | 21.4286  | 150  | 3.2563          | 107.3529 |
| 2.7277        | 28.5714  | 200  | 3.1662          | 105.3476 |
| 2.462         | 35.7143  | 250  | 3.1332          | 104.8797 |
| 2.0927        | 42.8571  | 300  | 3.1455          | 105.1471 |
| 1.7015        | 50.0     | 350  | 3.3032          | 107.5535 |
| 1.3212        | 57.1429  | 400  | 3.3018          | 106.7513 |
| 1.1036        | 64.2857  | 450  | 3.4413          | 106.6176 |
| 0.8124        | 71.4286  | 500  | 3.4884          | 104.9465 |
| 0.674         | 78.5714  | 550  | 3.5686          | 106.3503 |
| 0.565         | 85.7143  | 600  | 3.5688          | 106.4171 |
| 0.488         | 92.8571  | 650  | 3.6488          | 106.2834 |
| 0.4201        | 100.0    | 700  | 3.6693          | 106.5508 |
| 0.3683        | 107.1429 | 750  | 3.6873          | 107.4198 |
| 0.313         | 114.2857 | 800  | 3.6209          | 104.5455 |
| 0.3457        | 121.4286 | 850  | 3.6447          | 105.4144 |
| 0.323         | 128.5714 | 900  | 3.6727          | 102.8743 |
| 0.2825        | 135.7143 | 950  | 3.6922          | 109.8262 |
| 0.2748        | 142.8571 | 1000 | 3.8867          | 102.5401 |
| 0.2109        | 150.0    | 1050 | 3.8843          | 104.8128 |
| 0.255         | 157.1429 | 1100 | 3.8725          | 104.4118 |
| 0.2079        | 164.2857 | 1150 | 3.9449          | 103.3422 |
| 0.1968        | 171.4286 | 1200 | 3.8255          | 104.7460 |
| 0.1641        | 178.5714 | 1250 | 3.8195          | 105.0802 |
| 0.1488        | 185.7143 | 1300 | 3.8621          | 103.7433 |
| 0.1487        | 192.8571 | 1350 | 3.7639          | 104.9465 |
| 0.1445        | 200.0    | 1400 | 3.7507          | 106.6845 |
| 0.1194        | 207.1429 | 1450 | 3.8920          | 104.4786 |
| 0.0906        | 214.2857 | 1500 | 3.9927          | 106.0829 |
| 0.093         | 221.4286 | 1550 | 3.9899          | 107.0856 |
| 0.1008        | 228.5714 | 1600 | 3.9051          | 104.4118 |
| 0.0911        | 235.7143 | 1650 | 3.9279          | 103.4759 |
| 0.0859        | 242.8571 | 1700 | 3.9341          | 104.1444 |
| 0.0876        | 250.0    | 1750 | 4.0044          | 105.2807 |
| 0.0768        | 257.1429 | 1800 | 3.9745          | 103.7433 |
| 0.0747        | 264.2857 | 1850 | 4.0634          | 105.7487 |
| 0.0672        | 271.4286 | 1900 | 4.1271          | 106.1497 |
| 0.0654        | 278.5714 | 1950 | 4.0620          | 106.3503 |
| 0.0559        | 285.7143 | 2000 | 4.1474          | 106.9519 |
| 0.0335        | 292.8571 | 2050 | 4.0337          | 104.4118 |
| 0.0661        | 300.0    | 2100 | 4.2306          | 102.8743 |
| 0.0517        | 307.1429 | 2150 | 4.1846          | 104.2781 |
| 0.0328        | 314.2857 | 2200 | 4.1616          | 105.2139 |
| 0.0488        | 321.4286 | 2250 | 4.1904          | 106.4171 |
| 0.0307        | 328.5714 | 2300 | 4.2941          | 104.9465 |
| 0.0304        | 335.7143 | 2350 | 4.2107          | 103.6096 |
| 0.0472        | 342.8571 | 2400 | 4.2483          | 105.1471 |
| 0.0181        | 350.0    | 2450 | 4.2711          | 104.3449 |
| 0.015         | 357.1429 | 2500 | 4.2480          | 104.2781 |
| 0.0122        | 364.2857 | 2550 | 4.3409          | 104.4118 |
| 0.008         | 371.4286 | 2600 | 4.3071          | 104.8797 |
| 0.0159        | 378.5714 | 2650 | 4.2340          | 104.6791 |
| 0.0297        | 385.7143 | 2700 | 4.3997          | 104.2781 |
| 0.0065        | 392.8571 | 2750 | 4.3840          | 104.0107 |
| 0.0154        | 400.0    | 2800 | 4.3097          | 103.6096 |
| 0.0065        | 407.1429 | 2850 | 4.3581          | 104.0775 |
| 0.0035        | 414.2857 | 2900 | 4.3293          | 104.8128 |
| 0.0201        | 421.4286 | 2950 | 4.3565          | 103.9439 |
| 0.0095        | 428.5714 | 3000 | 4.3462          | 104.4118 |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.9.1+cu128
- Datasets 4.4.1
- Tokenizers 0.21.4