transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: torgo_xlsr_finetune_F04_keep_all
  results: []
---

<!-- 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. -->

# torgo_xlsr_finetune_F04_keep_all

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6928
- Wer: 0.2375

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.6072        | 0.56  | 1000  | 3.3393          | 1.0    |
| 2.6659        | 1.11  | 2000  | 2.2682          | 0.9615 |
| 1.1671        | 1.67  | 3000  | 1.5754          | 0.7076 |
| 0.8034        | 2.22  | 4000  | 1.6181          | 0.5738 |
| 0.681         | 2.78  | 5000  | 1.4303          | 0.4888 |
| 0.5537        | 3.33  | 6000  | 1.6473          | 0.4605 |
| 0.5213        | 3.89  | 7000  | 1.5112          | 0.4377 |
| 0.4445        | 4.44  | 8000  | 1.3818          | 0.4182 |
| 0.4396        | 5.0   | 9000  | 1.5070          | 0.4274 |
| 0.4179        | 5.55  | 10000 | 1.4717          | 0.3995 |
| 0.3641        | 6.11  | 11000 | 1.3974          | 0.3359 |
| 0.3264        | 6.66  | 12000 | 1.6107          | 0.3607 |
| 0.3252        | 7.22  | 13000 | 1.2008          | 0.3023 |
| 0.2894        | 7.77  | 14000 | 1.4290          | 0.3039 |
| 0.2959        | 8.33  | 15000 | 1.3412          | 0.3126 |
| 0.2778        | 8.88  | 16000 | 1.4307          | 0.3035 |
| 0.2495        | 9.44  | 17000 | 1.3922          | 0.3092 |
| 0.2704        | 9.99  | 18000 | 1.3564          | 0.2627 |
| 0.2307        | 10.55 | 19000 | 1.4333          | 0.2612 |
| 0.2211        | 11.1  | 20000 | 1.6846          | 0.2775 |
| 0.1995        | 11.66 | 21000 | 1.4738          | 0.2856 |
| 0.2208        | 12.22 | 22000 | 1.5382          | 0.2695 |
| 0.2087        | 12.77 | 23000 | 1.3165          | 0.2722 |
| 0.1769        | 13.33 | 24000 | 1.9005          | 0.2791 |
| 0.1883        | 13.88 | 25000 | 1.7298          | 0.2768 |
| 0.1835        | 14.44 | 26000 | 1.6170          | 0.2608 |
| 0.1829        | 14.99 | 27000 | 1.8436          | 0.2711 |
| 0.1563        | 15.55 | 28000 | 1.7982          | 0.2627 |
| 0.1474        | 16.1  | 29000 | 1.6996          | 0.2398 |
| 0.155         | 16.66 | 30000 | 1.6696          | 0.2482 |
| 0.1295        | 17.21 | 31000 | 1.8057          | 0.2429 |
| 0.1345        | 17.77 | 32000 | 1.8119          | 0.2474 |
| 0.1475        | 18.32 | 33000 | 1.8016          | 0.2505 |
| 0.1246        | 18.88 | 34000 | 1.7389          | 0.2425 |
| 0.1395        | 19.43 | 35000 | 1.7249          | 0.2421 |
| 0.1223        | 19.99 | 36000 | 1.6928          | 0.2375 |


### Framework versions

- Transformers 4.26.1
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.13.3
