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_F01
  results: []
---

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# torgo_xlsr_finetune_F01

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2809
- Wer: 0.2462

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.7203        | 0.83  | 1000  | 3.5860          | 1.0    |
| 3.3181        | 1.66  | 2000  | 3.2148          | 1.0    |
| 1.547         | 2.49  | 3000  | 1.6683          | 0.8497 |
| 0.8041        | 3.32  | 4000  | 1.1466          | 0.6036 |
| 0.5827        | 4.15  | 5000  | 1.1892          | 0.5093 |
| 0.5022        | 4.98  | 6000  | 1.1251          | 0.4295 |
| 0.4373        | 5.81  | 7000  | 1.4837          | 0.4270 |
| 0.3729        | 6.64  | 8000  | 1.1526          | 0.3430 |
| 0.3448        | 7.47  | 9000  | 1.0968          | 0.3489 |
| 0.2964        | 8.3   | 10000 | 1.2292          | 0.3014 |
| 0.2806        | 9.13  | 11000 | 1.4337          | 0.3345 |
| 0.2684        | 9.96  | 12000 | 1.4606          | 0.3472 |
| 0.2587        | 10.79 | 13000 | 1.1020          | 0.2937 |
| 0.2187        | 11.62 | 14000 | 1.3086          | 0.3192 |
| 0.2052        | 12.45 | 15000 | 1.3270          | 0.2912 |
| 0.1757        | 13.28 | 16000 | 1.2323          | 0.2784 |
| 0.2061        | 14.11 | 17000 | 1.1289          | 0.2716 |
| 0.1749        | 14.94 | 18000 | 1.2851          | 0.2784 |
| 0.1476        | 15.77 | 19000 | 1.1545          | 0.2547 |
| 0.1446        | 16.6  | 20000 | 1.2718          | 0.2487 |
| 0.1319        | 17.43 | 21000 | 1.2026          | 0.2487 |
| 0.1533        | 18.26 | 22000 | 1.2299          | 0.2530 |
| 0.1349        | 19.09 | 23000 | 1.3010          | 0.2513 |
| 0.1185        | 19.92 | 24000 | 1.2809          | 0.2462 |


### Framework versions

- Transformers 4.26.1
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.13.3
