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_M04
  results: []
---

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# torgo_xlsr_finetune_M04

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: 1.6944
- Wer: 0.2951

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5657        | 0.75  | 1000  | 3.3394          | 1.0    |
| 1.6728        | 1.5   | 2000  | 2.0240          | 0.8222 |
| 0.8728        | 2.25  | 3000  | 1.6539          | 0.6160 |
| 0.6675        | 3.0   | 4000  | 1.6387          | 0.5754 |
| 0.5636        | 3.75  | 5000  | 1.6527          | 0.5277 |
| 0.4598        | 4.5   | 6000  | 1.6385          | 0.4130 |
| 0.3835        | 5.25  | 7000  | 1.5931          | 0.4311 |
| 0.3881        | 6.0   | 8000  | 1.1019          | 0.3776 |
| 0.3276        | 6.75  | 9000  | 1.4476          | 0.4317 |
| 0.3099        | 7.5   | 10000 | 1.5607          | 0.3885 |
| 0.2973        | 8.25  | 11000 | 1.5421          | 0.3338 |
| 0.2961        | 9.0   | 12000 | 1.6224          | 0.3802 |
| 0.2702        | 9.74  | 13000 | 1.9051          | 0.3563 |
| 0.2437        | 10.49 | 14000 | 1.8258          | 0.3789 |
| 0.2226        | 11.24 | 15000 | 1.5751          | 0.3351 |
| 0.2059        | 11.99 | 16000 | 1.5211          | 0.3260 |
| 0.2244        | 12.74 | 17000 | 1.3021          | 0.3196 |
| 0.1904        | 13.49 | 18000 | 1.8146          | 0.3557 |
| 0.2026        | 14.24 | 19000 | 1.6795          | 0.3305 |
| 0.1696        | 14.99 | 20000 | 1.4434          | 0.2990 |
| 0.1753        | 15.74 | 21000 | 1.5751          | 0.2957 |
| 0.1648        | 16.49 | 22000 | 1.7432          | 0.3048 |
| 0.1364        | 17.24 | 23000 | 1.8501          | 0.3267 |
| 0.1499        | 17.99 | 24000 | 1.6999          | 0.3048 |
| 0.1452        | 18.74 | 25000 | 1.7274          | 0.2970 |
| 0.1367        | 19.49 | 26000 | 1.6944          | 0.2951 |


### Framework versions

- Transformers 4.26.1
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.13.3
