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_M02_keep_all
  results: []
---

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# torgo_xlsr_finetune_M02_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: 1.6539
- Wer: 0.2436

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5043        | 0.56  | 1000  | 3.3139          | 1.0    |
| 2.1248        | 1.12  | 2000  | 1.9926          | 0.8898 |
| 1.0178        | 1.67  | 3000  | 1.5324          | 0.6683 |
| 0.7315        | 2.23  | 4000  | 1.7989          | 0.5959 |
| 0.6289        | 2.79  | 5000  | 1.3984          | 0.4987 |
| 0.5123        | 3.35  | 6000  | 1.2977          | 0.4228 |
| 0.4751        | 3.91  | 7000  | 1.3967          | 0.3988 |
| 0.4354        | 4.47  | 8000  | 1.5080          | 0.4274 |
| 0.3817        | 5.03  | 9000  | 1.7897          | 0.4014 |
| 0.3758        | 5.58  | 10000 | 1.3421          | 0.3385 |
| 0.358         | 6.14  | 11000 | 1.6429          | 0.3427 |
| 0.3083        | 6.7   | 12000 | 1.2683          | 0.3084 |
| 0.2805        | 7.26  | 13000 | 1.7095          | 0.3122 |
| 0.2856        | 7.82  | 14000 | 1.7918          | 0.3317 |
| 0.2574        | 8.38  | 15000 | 1.5411          | 0.2947 |
| 0.2495        | 8.93  | 16000 | 1.4551          | 0.2997 |
| 0.2651        | 9.49  | 17000 | 1.5073          | 0.2825 |
| 0.2517        | 10.05 | 18000 | 1.6405          | 0.2920 |
| 0.2274        | 10.61 | 19000 | 1.4440          | 0.2604 |
| 0.2278        | 11.17 | 20000 | 1.4020          | 0.2875 |
| 0.2472        | 11.73 | 21000 | 1.6264          | 0.2897 |
| 0.1875        | 12.28 | 22000 | 1.5901          | 0.2783 |
| 0.175         | 12.84 | 23000 | 1.4056          | 0.2501 |
| 0.1751        | 13.4  | 24000 | 1.4809          | 0.2631 |
| 0.1607        | 13.96 | 25000 | 1.4363          | 0.2551 |
| 0.1712        | 14.52 | 26000 | 1.6480          | 0.2524 |
| 0.1581        | 15.08 | 27000 | 1.5084          | 0.2615 |
| 0.1623        | 15.63 | 28000 | 1.4066          | 0.2482 |
| 0.1397        | 16.19 | 29000 | 1.7111          | 0.2619 |
| 0.1536        | 16.75 | 30000 | 1.4691          | 0.2402 |
| 0.1343        | 17.31 | 31000 | 1.5406          | 0.2329 |
| 0.1428        | 17.87 | 32000 | 1.5261          | 0.2413 |
| 0.1125        | 18.43 | 33000 | 1.6416          | 0.2337 |
| 0.1214        | 18.98 | 34000 | 1.6803          | 0.2425 |
| 0.124         | 19.54 | 35000 | 1.6539          | 0.2436 |


### Framework versions

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