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_keep_all
  results: []
---

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# torgo_xlsr_finetune_F01_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: 1.4829
- Wer: 0.2307

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5238        | 0.54  | 1000  | 3.3622          | 1.0    |
| 2.0516        | 1.08  | 2000  | 1.8955          | 0.8696 |
| 1.0289        | 1.62  | 3000  | 1.6729          | 0.7099 |
| 0.7811        | 2.15  | 4000  | 1.4110          | 0.5810 |
| 0.6504        | 2.69  | 5000  | 1.3730          | 0.4746 |
| 0.5201        | 3.23  | 6000  | 1.3490          | 0.4285 |
| 0.4961        | 3.77  | 7000  | 1.4873          | 0.4758 |
| 0.4362        | 4.31  | 8000  | 1.4745          | 0.4083 |
| 0.4124        | 4.85  | 9000  | 1.5057          | 0.3881 |
| 0.3923        | 5.39  | 10000 | 1.2302          | 0.3473 |
| 0.364         | 5.92  | 11000 | 1.4894          | 0.3351 |
| 0.3295        | 6.46  | 12000 | 1.4680          | 0.3412 |
| 0.3246        | 7.0   | 13000 | 1.3344          | 0.3206 |
| 0.2864        | 7.54  | 14000 | 1.4118          | 0.3050 |
| 0.2897        | 8.08  | 15000 | 1.4808          | 0.2993 |
| 0.2376        | 8.62  | 16000 | 1.7672          | 0.3145 |
| 0.2539        | 9.15  | 17000 | 1.5791          | 0.3035 |
| 0.2424        | 9.69  | 18000 | 1.3562          | 0.2867 |
| 0.2266        | 10.23 | 19000 | 1.5603          | 0.2753 |
| 0.213         | 10.77 | 20000 | 1.3815          | 0.2673 |
| 0.2296        | 11.31 | 21000 | 1.1711          | 0.2482 |
| 0.2094        | 11.85 | 22000 | 1.2037          | 0.2490 |
| 0.1901        | 12.39 | 23000 | 1.3386          | 0.2451 |
| 0.1849        | 12.92 | 24000 | 1.5142          | 0.2482 |
| 0.2062        | 13.46 | 25000 | 1.4039          | 0.2409 |
| 0.1654        | 14.0  | 26000 | 1.4757          | 0.2398 |
| 0.1926        | 14.54 | 27000 | 1.3155          | 0.2276 |
| 0.153         | 15.08 | 28000 | 1.6762          | 0.2551 |
| 0.1513        | 15.62 | 29000 | 1.4876          | 0.2505 |
| 0.1491        | 16.16 | 30000 | 1.3197          | 0.2280 |
| 0.1429        | 16.69 | 31000 | 1.4292          | 0.2371 |
| 0.1438        | 17.23 | 32000 | 1.3700          | 0.2310 |
| 0.1335        | 17.77 | 33000 | 1.4660          | 0.2265 |
| 0.1386        | 18.31 | 34000 | 1.5169          | 0.2291 |
| 0.1201        | 18.85 | 35000 | 1.5295          | 0.2291 |
| 0.1238        | 19.39 | 36000 | 1.4762          | 0.2287 |
| 0.1192        | 19.92 | 37000 | 1.4829          | 0.2307 |


### Framework versions

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