pytorch tensorboard wav2vec2 automatic-speech-recognition transformers generated_from_trainer license:apache-2.0 model-index
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-xlsr-persian-50p
  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. -->

# wav2vec2-xlsr-persian-50p

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: 0.6846
- Wer: 0.4339

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 1.05  | 250  | 3.2104          | 1.0    |
| 3.2437        | 2.11  | 500  | 2.9131          | 1.0    |
| 3.2437        | 3.16  | 750  | 1.0335          | 0.7303 |
| 1.4382        | 4.22  | 1000 | 0.8335          | 0.6155 |
| 1.4382        | 5.27  | 1250 | 0.7640          | 0.5904 |
| 0.6923        | 6.33  | 1500 | 0.6923          | 0.5468 |
| 0.6923        | 7.38  | 1750 | 0.6627          | 0.5238 |
| 0.5137        | 8.44  | 2000 | 0.6606          | 0.5112 |
| 0.5137        | 9.49  | 2250 | 0.6600          | 0.5125 |
| 0.4258        | 10.55 | 2500 | 0.6337          | 0.4939 |
| 0.4258        | 11.6  | 2750 | 0.6454          | 0.4851 |
| 0.362         | 12.66 | 3000 | 0.6481          | 0.4793 |
| 0.362         | 13.71 | 3250 | 0.6487          | 0.4801 |
| 0.3179        | 14.77 | 3500 | 0.6602          | 0.4668 |
| 0.3179        | 15.82 | 3750 | 0.6757          | 0.4683 |
| 0.2861        | 16.88 | 4000 | 0.6544          | 0.4591 |
| 0.2861        | 17.93 | 4250 | 0.6659          | 0.4634 |
| 0.2529        | 18.99 | 4500 | 0.6311          | 0.4556 |
| 0.2529        | 20.04 | 4750 | 0.6574          | 0.4525 |
| 0.235         | 21.1  | 5000 | 0.7019          | 0.4462 |
| 0.235         | 22.15 | 5250 | 0.6783          | 0.4426 |
| 0.2203        | 23.21 | 5500 | 0.6789          | 0.4361 |
| 0.2203        | 24.26 | 5750 | 0.6779          | 0.4336 |
| 0.2014        | 25.32 | 6000 | 0.6805          | 0.4406 |
| 0.2014        | 26.37 | 6250 | 0.6918          | 0.4407 |
| 0.1957        | 27.43 | 6500 | 0.6919          | 0.4360 |
| 0.1957        | 28.48 | 6750 | 0.6795          | 0.4332 |
| 0.1837        | 29.53 | 7000 | 0.6846          | 0.4339 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
