---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- openslr
metrics:
- wer
model-index:
- name: wav2vec2-telugu
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: openslr
      type: openslr
      config: SLR66
      split: train
      args: SLR66
    metrics:
    - name: Wer
      type: wer
      value: 0.2884547694473777
---

<!-- 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-telugu

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the openslr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2982
- Wer: 0.2885

## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.6905        | 3.84  | 400  | 0.7109          | 0.7800 |
| 0.4532        | 7.69  | 800  | 0.2972          | 0.3977 |
| 0.1957        | 11.54 | 1200 | 0.2907          | 0.3522 |
| 0.1284        | 15.38 | 1600 | 0.3117          | 0.3317 |
| 0.0979        | 19.23 | 2000 | 0.3000          | 0.3353 |
| 0.0749        | 23.08 | 2400 | 0.2823          | 0.3045 |
| 0.0584        | 26.92 | 2800 | 0.2982          | 0.2885 |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.2