---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: Marathi_ASR_using_xlsr_wav2vec
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_13_0
      type: common_voice_13_0
      config: mr
      split: test
      args: mr
    metrics:
    - name: Wer
      type: wer
      value: 0.7180765086206896
---

<!-- 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. -->

# Marathi_ASR_using_xlsr_wav2vec

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7582
- Wer: 0.7181

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| No log        | 2.6667  | 200  | 0.7381          | 0.7263 |
| 0.336         | 5.3333  | 400  | 0.7472          | 0.7289 |
| 0.336         | 8.0     | 600  | 0.7452          | 0.7215 |
| 0.3237        | 10.6667 | 800  | 0.7449          | 0.7212 |
| 0.3237        | 13.3333 | 1000 | 0.7546          | 0.7192 |
| 0.3104        | 16.0    | 1200 | 0.7565          | 0.7210 |
| 0.3104        | 18.6667 | 1400 | 0.7550          | 0.7193 |
| 0.3089        | 21.3333 | 1600 | 0.7551          | 0.7186 |
| 0.3089        | 24.0    | 1800 | 0.7572          | 0.7185 |
| 0.2993        | 26.6667 | 2000 | 0.7571          | 0.7175 |
| 0.2993        | 29.3333 | 2200 | 0.7582          | 0.7181 |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.1.2
- Datasets 2.19.0
- Tokenizers 0.19.1