---
base_model: facebook/wav2vec2-large-xlsr-53
datasets:
- fleurs
library_name: transformers
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xlsr-53-Hindi-Version3
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: fleurs
      type: fleurs
      config: hi_in
      split: None
      args: hi_in
    metrics:
    - type: wer
      value: 0.282143903153373
      name: Wer
language:
- hi
pipeline_tag: automatic-speech-recognition
---

<!-- 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-large-xlsr-53-Hindi-Version3

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7311
- Wer: 0.2821

## 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: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 3.362         | 6.7568  | 500  | 3.3795          | 1.0    |
| 0.5309        | 13.5135 | 1000 | 0.5572          | 0.4268 |
| 0.2342        | 20.2703 | 1500 | 0.5206          | 0.3341 |
| 0.1408        | 27.0270 | 2000 | 0.5516          | 0.3292 |
| 0.1126        | 33.7838 | 2500 | 0.6105          | 0.3199 |
| 0.0892        | 40.5405 | 3000 | 0.6489          | 0.3123 |
| 0.0721        | 47.2973 | 3500 | 0.6533          | 0.3067 |
| 0.0719        | 54.0541 | 4000 | 0.6898          | 0.3050 |
| 0.0592        | 60.8108 | 4500 | 0.7007          | 0.2990 |
| 0.0737        | 67.5676 | 5000 | 0.7106          | 0.2921 |
| 0.0399        | 74.3243 | 5500 | 0.7271          | 0.2916 |
| 0.0409        | 81.0811 | 6000 | 0.7298          | 0.2871 |
| 0.0322        | 87.8378 | 6500 | 0.7311          | 0.2835 |
| 0.0285        | 94.5946 | 7000 | 0.7311          | 0.2821 |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1