---
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: wav2vec2-large-mms-1b-nhi-ft-3hrs
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: train
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 0.6467391304347826
---

<!-- 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-mms-1b-nhi-ft-3hrs

This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7237
- Wer: 0.6467

## 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: 0.001
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 4.6463        | 0.4149 | 100  | 1.0969          | 0.7919 |
| 1.1669        | 0.8299 | 200  | 0.8578          | 0.7023 |
| 0.987         | 1.2448 | 300  | 0.7607          | 0.6603 |
| 0.9324        | 1.6598 | 400  | 0.7237          | 0.6467 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.4.0
- Datasets 2.19.1
- Tokenizers 0.19.1