---
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-all-lingala-ojpl-4
  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: 0.2868821999256782
---

<!-- 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-all-lingala-ojpl-4

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: 0.7849
- Wer: 0.2869

## 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: 4
- 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: 100
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.5152        | 0.54  | 100  | 0.9315          | 0.3430 |
| 0.4837        | 1.08  | 200  | 0.9377          | 0.3133 |
| 0.5943        | 1.61  | 300  | 0.8318          | 0.3177 |
| 0.6232        | 2.15  | 400  | 0.7491          | 0.3107 |
| 0.5186        | 2.69  | 500  | 0.7888          | 0.2899 |
| 0.6024        | 3.23  | 600  | 0.7674          | 0.2913 |
| 0.4522        | 3.76  | 700  | 0.7849          | 0.2869 |


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3