---
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
  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: 0.2697881828316611
---

<!-- 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

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: 0.8394
- Wer: 0.2698

## 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: 1
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5442        | 0.13  | 100  | 0.9396          | 0.3307 |
| 0.9882        | 0.27  | 200  | 0.9189          | 0.3389 |
| 0.5845        | 0.4   | 300  | 0.9322          | 0.3129 |
| 0.4162        | 0.54  | 400  | 1.0742          | 0.2939 |
| 0.506         | 0.67  | 500  | 0.9626          | 0.3077 |
| 0.8789        | 0.81  | 600  | 1.0502          | 0.3055 |
| 0.6166        | 0.94  | 700  | 0.9560          | 0.2984 |
| 0.4101        | 1.08  | 800  | 0.9520          | 0.2995 |
| 0.6536        | 1.21  | 900  | 1.1213          | 0.2988 |
| 0.4921        | 1.34  | 1000 | 1.0319          | 0.3010 |
| 0.856         | 1.48  | 1100 | 0.9514          | 0.3043 |
| 0.4479        | 1.61  | 1200 | 0.9079          | 0.2843 |
| 0.7249        | 1.75  | 1300 | 0.9612          | 0.2895 |
| 0.5384        | 1.88  | 1400 | 0.9050          | 0.2928 |
| 0.709         | 2.02  | 1500 | 0.9844          | 0.2735 |
| 0.6575        | 2.15  | 1600 | 0.9377          | 0.2772 |
| 0.6115        | 2.28  | 1700 | 0.9690          | 0.2876 |
| 0.3119        | 2.42  | 1800 | 0.9222          | 0.2798 |
| 0.3591        | 2.55  | 1900 | 0.9358          | 0.2783 |
| 0.3979        | 2.69  | 2000 | 0.9156          | 0.2702 |
| 0.7541        | 2.82  | 2100 | 0.8838          | 0.2761 |
| 0.81          | 2.96  | 2200 | 0.8460          | 0.2813 |
| 0.2224        | 3.09  | 2300 | 0.9377          | 0.2694 |
| 0.2338        | 3.23  | 2400 | 0.8870          | 0.2746 |
| 0.5315        | 3.36  | 2500 | 0.8782          | 0.2672 |
| 0.4045        | 3.49  | 2600 | 0.8811          | 0.2653 |
| 0.4874        | 3.63  | 2700 | 0.9059          | 0.2620 |
| 0.304         | 3.76  | 2800 | 0.8801          | 0.2690 |
| 1.4688        | 3.9   | 2900 | 0.8394          | 0.2698 |


### Framework versions

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