---
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-even-pakendorf
  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: 0.7591335595927331
---

<!-- 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-even-pakendorf

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: inf
- Wer: 0.7591
- Cer: 0.2779

## 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 1.847         | 0.1895 | 300  | inf             | 0.9027 | 0.3662 |
| 1.8253        | 0.3790 | 600  | inf             | 0.9087 | 0.3658 |
| 1.6956        | 0.5685 | 900  | inf             | 0.8723 | 0.3412 |
| 1.6616        | 0.7581 | 1200 | inf             | 0.8437 | 0.3209 |
| 1.5962        | 0.9476 | 1500 | inf             | 0.8392 | 0.3217 |
| 1.6299        | 1.1371 | 1800 | inf             | 0.8447 | 0.3201 |
| 1.5242        | 1.3266 | 2100 | inf             | 0.8191 | 0.3076 |
| 1.582         | 1.5161 | 2400 | inf             | 0.8157 | 0.3070 |
| 1.5555        | 1.7056 | 2700 | inf             | 0.8092 | 0.3061 |
| 1.5476        | 1.8951 | 3000 | inf             | 0.7999 | 0.3009 |
| 1.4725        | 2.0846 | 3300 | inf             | 0.7945 | 0.2952 |
| 1.4902        | 2.2742 | 3600 | inf             | 0.7834 | 0.2936 |
| 1.3984        | 2.4637 | 3900 | inf             | 0.7836 | 0.2900 |
| 1.4633        | 2.6532 | 4200 | inf             | 0.7942 | 0.2872 |
| 1.4533        | 2.8427 | 4500 | inf             | 0.7804 | 0.2863 |
| 1.4814        | 3.0322 | 4800 | inf             | 0.7728 | 0.2859 |
| 1.4397        | 3.2217 | 5100 | inf             | 0.7693 | 0.2818 |
| 1.4218        | 3.4112 | 5400 | inf             | 0.7702 | 0.2831 |
| 1.3655        | 3.6008 | 5700 | inf             | 0.7650 | 0.2795 |
| 1.34          | 3.7903 | 6000 | inf             | 0.7615 | 0.2792 |
| 1.3351        | 3.9798 | 6300 | inf             | 0.7591 | 0.2779 |


### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1