---
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- automatic-speech-recognition
- natbed
- mms
- generated_from_trainer
metrics:
- wer
model-index:
- name: mms-1b-all-bem-natbed-n-model
  results: []
---

<!-- 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. -->

# mms-1b-all-bem-natbed-n-model

This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co./facebook/mms-1b-all) on the NATBED - BEM dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5309
- Wer: 0.4631

## 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: 8
- 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: 30.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 7.3623        | 0.2809 | 100  | 0.9287          | 0.7283 |
| 0.9213        | 0.5618 | 200  | 0.6511          | 0.5931 |
| 0.7224        | 0.8427 | 300  | 0.6387          | 0.5434 |
| 0.7132        | 1.1236 | 400  | 0.6140          | 0.5213 |
| 0.7195        | 1.4045 | 500  | 0.6097          | 0.5146 |
| 0.7054        | 1.6854 | 600  | 0.6145          | 0.5126 |
| 0.7417        | 1.9663 | 700  | 0.6062          | 0.5257 |
| 0.7029        | 2.2472 | 800  | 0.6022          | 0.4947 |
| 0.6845        | 2.5281 | 900  | 0.5886          | 0.5023 |
| 0.663         | 2.8090 | 1000 | 0.5915          | 0.4926 |
| 0.7129        | 3.0899 | 1100 | 0.5833          | 0.4920 |
| 0.6735        | 3.3708 | 1200 | 0.5877          | 0.4832 |
| 0.672         | 3.6517 | 1300 | 0.5863          | 0.5151 |
| 0.6494        | 3.9326 | 1400 | 0.5795          | 0.4844 |
| 0.7049        | 4.2135 | 1500 | 0.5724          | 0.4716 |
| 0.5898        | 4.4944 | 1600 | 0.5640          | 0.4762 |
| 0.6581        | 4.7753 | 1700 | 0.5582          | 0.4724 |
| 0.6262        | 5.0562 | 1800 | 0.5447          | 0.4751 |
| 0.6179        | 5.3371 | 1900 | 0.5497          | 0.4656 |
| 0.5896        | 5.6180 | 2000 | 0.5444          | 0.4779 |
| 0.6438        | 5.8989 | 2100 | 0.5399          | 0.4700 |
| 0.6086        | 6.1798 | 2200 | 0.5520          | 0.4598 |
| 0.6226        | 6.4607 | 2300 | 0.5386          | 0.4797 |
| 0.6148        | 6.7416 | 2400 | 0.5574          | 0.4680 |
| 0.5838        | 7.0225 | 2500 | 0.5497          | 0.4639 |
| 0.5407        | 7.3034 | 2600 | 0.5377          | 0.4631 |
| 0.6186        | 7.5843 | 2700 | 0.5404          | 0.4715 |
| 0.5922        | 7.8652 | 2800 | 0.5381          | 0.4609 |
| 0.5799        | 8.1461 | 2900 | 0.5312          | 0.4620 |
| 0.5914        | 8.4270 | 3000 | 0.5309          | 0.4631 |
| 0.6194        | 8.7079 | 3100 | 0.5317          | 0.4678 |
| 0.5851        | 8.9888 | 3200 | 0.5389          | 0.4575 |
| 0.5764        | 9.2697 | 3300 | 0.5579          | 0.4550 |


### Framework versions

- Transformers 4.46.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0