---
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- automatic-speech-recognition
- genbed
- mms
- generated_from_trainer
metrics:
- wer
model-index:
- name: mms-1b-bemgen-combined-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-bemgen-combined-model

This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the GENBED - BEM dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2591
- Wer: 0.4134

## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- 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    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 6.7553        | 0.0516 | 100  | 0.8774          | 0.8476 |
| 0.5648        | 0.1031 | 200  | 0.3408          | 0.5032 |
| 0.4827        | 0.1547 | 300  | 0.3261          | 0.4930 |
| 0.4321        | 0.2063 | 400  | 0.3036          | 0.4854 |
| 0.4168        | 0.2579 | 500  | 0.2989          | 0.4783 |
| 0.3965        | 0.3094 | 600  | 0.2907          | 0.4513 |
| 0.4199        | 0.3610 | 700  | 0.2926          | 0.4718 |
| 0.3975        | 0.4126 | 800  | 0.2886          | 0.4459 |
| 0.3839        | 0.4642 | 900  | 0.2908          | 0.4722 |
| 0.3673        | 0.5157 | 1000 | 0.2836          | 0.4445 |
| 0.3777        | 0.5673 | 1100 | 0.2784          | 0.4365 |
| 0.3764        | 0.6189 | 1200 | 0.2791          | 0.4278 |
| 0.3918        | 0.6704 | 1300 | 0.2757          | 0.4251 |
| 0.3669        | 0.7220 | 1400 | 0.2721          | 0.4182 |
| 0.377         | 0.7736 | 1500 | 0.2728          | 0.4757 |
| 0.4174        | 0.8252 | 1600 | 0.2684          | 0.4242 |
| 0.3641        | 0.8767 | 1700 | 0.2649          | 0.4195 |
| 0.3882        | 0.9283 | 1800 | 0.2647          | 0.4125 |
| 0.3861        | 0.9799 | 1900 | 0.2668          | 0.4425 |
| 0.3647        | 1.0315 | 2000 | 0.2675          | 0.4246 |
| 0.3467        | 1.0830 | 2100 | 0.2629          | 0.4098 |
| 0.3579        | 1.1346 | 2200 | 0.2587          | 0.4186 |
| 0.3544        | 1.1862 | 2300 | 0.2609          | 0.4127 |
| 0.35          | 1.2378 | 2400 | 0.2592          | 0.4062 |
| 0.3519        | 1.2893 | 2500 | 0.2591          | 0.4135 |


### Framework versions

- Transformers 4.48.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0