---
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-nyagen-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-nyagen-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: 0.1727
- Wer: 0.2465

## 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: 4
- 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    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 6.9978        | 0.1364 | 100  | 0.6384          | 0.5015 |
| 0.482         | 0.2729 | 200  | 0.2777          | 0.3713 |
| 0.3907        | 0.4093 | 300  | 0.2484          | 0.3481 |
| 0.3782        | 0.5457 | 400  | 0.2290          | 0.3232 |
| 0.3316        | 0.6821 | 500  | 0.2222          | 0.3148 |
| 0.3158        | 0.8186 | 600  | 0.2127          | 0.3042 |
| 0.3199        | 0.9550 | 700  | 0.2106          | 0.2932 |
| 0.3223        | 1.0914 | 800  | 0.2013          | 0.2826 |
| 0.3075        | 1.2278 | 900  | 0.1975          | 0.2709 |
| 0.3015        | 1.3643 | 1000 | 0.1942          | 0.2762 |
| 0.3049        | 1.5007 | 1100 | 0.1895          | 0.2729 |
| 0.3029        | 1.6371 | 1200 | 0.1888          | 0.2718 |
| 0.2626        | 1.7735 | 1300 | 0.1866          | 0.2683 |
| 0.2803        | 1.9100 | 1400 | 0.1830          | 0.2615 |
| 0.2725        | 2.0464 | 1500 | 0.1814          | 0.2626 |
| 0.2732        | 2.1828 | 1600 | 0.1783          | 0.2641 |
| 0.249         | 2.3192 | 1700 | 0.1828          | 0.2560 |
| 0.2423        | 2.4557 | 1800 | 0.1762          | 0.2480 |
| 0.2668        | 2.5921 | 1900 | 0.1732          | 0.2458 |
| 0.2653        | 2.7285 | 2000 | 0.1727          | 0.2460 |
| 0.2614        | 2.8649 | 2100 | 0.1749          | 0.2533 |
| 0.2474        | 3.0014 | 2200 | 0.1733          | 0.2438 |
| 0.2317        | 3.1378 | 2300 | 0.1767          | 0.2447 |


### Framework versions

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