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

This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the BEMGEN - BEM dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1921
- Wer: 0.3440

## 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    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 6.6524        | 0.1028 | 100  | 0.6406          | 0.6924 |
| 0.4626        | 0.2055 | 200  | 0.2750          | 0.4520 |
| 0.3384        | 0.3083 | 300  | 0.2556          | 0.4587 |
| 0.3391        | 0.4111 | 400  | 0.2391          | 0.4119 |
| 0.2928        | 0.5139 | 500  | 0.2326          | 0.4009 |
| 0.3126        | 0.6166 | 600  | 0.2275          | 0.4031 |
| 0.3305        | 0.7194 | 700  | 0.2208          | 0.3978 |
| 0.3043        | 0.8222 | 800  | 0.2123          | 0.3804 |
| 0.2989        | 0.9250 | 900  | 0.2135          | 0.3793 |
| 0.2911        | 1.0277 | 1000 | 0.2117          | 0.3890 |
| 0.2994        | 1.1305 | 1100 | 0.2066          | 0.3868 |
| 0.2849        | 1.2333 | 1200 | 0.2113          | 0.3932 |
| 0.2864        | 1.3361 | 1300 | 0.2018          | 0.3713 |
| 0.2611        | 1.4388 | 1400 | 0.2005          | 0.4004 |
| 0.2995        | 1.5416 | 1500 | 0.1996          | 0.3729 |
| 0.2787        | 1.6444 | 1600 | 0.2015          | 0.3647 |
| 0.2444        | 1.7472 | 1700 | 0.1988          | 0.3603 |
| 0.2734        | 1.8499 | 1800 | 0.1950          | 0.3590 |
| 0.2794        | 1.9527 | 1900 | 0.1953          | 0.3546 |
| 0.2708        | 2.0555 | 2000 | 0.1934          | 0.3610 |
| 0.2545        | 2.1583 | 2100 | 0.1953          | 0.3616 |
| 0.2529        | 2.2610 | 2200 | 0.1940          | 0.3559 |
| 0.2628        | 2.3638 | 2300 | 0.1926          | 0.3585 |
| 0.2788        | 2.4666 | 2400 | 0.1925          | 0.3510 |
| 0.2473        | 2.5694 | 2500 | 0.1878          | 0.3499 |
| 0.2595        | 2.6721 | 2600 | 0.1911          | 0.3705 |
| 0.2516        | 2.7749 | 2700 | 0.1883          | 0.3480 |
| 0.2445        | 2.8777 | 2800 | 0.1921          | 0.3442 |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0