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

This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the SWAGEN - SWA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2065
- Wer: 0.1900

## 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
- training_steps: 2500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 8.5907        | 0.1176 | 100  | 4.5590          | 1.0    |
| 4.3006        | 0.2353 | 200  | 4.2573          | 1.0    |
| 4.1906        | 0.3529 | 300  | 3.8436          | 1.0    |
| 1.1198        | 0.4706 | 400  | 0.2569          | 0.1996 |
| 0.3012        | 0.5882 | 500  | 0.2466          | 0.1942 |
| 0.2919        | 0.7059 | 600  | 0.2501          | 0.1990 |
| 0.267         | 0.8235 | 700  | 0.2353          | 0.1894 |
| 0.2666        | 0.9412 | 800  | 0.2280          | 0.1904 |
| 0.2396        | 1.0588 | 900  | 0.2238          | 0.1892 |
| 0.258         | 1.1765 | 1000 | 0.2202          | 0.1898 |
| 0.2192        | 1.2941 | 1100 | 0.2196          | 0.1938 |
| 0.2353        | 1.4118 | 1200 | 0.2169          | 0.1875 |
| 0.2398        | 1.5294 | 1300 | 0.2164          | 0.1896 |
| 0.2419        | 1.6471 | 1400 | 0.2146          | 0.1913 |
| 0.2582        | 1.7647 | 1500 | 0.2122          | 0.1905 |
| 0.2417        | 1.8824 | 1600 | 0.2105          | 0.1861 |
| 0.2375        | 2.0    | 1700 | 0.2091          | 0.1892 |
| 0.2353        | 2.1176 | 1800 | 0.2087          | 0.1882 |
| 0.23          | 2.2353 | 1900 | 0.2091          | 0.1904 |
| 0.2378        | 2.3529 | 2000 | 0.2067          | 0.1898 |
| 0.2343        | 2.4706 | 2100 | 0.2061          | 0.1890 |
| 0.2083        | 2.5882 | 2200 | 0.2084          | 0.1884 |
| 0.2058        | 2.7059 | 2300 | 0.2085          | 0.1882 |
| 0.2197        | 2.8235 | 2400 | 0.2062          | 0.1904 |
| 0.2317        | 2.9412 | 2500 | 0.2065          | 0.1896 |


### Framework versions

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