---
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-combined-30hrs-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-combined-30hrs-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: 0.2278
- Wer: 0.1922

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 16.9208       | 0.0399 | 100  | 4.2681          | 0.9999 |
| 7.2021        | 0.0798 | 200  | 3.4068          | 1.0027 |
| 6.725         | 0.1196 | 300  | 3.1580          | 1.0146 |
| 6.2214        | 0.1595 | 400  | 3.0439          | 1.0027 |
| 5.9996        | 0.1994 | 500  | 3.0050          | 1.0016 |
| 5.8133        | 0.2393 | 600  | 2.9415          | 1.0    |
| 5.805         | 0.2792 | 700  | 2.8951          | 0.9997 |
| 5.7621        | 0.3190 | 800  | 2.9054          | 0.9996 |
| 5.6973        | 0.3589 | 900  | 2.8109          | 0.9909 |
| 5.6541        | 0.3988 | 1000 | 2.8355          | 0.9911 |
| 5.4159        | 0.4387 | 1100 | 2.7632          | 0.9783 |
| 5.4112        | 0.4786 | 1200 | 2.6499          | 0.9793 |
| 4.2059        | 0.5184 | 1300 | 0.3675          | 0.2617 |
| 0.6493        | 0.5583 | 1400 | 0.2747          | 0.2077 |
| 0.5624        | 0.5982 | 1500 | 0.2649          | 0.2023 |
| 0.5197        | 0.6381 | 1600 | 0.2619          | 0.1988 |
| 0.4715        | 0.6780 | 1700 | 0.2589          | 0.1982 |
| 0.5126        | 0.7178 | 1800 | 0.2518          | 0.1979 |
| 0.4916        | 0.7577 | 1900 | 0.2549          | 0.1958 |
| 0.4667        | 0.7976 | 2000 | 0.2501          | 0.1947 |
| 0.4713        | 0.8375 | 2100 | 0.2479          | 0.1943 |
| 0.4875        | 0.8774 | 2200 | 0.2449          | 0.1931 |
| 0.4611        | 0.9172 | 2300 | 0.2436          | 0.1935 |
| 0.4587        | 0.9571 | 2400 | 0.2434          | 0.1928 |
| 0.4679        | 0.9970 | 2500 | 0.2409          | 0.1895 |
| 0.4141        | 1.0367 | 2600 | 0.2331          | 0.1896 |
| 0.4263        | 1.0766 | 2700 | 0.2329          | 0.1920 |
| 0.4142        | 1.1165 | 2800 | 0.2324          | 0.1918 |
| 0.4606        | 1.1563 | 2900 | 0.2257          | 0.1943 |
| 0.4048        | 1.1962 | 3000 | 0.2289          | 0.1928 |
| 0.4172        | 1.2361 | 3100 | 0.2326          | 0.1938 |
| 0.4294        | 1.2760 | 3200 | 0.2327          | 0.1941 |
| 0.468         | 1.3159 | 3300 | 0.2277          | 0.1922 |


### Framework versions

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