---
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-all-bem-genbed-m-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-all-bem-genbed-m-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: 0.2887
- Wer: 0.4119

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- 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    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 6.6677        | 0.1379 | 100  | 1.1908          | 0.9662 |
| 0.7337        | 0.2759 | 200  | 0.4132          | 0.5658 |
| 0.5661        | 0.4138 | 300  | 0.3767          | 0.5508 |
| 0.523         | 0.5517 | 400  | 0.3591          | 0.5009 |
| 0.5391        | 0.6897 | 500  | 0.3530          | 0.5054 |
| 0.4978        | 0.8276 | 600  | 0.3496          | 0.5096 |
| 0.4737        | 0.9655 | 700  | 0.3478          | 0.5079 |
| 0.4878        | 1.1034 | 800  | 0.3390          | 0.4737 |
| 0.4737        | 1.2414 | 900  | 0.3304          | 0.4905 |
| 0.4603        | 1.3793 | 1000 | 0.3328          | 0.4842 |
| 0.4793        | 1.5172 | 1100 | 0.3252          | 0.4640 |
| 0.4359        | 1.6552 | 1200 | 0.3252          | 0.4659 |
| 0.457         | 1.7931 | 1300 | 0.3204          | 0.4717 |
| 0.469         | 1.9310 | 1400 | 0.3185          | 0.4697 |
| 0.4894        | 2.0690 | 1500 | 0.3167          | 0.4622 |
| 0.4386        | 2.2069 | 1600 | 0.3185          | 0.4646 |
| 0.4441        | 2.3448 | 1700 | 0.3099          | 0.4607 |
| 0.444         | 2.4828 | 1800 | 0.3154          | 0.4551 |
| 0.4065        | 2.6207 | 1900 | 0.3138          | 0.4623 |
| 0.4163        | 2.7586 | 2000 | 0.3087          | 0.4377 |
| 0.4518        | 2.8966 | 2100 | 0.3054          | 0.4495 |
| 0.4208        | 3.0345 | 2200 | 0.3037          | 0.4512 |
| 0.381         | 3.1724 | 2300 | 0.3074          | 0.4386 |
| 0.4203        | 3.3103 | 2400 | 0.2989          | 0.4244 |
| 0.4556        | 3.4483 | 2500 | 0.3080          | 0.4835 |
| 0.4143        | 3.5862 | 2600 | 0.2956          | 0.4222 |
| 0.4055        | 3.7241 | 2700 | 0.3023          | 0.4581 |
| 0.4102        | 3.8621 | 2800 | 0.2955          | 0.4412 |
| 0.4451        | 4.0    | 2900 | 0.2944          | 0.4181 |
| 0.3857        | 4.1379 | 3000 | 0.2985          | 0.4426 |
| 0.4071        | 4.2759 | 3100 | 0.2918          | 0.4290 |
| 0.4           | 4.4138 | 3200 | 0.2951          | 0.4321 |
| 0.4257        | 4.5517 | 3300 | 0.3035          | 0.4608 |
| 0.3929        | 4.6897 | 3400 | 0.2965          | 0.4131 |
| 0.3957        | 4.8276 | 3500 | 0.2938          | 0.4397 |
| 0.3974        | 4.9655 | 3600 | 0.2887          | 0.4119 |
| 0.3733        | 5.1034 | 3700 | 0.2890          | 0.4050 |
| 0.382         | 5.2414 | 3800 | 0.2917          | 0.4233 |
| 0.3669        | 5.3793 | 3900 | 0.2900          | 0.4313 |


### Framework versions

- Transformers 4.46.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0