---
library_name: transformers
license: cc-by-nc-4.0
base_model: mms-meta/mms-zeroshot-300m
tags:
- automatic-speech-recognition
- BembaSpeech
- mms
- generated_from_trainer
metrics:
- wer
model-index:
- name: mms-zeroshot-300m-bem
  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-zeroshot-300m-bem

This model is a fine-tuned version of [mms-meta/mms-zeroshot-300m](https://huggingface.co/mms-meta/mms-zeroshot-300m) on the BEMBASPEECH - BEM dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1787
- Wer: 0.3583

## 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: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 4.6629        | 0.1778 | 500   | 0.3540          | 0.5421 |
| 0.6579        | 0.3556 | 1000  | 0.2588          | 0.4883 |
| 0.591         | 0.5334 | 1500  | 0.2552          | 0.4720 |
| 0.5467        | 0.7112 | 2000  | 0.2370          | 0.4542 |
| 0.5405        | 0.8890 | 2500  | 0.2376          | 0.4556 |
| 0.5027        | 1.0669 | 3000  | 0.2234          | 0.4307 |
| 0.5001        | 1.2447 | 3500  | 0.2176          | 0.4213 |
| 0.4962        | 1.4225 | 4000  | 0.2199          | 0.4205 |
| 0.486         | 1.6003 | 4500  | 0.2145          | 0.4167 |
| 0.47          | 1.7781 | 5000  | 0.2159          | 0.4169 |
| 0.4557        | 1.9559 | 5500  | 0.2099          | 0.4135 |
| 0.4514        | 2.1337 | 6000  | 0.2091          | 0.4100 |
| 0.4539        | 2.3115 | 6500  | 0.2038          | 0.4016 |
| 0.439         | 2.4893 | 7000  | 0.2041          | 0.4025 |
| 0.4378        | 2.6671 | 7500  | 0.2002          | 0.3916 |
| 0.4347        | 2.8450 | 8000  | 0.1961          | 0.3911 |
| 0.4278        | 3.0228 | 8500  | 0.1995          | 0.3923 |
| 0.4117        | 3.2006 | 9000  | 0.1959          | 0.3892 |
| 0.4149        | 3.3784 | 9500  | 0.1926          | 0.3859 |
| 0.4148        | 3.5562 | 10000 | 0.1958          | 0.3804 |
| 0.4009        | 3.7340 | 10500 | 0.1930          | 0.3790 |
| 0.4174        | 3.9118 | 11000 | 0.1955          | 0.3823 |
| 0.4012        | 4.0896 | 11500 | 0.1950          | 0.3812 |
| 0.3974        | 4.2674 | 12000 | 0.1934          | 0.3773 |
| 0.3943        | 4.4452 | 12500 | 0.1845          | 0.3720 |
| 0.4071        | 4.6230 | 13000 | 0.1920          | 0.3839 |
| 0.3968        | 4.8009 | 13500 | 0.1867          | 0.3743 |
| 0.3795        | 4.9787 | 14000 | 0.1872          | 0.3713 |
| 0.3856        | 5.1565 | 14500 | 0.1869          | 0.3737 |
| 0.3706        | 5.3343 | 15000 | 0.1903          | 0.3766 |
| 0.3784        | 5.5121 | 15500 | 0.1861          | 0.3683 |
| 0.3777        | 5.6899 | 16000 | 0.1866          | 0.3713 |
| 0.3861        | 5.8677 | 16500 | 0.1812          | 0.3637 |
| 0.3711        | 6.0455 | 17000 | 0.1842          | 0.3667 |
| 0.374         | 6.2233 | 17500 | 0.1815          | 0.3618 |
| 0.3539        | 6.4011 | 18000 | 0.1815          | 0.3647 |
| 0.3625        | 6.5789 | 18500 | 0.1785          | 0.3589 |
| 0.3599        | 6.7568 | 19000 | 0.1795          | 0.3621 |
| 0.3654        | 6.9346 | 19500 | 0.1822          | 0.3624 |
| 0.3693        | 7.1124 | 20000 | 0.1792          | 0.3612 |
| 0.3519        | 7.2902 | 20500 | 0.1800          | 0.3675 |
| 0.3553        | 7.4680 | 21000 | 0.1808          | 0.3640 |
| 0.3451        | 7.6458 | 21500 | 0.1808          | 0.3620 |
| 0.3558        | 7.8236 | 22000 | 0.1794          | 0.3610 |
| 0.3595        | 8.0014 | 22500 | 0.1772          | 0.3576 |
| 0.3404        | 8.1792 | 23000 | 0.1788          | 0.3581 |
| 0.3593        | 8.3570 | 23500 | 0.1782          | 0.3580 |
| 0.3471        | 8.5349 | 24000 | 0.1797          | 0.3606 |
| 0.3497        | 8.7127 | 24500 | 0.1778          | 0.3588 |
| 0.3398        | 8.8905 | 25000 | 0.1775          | 0.3583 |
| 0.3444        | 9.0683 | 25500 | 0.1796          | 0.3586 |
| 0.3366        | 9.2461 | 26000 | 0.1785          | 0.3574 |
| 0.3434        | 9.4239 | 26500 | 0.1781          | 0.3592 |
| 0.3426        | 9.6017 | 27000 | 0.1786          | 0.3593 |
| 0.3496        | 9.7795 | 27500 | 0.1787          | 0.3590 |
| 0.334         | 9.9573 | 28000 | 0.1788          | 0.3588 |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1