---
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: MMS-Adapter-Testing
  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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/tamimhasanbhuiyan/huggingface/runs/qiuhht9t)
# MMS-Adapter-Testing

This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9386
- Wer: 0.6378

## 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: 0.001
- train_batch_size: 2
- 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: 10
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 5.768         | 0.0150 | 100  | 2.1552          | 0.9390 |
| 2.3489        | 0.0299 | 200  | 1.1714          | 0.7209 |
| 1.7924        | 0.0449 | 300  | 1.1720          | 0.7586 |
| 1.7483        | 0.0598 | 400  | 1.0868          | 0.7237 |
| 1.8404        | 0.0748 | 500  | 1.0824          | 0.6963 |
| 1.8122        | 0.0897 | 600  | 1.0771          | 0.6866 |
| 1.7504        | 0.1047 | 700  | 1.0705          | 0.6970 |
| 1.6675        | 0.1196 | 800  | 1.0688          | 0.6913 |
| 1.6123        | 0.1346 | 900  | 1.0446          | 0.6888 |
| 1.6237        | 0.1495 | 1000 | 1.0586          | 0.7034 |
| 1.6714        | 0.1645 | 1100 | 1.0562          | 0.6866 |
| 1.8129        | 0.1795 | 1200 | 1.0363          | 0.6891 |
| 1.7839        | 0.1944 | 1300 | 1.0374          | 0.6631 |
| 1.7305        | 0.2094 | 1400 | 1.0211          | 0.6834 |
| 1.5496        | 0.2243 | 1500 | 1.0225          | 0.6856 |
| 1.5106        | 0.2393 | 1600 | 1.0387          | 0.7127 |
| 1.7517        | 0.2542 | 1700 | 1.0561          | 0.6898 |
| 1.7117        | 0.2692 | 1800 | 1.0303          | 0.6866 |
| 1.6854        | 0.2841 | 1900 | 1.0240          | 0.6888 |
| 1.5186        | 0.2991 | 2000 | 1.0207          | 0.6873 |
| 1.5631        | 0.3140 | 2100 | 0.9964          | 0.6677 |
| 1.6909        | 0.3290 | 2200 | 1.0090          | 0.6738 |
| 1.5698        | 0.3440 | 2300 | 1.0016          | 0.6809 |
| 1.6702        | 0.3589 | 2400 | 0.9996          | 0.6749 |
| 1.628         | 0.3739 | 2500 | 1.0074          | 0.6699 |
| 1.8025        | 0.3888 | 2600 | 1.0312          | 0.6934 |
| 1.5986        | 0.4038 | 2700 | 0.9871          | 0.6667 |
| 1.5687        | 0.4187 | 2800 | 0.9893          | 0.6567 |
| 1.6444        | 0.4337 | 2900 | 0.9943          | 0.6674 |
| 1.5869        | 0.4486 | 3000 | 0.9831          | 0.6706 |
| 1.443         | 0.4636 | 3100 | 1.0192          | 0.7045 |
| 1.569         | 0.4785 | 3200 | 0.9783          | 0.6635 |
| 1.5302        | 0.4935 | 3300 | 0.9898          | 0.6727 |
| 1.5879        | 0.5084 | 3400 | 0.9773          | 0.6670 |
| 1.5739        | 0.5234 | 3500 | 0.9837          | 0.6895 |
| 1.5684        | 0.5384 | 3600 | 0.9836          | 0.6667 |
| 1.6397        | 0.5533 | 3700 | 0.9673          | 0.6578 |
| 1.5639        | 0.5683 | 3800 | 0.9888          | 0.6599 |
| 1.6773        | 0.5832 | 3900 | 0.9788          | 0.6613 |
| 1.5069        | 0.5982 | 4000 | 0.9801          | 0.6542 |
| 1.4801        | 0.6131 | 4100 | 0.9587          | 0.6545 |
| 1.7308        | 0.6281 | 4200 | 0.9599          | 0.6706 |
| 1.4852        | 0.6430 | 4300 | 0.9728          | 0.6663 |
| 1.4654        | 0.6580 | 4400 | 0.9468          | 0.6417 |
| 1.801         | 0.6729 | 4500 | 0.9591          | 0.6556 |
| 2.0928        | 0.6879 | 4600 | 0.9857          | 0.6670 |
| 1.561         | 0.7029 | 4700 | 0.9550          | 0.6503 |
| 1.6623        | 0.7178 | 4800 | 0.9587          | 0.6524 |
| 1.5252        | 0.7328 | 4900 | 0.9551          | 0.6531 |
| 1.5539        | 0.7477 | 5000 | 0.9660          | 0.6513 |
| 1.5571        | 0.7627 | 5100 | 0.9557          | 0.6531 |
| 1.6584        | 0.7776 | 5200 | 0.9649          | 0.6563 |
| 1.5072        | 0.7926 | 5300 | 0.9604          | 0.6481 |
| 1.5362        | 0.8075 | 5400 | 0.9457          | 0.6314 |
| 1.4772        | 0.8225 | 5500 | 0.9491          | 0.6449 |
| 1.3731        | 0.8374 | 5600 | 0.9609          | 0.6478 |
| 1.5795        | 0.8524 | 5700 | 0.9568          | 0.6567 |
| 1.4013        | 0.8674 | 5800 | 0.9457          | 0.6406 |
| 1.5817        | 0.8823 | 5900 | 0.9437          | 0.6513 |
| 1.4211        | 0.8973 | 6000 | 0.9433          | 0.6381 |
| 1.4341        | 0.9122 | 6100 | 0.9420          | 0.6353 |
| 1.4818        | 0.9272 | 6200 | 0.9407          | 0.6456 |
| 1.5241        | 0.9421 | 6300 | 0.9400          | 0.6381 |
| 1.575         | 0.9571 | 6400 | 0.9374          | 0.6392 |
| 1.5232        | 0.9720 | 6500 | 0.9385          | 0.6364 |
| 1.8634        | 0.9870 | 6600 | 0.9386          | 0.6378 |


### Framework versions

- Transformers 4.41.0.dev0
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.19.1