---
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: mms-SADA
  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-SADA

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: 1.1720
- Wer: 0.6627

## 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: 1e-05
- train_batch_size: 14
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 10000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.7672        | 0.0   | 250   | 1.3082          | 0.6868 |
| 1.7864        | 0.01  | 500   | 1.3002          | 0.6852 |
| 1.445         | 0.01  | 750   | 1.2947          | 0.6846 |
| 1.6083        | 0.01  | 1000  | 1.2921          | 0.6831 |
| 1.6405        | 0.02  | 1250  | 1.2864          | 0.6819 |
| 1.6112        | 0.02  | 1500  | 1.2774          | 0.6826 |
| 1.5307        | 0.02  | 1750  | 1.2730          | 0.6812 |
| 1.8135        | 0.02  | 2000  | 1.2694          | 0.6795 |
| 1.6133        | 0.03  | 2250  | 1.2660          | 0.6783 |
| 1.8358        | 0.03  | 2500  | 1.2633          | 0.6758 |
| 1.507         | 0.03  | 2750  | 1.2563          | 0.6774 |
| 1.7197        | 0.04  | 3000  | 1.2553          | 0.6750 |
| 1.5191        | 0.04  | 3250  | 1.2498          | 0.6737 |
| 1.4389        | 0.04  | 3500  | 1.2478          | 0.6734 |
| 1.6184        | 0.05  | 3750  | 1.2401          | 0.6723 |
| 1.6814        | 0.05  | 4000  | 1.2357          | 0.6716 |
| 1.4742        | 0.05  | 4250  | 1.2304          | 0.6708 |
| 1.4276        | 0.06  | 4500  | 1.2302          | 0.6700 |
| 1.4855        | 0.06  | 4750  | 1.2224          | 0.6693 |
| 1.4409        | 0.06  | 5000  | 1.2197          | 0.6693 |
| 1.4562        | 0.07  | 5250  | 1.2162          | 0.6688 |
| 1.5353        | 0.07  | 5500  | 1.2119          | 0.6689 |
| 1.5601        | 0.07  | 5750  | 1.2105          | 0.6696 |
| 1.4666        | 0.07  | 6000  | 1.2066          | 0.6679 |
| 1.6642        | 0.08  | 6250  | 1.2010          | 0.6687 |
| 1.5008        | 0.08  | 6500  | 1.2005          | 0.6669 |
| 1.6213        | 0.08  | 6750  | 1.2008          | 0.6665 |
| 1.7335        | 0.09  | 7000  | 1.1938          | 0.6675 |
| 1.421         | 0.09  | 7250  | 1.1921          | 0.6666 |
| 1.6255        | 0.09  | 7500  | 1.1919          | 0.6645 |
| 1.4785        | 0.1   | 7750  | 1.1895          | 0.6646 |
| 1.6736        | 0.1   | 8000  | 1.1918          | 0.6634 |
| 1.4629        | 0.1   | 8250  | 1.1841          | 0.6645 |
| 1.6599        | 0.11  | 8500  | 1.1832          | 0.6628 |
| 1.4726        | 0.11  | 8750  | 1.1790          | 0.6649 |
| 1.6825        | 0.11  | 9000  | 1.1774          | 0.6636 |
| 1.6216        | 0.11  | 9250  | 1.1815          | 0.6630 |
| 1.4291        | 0.12  | 9500  | 1.1768          | 0.6637 |
| 1.2947        | 0.12  | 9750  | 1.1743          | 0.6623 |
| 1.4702        | 0.12  | 10000 | 1.1720          | 0.6627 |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1
- Datasets 2.19.1
- Tokenizers 0.13.3