---
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
datasets:
- fleurs
metrics:
- wer
model-index:
- name: mms-1b-all-lg-CV-Fleurs_filtered-100hrs-v2
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: fleurs
      type: fleurs
      config: lg_ug
      split: test
      args: lg_ug
    metrics:
    - name: Wer
      type: wer
      value: 0.4090379008746356
---

<!-- 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-lg-CV-Fleurs_filtered-100hrs-v2

This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2933
- Wer: 0.4090
- Cer: 0.0749

## 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: 4
- eval_batch_size: 2
- 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
- num_epochs: 70
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|
| 0.7083        | 1.0   | 7125   | 0.3181          | 0.4363 | 0.0781 |
| 0.2153        | 2.0   | 14250  | 0.3057          | 0.4327 | 0.0775 |
| 0.2092        | 3.0   | 21375  | 0.2982          | 0.4040 | 0.0738 |
| 0.207         | 4.0   | 28500  | 0.3020          | 0.4057 | 0.0740 |
| 0.2047        | 5.0   | 35625  | 0.3008          | 0.4136 | 0.0790 |
| 0.2025        | 6.0   | 42750  | 0.3010          | 0.4156 | 0.0763 |
| 0.1989        | 7.0   | 49875  | 0.3064          | 0.4101 | 0.0754 |
| 0.1989        | 8.0   | 57000  | 0.2903          | 0.4086 | 0.0751 |
| 0.1973        | 9.0   | 64125  | 0.2927          | 0.4    | 0.0737 |
| 0.1961        | 10.0  | 71250  | 0.2882          | 0.3986 | 0.0736 |
| 0.1952        | 11.0  | 78375  | 0.2895          | 0.4068 | 0.0741 |
| 0.1943        | 12.0  | 85500  | 0.2950          | 0.4096 | 0.0754 |
| 0.1933        | 13.0  | 92625  | 0.2945          | 0.4086 | 0.0749 |
| 0.1926        | 14.0  | 99750  | 0.2933          | 0.4004 | 0.0734 |
| 0.1912        | 15.0  | 106875 | 0.2925          | 0.4180 | 0.0755 |
| 0.1909        | 16.0  | 114000 | 0.2949          | 0.4149 | 0.0751 |
| 0.1902        | 17.0  | 121125 | 0.2888          | 0.4045 | 0.0740 |
| 0.189         | 18.0  | 128250 | 0.2856          | 0.4086 | 0.0744 |
| 0.1885        | 19.0  | 135375 | 0.2933          | 0.4125 | 0.0745 |
| 0.187         | 20.0  | 142500 | 0.2930          | 0.4115 | 0.0746 |
| 0.1877        | 21.0  | 149625 | 0.2886          | 0.4023 | 0.0737 |
| 0.1867        | 22.0  | 156750 | 0.2933          | 0.4009 | 0.0730 |
| 0.1863        | 23.0  | 163875 | 0.2893          | 0.4040 | 0.0738 |
| 0.1846        | 24.0  | 171000 | 0.2920          | 0.4146 | 0.0753 |
| 0.185         | 25.0  | 178125 | 0.2907          | 0.4017 | 0.0730 |
| 0.1836        | 26.0  | 185250 | 0.2939          | 0.3992 | 0.0730 |
| 0.1827        | 27.0  | 192375 | 0.2934          | 0.4144 | 0.0760 |
| 0.1827        | 28.0  | 199500 | 0.2962          | 0.4038 | 0.0736 |
| 0.1818        | 29.0  | 206625 | 0.2917          | 0.4063 | 0.0750 |
| 0.1818        | 30.0  | 213750 | 0.2933          | 0.4090 | 0.0749 |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.1.0+cu118
- Datasets 3.1.0
- Tokenizers 0.20.3