---
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-Wolof-20-hour-Mixed-dataset
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: fleurs
      type: fleurs
      config: wo_sn
      split: None
      args: wo_sn
    metrics:
    - name: Wer
      type: wer
      value: 0.4748502317169662
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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[<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/asr-africa-research-team/ASR%20Africa/runs/j93wiewo)
# MMS-Wolof-20-hour-Mixed-dataset

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: 1.3654
- Wer: 0.4749
- Cer: 0.1708

## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer    | Cer    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
| 9.5604        | 0.7369  | 500   | 0.7373          | 0.5227 | 0.1732 |
| 2.6975        | 1.4738  | 1000  | 0.6973          | 0.5301 | 0.1779 |
| 2.726         | 2.2108  | 1500  | 0.8814          | 0.5966 | 0.2092 |
| 2.8093        | 2.9477  | 2000  | 0.8858          | 0.6151 | 0.2303 |
| 2.78          | 3.6846  | 2500  | 0.8017          | 0.5918 | 0.2146 |
| 2.8066        | 4.4215  | 3000  | 1.0143          | 0.6655 | 0.2453 |
| 2.8916        | 5.1584  | 3500  | 0.8609          | 0.6299 | 0.2351 |
| 2.7853        | 5.8954  | 4000  | 0.8444          | 0.6100 | 0.2304 |
| 2.6374        | 6.6323  | 4500  | 0.9584          | 0.6420 | 0.2505 |
| 2.5197        | 7.3692  | 5000  | 0.9859          | 0.6703 | 0.2562 |
| 2.4943        | 8.1061  | 5500  | 0.8587          | 0.6267 | 0.2405 |
| 2.39          | 8.8430  | 6000  | 0.9456          | 0.6221 | 0.2388 |
| 2.2478        | 9.5800  | 6500  | 0.9030          | 0.6598 | 0.2536 |
| 2.1688        | 10.3169 | 7000  | 0.8462          | 0.6527 | 0.2567 |
| 2.0468        | 11.0538 | 7500  | 0.8851          | 0.6080 | 0.2302 |
| 1.9318        | 11.7907 | 8000  | 0.8898          | 0.6276 | 0.2301 |
| 1.8937        | 12.5276 | 8500  | 0.7846          | 0.5826 | 0.2191 |
| 1.7943        | 13.2646 | 9000  | 0.8560          | 0.6137 | 0.2326 |
| 1.7337        | 14.0015 | 9500  | 0.8789          | 0.6005 | 0.2271 |
| 1.608         | 14.7384 | 10000 | 0.8736          | 0.6092 | 0.2270 |
| 1.5533        | 15.4753 | 10500 | 0.9045          | 0.5951 | 0.2287 |
| 1.4501        | 16.2122 | 11000 | 0.8505          | 0.6288 | 0.2415 |
| 1.4074        | 16.9492 | 11500 | 0.8023          | 0.5853 | 0.2227 |
| 1.3339        | 17.6861 | 12000 | 0.8177          | 0.5967 | 0.2211 |
| 1.2208        | 18.4230 | 12500 | 0.8922          | 0.5761 | 0.2126 |
| 1.1803        | 19.1599 | 13000 | 0.8207          | 0.5637 | 0.2076 |
| 1.1159        | 19.8968 | 13500 | 0.8114          | 0.5473 | 0.2013 |
| 1.0415        | 20.6338 | 14000 | 0.8646          | 0.5533 | 0.2036 |
| 0.9767        | 21.3707 | 14500 | 0.9001          | 0.5569 | 0.2088 |
| 0.9803        | 22.1076 | 15000 | 0.8485          | 0.5696 | 0.2100 |
| 0.8933        | 22.8445 | 15500 | 0.8164          | 0.5452 | 0.2041 |
| 0.8509        | 23.5814 | 16000 | 0.9136          | 0.5545 | 0.2037 |
| 0.8398        | 24.3183 | 16500 | 0.8095          | 0.5350 | 0.1950 |
| 0.7741        | 25.0553 | 17000 | 0.9116          | 0.5448 | 0.1998 |
| 0.7303        | 25.7922 | 17500 | 0.9380          | 0.5339 | 0.1948 |
| 0.7132        | 26.5291 | 18000 | 0.8357          | 0.5143 | 0.1888 |
| 0.6655        | 27.2660 | 18500 | 0.9127          | 0.5495 | 0.2049 |
| 0.6452        | 28.0029 | 19000 | 0.8722          | 0.5258 | 0.1933 |
| 0.5913        | 28.7399 | 19500 | 0.9262          | 0.5227 | 0.1929 |
| 0.5792        | 29.4768 | 20000 | 0.9722          | 0.5239 | 0.1883 |
| 0.5528        | 30.2137 | 20500 | 0.9868          | 0.5259 | 0.1937 |
| 0.5488        | 30.9506 | 21000 | 0.9860          | 0.5268 | 0.1945 |
| 0.5023        | 31.6875 | 21500 | 0.9549          | 0.5134 | 0.1874 |
| 0.4668        | 32.4245 | 22000 | 1.0188          | 0.5200 | 0.1943 |
| 0.4751        | 33.1614 | 22500 | 1.0139          | 0.5112 | 0.1852 |
| 0.434         | 33.8983 | 23000 | 1.0354          | 0.5073 | 0.1815 |
| 0.4149        | 34.6352 | 23500 | 0.9920          | 0.5170 | 0.1874 |
| 0.4044        | 35.3721 | 24000 | 1.1387          | 0.5051 | 0.1840 |
| 0.3839        | 36.1091 | 24500 | 1.1052          | 0.5034 | 0.1848 |
| 0.3576        | 36.8460 | 25000 | 1.0593          | 0.4889 | 0.1811 |
| 0.3379        | 37.5829 | 25500 | 1.0930          | 0.5007 | 0.1823 |
| 0.336         | 38.3198 | 26000 | 1.1091          | 0.4968 | 0.1808 |
| 0.3148        | 39.0567 | 26500 | 1.1871          | 0.4993 | 0.1810 |
| 0.3005        | 39.7937 | 27000 | 1.1890          | 0.4993 | 0.1801 |
| 0.2964        | 40.5306 | 27500 | 1.1436          | 0.4899 | 0.1758 |
| 0.2731        | 41.2675 | 28000 | 1.1677          | 0.4940 | 0.1780 |
| 0.2641        | 42.0044 | 28500 | 1.1943          | 0.4943 | 0.1783 |
| 0.2435        | 42.7413 | 29000 | 1.2838          | 0.4906 | 0.1787 |
| 0.2486        | 43.4783 | 29500 | 1.2935          | 0.4938 | 0.1767 |
| 0.2259        | 44.2152 | 30000 | 1.3013          | 0.4898 | 0.1749 |
| 0.218         | 44.9521 | 30500 | 1.2965          | 0.4856 | 0.1749 |
| 0.2143        | 45.6890 | 31000 | 1.2891          | 0.4823 | 0.1739 |
| 0.195         | 46.4259 | 31500 | 1.3284          | 0.4791 | 0.1724 |
| 0.1943        | 47.1629 | 32000 | 1.3182          | 0.4805 | 0.1734 |
| 0.1851        | 47.8998 | 32500 | 1.3429          | 0.4769 | 0.1719 |
| 0.1833        | 48.6367 | 33000 | 1.3515          | 0.4723 | 0.1708 |
| 0.1739        | 49.3736 | 33500 | 1.3654          | 0.4749 | 0.1708 |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.1.0+cu118
- Datasets 2.17.0
- Tokenizers 0.20.3