---
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
datasets:
- common_voice_16_1
metrics:
- wer
model-index:
- name: wav2vec2-large-mms-1b-yoruba-test
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_16_1
      type: common_voice_16_1
      config: yo
      split: test
      args: yo
    metrics:
    - name: Wer
      type: wer
      value: 0.6802364381733245
language:
- yo
---

<!-- 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. -->

# wav2vec2-large-mms-1b-yoruba-test

This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the common_voice_16_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6682
- Wer: 0.6802


Finetuned by Daniel Ogbuigwe

## Model description

This checkpoint is a model fine-tuned for multi-lingual ASR using Facebook's Massive Multilingual Speech project. This checkpoint is based on the Wav2Vec2 architecture and makes use of adapter models to transcribe 1000+ languages. The checkpoint consists of 1 billion parameters and has been fine-tuned from facebook/mms-1b on Yoruba.

## Intended uses & limitations

More information needed

## Training and evaluation data

Common Voice 16.1 Yoruba data

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- 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: 100
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.8923        | 0.77  | 100  | 0.7710          | 0.7413 |
| 0.7507        | 1.54  | 200  | 0.7249          | 0.7585 |
| 0.7033        | 2.31  | 300  | 0.7105          | 0.7247 |
| 0.6888        | 3.08  | 400  | 0.6829          | 0.7229 |
| 0.6471        | 3.85  | 500  | 0.6682          | 0.6802 |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0