---
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/mms-1b-fl102
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: mms-1b-fl102-xho-5
  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-1b-fl102-xho-5

This model is a fine-tuned version of [facebook/mms-1b-fl102](https://huggingface.co/facebook/mms-1b-fl102) on the [lelapa/Vukuzenzele_isiXhosa_Speech_Dataset_ViXSD](https://huggingface.co/datasets/lelapa/Vukuzenzele_isiXhosa_Speech_Dataset_ViXSD) for 5 epochs.

It achieves the following results on the evaluation set:
- Loss: 0.2675
- Wer: 0.3656

## Model description

Massively Multilingual Speech (MMS) - Finetuned ASR - FL102

We finetune the MMS - FL102 checkpoint on 7hrs of isiXhosa Speech Data, which is a model fine-tuned for multi-lingual ASR and part of Facebook's Massive Multilingual Speech project. The checkpoint is based on the Wav2Vec2 architecture and makes use of adapter models to transcribe 100+ languages. The checkpoint consists of 1 billion parameters and has been fine-tuned from facebook/mms-1b on 102 languages of Fleurs.

## Intended uses & limitations

The datasets created and used for the model benchmarks are taken solely from South African government magazine resources. Therefore it is high-lighted that this model might ignore certain social/societal structures and will be representative of the dominant political views at the time the dataset was sourced. 

## Training and evaluation data

[lelapa/Vukuzenzele_isiXhosa_Speech_Dataset_ViXSD](https://huggingface.co/datasets/lelapa/Vukuzenzele_isiXhosa_Speech_Dataset_ViXSD)

## 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
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.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_steps: 8
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 9.1848        | 1.0   | 10   | 3.3243          | 0.9999 |
| 1.8736        | 2.0   | 20   | 0.5765          | 0.5415 |
| 0.3856        | 3.0   | 30   | 0.3047          | 0.3908 |
| 0.2553        | 4.0   | 40   | 0.2675          | 0.3656 |


### Framework versions

- Transformers 4.49.0.dev0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0