---
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
- bleu
model-index:
- name: wav2vec2-mms-1b-malayalam-colab-CV17.0-v2
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: ml
      split: test
      args: ml
    metrics:
    - name: Wer
      type: wer
      value: 0.5283687943262412
    - name: Bleu
      type: bleu
      value: 0.1996948603256558
---

<!-- 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-mms-1b-malayalam-colab-CV17.0-v2

This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2965
- Wer: 0.5284
- Cer: 0.0934
- Bleu: 0.1997

## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.15
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    | Cer    | Bleu   |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|:------:|
| 5.5563        | 3.1496  | 200  | 0.3157          | 0.5580 | 0.1055 | 0.1800 |
| 0.3888        | 6.2992  | 400  | 0.2983          | 0.5471 | 0.1003 | 0.1906 |
| 0.3328        | 9.4488  | 600  | 0.3008          | 0.5542 | 0.1002 | 0.1634 |
| 0.3006        | 12.5984 | 800  | 0.2821          | 0.5368 | 0.0984 | 0.1888 |
| 0.2743        | 15.7480 | 1000 | 0.2913          | 0.5329 | 0.0968 | 0.1813 |
| 0.2461        | 18.8976 | 1200 | 0.2822          | 0.5319 | 0.0957 | 0.1937 |
| 0.2346        | 22.0472 | 1400 | 0.2933          | 0.5335 | 0.0942 | 0.1848 |
| 0.2112        | 25.1969 | 1600 | 0.2885          | 0.5300 | 0.0947 | 0.1900 |
| 0.2006        | 28.3465 | 1800 | 0.2944          | 0.5329 | 0.0939 | 0.1870 |
| 0.1879        | 31.4961 | 2000 | 0.2965          | 0.5284 | 0.0934 | 0.1997 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1