---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-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: 0.7946486137975499
---

<!-- 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-xls-r-300m-malayalam-colab-CV17.0-v2

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9415
- Wer: 0.7946
- Cer: 0.1990

## 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: 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    |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 8.3824        | 3.1496  | 200  | 3.5244          | 1.0    | 1.0    |
| 2.8615        | 6.2992  | 400  | 1.4480          | 0.9716 | 0.3680 |
| 0.8112        | 9.4488  | 600  | 0.9231          | 0.9188 | 0.2573 |
| 0.4211        | 12.5984 | 800  | 0.9136          | 0.8843 | 0.2477 |
| 0.2862        | 15.7480 | 1000 | 0.9257          | 0.8533 | 0.2370 |
| 0.21          | 18.8976 | 1200 | 0.9450          | 0.8185 | 0.2188 |
| 0.1772        | 22.0472 | 1400 | 0.9285          | 0.8343 | 0.2151 |
| 0.1432        | 25.1969 | 1600 | 0.9596          | 0.8262 | 0.2110 |
| 0.117         | 28.3465 | 1800 | 0.9419          | 0.7985 | 0.2026 |
| 0.1047        | 31.4961 | 2000 | 0.9415          | 0.7946 | 0.1990 |


### Framework versions

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