---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- thennal/IMaSC
- vrclc/openslr63
- thennal/indic_tts_ml
- kavyamanohar/ml-sentences
model-index:
- name: XLSR-WithLM-Malayalam
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: OpenSLR Malayalam -Test
      type: vrclc/openslr63
      config: ml
      split: test
      args: ml
    metrics:
    - type: wer
      value: 27.3
      name: WER
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Goole Fleurs
      type: google/fleurs
      config: ml
      split: test
      args: ml
    metrics:
    - type: wer
      value: 37.2
      name: WER
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: MSC
      type: smcproject/msc
      config: ml
      split: train
      args: ml
    metrics:
    - type: wer
      value: 52.9
      name: WER
---
# XLSR-WithLM-Malayalam

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the [IMASC](https://huggingface.co/datasets/thennal/IMaSC), [Indic TTS Malayalam](https://huggingface.co/datasets/thennal/indic_tts_ml), [OpenSLR Malayalam Train split](https://huggingface.co/datasets/vrclc/openslr63) datasets.
It achieves the following results on the evaluation set:
- Loss: 0.1395
- Wer: 0.2952

Trigram Language Model Trained using KENLM Library on [kavyamanohar/ml-sentences](https://huggingface.co/datasets/kavyamanohar/ml-sentences) dataset


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.00024
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 800
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 1.4912        | 0.1165 | 1000 | 0.5497          | 0.7011 |
| 0.5377        | 0.2330 | 2000 | 0.3292          | 0.5364 |
| 0.4343        | 0.3494 | 3000 | 0.2475          | 0.4424 |
| 0.3678        | 0.4659 | 4000 | 0.2145          | 0.4014 |
| 0.3345        | 0.5824 | 5000 | 0.1898          | 0.3774 |
| 0.3029        | 0.6989 | 6000 | 0.1718          | 0.3441 |
| 0.2685        | 0.8153 | 7000 | 0.1517          | 0.3135 |
| 0.2385        | 0.9318 | 8000 | 0.1395          | 0.2952 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1