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---
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 |