---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
metrics:
- wer
datasets:
- thennal/IMaSC
- vrclc/festvox-iiith-ml
- vrclc/openslr63
language:
- ml
library_name: transformers
pipeline_tag: text-generation
model-index:
- name: w2v2bert-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: 8.82
      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: 32.09
      name: WER
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset: 
      name: Common Voice 16 Malayalam
      type: mozilla-foundation/common_voice_16_1
      config: ml
      split: test
      args: ml
    metrics:
    - type: wer
      value: 52.72
      name: WER
---

<!-- 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. -->

# W2V2-BERT-withLM-Studio-Malayalam

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on [IMASC](https://huggingface.co/datasets/thennal/IMaSC), [OpenSLR Malayalam Train split](https://huggingface.co/datasets/vrclc/openslr63), [Festvox Malayalam](https://huggingface.co/datasets/vrclc/festvox-iiith-ml)dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1587
- Wer: 0.1157

## 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: 5e-05
- 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_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 1.0335        | 0.4932 | 600   | 0.3654          | 0.4387 |
| 0.1531        | 0.9864 | 1200  | 0.2373          | 0.3332 |
| 0.1074        | 1.4797 | 1800  | 0.2069          | 0.2953 |
| 0.0928        | 1.9729 | 2400  | 0.2146          | 0.2814 |
| 0.0734        | 2.4661 | 3000  | 0.1947          | 0.2433 |
| 0.0678        | 2.9593 | 3600  | 0.1938          | 0.2406 |
| 0.0522        | 3.4525 | 4200  | 0.1566          | 0.2053 |
| 0.0493        | 3.9457 | 4800  | 0.1649          | 0.1988 |
| 0.0366        | 4.4390 | 5400  | 0.1417          | 0.1834 |
| 0.0372        | 4.9322 | 6000  | 0.1542          | 0.1749 |
| 0.028         | 5.4254 | 6600  | 0.1476          | 0.1620 |
| 0.0263        | 5.9186 | 7200  | 0.1388          | 0.1622 |
| 0.0195        | 6.4118 | 7800  | 0.1384          | 0.1495 |
| 0.0185        | 6.9051 | 8400  | 0.1351          | 0.1383 |
| 0.0136        | 7.3983 | 9000  | 0.1404          | 0.1344 |
| 0.0119        | 7.8915 | 9600  | 0.1253          | 0.1276 |
| 0.0087        | 8.3847 | 10200 | 0.1443          | 0.1284 |
| 0.0066        | 8.8779 | 10800 | 0.1475          | 0.1252 |
| 0.0049        | 9.3711 | 11400 | 0.1577          | 0.1227 |
| 0.0038        | 9.8644 | 12000 | 0.1587          | 0.1157 |


### Framework versions

- Transformers 4.42.2
- Pytorch 2.1.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1