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---
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: 32.01
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 |