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---
base_model: facebook/w2v-bert-2.0
license: mit
metrics:
- wer
model-index:
- name: W2V2-BERT-withLM-Malayalam by Bajiyo Baiju, Kavya Manohar
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: 18.23
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Google Fleurs
type: google/fleurs
config: ml
split: test
args: ml
metrics:
- type: wer
value: 31.92
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Mozilla Common Voice
type: mozilla-foundation/common_voice_16_1
config: ml
split: test
args: ml
metrics:
- type: wer
value: 49.79
name: WER
datasets:
- vrclc/festvox-iiith-ml
- vrclc/openslr63
- vrclc/imasc_slr
- mozilla-foundation/common_voice_17_0
- smcproject/MSC
- kavyamanohar/ml-sentences
language:
- ml
pipeline_tag: automatic-speech-recognition
---
<!-- 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-Malayalam
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the [IMASC](https://huggingface.co/datasets/thennal/IMaSC), [MSC](https://huggingface.co/datasets/smcproject/MSC), [OpenSLR Malayalam Train split](https://huggingface.co/datasets/vrclc/openslr63), [Festvox Malayalam](https://huggingface.co/datasets/vrclc/openslr63), [CV16](https://huggingface.co/datasets/mozilla-foundation/common_voice_16_0) .
It achieves the following results on the validation set : [OpenSLR-Test](https://huggingface.co/vrclc/openslr63):
- Loss: 0.1722
- Wer: 0.1299
Trigram Language Model Trained using KENLM Library on [kavyamanohar/ml-sentences](https://huggingface.co/datasets/kavyamanohar/ml-sentences) dataset
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.1416 | 0.46 | 600 | 0.3393 | 0.4616 |
| 0.1734 | 0.92 | 1200 | 0.2414 | 0.3493 |
| 0.1254 | 1.38 | 1800 | 0.2205 | 0.2963 |
| 0.1097 | 1.84 | 2400 | 0.2157 | 0.3133 |
| 0.0923 | 2.3 | 3000 | 0.1854 | 0.2473 |
| 0.0792 | 2.76 | 3600 | 0.1939 | 0.2471 |
| 0.0696 | 3.22 | 4200 | 0.1720 | 0.2282 |
| 0.0589 | 3.68 | 4800 | 0.1768 | 0.2013 |
| 0.0552 | 4.14 | 5400 | 0.1635 | 0.1864 |
| 0.0437 | 4.6 | 6000 | 0.1501 | 0.1826 |
| 0.0408 | 5.06 | 6600 | 0.1500 | 0.1645 |
| 0.0314 | 5.52 | 7200 | 0.1559 | 0.1655 |
| 0.0317 | 5.98 | 7800 | 0.1448 | 0.1553 |
| 0.022 | 6.44 | 8400 | 0.1592 | 0.1590 |
| 0.0218 | 6.9 | 9000 | 0.1431 | 0.1458 |
| 0.0154 | 7.36 | 9600 | 0.1514 | 0.1366 |
| 0.0141 | 7.82 | 10200 | 0.1540 | 0.1383 |
| 0.0113 | 8.28 | 10800 | 0.1558 | 0.1391 |
| 0.0085 | 8.74 | 11400 | 0.1612 | 0.1356 |
| 0.0072 | 9.2 | 12000 | 0.1697 | 0.1289 |
| 0.0046 | 9.66 | 12600 | 0.1722 | 0.1299 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 |