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---
license: mit
tags:
- generated_from_trainer
base_model: facebook/w2v-bert-2.0
datasets:
- common_voice_16_0
metrics:
- wer
model-index:
- name: w2v-bert-2.0-bangala-gpu-CV16.0_v2
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: common_voice_16_0
type: common_voice_16_0
config: bn
split: test
args: bn
metrics:
- type: wer
value: 0.4811011116993118
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. -->
# w2v-bert-2.0-bangala-gpu-CV16.0_v2
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the common_voice_16_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4490
- Wer: 0.4811
## 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: 4.42184e-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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.5221 | 0.31 | 300 | 0.5900 | 0.6271 |
| 1.2024 | 0.63 | 600 | 0.4088 | 0.4071 |
| 0.9149 | 0.94 | 900 | 0.3200 | 0.3270 |
| 0.8124 | 1.26 | 1200 | 0.2965 | 0.3080 |
| 0.7028 | 1.57 | 1500 | 0.2759 | 0.2884 |
| 0.6301 | 1.89 | 1800 | 0.2435 | 0.2671 |
| 0.6147 | 2.2 | 2100 | 0.2335 | 0.2477 |
| 0.6304 | 2.52 | 2400 | 0.2248 | 0.2458 |
| 0.5921 | 2.83 | 2700 | 0.2326 | 0.2441 |
| 0.495 | 3.15 | 3000 | 0.2180 | 0.2378 |
| 0.4987 | 3.46 | 3300 | 0.2139 | 0.2227 |
| 0.5669 | 3.78 | 3600 | 0.2097 | 0.2236 |
| 0.5904 | 4.09 | 3900 | 0.2038 | 0.2178 |
| 0.6016 | 4.41 | 4200 | 0.2091 | 0.2131 |
| 0.5325 | 4.72 | 4500 | 0.2064 | 0.2147 |
| 0.5271 | 5.04 | 4800 | 0.2002 | 0.2159 |
| 0.5229 | 5.35 | 5100 | 0.2069 | 0.2209 |
| 0.5843 | 5.67 | 5400 | 0.2090 | 0.2202 |
| 0.5477 | 5.98 | 5700 | 0.2085 | 0.2175 |
| 0.508 | 6.3 | 6000 | 0.2046 | 0.2158 |
| 0.5226 | 6.61 | 6300 | 0.2515 | 0.3250 |
| 0.7576 | 6.93 | 6600 | 0.2343 | 0.2364 |
| 1.0089 | 7.24 | 6900 | 0.2731 | 0.2713 |
| 0.9462 | 7.56 | 7200 | 0.2588 | 0.2648 |
| 0.8648 | 7.87 | 7500 | 0.2916 | 0.3393 |
| 1.1282 | 8.19 | 7800 | 0.3830 | 0.4583 |
| 1.3279 | 8.5 | 8100 | 0.3910 | 0.4117 |
| 1.2722 | 8.82 | 8400 | 0.4424 | 0.3442 |
| 1.2886 | 9.13 | 8700 | 0.4421 | 0.4011 |
| 1.3274 | 9.45 | 9000 | 0.4483 | 0.4769 |
| 1.3235 | 9.76 | 9300 | 0.4490 | 0.4811 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0
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