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---
language:
- km
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- automatic-speech-recognition
- openslr
- generated_from_trainer
datasets:
- openslr
model-index:
- name: training
results: []
---
<!-- 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. -->
# Wav2VecBert 2.0 Khmer
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the OpenSLR 42 dataset.
```python
from transformers import pipeline
recognizer = pipeline("automatic-speech-recognition", model="seanghay/w2v-bert-2.0-khmer", device="cuda")
text = recognizer("audio.mp3", chunk_length_s=10, stride_length_s=(4, 2))["text"]
```
## Training and evaluation data
25.79% WER (Eval with 10% of OpenSLR seed: 42)
```
{
"epoch": 14.634146341463415,
"eval_loss": 0.36365753412246704,
"eval_runtime": 8.7546,
"eval_samples_per_second": 33.24,
"eval_steps_per_second": 4.226,
"eval_wer": 0.2579008973858759,
"step": 2400
}
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Framework versions
- Transformers 4.42.4
- Pytorch 2.0.1
- Datasets 2.20.0
- Tokenizers 0.19.1
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