Wav2VecBert 2.0 Khmer
This model is a fine-tuned version of facebook/w2v-bert-2.0 on the OpenSLR 42 dataset.
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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facebook/w2v-bert-2.0