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--- |
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language: |
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- km |
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license: mit |
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base_model: facebook/w2v-bert-2.0 |
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tags: |
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- automatic-speech-recognition |
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- openslr |
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- generated_from_trainer |
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datasets: |
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- openslr |
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model-index: |
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- name: training |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Wav2VecBert 2.0 Khmer |
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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. |
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```python |
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from transformers import pipeline |
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recognizer = pipeline("automatic-speech-recognition", model="seanghay/w2v-bert-2.0-khmer", device="cuda") |
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text = recognizer("audio.mp3", chunk_length_s=10, stride_length_s=(4, 2))["text"] |
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``` |
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## Training and evaluation data |
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25.79% WER (Eval with 10% of OpenSLR seed: 42) |
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``` |
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{ |
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"epoch": 14.634146341463415, |
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"eval_loss": 0.36365753412246704, |
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"eval_runtime": 8.7546, |
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"eval_samples_per_second": 33.24, |
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"eval_steps_per_second": 4.226, |
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"eval_wer": 0.2579008973858759, |
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"step": 2400 |
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} |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 15 |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.0.1 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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