Instructions to use Hanhpt23/whisper-base-Encod-vietmed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hanhpt23/whisper-base-Encod-vietmed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Hanhpt23/whisper-base-Encod-vietmed")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Hanhpt23/whisper-base-Encod-vietmed") model = AutoModelForSpeechSeq2Seq.from_pretrained("Hanhpt23/whisper-base-Encod-vietmed") - Notebooks
- Google Colab
- Kaggle
openai/whisper-base
This model is a fine-tuned version of openai/whisper-base on the pphuc25/VietMed-split-8-2 dataset. It achieves the following results on the evaluation set:
- Loss: 0.6028
- Wer: 23.6903
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.6717 | 1.0 | 569 | 0.6778 | 31.9019 |
| 0.4003 | 2.0 | 1138 | 0.5956 | 27.1280 |
| 0.2279 | 3.0 | 1707 | 0.5805 | 23.7415 |
| 0.0875 | 4.0 | 2276 | 0.6028 | 23.6903 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for Hanhpt23/whisper-base-Encod-vietmed
Base model
openai/whisper-base