Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Chinese
whisper
Generated from Trainer
Instructions to use wanglynn/zhtw1-1218 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wanglynn/zhtw1-1218 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="wanglynn/zhtw1-1218")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("wanglynn/zhtw1-1218") model = AutoModelForSpeechSeq2Seq.from_pretrained("wanglynn/zhtw1-1218") - Notebooks
- Google Colab
- Kaggle
zhtw1-1218
This model is a fine-tuned version of openai/whisper-small on the your_dataset_name dataset. It achieves the following results on the evaluation set:
- Loss: 0.1482
- Cer: 31.7073
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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|---|---|---|---|---|
| 0.0 | 71.4286 | 500 | 0.1482 | 31.7073 |
Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.4.1+cu118
- Datasets 3.0.2
- Tokenizers 0.21.0
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Model tree for wanglynn/zhtw1-1218
Base model
openai/whisper-small