Instructions to use max-at-Parami/whisper-small-zh-hk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use max-at-Parami/whisper-small-zh-hk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="max-at-Parami/whisper-small-zh-hk")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("max-at-Parami/whisper-small-zh-hk") model = AutoModelForSpeechSeq2Seq.from_pretrained("max-at-Parami/whisper-small-zh-hk") - Notebooks
- Google Colab
- Kaggle
whisper-small-zh-hk
This model is a fine-tuned version of openai/whisper-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2991
- Wer: 94.4078
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: 32
- 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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.142 | 2.28 | 1000 | 0.2684 | 95.6723 |
| 0.0297 | 4.57 | 2000 | 0.2714 | 103.3838 |
| 0.0056 | 6.85 | 3000 | 0.2910 | 105.6278 |
| 0.0028 | 9.13 | 4000 | 0.2991 | 94.4078 |
Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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Model tree for max-at-Parami/whisper-small-zh-hk
Base model
openai/whisper-small