Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Yue Chinese
whisper
Generated from Trainer
Instructions to use KyleCYCC/whisper-small-mystt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KyleCYCC/whisper-small-mystt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="KyleCYCC/whisper-small-mystt")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("KyleCYCC/whisper-small-mystt") model = AutoModelForSpeechSeq2Seq.from_pretrained("KyleCYCC/whisper-small-mystt") - Notebooks
- Google Colab
- Kaggle
Whisper Small cantonese
This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.6546
- Cer: 36.2949
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: 8
- 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: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|---|---|---|---|---|
| 0.5332 | 0.0571 | 50 | 0.6995 | 33.2703 |
| 0.4478 | 0.1142 | 100 | 0.6546 | 36.2949 |
Framework versions
- Transformers 4.46.0.dev0
- Pytorch 2.5.0+cu121
- Datasets 3.0.2
- Tokenizers 0.20.1
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Model tree for KyleCYCC/whisper-small-mystt
Base model
openai/whisper-small