Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Chinese
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use chandc/whisper-small-Cantonese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chandc/whisper-small-Cantonese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="chandc/whisper-small-Cantonese")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("chandc/whisper-small-Cantonese") model = AutoModelForSpeechSeq2Seq.from_pretrained("chandc/whisper-small-Cantonese", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Whisper Small Cantonese - Daniel Chan
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: 0.2611
- Wer: 55.8860
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2222 | 1.14 | 1000 | 0.2847 | 63.1879 |
| 0.1146 | 2.28 | 2000 | 0.2592 | 58.2725 |
| 0.0382 | 3.42 | 3000 | 0.2575 | 55.9216 |
| 0.024 | 4.57 | 4000 | 0.2611 | 55.8860 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2
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Model tree for chandc/whisper-small-Cantonese
Base model
openai/whisper-smallEvaluation results
- Wer on Common Voice 11.0self-reported55.886