Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
cn
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Svetlana0303/whisper-small-cn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Svetlana0303/whisper-small-cn with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Svetlana0303/whisper-small-cn")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Svetlana0303/whisper-small-cn") model = AutoModelForSpeechSeq2Seq.from_pretrained("Svetlana0303/whisper-small-cn", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| language: | |
| - cn | |
| license: apache-2.0 | |
| base_model: openai/whisper-small | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - Svetlana0303/my_CN_ds | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: Whisper Small CN - my voice | |
| results: | |
| - task: | |
| name: Automatic Speech Recognition | |
| type: automatic-speech-recognition | |
| dataset: | |
| name: my_CN_ds | |
| type: Svetlana0303/my_CN_ds | |
| args: 'split: test' | |
| metrics: | |
| - name: Wer | |
| type: wer | |
| value: 100.0 | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # Whisper Small CN - my voice | |
| This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the my_CN_ds dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.7879 | |
| - Wer: 100.0 | |
| ## 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: 50 | |
| - training_steps: 400 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer | | |
| |:-------------:|:-----:|:----:|:---------------:|:-----:| | |
| | 0.0 | 100.0 | 100 | 0.7750 | 100.0 | | |
| | 0.0 | 200.0 | 200 | 0.7819 | 100.0 | | |
| | 0.0 | 300.0 | 300 | 0.7860 | 100.0 | | |
| | 0.0 | 400.0 | 400 | 0.7879 | 100.0 | | |
| ### Framework versions | |
| - Transformers 4.40.1 | |
| - Pytorch 2.2.1+cu121 | |
| - Datasets 2.19.0 | |
| - Tokenizers 0.19.1 | |