Automatic Speech Recognition
Transformers
Safetensors
lite-whisper
feature-extraction
audio
whisper
hf-asr-leaderboard
custom_code
Instructions to use efficient-speech/lite-whisper-tiny-acc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use efficient-speech/lite-whisper-tiny-acc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="efficient-speech/lite-whisper-tiny-acc", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("efficient-speech/lite-whisper-tiny-acc", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| from transformers import WhisperConfig | |
| class LiteWhisperConfig(WhisperConfig): | |
| model_type = "lite-whisper" | |
| def __init__( | |
| self, | |
| low_rank_config: list[dict[str, int]] = None, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| self.low_rank_config = low_rank_config | |