Instructions to use Shubham09/LISA_Whisper_small_latest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shubham09/LISA_Whisper_small_latest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Shubham09/LISA_Whisper_small_latest")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Shubham09/LISA_Whisper_small_latest") model = AutoModelForSpeechSeq2Seq.from_pretrained("Shubham09/LISA_Whisper_small_latest") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_bos_token": false, | |
| "add_prefix_space": false, | |
| "bos_token": { | |
| "__type": "AddedToken", | |
| "content": "<|endoftext|>", | |
| "lstrip": false, | |
| "normalized": true, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "eos_token": { | |
| "__type": "AddedToken", | |
| "content": "<|endoftext|>", | |
| "lstrip": false, | |
| "normalized": true, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "errors": "replace", | |
| "model_max_length": 1024, | |
| "name_or_path": "openai/whisper-small.en", | |
| "pad_token": null, | |
| "processor_class": "WhisperProcessor", | |
| "return_attention_mask": false, | |
| "special_tokens_map_file": null, | |
| "task": "transcribe", | |
| "tokenizer_class": "WhisperTokenizer", | |
| "unk_token": { | |
| "__type": "AddedToken", | |
| "content": "<|endoftext|>", | |
| "lstrip": false, | |
| "normalized": true, | |
| "rstrip": false, | |
| "single_word": false | |
| } | |
| } | |