Instructions to use devanshty/Babel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use devanshty/Babel with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("openai/whisper-large-v3") model = PeftModel.from_pretrained(base_model, "devanshty/Babel") - Notebooks
- Google Colab
- Kaggle
Download processor_config.json from devanshty/Babel: direct link, hf CLI and curl.
- Browser
- Download file 427 Bytes
-
https://huggingface.co/devanshty/Babel/resolve/main/processor_config.json
- Command line
-
hf download hf://devanshty/Babel/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/devanshty/Babel/resolve/main/processor_config.json
427 Bytes
| { | |
| "feature_extractor": { | |
| "chunk_length": 30, | |
| "dither": 0.0, | |
| "feature_extractor_type": "WhisperFeatureExtractor", | |
| "feature_size": 128, | |
| "hop_length": 160, | |
| "n_fft": 400, | |
| "n_samples": 480000, | |
| "nb_max_frames": 3000, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": false, | |
| "sampling_rate": 16000 | |
| }, | |
| "processor_class": "WhisperProcessor" | |
| } | |