Automatic Speech Recognition
PEFT
Safetensors
Transformers
Nepali
English
whisper
nepali
english
code-switching
lora
Instructions to use DontHash/neptrans-v4-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DontHash/neptrans-v4-lora with PEFT:
Task type is invalid.
- Transformers
How to use DontHash/neptrans-v4-lora with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="DontHash/neptrans-v4-lora")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DontHash/neptrans-v4-lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from DontHash/neptrans-v4-lora: direct link, hf CLI and curl.
- Browser
- Download file 372 Bytes
-
https://huggingface.co/DontHash/neptrans-v4-lora/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://DontHash/neptrans-v4-lora/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/DontHash/neptrans-v4-lora/resolve/main/preprocessor_config.json
372 Bytes
| { | |
| "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, | |
| "processor_class": "WhisperProcessor", | |
| "return_attention_mask": false, | |
| "sampling_rate": 16000 | |
| } | |