Automatic Speech Recognition
PEFT
Safetensors
Transformers
English
lora
singapore
education
speech
nmlp
Instructions to use munyew/meralion-education-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use munyew/meralion-education-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("C:/Projects/meralion-server/models/meralion_2_3b") model = PeftModel.from_pretrained(base_model, "munyew/meralion-education-lora") - Transformers
How to use munyew/meralion-education-lora with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="munyew/meralion-education-lora")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("munyew/meralion-education-lora", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 765 Bytes
c218836 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | {
"auto_map": {
"AutoProcessor": "processing_meralion2.MERaLiON2Processor"
},
"do_normalize": true,
"feature_extractor": {
"auto_map": {
"AutoProcessor": "processing_meralion2.MERaLiON2Processor"
},
"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
},
"fixed_speech_embeds_length": 100,
"processor_class": "MERaLiON2Processor",
"speech_token_index": 255999,
"time_duration_limit": 300,
"whisper_chunk_size": 30
}
|