Automatic Speech Recognition
PEFT
Safetensors
English
whisper
lora
audio
machine-sound
anomaly-detection
predictive-maintenance
manufacturing
voice-agent
text-to-speech
non-commercial
Eval Results (legacy)
Instructions to use muruganmath/Machine-fault-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use muruganmath/Machine-fault-detector with PEFT:
from peft import PeftModel from transformers import AutoModelForSpeechSeq2Seq base_model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-large-v3") model = PeftModel.from_pretrained(base_model, "muruganmath/Machine-fault-detector") - Notebooks
- Google Colab
- Kaggle
Add trained LoRA adapter (main), tokenizer/processor, lineage, standalone inference and model card
36ba721 verified Download processor_config.json from muruganmath/Machine-fault-detector: direct link, hf CLI and curl.
- Browser
- Download file 410 Bytes
-
https://huggingface.co/muruganmath/Machine-fault-detector/resolve/main/processor_config.json
- Command line
-
hf download hf://muruganmath/Machine-fault-detector/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/muruganmath/Machine-fault-detector/resolve/main/processor_config.json
410 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" | |
| } | |