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
adapter_config.json: set peft task_type to SEQ_2_SEQ_LM (Hub requires a string)
Browse files- adapter_config.json +1 -1
adapter_config.json
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"q_proj"
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"target_parameters": null,
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"task_type":
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"trainable_token_indices": null,
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"use_bdlora": null,
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"use_dora": false,
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"q_proj"
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"target_parameters": null,
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"task_type": "SEQ_2_SEQ_LM",
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"trainable_token_indices": null,
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"use_bdlora": null,
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"use_dora": false,
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