| --- |
| base_model: Qwen/Qwen2.5-7B-Instruct |
| library_name: peft |
| license: mit |
| tags: |
| - speech-enhancement |
| - audio |
| - lora |
| - dpo |
| --- |
| |
| # LLM-Guided Speech Enhancement adapters |
|
|
| This repository hosts LoRA adapters for **LLM-Guided Speech Enhancement**. The recommended `dpo-adapter/` is based on `Qwen/Qwen2.5-7B-Instruct`; `sft-adapter/` is the checkpoint before DPO. |
|
|
| ## Intended use |
|
|
| Given structured acoustic evidence, the adapter produces an interpretable response comprising a degradation diagnosis, a DSP-oriented enhancement strategy, and a rationale. It is intended as a research and prototyping control layer, not as a standalone waveform denoiser. |
|
|
| ## Training |
|
|
| Training examples were generated from AISHELL-1 clean-speech metadata and programmatically sampled degradations. Supervised fine-tuning was followed by Direct Preference Optimization. The synthetic construction makes the response format highly regular. |
|
|
| ## Limitations |
|
|
| Reported held-out format and diagnosis metrics are in-distribution metrics against programmatically constructed targets. They do not demonstrate generalization to arbitrary recordings or superiority on perceptual speech-enhancement benchmarks. Use the adapters with the Qwen and AISHELL-1 licenses/terms in mind. |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| from peft import PeftModel |
| |
| base_id = "Qwen/Qwen2.5-7B-Instruct" |
| tokenizer = AutoTokenizer.from_pretrained(base_id, trust_remote_code=True) |
| base = AutoModelForCausalLM.from_pretrained(base_id, device_map="auto", trust_remote_code=True) |
| model = PeftModel.from_pretrained(base, "jatshi/llm-guided-speech-enhancement", subfolder="dpo-adapter") |
| ``` |
|
|
| Source code: https://github.com/Jatshi/llm-guided-speech-enhancement |
|
|
