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README.md

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 ---
+language: en
 license: mit
+tags:
+- pytorch
 ---
+
+<h1 align="center">
+  FISHER
+</h1>
+
+<div align="center">
+    <img src="assets/rmis_curve.png" alt="Model Performances on the RMIS Benchmark" style="width:80%; max-width: 1000px">
+</div>
+
+
+## Introduction
+
+<div align="center">
+    <img src="assets/model_pipe.png" alt="Model Performances on the RMIS Benchmark" style="width:100%; max-width: 1500px">
+</div>
+
+FISHER is a **F**oundation model for **I**ndustrial **S**ignal compre**HE**nsive **R**epresentation, which models heterogeneous industrial signals (sound, vibration, voltage, etc.) in a unified manner. FISHER accepts arbitrary sampling rates and models the increment of sampling rate as the concatenation of sub-band information, which first splits a STFT spectrogram into sub-bands before processsing it by the ViT encoder. FISHER is trained by teacher student EMA self-distillation.
+
+To evaluate the model, we develop the RMIS benchmark, which will also be open-sourced in the near future. FISHER achieves the SOTA performances on the RMIS benchmark with much more efficient scaling properties.
+
+## Inference
+
+Please use the following code to infer the signal representation by FISHER.
+
+```python
+import torch
+import torchaudio
+import torch.nn.functional as F
+from transformers import AutoModel
+
+model = AutoModel.from_pretrained('jiangab/FISHER-tiny-0723', trust_remote_code=True)
+model = model.cuda()
+model.eval()
+
+wav, sr = torchaudio.load('/path/to/local/signal.wav')
+# You can replace it with your custom loading function for other signals
+
+wav = wav - wav.mean()
+STFT = torchaudio.transforms.Spectrogram(
+    n_fft=25 * sr // 1000,
+    win_length=None,
+    hop_length=10 * sr // 1000,
+    power=1,
+    center=False
+)
+spec = torch.log(torch.abs(STFT(wav)) + 1e-10)
+spec = spec.transpose(-2, -1)  # [1, time, freq]
+spec = (spec + 3.017344307886898) / (2.1531635155379805 * 2)
+
+# time-wise cutoff
+if spec.shape[-2] > 1024:
+    spec = spec[:, :1024]
+# freq-wise padding
+if spec.shape[-1] < model.cfg.band_width:
+    spec = F.pad(spec, (0, model.cfg.band_width - spec.shape[-1]))
+spec = spec.unsqueeze(1).cuda()
+
+with torch.no_grad():
+    # Use autocast for mixed precision inference. You can disable it for full precision.
+    with torch.autocast('cuda'):
+        repre = model.extract_features(spec)
+print(repre.shape)
+```
+
+## Acknowledgements
+
+FISHER is developed based on [EAT](https://github.com/cwx-worst-one/EAT) and [fairseq](https://github.com/facebookresearch/fairseq). We thank these authors for open-sourcing their works.
+
+## Citation
+
+If you find FISHER useful, please cite the following paper.
+
+```bibtex
+@article{fan2025fisher,
+  title={FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation},
+  author={Fan, Pingyi and Jiang, Anbai and Zhang, Shuwei and Lv, Zhiqiang and Han, Bing and Zheng, Xinhu and Liang, Wenrui and Li, Junjie and Zhang, Wei-Qiang and Qian, Yanmin and Chen, Xie and Lu, Cheng and Liu, Jia},
+  journal={arXiv preprint arXiv:2507.16696},
+  year={2025}
+}
+```