add lighthubert profiling results

README.md

@@ -65,6 +65,20 @@ hs = model.extract_features(wav_input_16khz, ret_hs=True)[0]
 print(f"Representation at bottom hidden states: {torch.allclose(rep, hs[-1])}")
 ```
 
+### Profiling LightHuBERT
+
+As mentioned in [Profiling Tool for SLT2022 SUPERB Challenge](https://github.com/B06901052/DeepSpeed/tree/superb-challenge), we profiling the `lighthubert` in s3prl.
+
+```sh
+cd DeepSpeed
+# lighthubert_small
+python testing/s3prl_profiling_test.py -u lighthubert_small --libri_root "libri_root"
+# lighthubert_base
+python testing/s3prl_profiling_test.py -u lighthubert_base --libri_root "libri_root"
+# lighthubert_stage1
+python testing/s3prl_profiling_test.py -u lighthubert_stage1 --libri_root "libri_root"
+```
+
 ### Reference
 
 If you find our work is useful in your research, please cite the following paper:

lighthubert_profile.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:783c7cb607506d889f85e176b3ad21859396d984ada3ad11d85036e491b7e079
+size 46849