Update 2022-3-8-introducing-torchrec-and-other-domain-library-updates-in-pytorch-1-11.md

TerryCE · web-flow · commit 83992ff649a9 · 2022-03-09T15:32:58.000-08:00
Updates from github to GitHub
diff --git a/_posts/2022-3-8-introducing-torchrec-and-other-domain-library-updates-in-pytorch-1-11.md b/_posts/2022-3-8-introducing-torchrec-and-other-domain-library-updates-in-pytorch-1-11.md
@@ -47,11 +47,11 @@ The TorchAudio v0.11 release includes the following beta features:
 * Implementation of Emformer ([docs](https://pytorch.org/audio/main/models.html#emformer))
 * Recurrent neural network transducer (RNN-T) streaming ASR model that uses Emformer for its transcription network ([docs](https://pytorch.org/audio/main/models.html#rnn-t))
 * RNN-T beam search decoder with TorchScript support ([docs](https://pytorch.org/audio/main/models.html#rnntbeamsearch))
-* LibriSpeech Emformer RNN-T training recipe ([github](https://github.com/pytorch/audio/tree/release/0.11/examples/asr/librispeech_emformer_rnnt)) and corresponding pre-trained streaming ASR inference pipeline ([docs](https://pytorch.org/audio/main/pipelines.html#emformer-rnnt-base-librispeech))
+* LibriSpeech Emformer RNN-T training recipe ([GitHub](https://github.com/pytorch/audio/tree/release/0.11/examples/asr/librispeech_emformer_rnnt)) and corresponding pre-trained streaming ASR inference pipeline ([docs](https://pytorch.org/audio/main/pipelines.html#emformer-rnnt-base-librispeech))
 
 Also there are prototype features that are available from nightly builds or the main branch.
 
-* Training recipes trained on MuST-C and TED-LIUM3 datasets. ([github](https://github.com/pytorch/audio/tree/main/examples/asr/emformer_rnnt))
+* Training recipes trained on MuST-C and TED-LIUM3 datasets. ([GitHub](https://github.com/pytorch/audio/tree/main/examples/asr/emformer_rnnt))
 * Pre-trained pipelines corresponding to the recipes. ([docs](https://pytorch.org/audio/main/prototype.pipelines.html))
 * Tutorial that steps through performing online speech recognition with RNN-T Emformer model. ([docs](https://pytorch.org/audio/main/tutorials/online_asr_tutorial.html))
 
@@ -217,7 +217,6 @@ More specifically the new, influence, subsection of Captum includes:
 
 More about the implementation of influential instances can be found on our [GitHub](https://github.com/pytorch/captum/tree/master/captum/influence) page and [tutorials](https://captum.ai/tutorials/TracInCP_Tutorial).
 
-
 Thanks for reading, If you’re interested in these updates and want to join the PyTorch community, we encourage you to join the [discussion forums](https://discuss.pytorch.org/) and [open GitHub issues](https://github.com/pytorch/pytorch/issues). To get the latest news from PyTorch, follow us on [Twitter](https://twitter.com/PyTorch), [Medium](https://medium.com/pytorch), [YouTube](https://www.youtube.com/pytorch), and [LinkedIn](https://www.linkedin.com/company/pytorch).
 
 Cheers!
