speech_recognition/FunASR
1
0
Fork 0
mirror of https://github.com/modelscope/FunASR synced 2025-09-15 14:48:36 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

docs

Browse Source
This commit is contained in:
游雁 2023-05-19 10:59:08 +08:00
parent 38b69b021a
commit 0eb6318fc3
2 changed files with 3 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
docs/installation/installation.md
View File

@ -34,9 +34,7 @@ Ref to [docs](https://docs.conda.io/en/latest/miniconda.html#windows-installers)
```sh ```sh
pip install torch torchaudio pip install torch torchaudio
``` ```
If there exists CUDAs in your environments, you should install the pytorch with the version matching the CUDA. The matching list could be found in [docs](https://pytorch.org/get-started/previous-versions/).
For more details about torch, please see [https://pytorch.org/get-started/locally](https://pytorch.org/get-started/locally)
### Install funasr ### Install funasr
#### Install from pip #### Install from pip

3
docs/reference/papers.md
View File

@ -3,7 +3,8 @@
FunASR have implemented the following paper code FunASR have implemented the following paper code
### Speech Recognition ### Speech Recognition
- [Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition](https://arxiv.org/abs/2206.08317), INTERSPEECH 2022. - [FunASR: A Fundamental End-to-End Speech Recognition Toolkit](https://arxiv.org/abs/2305.11013), INTERSPEECH 2023
- [Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition](https://arxiv.org/abs/2206.08317), INTERSPEECH 2022
- [Universal ASR: Unifying Streaming and Non-Streaming ASR Using a Single Encoder-Decoder Model](https://arxiv.org/abs/2010.14099), arXiv preprint arXiv:2010.14099, 2020. - [Universal ASR: Unifying Streaming and Non-Streaming ASR Using a Single Encoder-Decoder Model](https://arxiv.org/abs/2010.14099), arXiv preprint arXiv:2010.14099, 2020.
- [San-m: Memory equipped self-attention for end-to-end speech recognition](https://arxiv.org/pdf/2006.01713), INTERSPEECH 2020 - [San-m: Memory equipped self-attention for end-to-end speech recognition](https://arxiv.org/pdf/2006.01713), INTERSPEECH 2020
- [Streaming Chunk-Aware Multihead Attention for Online End-to-End Speech Recognition](https://arxiv.org/abs/2006.01712), INTERSPEECH 2020 - [Streaming Chunk-Aware Multihead Attention for Online End-to-End Speech Recognition](https://arxiv.org/abs/2006.01712), INTERSPEECH 2020