From d2dc3af1a69ee4075bcfc0c83dc0fb8e3fc1db4e Mon Sep 17 00:00:00 2001
From: yhliang <68215459+yhliang-aslp@users.noreply.github.com>
Date: 星期四, 11 五月 2023 16:31:40 +0800
Subject: [PATCH] Merge pull request #492 from alibaba-damo-academy/dev_smohan
---
egs/alimeeting/sa-asr/README.md | 18 +++++++++---------
1 files changed, 9 insertions(+), 9 deletions(-)
diff --git a/egs/alimeeting/sa-asr/README.md b/egs/alimeeting/sa-asr/README.md
index bc6d04c..951670b 100644
--- a/egs/alimeeting/sa-asr/README.md
+++ b/egs/alimeeting/sa-asr/README.md
@@ -1,7 +1,7 @@
# Get Started
Speaker Attributed Automatic Speech Recognition (SA-ASR) is a task proposed to solve "who spoke what". Specifically, the goal of SA-ASR is not only to obtain multi-speaker transcriptions, but also to identify the corresponding speaker for each utterance. The method used in this example is referenced in the paper: [End-to-End Speaker-Attributed ASR with Transformer](https://www.isca-speech.org/archive/pdfs/interspeech_2021/kanda21b_interspeech.pdf).
To run this receipe, first you need to install FunASR and ModelScope. ([installation](https://alibaba-damo-academy.github.io/FunASR/en/installation.html))
-There are two startup scripts, `run.sh` for training and evaluating on the old eval and test sets, and `run_m2met_2023_infer.sh` for inference on the new test set of the Multi-Channel Multi-Party Meeting Transcription 2.0 ([M2MET2.0](https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html)) Challenge.
+There are two startup scripts, `run.sh` for training and evaluating on the old eval and test sets, and `run_m2met_2023_infer.sh` for inference on the new test set of the Multi-Channel Multi-Party Meeting Transcription 2.0 ([M2MeT2.0](https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html)) Challenge.
Before running `run.sh`, you must manually download and unpack the [AliMeeting](http://www.openslr.org/119/) corpus and place it in the `./dataset` directory:
```shell
dataset
@@ -65,17 +65,17 @@
</tr>
<tr>
<td>oracle profile</td>
- <td>31.93</td>
- <td>32.75</td>
- <td>48.56</td>
- <td>53.33</td>
+ <td>32.05</td>
+ <td>32.70</td>
+ <td>47.40</td>
+ <td>52.57</td>
</tr>
<tr>
<td>cluster profile</td>
- <td>31.94</td>
- <td>32.77</td>
- <td>55.49</td>
- <td>58.17</td>
+ <td>32.05</td>
+ <td>32.70</td>
+ <td>53.76</td>
+ <td>55.95</td>
</tr>
</table>
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