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| [**M2MET2.0 Challenge**](https://github.com/alibaba-damo-academy/FunASR#multi-channel-multi-party-meeting-transcription-20-m2met20-challenge)
## What's new:
### Multi-Channel Multi-Party Meeting Transcription 2.0 (M2MET2.0) Challenge
We are pleased to announce that the M2MeT2.0 challenge has been accepted by the ASRU2023 challenge special session. The registration is now open. The baseline system is conducted on FunASR and is provided as a receipe of AliMeeting corpus. For more details you can see the guidence of M2MET2.0 ([CN](https://alibaba-damo-academy.github.io/FunASR/m2met2_cn/index.html)/[EN](https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html)).
### Multi-Channel Multi-Party Meeting Transcription 2.0 (M2MeT2.0) Challenge
We are pleased to announce that the M2MeT2.0 challenge has been accepted by the ASRU 2023 challenge special session. The registration is now open. The baseline system is conducted on FunASR and is provided as a receipe of AliMeeting corpus. For more details you can see the guidence of M2MET2.0 ([CN](https://alibaba-damo-academy.github.io/FunASR/m2met2_cn/index.html)/[EN](https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html)).
### Release notes
For the release notes, please ref to [news](https://github.com/alibaba-damo-academy/FunASR/releases)

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@ -6,7 +6,7 @@ We will release an E2E SA-ASR baseline conducted on [FunASR](https://github.com/
## Quick start
To run the baseline, 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

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# Contact
If you have any questions about M2MET2.0 challenge, please contact us by
If you have any questions about M2MeT2.0 challenge, please contact us by
- email: [m2met.alimeeting@gmail.com](mailto:m2met.alimeeting@gmail.com)

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The ICASSP2022 M2MeT challenge focuses on meeting scenarios, and it comprises two main tasks: speaker diarization and multi-speaker automatic speech recognition. The former involves identifying who spoke when in the meeting, while the latter aims to transcribe speech from multiple speakers simultaneously, which poses significant technical difficulties due to overlapping speech and acoustic interferences.
Building on the success of the previous M2MeT challenge, we are excited to propose the M2MeT2.0 challenge as an ASRU2023 challenge special session. In the original M2MeT challenge, the evaluation metric was speaker-independent, which meant that the transcription could be determined, but not the corresponding speaker. To address this limitation and further advance the current multi-talker ASR system towards practicality, the M2MeT2.0 challenge proposes the speaker-attributed ASR task with two sub-tracks: fixed and open training conditions. The speaker-attribute automatic speech recognition (ASR) task aims to tackle the practical and challenging problem of identifying "who spoke what at when". To facilitate reproducible research in this field, we offer a comprehensive overview of the dataset, rules, evaluation metrics, and baseline systems. Furthermore, we will release a carefully curated test set, comprising approximately 10 hours of audio, according to the timeline. The new test set is designed to enable researchers to validate and compare their models' performance and advance the state of the art in this area.
Building on the success of the previous M2MeT challenge, we are excited to propose the M2MeT2.0 challenge as an ASRU 2023 challenge special session. In the original M2MeT challenge, the evaluation metric was speaker-independent, which meant that the transcription could be determined, but not the corresponding speaker. To address this limitation and further advance the current multi-talker ASR system towards practicality, the M2MeT2.0 challenge proposes the speaker-attributed ASR task with two sub-tracks: fixed and open training conditions. The speaker-attribute automatic speech recognition (ASR) task aims to tackle the practical and challenging problem of identifying "who spoke what at when". To facilitate reproducible research in this field, we offer a comprehensive overview of the dataset, rules, evaluation metrics, and baseline systems. Furthermore, we will release a carefully curated test set, comprising approximately 10 hours of audio, according to the timeline. The new test set is designed to enable researchers to validate and compare their models' performance and advance the state of the art in this area.
## Timeline(AOE Time)
- $ April~29, 2023: $ Challenge and registration open.
@ -21,8 +21,8 @@ Building on the success of the previous M2MeT challenge, we are excited to propo
## Guidelines
Interested participants, whether from academia or industry, must register for the challenge by completing the Google form below. The deadline for registration is May 22, 2023. Participants are also welcome to join the [wechat group](https://alibaba-damo-academy.github.io/FunASR/m2met2/Contact.html) of M2MET2.0 and keep up to date with the latest updates about the challenge.
Interested participants, whether from academia or industry, must register for the challenge by completing the Google form below. The deadline for registration is May 22, 2023. Participants are also welcome to join the [wechat group](https://alibaba-damo-academy.github.io/FunASR/m2met2/Contact.html) of M2MeT2.0 and keep up to date with the latest updates about the challenge.
[M2MET2.0 Registration](https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link)
[M2MeT2.0 Registration](https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link)
Within three working days, the challenge organizer will send email invitations to eligible teams to participate in the challenge. All qualified teams are required to adhere to the challenge rules, which will be published on the challenge page. Prior to the ranking release time, each participant must submit a system description document detailing their approach and methods. The organizer will select the top ranking submissions to be included in the ASRU2023 Proceedings.

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<section id="quick-start">
<h2>Quick start<a class="headerlink" href="#quick-start" title="Permalink to this heading"></a></h2>
<p>To run the baseline, first you need to install FunASR and ModelScope. (<a class="reference external" href="https://alibaba-damo-academy.github.io/FunASR/en/installation.html">installation</a>)<br />
There are two startup scripts, <code class="docutils literal notranslate"><span class="pre">run.sh</span></code> for training and evaluating on the old eval and test sets, and <code class="docutils literal notranslate"><span class="pre">run_m2met_2023_infer.sh</span></code> for inference on the new test set of the Multi-Channel Multi-Party Meeting Transcription 2.0 (<a class="reference external" href="https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html">M2MET2.0</a>) Challenge.<br />
There are two startup scripts, <code class="docutils literal notranslate"><span class="pre">run.sh</span></code> for training and evaluating on the old eval and test sets, and <code class="docutils literal notranslate"><span class="pre">run_m2met_2023_infer.sh</span></code> for inference on the new test set of the Multi-Channel Multi-Party Meeting Transcription 2.0 (<a class="reference external" href="https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html">M2MeT2.0</a>) Challenge.<br />
Before running <code class="docutils literal notranslate"><span class="pre">run.sh</span></code>, you must manually download and unpack the <a class="reference external" href="http://www.openslr.org/119/">AliMeeting</a> corpus and place it in the <code class="docutils literal notranslate"><span class="pre">./dataset</span></code> directory:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>dataset
<span class="p">|</span>——<span class="w"> </span>Eval_Ali_far

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<section id="contact">
<h1>Contact<a class="headerlink" href="#contact" title="Permalink to this heading"></a></h1>
<p>If you have any questions about M2MET2.0 challenge, please contact us by</p>
<p>If you have any questions about M2MeT2.0 challenge, please contact us by</p>
<ul class="simple">
<li><p>email: <a class="reference external" href="mailto:m2met&#46;alimeeting&#37;&#52;&#48;gmail&#46;com">m2met<span>&#46;</span>alimeeting<span>&#64;</span>gmail<span>&#46;</span>com</a></p></li>
</ul>

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</section>
<section id="guidelines">
<h2>Guidelines<a class="headerlink" href="#guidelines" title="Permalink to this heading"></a></h2>
<p>Interested participants, whether from academia or industry, must register for the challenge by completing the Google form below. The deadline for registration is May 22, 2023. Participants are also welcome to join the <a class="reference external" href="https://alibaba-damo-academy.github.io/FunASR/m2met2/Contact.html">wechat group</a> of M2MET2.0 and keep up to date with the latest updates about the challenge.</p>
<p><a class="reference external" href="https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link">M2MET2.0 Registration</a></p>
<p>Interested participants, whether from academia or industry, must register for the challenge by completing the Google form below. The deadline for registration is May 22, 2023. Participants are also welcome to join the <a class="reference external" href="https://alibaba-damo-academy.github.io/FunASR/m2met2/Contact.html">wechat group</a> of M2MeT2.0 and keep up to date with the latest updates about the challenge.</p>
<p><a class="reference external" href="https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link">M2MeT2.0 Registration</a></p>
<p>Within three working days, the challenge organizer will send email invitations to eligible teams to participate in the challenge. All qualified teams are required to adhere to the challenge rules, which will be published on the challenge page. Prior to the ranking release time, each participant must submit a system description document detailing their approach and methods. The organizer will select the top ranking submissions to be included in the ASRU2023 Proceedings.</p>
</section>
</section>

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@ -6,7 +6,7 @@ We will release an E2E SA-ASR baseline conducted on [FunASR](https://github.com/
## Quick start
To run the baseline, 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

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# Contact
If you have any questions about M2MET2.0 challenge, please contact us by
If you have any questions about M2MeT2.0 challenge, please contact us by
- email: [m2met.alimeeting@gmail.com](mailto:m2met.alimeeting@gmail.com)

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@ -21,8 +21,8 @@ Building on the success of the previous M2MeT challenge, we are excited to propo
## Guidelines
Interested participants, whether from academia or industry, must register for the challenge by completing the Google form below. The deadline for registration is May 22, 2023. Participants are also welcome to join the [wechat group](https://alibaba-damo-academy.github.io/FunASR/m2met2/Contact.html) of M2MET2.0 and keep up to date with the latest updates about the challenge.
Interested participants, whether from academia or industry, must register for the challenge by completing the Google form below. The deadline for registration is May 22, 2023. Participants are also welcome to join the [wechat group](https://alibaba-damo-academy.github.io/FunASR/m2met2/Contact.html) of M2MeT2.0 and keep up to date with the latest updates about the challenge.
[M2MET2.0 Registration](https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link)
[M2MeT2.0 Registration](https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link)
Within three working days, the challenge organizer will send email invitations to eligible teams to participate in the challenge. All qualified teams are required to adhere to the challenge rules, which will be published on the challenge page. Prior to the ranking release time, each participant must submit a system description document detailing their approach and methods. The organizer will select the top ranking submissions to be included in the ASRU2023 Proceedings.

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ASRU 2023 多通道多方会议转录挑战 2.0
==================================================================================
在上一届M2MET竞赛成功举办的基础上，我们将在ASRU2023上继续举办M2MET2.0挑战赛。
为了将现在的多说话人语音识别系统推向实用化，M2MET2.0挑战赛将在说话人相关的人物上评估，并且同时设立限定数据与不限定数据两个子赛道。
在上一届M2MeT竞赛成功举办的基础上，我们将在ASRU2023上继续举办M2MeT2.0挑战赛。
为了将现在的多说话人语音识别系统推向实用化，M2MeT2.0挑战赛将在说话人相关的人物上评估，并且同时设立限定数据与不限定数据两个子赛道。
我们对数据集、规则、基线系统和评估方法进行了详细介绍，以进一步促进多说话人语音识别领域研究的发展。
.. toctree::

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## 快速开始
首先需要安装FunASR和ModelScope. ([installation](https://alibaba-damo-academy.github.io/FunASR/en/installation.html))
基线系统有训练和测试两个脚本，`run.sh` 是用于训练基线系统并在M2MET的验证与测试集上评估的，而 `run_m2met_2023_infer.sh` 用于此次竞赛预备开放的全新测试集上测试同时生成符合竞赛最终提交格式的文件。
基线系统有训练和测试两个脚本，`run.sh` 是用于训练基线系统并在M2MeT的验证与测试集上评估的，而 `run_m2met_2023_infer.sh` 用于此次竞赛预备开放的全新测试集上测试同时生成符合竞赛最终提交格式的文件。
在运行 `run.sh`前，需要自行下载并解压[AliMeeting](http://www.openslr.org/119/)数据集并放置于`./dataset`目录下：
```shell
dataset

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IASSP2022 M2MeT挑战的侧重点是会议场景，它包括两个赛道：说话人日记和多说话人自动语音识别。前者涉及识别“谁在什么时候说了话”，而后者旨在同时识别来自多个说话人的语音，语音重叠和各种噪声带来了巨大的技术困难。
在上一届M2MET成功举办的基础上，我们将在ASRU2023上继续举办M2MET2.0挑战赛。在上一届M2MET挑战赛中，评估指标是说话人无关的，我们只能得到识别文本，而不能确定相应的说话人。
为了解决这一局限性并将现在的多说话人语音识别系统推向实用化，M2MET2.0挑战赛将在说话人相关的人物上评估，并且同时设立限定数据与不限定数据两个子赛道。通过将语音归属于特定的说话人，这项任务旨在提高多说话人ASR系统在真实世界环境中的准确性和适用性。
在上一届M2MeT成功举办的基础上，我们将在ASRU2023上继续举办M2MeT2.0挑战赛。在上一届M2MeT挑战赛中，评估指标是说话人无关的，我们只能得到识别文本，而不能确定相应的说话人。
为了解决这一局限性并将现在的多说话人语音识别系统推向实用化，M2MeT2.0挑战赛将在说话人相关的人物上评估，并且同时设立限定数据与不限定数据两个子赛道。通过将语音归属于特定的说话人，这项任务旨在提高多说话人ASR系统在真实世界环境中的准确性和适用性。
我们对数据集、规则、基线系统和评估方法进行了详细介绍，以进一步促进多说话人语音识别领域研究的发展。此外，我们将根据时间表发布一个全新的测试集，包括大约10小时的音频。
@ -28,6 +28,6 @@ IASSP2022 M2MeT挑战的侧重点是会议场景，它包括两个赛道：说
来自学术界和工业界的有意向参赛者均应在2023年5月22日及之前填写下方的谷歌表单。同时欢迎广大参赛者加入[官方交流微信群](https://alibaba-damo-academy.github.io/FunASR/m2met2_cn/%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html)交流并及时获取竞赛最新消息：
[M2MET2.0报名](https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link)
[M2MeT2.0报名](https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link)
主办方将在3个工作日内通过电子邮件通知符合条件的参赛团队，团队必须遵守将在挑战网站上发布的挑战规则。在排名发布之前，每个参赛者必须提交一份系统描述文件，详细说明使用的方法和模型。主办方将排名前列的队伍纳入ASRU2023论文集。

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# 联系方式
如果对M2MET2.0竞赛有任何疑问，欢迎通过以下方式联系我们：
如果对M2MeT2.0竞赛有任何疑问，欢迎通过以下方式联系我们：
- 邮件: [m2met.alimeeting@gmail.com](mailto:m2met.alimeeting@gmail.com)
| M2MET2.0竞赛官方微信群 |
| M2MeT2.0竞赛官方微信群 |
|:------------------------------------------:|
| <img src="images/qrcode.png" width="300"/> |

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# 赛道设置与评估
## 说话人相关的语音识别
说话人相关的ASR任务需要从重叠的语音中识别每个说话人的语音，并为识别内容分配一个说话人标签。图2展示了说话人相关语音识别任务和多说话人语音识别任务的主要区别。在本次竞赛中AliMeeting、Aishell4和Cn-Celeb数据集可作为受限数据源。在M2MeT挑战赛中使用的AliMeeting数据集包含训练、评估和测试集，在M2MET2.0可以在训练和评估中使用。此外，一个包含约10小时会议数据的新的Test-2023集将根据赛程安排发布并用于挑战赛的评分和排名。值得注意的是，对于Test-2023测试集，主办方将不再提供耳机的近场音频、转录以及真实时间戳。而是提供可以通过一个简单的VAD模型得到的包含多个说话人的片段。
说话人相关的ASR任务需要从重叠的语音中识别每个说话人的语音，并为识别内容分配一个说话人标签。图2展示了说话人相关语音识别任务和多说话人语音识别任务的主要区别。在本次竞赛中AliMeeting、Aishell4和Cn-Celeb数据集可作为受限数据源。在M2MeT挑战赛中使用的AliMeeting数据集包含训练、评估和测试集，在M2MeT2.0可以在训练和评估中使用。此外，一个包含约10小时会议数据的新的Test-2023集将根据赛程安排发布并用于挑战赛的评分和排名。值得注意的是，对于Test-2023测试集，主办方将不再提供耳机的近场音频、转录以及真实时间戳。而是提供可以通过一个简单的VAD模型得到的包含多个说话人的片段。
![task difference](images/task_diff.png)

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<section id="asru-2023-2-0">
<h1>ASRU 2023 多通道多方会议转录挑战 2.0<a class="headerlink" href="#asru-2023-2-0" title="此标题的永久链接"></a></h1>
<p>在上一届M2MET竞赛成功举办的基础上，我们将在ASRU2023上继续举办M2MET2.0挑战赛。
为了将现在的多说话人语音识别系统推向实用化，M2MET2.0挑战赛将在说话人相关的人物上评估，并且同时设立限定数据与不限定数据两个子赛道。
<p>在上一届M2MeT竞赛成功举办的基础上，我们将在ASRU2023上继续举办M2MeT2.0挑战赛。
为了将现在的多说话人语音识别系统推向实用化，M2MeT2.0挑战赛将在说话人相关的人物上评估，并且同时设立限定数据与不限定数据两个子赛道。
我们对数据集、规则、基线系统和评估方法进行了详细介绍，以进一步促进多说话人语音识别领域研究的发展。</p>
<div class="toctree-wrapper compound">
<p class="caption" role="heading"><span class="caption-text">目录:</span></p>

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<section id="id3">
<h2>快速开始<a class="headerlink" href="#id3" title="此标题的永久链接"></a></h2>
<p>首先需要安装FunASR和ModelScope. (<a class="reference external" href="https://alibaba-damo-academy.github.io/FunASR/en/installation.html">installation</a>)<br />
基线系统有训练和测试两个脚本，<code class="docutils literal notranslate"><span class="pre">run.sh</span></code> 是用于训练基线系统并在M2MET的验证与测试集上评估的，而 <code class="docutils literal notranslate"><span class="pre">run_m2met_2023_infer.sh</span></code> 用于此次竞赛预备开放的全新测试集上测试同时生成符合竞赛最终提交格式的文件。
基线系统有训练和测试两个脚本，<code class="docutils literal notranslate"><span class="pre">run.sh</span></code> 是用于训练基线系统并在M2MeT的验证与测试集上评估的，而 <code class="docutils literal notranslate"><span class="pre">run_m2met_2023_infer.sh</span></code> 用于此次竞赛预备开放的全新测试集上测试同时生成符合竞赛最终提交格式的文件。
在运行 <code class="docutils literal notranslate"><span class="pre">run.sh</span></code>前，需要自行下载并解压<a class="reference external" href="http://www.openslr.org/119/">AliMeeting</a>数据集并放置于<code class="docutils literal notranslate"><span class="pre">./dataset</span></code>目录下：</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>dataset
<span class="p">|</span>——<span class="w"> </span>Eval_Ali_far

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<p>语音识别（Automatic Speech Recognition）、说话人日志（Speaker Diarization）等语音处理技术的最新发展激发了众多智能语音的广泛应用。然而会议场景由于其复杂的声学条件和不同的讲话风格，包括重叠的讲话、不同数量的发言者、大会议室的远场信号以及环境噪声和混响，仍然属于一项极具挑战性的任务。</p>
<p>为了推动会议场景语音识别的发展，已经有很多相关的挑战赛，如 Rich Transcription evaluation 和 CHIME（Computational Hearing in Multisource Environments） 挑战赛。最新的CHIME挑战赛关注于远距离自动语音识别和开发能在各种不同拓扑结构的阵列和应用场景中通用的系统。然而不同语言之间的差异限制了非英语会议转录的进展。MISP（Multimodal Information Based Speech Processing）和M2MeT（Multi-Channel Multi-Party Meeting Transcription）挑战赛为推动普通话会议场景语音识别做出了贡献。MISP挑战赛侧重于用视听多模态的方法解决日常家庭环境中的远距离多麦克风信号处理问题，而M2MeT挑战则侧重于解决离线会议室中会议转录的语音重叠问题。</p>
<p>IASSP2022 M2MeT挑战的侧重点是会议场景，它包括两个赛道：说话人日记和多说话人自动语音识别。前者涉及识别“谁在什么时候说了话”，而后者旨在同时识别来自多个说话人的语音，语音重叠和各种噪声带来了巨大的技术困难。</p>
<p>在上一届M2MET成功举办的基础上，我们将在ASRU2023上继续举办M2MET2.0挑战赛。在上一届M2MET挑战赛中，评估指标是说话人无关的，我们只能得到识别文本，而不能确定相应的说话人。
为了解决这一局限性并将现在的多说话人语音识别系统推向实用化，M2MET2.0挑战赛将在说话人相关的人物上评估，并且同时设立限定数据与不限定数据两个子赛道。通过将语音归属于特定的说话人，这项任务旨在提高多说话人ASR系统在真实世界环境中的准确性和适用性。
<p>在上一届M2MeT成功举办的基础上，我们将在ASRU2023上继续举办M2MeT2.0挑战赛。在上一届M2MeT挑战赛中，评估指标是说话人无关的，我们只能得到识别文本，而不能确定相应的说话人。
为了解决这一局限性并将现在的多说话人语音识别系统推向实用化，M2MeT2.0挑战赛将在说话人相关的人物上评估，并且同时设立限定数据与不限定数据两个子赛道。通过将语音归属于特定的说话人，这项任务旨在提高多说话人ASR系统在真实世界环境中的准确性和适用性。
我们对数据集、规则、基线系统和评估方法进行了详细介绍，以进一步促进多说话人语音识别领域研究的发展。此外，我们将根据时间表发布一个全新的测试集，包括大约10小时的音频。</p>
</section>
<section id="aoe">
@ -152,7 +152,7 @@
<section id="id3">
<h2>竞赛报名<a class="headerlink" href="#id3" title="此标题的永久链接"></a></h2>
<p>来自学术界和工业界的有意向参赛者均应在2023年5月22日及之前填写下方的谷歌表单。同时欢迎广大参赛者加入<a class="reference external" href="https://alibaba-damo-academy.github.io/FunASR/m2met2_cn/%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html">官方交流微信群</a>交流并及时获取竞赛最新消息：</p>
<p><a class="reference external" href="https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link">M2MET2.0报名</a></p>
<p><a class="reference external" href="https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link">M2MeT2.0报名</a></p>
<p>主办方将在3个工作日内通过电子邮件通知符合条件的参赛团队，团队必须遵守将在挑战网站上发布的挑战规则。在排名发布之前，每个参赛者必须提交一份系统描述文件，详细说明使用的方法和模型。主办方将排名前列的队伍纳入ASRU2023论文集。</p>
</section>
</section>

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<section id="id1">
<h1>联系方式<a class="headerlink" href="#id1" title="此标题的永久链接"></a></h1>
<p>如果对M2MET2.0竞赛有任何疑问，欢迎通过以下方式联系我们：</p>
<p>如果对M2MeT2.0竞赛有任何疑问，欢迎通过以下方式联系我们：</p>
<ul class="simple">
<li><p>邮件: <a class="reference external" href="mailto:m2met&#46;alimeeting&#37;&#52;&#48;gmail&#46;com">m2met<span>&#46;</span>alimeeting<span>&#64;</span>gmail<span>&#46;</span>com</a></p></li>
</ul>
<table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head text-center"><p>M2MET2.0竞赛官方微信群</p></th>
<tr class="row-odd"><th class="head text-center"><p>M2MeT2.0竞赛官方微信群</p></th>
</tr>
</thead>
<tbody>

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<h1>赛道设置与评估<a class="headerlink" href="#id1" title="此标题的永久链接"></a></h1>
<section id="id2">
<h2>说话人相关的语音识别<a class="headerlink" href="#id2" title="此标题的永久链接"></a></h2>
<p>说话人相关的ASR任务需要从重叠的语音中识别每个说话人的语音，并为识别内容分配一个说话人标签。图2展示了说话人相关语音识别任务和多说话人语音识别任务的主要区别。在本次竞赛中AliMeeting、Aishell4和Cn-Celeb数据集可作为受限数据源。在M2MeT挑战赛中使用的AliMeeting数据集包含训练、评估和测试集，在M2MET2.0可以在训练和评估中使用。此外，一个包含约10小时会议数据的新的Test-2023集将根据赛程安排发布并用于挑战赛的评分和排名。值得注意的是，对于Test-2023测试集，主办方将不再提供耳机的近场音频、转录以及真实时间戳。而是提供可以通过一个简单的VAD模型得到的包含多个说话人的片段。</p>
<p>说话人相关的ASR任务需要从重叠的语音中识别每个说话人的语音，并为识别内容分配一个说话人标签。图2展示了说话人相关语音识别任务和多说话人语音识别任务的主要区别。在本次竞赛中AliMeeting、Aishell4和Cn-Celeb数据集可作为受限数据源。在M2MeT挑战赛中使用的AliMeeting数据集包含训练、评估和测试集，在M2MeT2.0可以在训练和评估中使用。此外，一个包含约10小时会议数据的新的Test-2023集将根据赛程安排发布并用于挑战赛的评分和排名。值得注意的是，对于Test-2023测试集，主办方将不再提供耳机的近场音频、转录以及真实时间戳。而是提供可以通过一个简单的VAD模型得到的包含多个说话人的片段。</p>
<p><img alt="task difference" src="_images/task_diff.png" /></p>
</section>
<section id="id3">

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ASRU 2023 多通道多方会议转录挑战 2.0
==================================================================================
在上一届M2MET竞赛成功举办的基础上，我们将在ASRU2023上继续举办M2MET2.0挑战赛。
为了将现在的多说话人语音识别系统推向实用化，M2MET2.0挑战赛将在说话人相关的人物上评估，并且同时设立限定数据与不限定数据两个子赛道。
在上一届M2MeT竞赛成功举办的基础上，我们将在ASRU2023上继续举办M2MeT2.0挑战赛。
为了将现在的多说话人语音识别系统推向实用化，M2MeT2.0挑战赛将在说话人相关的人物上评估，并且同时设立限定数据与不限定数据两个子赛道。
我们对数据集、规则、基线系统和评估方法进行了详细介绍，以进一步促进多说话人语音识别领域研究的发展。
.. toctree::

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## 快速开始
首先需要安装FunASR和ModelScope. ([installation](https://alibaba-damo-academy.github.io/FunASR/en/installation.html))
基线系统有训练和测试两个脚本，`run.sh` 是用于训练基线系统并在M2MET的验证与测试集上评估的，而 `run_m2met_2023_infer.sh` 用于此次竞赛预备开放的全新测试集上测试同时生成符合竞赛最终提交格式的文件。
基线系统有训练和测试两个脚本，`run.sh` 是用于训练基线系统并在M2MeT的验证与测试集上评估的，而 `run_m2met_2023_infer.sh` 用于此次竞赛预备开放的全新测试集上测试同时生成符合竞赛最终提交格式的文件。
在运行 `run.sh`前，需要自行下载并解压[AliMeeting](http://www.openslr.org/119/)数据集并放置于`./dataset`目录下：
```shell
dataset

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IASSP2022 M2MeT挑战的侧重点是会议场景，它包括两个赛道：说话人日记和多说话人自动语音识别。前者涉及识别“谁在什么时候说了话”，而后者旨在同时识别来自多个说话人的语音，语音重叠和各种噪声带来了巨大的技术困难。
在上一届M2MET成功举办的基础上，我们将在ASRU2023上继续举办M2MET2.0挑战赛。在上一届M2MET挑战赛中，评估指标是说话人无关的，我们只能得到识别文本，而不能确定相应的说话人。
为了解决这一局限性并将现在的多说话人语音识别系统推向实用化，M2MET2.0挑战赛将在说话人相关的人物上评估，并且同时设立限定数据与不限定数据两个子赛道。通过将语音归属于特定的说话人，这项任务旨在提高多说话人ASR系统在真实世界环境中的准确性和适用性。
在上一届M2MeT成功举办的基础上，我们将在ASRU 2023上继续举办M2MeT2.0挑战赛。在上一届M2MeT挑战赛中，评估指标是说话人无关的，我们只能得到识别文本，而不能确定相应的说话人。
为了解决这一局限性并将现在的多说话人语音识别系统推向实用化，M2MeT2.0挑战赛将在说话人相关的人物上评估，并且同时设立限定数据与不限定数据两个子赛道。通过将语音归属于特定的说话人，这项任务旨在提高多说话人ASR系统在真实世界环境中的准确性和适用性。
我们对数据集、规则、基线系统和评估方法进行了详细介绍，以进一步促进多说话人语音识别领域研究的发展。此外，我们将根据时间表发布一个全新的测试集，包括大约10小时的音频。
@ -28,6 +28,6 @@ IASSP2022 M2MeT挑战的侧重点是会议场景，它包括两个赛道：说
来自学术界和工业界的有意向参赛者均应在2023年5月22日及之前填写下方的谷歌表单。同时欢迎广大参赛者加入[官方交流微信群](https://alibaba-damo-academy.github.io/FunASR/m2met2_cn/%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html)交流并及时获取竞赛最新消息：
[M2MET2.0报名](https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link)
[M2MeT2.0报名](https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link)
主办方将在3个工作日内通过电子邮件通知符合条件的参赛团队，团队必须遵守将在挑战网站上发布的挑战规则。在排名发布之前，每个参赛者必须提交一份系统描述文件，详细说明使用的方法和模型。主办方将排名前列的队伍纳入ASRU2023论文集。

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# 联系方式
如果对M2MET2.0竞赛有任何疑问，欢迎通过以下方式联系我们：
如果对M2MeT2.0竞赛有任何疑问，欢迎通过以下方式联系我们：
- 邮件: [m2met.alimeeting@gmail.com](mailto:m2met.alimeeting@gmail.com)
| M2MET2.0竞赛官方微信群 |
| M2MeT2.0竞赛官方微信群 |
|:------------------------------------------:|
| <img src="images/qrcode.png" width="300"/> |

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# 赛道设置与评估
## 说话人相关的语音识别
说话人相关的ASR任务需要从重叠的语音中识别每个说话人的语音，并为识别内容分配一个说话人标签。图2展示了说话人相关语音识别任务和多说话人语音识别任务的主要区别。在本次竞赛中AliMeeting、Aishell4和Cn-Celeb数据集可作为受限数据源。在M2MeT挑战赛中使用的AliMeeting数据集包含训练、评估和测试集，在M2MET2.0可以在训练和评估中使用。此外，一个包含约10小时会议数据的新的Test-2023集将根据赛程安排发布并用于挑战赛的评分和排名。值得注意的是，对于Test-2023测试集，主办方将不再提供耳机的近场音频、转录以及真实时间戳。而是提供可以通过一个简单的VAD模型得到的包含多个说话人的片段。
说话人相关的ASR任务需要从重叠的语音中识别每个说话人的语音，并为识别内容分配一个说话人标签。图2展示了说话人相关语音识别任务和多说话人语音识别任务的主要区别。在本次竞赛中AliMeeting、Aishell4和Cn-Celeb数据集可作为受限数据源。在M2MeT挑战赛中使用的AliMeeting数据集包含训练、评估和测试集，在M2MeT2.0可以在训练和评估中使用。此外，一个包含约10小时会议数据的新的Test-2023集将根据赛程安排发布并用于挑战赛的评分和排名。值得注意的是，对于Test-2023测试集，主办方将不再提供耳机的近场音频、转录以及真实时间戳。而是提供可以通过一个简单的VAD模型得到的包含多个说话人的片段。
![task difference](images/task_diff.png)