From a6d852d2aa3929e4ae6eb6e29c6583f92b542136 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 25 四月 2023 22:43:57 +0800
Subject: [PATCH] Merge pull request #422 from alibaba-damo-academy/main
---
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docs/m2met2/_build/html/Contact.html | 2
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docs/m2met2_cn/_build/html/_static/underscore-1.13.1.js | 0
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egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/infer.py | 1
docs/m2met2/_build/html/_static/fonts/glyphicons-halflings-regular.woff | 0
docs/m2met2/_build/html/_sources/Introduction.md.txt | 21
docs/m2met2_cn/_build/html/_static/fonts/open-sans/fonts/OpenSans-Light-webfont.ttf | 0
docs/m2met2_cn/_build/html/_static/guzzle.css | 0
docs/m2met2_cn/规则.md | 2
docs/m2met2_cn/_build/html/_static/translations.js | 0
docs/m2met2/_build/html/_static/file.png | 0
docs/m2met2/_build/html/index.html | 10
docs/m2met2_cn/_build/html/_static/jquery.min.map | 0
docs/m2met2/_build/doctrees/Contact.doctree | 0
README.md | 11
docs/m2met2/images/kong.png | 0
docs/m2met2_cn/_build/html/_static/fonts/source-serif-pro/OTF/SourceSerifPro-Bold.otf | 0
docs/m2met2/_build/html/_images/zsl.JPG | 0
docs/m2met2_cn/_build/html/简介.html | 13
docs/m2met2/_build/html/_static/fonts/open-sans/fonts/OpenSans-LightItalic-webfont.eot | 0
docs/m2met2_cn/_build/html/_images/lxie.jpeg | 0
egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/infer.sh | 70 ++++
docs/m2met2_cn/images/meeting_room.png | 0
docs/m2met2_cn/_build/html/_static/fonts/source-serif-pro/EOT/SourceSerifPro-Black.eot | 0
docs/m2met2_cn/_build/html/_static/js/bootstrap.min.js | 0
docs/m2met2_cn/_build/html/_sources/基线.md.txt | 0
docs/m2met2_cn/_build/html/_static/fonts/open-sans/fonts/OpenSans-SemiboldItalic-webfont.eot | 0
docs/m2met2_cn/_build/html/_static/fonts/source-serif-pro/TTF/SourceSerifPro-Semibold.ttf | 0
funasr/datasets/large_datasets/utils/tokenize.py | 10
docs/m2met2/_build/html/_sources/Contact.md.txt | 0
docs/m2met2/_build/html/_static/basic.css | 0
docs/m2met2_cn/_build/html/_sources/赛道设置与评估.md.txt | 6
docs/m2met2_cn/_build/html/_static/fonts/glyphicons-halflings-regular.eot | 0
docs/m2met2_cn/_build/html/_images/meeting_room.png | 0
egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/infer.py | 111 +------
docs/m2met2/_build/html/_images/sa_asr_arch.png | 0
docs/m2met2_cn/_build/html/_static/fonts/open-sans/fonts/OpenSans-LightItalic-webfont.woff | 0
docs/m2met2_cn/images/baseline_result.png | 0
docs/m2met2/_build/html/search.html | 2
docs/m2met2_cn/images/sa_asr_arch.png | 0
docs/m2met2/_build/html/_static/fonts/source-serif-pro/WOFF/TTF/SourceSerifPro-Bold.ttf.woff | 0
docs/m2met2_cn/_build/html/.buildinfo | 0
docs/m2met2/_build/html/_static/fonts/open-sans/fonts/OpenSans-Semibold-webfont.eot | 0
docs/m2met2/_build/html/_static/fonts/open-sans/fonts/OpenSans-ExtraBold-webfont.woff | 0
docs/m2met2_cn/_build/html/_sources/数据集.md.txt | 4
docs/m2met2/_build/html/_static/fonts/source-serif-pro/TTF/SourceSerifPro-Light.ttf | 0
funasr/datasets/preprocessor.py | 11
docs/m2met2_cn/_build/html/_static/plus.png | 0
docs/m2met2/_build/html/_images/wujian.jpg | 0
docs/m2met2_cn/_build/html/_static/fonts/source-serif-pro/EOT/SourceSerifPro-Semibold.eot | 0
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/infer.sh | 105 +++++++
docs/m2met2/_build/html/searchindex.js | 1
docs/m2met2_cn/_build/html/_static/fonts/open-sans/fonts/OpenSans-Regular-webfont.ttf | 0
docs/m2met2_cn/images/zsl.JPG | 0
docs/m2met2/_build/html/_static/fonts/source-serif-pro/WOFF/OTF/SourceSerifPro-Bold.otf.woff | 0
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docs/m2met2/_build/html/.buildinfo | 0
docs/m2met2/_build/html/Introduction.html | 28 +
docs/m2met2_cn/_build/html/数据集.html | 8
docs/m2met2/_build/doctrees/index.doctree | 0
docs/m2met2_cn/_build/html/_static/css/bootstrap.min.css | 0
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362 files changed, 510 insertions(+), 354 deletions(-)
diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index 75651b6..26a47c4 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -29,10 +29,10 @@
cp -r docs/_build/html/* public/en/
mkdir public/m2met2
touch public/m2met2/.nojekyll
- cp -r docs_m2met2/_build/html/* public/m2met2/
+ cp -r docs/m2met2/_build/html/* public/m2met2/
mkdir public/m2met2_cn
touch public/m2met2_cn/.nojekyll
- cp -r docs_m2met2_cn/_build/html/* public/m2met2_cn/
+ cp -r docs/m2met2_cn/_build/html/* public/m2met2_cn/
- name: deploy github.io pages
if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/dev_wjm' || github.ref == 'refs/heads/dev_lyh'
diff --git a/README.md b/README.md
index 48b5ccd..9393d06 100644
--- a/README.md
+++ b/README.md
@@ -18,14 +18,13 @@
| [**Runtime**](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime)
| [**Model Zoo**](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/modelscope_models.md)
| [**Contact**](#contact)
-|
-[**M2MET2.0 Guidence_CN**](https://alibaba-damo-academy.github.io/FunASR/m2met2_cn/index.html)
-| [**M2MET2.0 Guidence_EN**](https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html)
+| [**M2MET2.0_CN**](https://alibaba-damo-academy.github.io/FunASR/m2met2_cn/index.html)
+| [**M2MET2.0_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 will be held in the near future. 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)).
## What's new:
-
+### Multi-Channel Multi-Party Meeting Transcription 2.0 (M2MET2.0) Challenge
+We are pleased to announce that the M2MeT2.0 challenge will be held in the near future. 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)
## Highlights
diff --git a/docs_m2met2/Baseline.md b/docs/m2met2/Baseline.md
similarity index 100%
rename from docs_m2met2/Baseline.md
rename to docs/m2met2/Baseline.md
diff --git a/docs_m2met2/Contact.md b/docs/m2met2/Contact.md
similarity index 100%
rename from docs_m2met2/Contact.md
rename to docs/m2met2/Contact.md
diff --git a/docs_m2met2/Dataset.md b/docs/m2met2/Dataset.md
similarity index 92%
rename from docs_m2met2/Dataset.md
rename to docs/m2met2/Dataset.md
index c987aca..a897d67 100644
--- a/docs_m2met2/Dataset.md
+++ b/docs/m2met2/Dataset.md
@@ -2,7 +2,7 @@
## Overview of training data
In the fixed training condition, the training dataset is restricted to three publicly available corpora, namely, AliMeeting, AISHELL-4, and CN-Celeb. To evaluate the performance of the models trained on these datasets, we will release a new Test set called Test-2023 for scoring and ranking. We will describe the AliMeeting dataset and the Test-2023 set in detail.
## Detail of AliMeeting corpus
-AliMeeting contains 118.75 hours of speech data in total. The dataset is divided into 104.75 hours for training (Train), 4 hours for evaluation (Eval) and 10 hours as test set (Test) for scoring and ranking. Specifically, the Train and Eval sets contain 212 and 8 sessions, respectively. Each session consists of a 15 to 30-minute discussion by a group of participants. The total number of participants in Train and Eval sets is 456 and 25, respectively, with balanced gender coverage.
+AliMeeting contains 118.75 hours of speech data in total. The dataset is divided into 104.75 hours for training (Train), 4 hours for evaluation (Eval) and 10 hours as test set (Test) for scoring and ranking. Specifically, the Train, Eval and Test sets contain 212, 8 and 20 sessions, respectively. Each session consists of a 15 to 30-minute discussion by a group of participants. The total number of participants in Train, Eval and Test sets is 456, 25 and 60, respectively, with balanced gender coverage.
The dataset is collected in 13 meeting venues, which are categorized into three types: small, medium, and large rooms with sizes ranging from 8 m$^{2}$ to 55 m$^{2}$. Different rooms give us a variety of acoustic properties and layouts. The detailed parameters of each meeting venue will be released together with the Train data. The type of wall material of the meeting venues covers cement, glass, etc. Other furnishings in meeting venues include sofa, TV, blackboard, fan, air conditioner, plants, etc. During recording, the participants of the meeting sit around the microphone array which is placed on the table and conduct a natural conversation. The microphone-speaker distance ranges from 0.3 m to 5.0 m. All participants are native Chinese speakers speaking Mandarin without strong accents. During the meeting, various kinds of indoor noise including but not limited to clicking, keyboard, door opening/closing, fan, bubble noise, etc., are made naturally. For both Train and Eval sets, the participants are required to remain in the same position during recording. There is no speaker overlap between the Train and Eval set. An example of the recording venue from the Train set is shown in Fig 1.
diff --git a/docs_m2met2/Introduction.md b/docs/m2met2/Introduction.md
similarity index 82%
rename from docs_m2met2/Introduction.md
rename to docs/m2met2/Introduction.md
index e1f9fc7..eac9eb6 100644
--- a/docs_m2met2/Introduction.md
+++ b/docs/m2met2/Introduction.md
@@ -9,17 +9,20 @@
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.
## Timeline(AOE Time)
-
-- $ May~5^{th}, 2023: $ Registration deadline, the due date for participants to join the Challenge.
-- $ June~9^{th}, 2023: $ Test data release.
-- $ June~13^{rd}, 2023: $ Final submission deadline.
-- $ June~19^{th}, 2023: $ Evaluation result and ranking release.
-- $ July~3^{rd}, 2023: $ Deadline for paper submission.
-- $ July~10^{th}, 2023: $ Deadline for final paper submission.
-- $ December~12^{nd}\ to\ 16^{th}, 2023: $ ASRU Workshop
+- $ April~29, 2023: $ Challenge and registration open.
+- $ May~8, 2023: $ Baseline release.
+- $ May~15, 2023: $ Registration deadline, the due date for participants to join the Challenge.
+- $ June~9, 2023: $ Test data release and leaderboard open.
+- $ June~13, 2023: $ Final submission deadline.
+- $ June~19, 2023: $ Evaluation result and ranking release.
+- $ July~3, 2023: $ Deadline for paper submission.
+- $ July~10, 2023: $ Deadline for final paper submission.
+- $ December~12\ to\ 16, 2023: $ ASRU Workshop and challenge session
## Guidelines
-Interested participants, whether from academia or industry, must register for the challenge by completing a Google form, which will be available here. The deadline for registration is May 5, 2023.
+Interested participants, whether from academia or industry, must register for the challenge by completing the Google form below. The deadline for registration is May 15, 2023.
+
+[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 three submissions to be included in the ASRU2023 Proceedings.
diff --git a/docs_m2met2/Makefile b/docs/m2met2/Makefile
similarity index 100%
rename from docs_m2met2/Makefile
rename to docs/m2met2/Makefile
diff --git a/docs_m2met2/Organizers.md b/docs/m2met2/Organizers.md
similarity index 100%
rename from docs_m2met2/Organizers.md
rename to docs/m2met2/Organizers.md
diff --git a/docs_m2met2/Rules.md b/docs/m2met2/Rules.md
similarity index 92%
rename from docs_m2met2/Rules.md
rename to docs/m2met2/Rules.md
index 8bc1a19..340c2f5 100644
--- a/docs_m2met2/Rules.md
+++ b/docs/m2met2/Rules.md
@@ -4,8 +4,6 @@
- Data augmentation is allowed on the original training dataset, including, but not limited to, adding noise or reverberation, speed perturbation and tone change.
- Participants are permitted to use the Eval set for model training, but it is not allowed to use the Test set for this purpose. Instead, the Test set should only be utilized for parameter tuning and model selection. Any use of the Test-2023 dataset that violates these rules is strictly prohibited, including but not limited to the use of the Test set for fine-tuning or training the model.
-
-- Multi-system fusion is allowed, but the systems with same structure and different parameters is not encouraged.
- If the cpCER of the two systems on the Test dataset are the same, the system with lower computation complexity will be judged as the superior one.
diff --git a/docs_m2met2/Track_setting_and_evaluation.md b/docs/m2met2/Track_setting_and_evaluation.md
similarity index 94%
rename from docs_m2met2/Track_setting_and_evaluation.md
rename to docs/m2met2/Track_setting_and_evaluation.md
index b90c17a..2b75fca 100644
--- a/docs_m2met2/Track_setting_and_evaluation.md
+++ b/docs/m2met2/Track_setting_and_evaluation.md
@@ -1,6 +1,6 @@
# Track & Evaluation
-## Speaker-Attributed ASR (Main Track)
-The speaker-attributed ASR task poses a unique challenge of transcribing speech from multiple speakers and assigning a speaker label to the transcription. Figure 2 illustrates the difference between the speaker-attributed ASR task and the multi-speaker ASR task. This track allows for the use of the AliMeeting, Aishell4, and Cn-Celeb datasets as constrained data sources during both training and evaluation. The AliMeeting dataset, which was used in the M2MeT challenge, includes Train, Eval, and Test sets. Additionally, a new Test-2023 set, consisting of approximately 10 hours of meeting data recorded in an identical acoustic setting as the AliMeeting corpus, will be released soon for challenge scoring and ranking. It's worth noting that the organizers will not provide the near-field audio, transcriptions, or oracle timestamps. Instead, segments containing multiple speakers will be provided on the Test-2023 set, which can be obtained using a simple voice activity detection (VAD) model.
+## Speaker-Attributed ASR
+The speaker-attributed ASR task poses a unique challenge of transcribing speech from multiple speakers and assigning a speaker label to the transcription. Figure 2 illustrates the difference between the speaker-attributed ASR task and the multi-speaker ASR task. This track allows for the use of the AliMeeting, Aishell4, and Cn-Celeb datasets as constrained data sources during both training and evaluation. The AliMeeting dataset, which was used in the M2MeT challenge, includes Train, Eval, and Test sets. Additionally, a new Test-2023 set, consisting of approximately 10 hours of meeting data recorded in an identical acoustic setting as the AliMeeting corpus, will be released soon for challenge scoring and ranking. It's worth noting that the organizers will not provide the near-field audio, transcriptions, or oracle timestamps of the Test-2023 set. Instead, segments containing multiple speakers will be provided, which can be obtained using a simple voice activity detection (VAD) model.
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similarity index 100%
rename from docs_m2met2/_build/html/.buildinfo
rename to docs/m2met2/_build/html/.buildinfo
diff --git a/docs_m2met2/_build/html/Baseline.html b/docs/m2met2/_build/html/Baseline.html
similarity index 97%
rename from docs_m2met2/_build/html/Baseline.html
rename to docs/m2met2/_build/html/Baseline.html
index 282f235..e52d322 100644
--- a/docs_m2met2/_build/html/Baseline.html
+++ b/docs/m2met2/_build/html/Baseline.html
@@ -88,7 +88,7 @@
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="Track_setting_and_evaluation.html">Track & Evaluation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr-main-track">Speaker-Attributed ASR (Main Track)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr">Speaker-Attributed ASR</a></li>
<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#evaluation-metric">Evaluation metric</a></li>
<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#sub-track-arrangement">Sub-track arrangement</a></li>
</ul>
@@ -135,8 +135,8 @@
</section>
<section id="baseline-results">
<h2>Baseline results<a class="headerlink" href="#baseline-results" title="Permalink to this heading">露</a></h2>
-<p>The results of the baseline system are shown in Table 3. The speaker profile adopts the oracle speaker embedding during training. However, due to the lack of oracle speaker label during evaluation, the speaker profile provided by an additional spectral clustering is used. Meanwhile, the results of using the oracle speaker profile on Eval and Test Set are also provided to show the impact of speaker profile accuracy.
-<img alt="baseline result" src="_images/baseline_result.png" /></p>
+<p>The results of the baseline system are shown in Table 3. The speaker profile adopts the oracle speaker embedding during training. However, due to the lack of oracle speaker label during evaluation, the speaker profile provided by an additional spectral clustering is used. Meanwhile, the results of using the oracle speaker profile on Eval and Test Set are also provided to show the impact of speaker profile accuracy.</p>
+<p><img alt="baseline result" src="_images/baseline_result.png" /></p>
</section>
</section>
diff --git a/docs_m2met2/_build/html/Contact.html b/docs/m2met2/_build/html/Contact.html
similarity index 98%
rename from docs_m2met2/_build/html/Contact.html
rename to docs/m2met2/_build/html/Contact.html
index 44546d5..eafd2d5 100644
--- a/docs_m2met2/_build/html/Contact.html
+++ b/docs/m2met2/_build/html/Contact.html
@@ -84,7 +84,7 @@
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="Track_setting_and_evaluation.html">Track & Evaluation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr-main-track">Speaker-Attributed ASR (Main Track)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr">Speaker-Attributed ASR</a></li>
<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#evaluation-metric">Evaluation metric</a></li>
<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#sub-track-arrangement">Sub-track arrangement</a></li>
</ul>
diff --git a/docs_m2met2/_build/html/Dataset.html b/docs/m2met2/_build/html/Dataset.html
similarity index 96%
rename from docs_m2met2/_build/html/Dataset.html
rename to docs/m2met2/_build/html/Dataset.html
index 88531f6..43bf8a1 100644
--- a/docs_m2met2/_build/html/Dataset.html
+++ b/docs/m2met2/_build/html/Dataset.html
@@ -89,7 +89,7 @@
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="Track_setting_and_evaluation.html">Track & Evaluation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr-main-track">Speaker-Attributed ASR (Main Track)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr">Speaker-Attributed ASR</a></li>
<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#evaluation-metric">Evaluation metric</a></li>
<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#sub-track-arrangement">Sub-track arrangement</a></li>
</ul>
@@ -131,7 +131,7 @@
</section>
<section id="detail-of-alimeeting-corpus">
<h2>Detail of AliMeeting corpus<a class="headerlink" href="#detail-of-alimeeting-corpus" title="Permalink to this heading">露</a></h2>
-<p>AliMeeting contains 118.75 hours of speech data in total. The dataset is divided into 104.75 hours for training (Train), 4 hours for evaluation (Eval) and 10 hours as test set (Test) for scoring and ranking. Specifically, the Train and Eval sets contain 212 and 8 sessions, respectively. Each session consists of a 15 to 30-minute discussion by a group of participants. The total number of participants in Train and Eval sets is 456 and 25, respectively, with balanced gender coverage.</p>
+<p>AliMeeting contains 118.75 hours of speech data in total. The dataset is divided into 104.75 hours for training (Train), 4 hours for evaluation (Eval) and 10 hours as test set (Test) for scoring and ranking. Specifically, the Train, Eval and Test sets contain 212, 8 and 20 sessions, respectively. Each session consists of a 15 to 30-minute discussion by a group of participants. The total number of participants in Train, Eval and Test sets is 456, 25 and 60, respectively, with balanced gender coverage.</p>
<p>The dataset is collected in 13 meeting venues, which are categorized into three types: small, medium, and large rooms with sizes ranging from 8 m<span class="math notranslate nohighlight">\(^{2}\)</span> to 55 m<span class="math notranslate nohighlight">\(^{2}\)</span>. Different rooms give us a variety of acoustic properties and layouts. The detailed parameters of each meeting venue will be released together with the Train data. The type of wall material of the meeting venues covers cement, glass, etc. Other furnishings in meeting venues include sofa, TV, blackboard, fan, air conditioner, plants, etc. During recording, the participants of the meeting sit around the microphone array which is placed on the table and conduct a natural conversation. The microphone-speaker distance ranges from 0.3 m to 5.0 m. All participants are native Chinese speakers speaking Mandarin without strong accents. During the meeting, various kinds of indoor noise including but not limited to clicking, keyboard, door opening/closing, fan, bubble noise, etc., are made naturally. For both Train and Eval sets, the participants are required to remain in the same position during recording. There is no speaker overlap between the Train and Eval set. An example of the recording venue from the Train set is shown in Fig 1.</p>
<p><img alt="meeting room" src="_images/meeting_room.png" /></p>
<p>The number of participants within one meeting session ranges from 2 to 4. To ensure the coverage of different overlap ratios, we select various meeting topics during recording, including medical treatment, education, business, organization management, industrial production and other daily routine meetings. The average speech overlap ratio of Train, Eval and Test sets are 42.27%, 34.76% and 42.8%, respectively. More details of AliMeeting are shown in Table 1. A detailed overlap ratio distribution of meeting sessions with different numbers of speakers in the Train, Eval and Test set is shown in Table 2.</p>
diff --git a/docs_m2met2/_build/html/Introduction.html b/docs/m2met2/_build/html/Introduction.html
similarity index 76%
rename from docs_m2met2/_build/html/Introduction.html
rename to docs/m2met2/_build/html/Introduction.html
index 54de72d..2ddafe2 100644
--- a/docs_m2met2/_build/html/Introduction.html
+++ b/docs/m2met2/_build/html/Introduction.html
@@ -89,7 +89,7 @@
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="Track_setting_and_evaluation.html">Track & Evaluation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr-main-track">Speaker-Attributed ASR (Main Track)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr">Speaker-Attributed ASR</a></li>
<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#evaluation-metric">Evaluation metric</a></li>
<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#sub-track-arrangement">Sub-track arrangement</a></li>
</ul>
@@ -128,26 +128,28 @@
<section id="call-for-participation">
<h2>Call for participation<a class="headerlink" href="#call-for-participation" title="Permalink to this heading">露</a></h2>
<p>Automatic speech recognition (ASR) and speaker diarization have made significant strides in recent years, resulting in a surge of speech technology applications across various domains. However, meetings present unique challenges to speech technologies due to their complex acoustic conditions and diverse speaking styles, including overlapping speech, variable numbers of speakers, far-field signals in large conference rooms, and environmental noise and reverberation.</p>
-<p>Over the years, several challenges have been organized to advance the development of meeting transcription, including the Rich Transcription evaluation and Computational Hearing in Multisource Environments (CHIME) challenges. The latest iteration of the CHIME challenge has a particular focus on distant automatic speech recognition (ASR) and developing systems that can generalize across various array topologies and application scenarios. However, while progress has been made in English meeting transcription, language differences remain a significant barrier to achieving comparable results in non-English languages, such as Mandarin.</p>
-<p>The Multimodal Information Based Speech Processing (MISP) and Multi-Channel Multi-Party Meeting Transcription (M2MeT) challenges have been instrumental in advancing Mandarin meeting transcription. The MISP challenge seeks to address the problem of audio-visual distant multi-microphone signal processing in everyday home environments, while the M2MeT challenge focuses on tackling the speech overlap issue in offline meeting rooms.</p>
-<p>The ICASSP2022 M2MeT challenge focuses on meeting scenarios, and it comprises two main tasks: speaker diarization and multi-speaker automatic speech recognition (ASR). 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.</p>
-<p>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. By attributing speech to specific speakers, this task aims to improve the accuracy and applicability of multi-talker ASR systems in real-world settings. The challenge provides detailed datasets, rules, evaluation methods, and baseline systems to facilitate reproducible research in this field. The speaker-attribute automatic speech recognition (ASR) task aims to tackle the practical and challenging problem of identifying 鈥渨ho spoke what at when鈥�. 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鈥� performance and advance the state of the art in this area.</p>
+<p>Over the years, several challenges have been organized to advance the development of meeting transcription, including the Rich Transcription evaluation and Computational Hearing in Multisource Environments (CHIME) challenges. The latest iteration of the CHIME challenge has a particular focus on distant automatic speech recognition and developing systems that can generalize across various array topologies and application scenarios. However, while progress has been made in English meeting transcription, language differences remain a significant barrier to achieving comparable results in non-English languages, such as Mandarin. The Multimodal Information Based Speech Processing (MISP) and Multi-Channel Multi-Party Meeting Transcription (M2MeT) challenges have been instrumental in advancing Mandarin meeting transcription. The MISP challenge seeks to address the problem of audio-visual distant multi-microphone signal processing in everyday home environments, while the M2MeT challenge focuses on tackling the speech overlap issue in offline meeting rooms.</p>
+<p>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.</p>
+<p>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 鈥渨ho spoke what at when鈥�. 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鈥� performance and advance the state of the art in this area.</p>
</section>
<section id="timeline-aoe-time">
<h2>Timeline(AOE Time)<a class="headerlink" href="#timeline-aoe-time" title="Permalink to this heading">露</a></h2>
<ul class="simple">
-<li><p><span class="math notranslate nohighlight">\( May~5^{th}, 2023: \)</span> Registration deadline, the due date for participants to join the Challenge.</p></li>
-<li><p><span class="math notranslate nohighlight">\( June~9^{th}, 2023: \)</span> Test data release.</p></li>
-<li><p><span class="math notranslate nohighlight">\( June~13^{rd}, 2023: \)</span> Final submission deadline.</p></li>
-<li><p><span class="math notranslate nohighlight">\( June~19^{th}, 2023: \)</span> Evaluation result and ranking release.</p></li>
-<li><p><span class="math notranslate nohighlight">\( July~3^{rd}, 2023: \)</span> Deadline for paper submission.</p></li>
-<li><p><span class="math notranslate nohighlight">\( July~10^{th}, 2023: \)</span> Deadline for final paper submission.</p></li>
-<li><p><span class="math notranslate nohighlight">\( December~12^{nd}\ to\ 16^{th}, 2023: \)</span> ASRU Workshop</p></li>
+<li><p><span class="math notranslate nohighlight">\( April~29, 2023: \)</span> Challenge and registration open.</p></li>
+<li><p><span class="math notranslate nohighlight">\( May~8, 2023: \)</span> Baseline release.</p></li>
+<li><p><span class="math notranslate nohighlight">\( May~15, 2023: \)</span> Registration deadline, the due date for participants to join the Challenge.</p></li>
+<li><p><span class="math notranslate nohighlight">\( June~9, 2023: \)</span> Test data release and leaderboard open.</p></li>
+<li><p><span class="math notranslate nohighlight">\( June~13, 2023: \)</span> Final submission deadline.</p></li>
+<li><p><span class="math notranslate nohighlight">\( June~19, 2023: \)</span> Evaluation result and ranking release.</p></li>
+<li><p><span class="math notranslate nohighlight">\( July~3, 2023: \)</span> Deadline for paper submission.</p></li>
+<li><p><span class="math notranslate nohighlight">\( July~10, 2023: \)</span> Deadline for final paper submission.</p></li>
+<li><p><span class="math notranslate nohighlight">\( December~12\ to\ 16, 2023: \)</span> ASRU Workshop and challenge session</p></li>
</ul>
</section>
<section id="guidelines">
<h2>Guidelines<a class="headerlink" href="#guidelines" title="Permalink to this heading">露</a></h2>
-<p>Possible improved version: Interested participants, whether from academia or industry, must register for the challenge by completing a Google form, which will be available here. The deadline for registration is May 5, 2023.</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 15, 2023.</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 three submissions to be included in the ASRU2023 Proceedings.</p>
</section>
</section>
diff --git a/docs_m2met2/_build/html/Organizers.html b/docs/m2met2/_build/html/Organizers.html
similarity index 61%
rename from docs_m2met2/_build/html/Organizers.html
rename to docs/m2met2/_build/html/Organizers.html
index e5bb748..0a8811e 100644
--- a/docs_m2met2/_build/html/Organizers.html
+++ b/docs/m2met2/_build/html/Organizers.html
@@ -88,7 +88,7 @@
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="Track_setting_and_evaluation.html">Track & Evaluation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr-main-track">Speaker-Attributed ASR (Main Track)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr">Speaker-Attributed ASR</a></li>
<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#evaluation-metric">Evaluation metric</a></li>
<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#sub-track-arrangement">Sub-track arrangement</a></li>
</ul>
@@ -127,36 +127,27 @@
<p><em><strong>Lei Xie, Professor, Northwestern Polytechnical University, China</strong></em></p>
<p>Email: <a class="reference external" href="mailto:lxie%40nwpu.edu.cn">lxie<span>@</span>nwpu<span>.</span>edu<span>.</span>cn</a></p>
<a class="reference internal image-reference" href="_images/lxie.jpeg"><img alt="lxie" src="_images/lxie.jpeg" style="width: 20%;" /></a>
-<p>Lei Xie received the Ph.D. degree in computer science from Northwestern Polytechnical University, Xi鈥檃n, China, in 2004. From 2001 to 2002, he was with the Department of Electronics and Information Processing, Vrije Universiteit Brussel (VUB), Brussels, Belgium, as a Visiting Scientist. From 2004 to 2006, he was a Senior Research Associate with the Center for Media Technology, School of Creative Media, City University of Hong Kong, Hong Kong, China. From 2006 to 2007, he was a Postdoctoral Fellow with the Human-Computer Communications Laboratory (HCCL), The Chinese University of Hong Kong, Hong Kong, China. He is currently a Professor with School of Computer Science, Northwestern Polytechnical University, Xian, China and leads the Audio, Speech and Language Processing Group (ASLP@NPU). He has published over 200 papers in referred journals and conferences, such as IEEE/ACM Transactions on Audio, Speech and Language Processing, IEEE Transactions on Multimedia, Interspeech, ICASSP, ASRU, ACL and ACM Multimedia. He has achieved several best paper awards in flagship conferences. His current research interests include general topics in speech and language processing, multimedia, and human-computer interaction. Dr. Xie is currently an associate editor (AE) of IEEE/ACM Trans. on Audio, Speech and language Processing. He has actively served as Chairs in many conferences and technical committees. He serves as an IEEE Speech and Language Processing
-Technical Committee Member.</p>
<p><em><strong>Kong Aik Lee, Senior Scientist at Institute for Infocomm Research, A*Star, Singapore</strong></em></p>
<p>Email: <a class="reference external" href="mailto:kongaik.lee%40ieee.org">kongaik<span>.</span>lee<span>@</span>ieee<span>.</span>org</a></p>
<a class="reference internal image-reference" href="_images/kong.png"><img alt="kong" src="_images/kong.png" style="width: 20%;" /></a>
-<p>Kong Aik Lee started off him career as a researcher, then a team leader and a strategic planning manager, at the Institute Infocomm Research, A*STAR, Singapore, working on speaker and language recognition research. From 2018 to 2020, he spent two and a half years in NEC Corporation, Japan, focusing very much on voice biometrics and multi-modal biometrics products. He is proud to work with a great team on voice biometrics featured on NEC Bio-Idiom platform. He returned to Singapore in July 2020, and now leading the speech and audio analytics research at the Institute for Infocomm Research, as a Senior Scientist and PI. He also serve as an Editor for Elsevier Computer Speech and Language (since 2016), and was an Associate Editor for IEEE/ACM Transactions on Audio, Speech and Language Processing (2017 - 2021), and am an elected member of IEEE Speech and Language Technical Committee (2019 - 2021).</p>
<p><em><strong>Zhijie Yan, Principal Engineer at Alibaba, China</strong></em>
Email: <a class="reference external" href="mailto:zhijie.yzj%40alibaba-inc.com">zhijie<span>.</span>yzj<span>@</span>alibaba-inc<span>.</span>com</a></p>
<a class="reference internal image-reference" href="_images/zhijie.jpg"><img alt="zhijie" src="_images/zhijie.jpg" style="width: 20%;" /></a>
-<p>Zhijie Yan holds a PhD from the University of Science and Technology of China, and is a senior member of the Institute of Electrical and Electronics Engineers (IEEE). He is also an expert reviewer of top academic conferences and journals in the speech field. His research fields include speech recognition, speech synthesis, voiceprints, and speech interaction. His research results are applied in speech services provided by Alibaba Group and Ant Financial. He was awarded the title of 鈥淥ne of the Top 100 Grassroots Scientists鈥� by the China Association for Science and Technology.</p>
<p><em><strong>Shiliang Zhang, Senior Engineer at Alibaba, China</strong></em>
Email: <a class="reference external" href="mailto:sly.zsl%40alibaba-inc.com">sly<span>.</span>zsl<span>@</span>alibaba-inc<span>.</span>com</a></p>
<a class="reference internal image-reference" href="_images/zsl.JPG"><img alt="zsl" src="_images/zsl.JPG" style="width: 20%;" /></a>
-<p>Shiliang Zhang graduated with a Ph.D. from the University of Science and Technology of China in 2017. His research areas mainly include speech recognition, natural language understanding, and machine learning. Currently, he has published over 40 papers in mainstream academic journals and conferences in the fields of speech and machine learning, and has applied for dozens of patents. After obtaining his doctorate degree, he joined the Alibaba Intelligent Speech team. He is currently leading the direction of speech recognition and fundamental technology at DAMO Academy鈥檚 speech laboratory.</p>
<p><em><strong>Yanmin Qian, Professor, Shanghai Jiao Tong University, China</strong></em></p>
<p>Email: <a class="reference external" href="mailto:yanminqian%40sjtu.edu.cn">yanminqian<span>@</span>sjtu<span>.</span>edu<span>.</span>cn</a></p>
<a class="reference internal image-reference" href="_images/qian.jpeg"><img alt="qian" src="_images/qian.jpeg" style="width: 20%;" /></a>
-<p>Yanmin Qian received the B.S. degree from the Department of Electronic and Information Engineering,Huazhong University of Science and Technology, Wuhan, China, in 2007, and the Ph.D. degree from the Department of Electronic Engineering, Tsinghua University, Beijing, China, in 2012. Since 2013, he has been with the Department of Computer Science and Engineering, Shanghai Jiao Tong University (SJTU), Shanghai, China, where he is currently an Associate Professor. From 2015 to 2016, he also worked as an Associate Research in the Speech Group, Cambridge University Engineering Department, Cambridge, U.K. He is a senior member of IEEE and a member of ISCA, and one of the founding members of Kaldi Speech Recognition Toolkit. He has published more than 110 papers on speech and language processing with 4000+ citations, including the top conference: ICASSP, INTERSPEECH and ASRU. His current research interests include the acoustic and language modeling in speech recognition, speaker and language recognition, key word spotting, and multimedia signal processing.</p>
<p><em><strong>Zhuo Chen, Applied Scientist in Microsoft, USA</strong></em></p>
<p>Email: <a class="reference external" href="mailto:zhuc%40microsoft.com">zhuc<span>@</span>microsoft<span>.</span>com</a></p>
<a class="reference internal image-reference" href="_images/chenzhuo.jpg"><img alt="chenzhuo" src="_images/chenzhuo.jpg" style="width: 20%;" /></a>
-<p>Zhuo Chen received the Ph.D. degree from Columbia University, New York, NY, USA, in 2017. He is currently a Principal Applied Data Scientist with Microsoft. He has authored or coauthored more than 80 papers in peer-reviewed journals and conferences with around 6000 citations. He is a reviewer or technical committee member for more than ten journals and conferences. His research interests include automatic conversation recognition, speech separation, diarisation, and speaker information extraction. He actively participated in the academic events and challenges, and won several awards. Meanwhile, he contributed to open-sourced datasets, such as WSJ0-2mix, LibriCSS, and AISHELL-4, that have been main benchmark datasets for multi-speaker processing research. In 2020, he was the Team Leader in 2020 Jelinek workshop, leading more than 30 researchers and students to push the state of the art in conversation transcription.</p>
<p><em><strong>Jian Wu, Applied Scientist in Microsoft, USA</strong></em></p>
<p>Email: <a class="reference external" href="mailto:wujian%40microsoft.com">wujian<span>@</span>microsoft<span>.</span>com</a></p>
<a class="reference internal image-reference" href="_images/wujian.jpg"><img alt="wujian" src="_images/wujian.jpg" style="width: 20%;" /></a>
-<p>Jian Wu received a master degree from Northwestern Polytechnical University, Xi鈥檃n, China, in 2020 and currently he is a Applied Scientist in Microsoft, USA. His research interests cover multi-channel signal processing, robust and multi-talker speech recognition, speech enhancement, dereverberation and separation. He has around 30 conference publications with a total citation over 1200. He participated in several challenges such as CHiME5, DNS 2020 and FFSVC 2020 and contributed to the open-sourced datasets including LibriCSS and AISHELL-4. He is also a reviewer for several journals and conferences such as ICASSP, SLT, TASLP and SPL.</p>
<p><em><strong>Hui Bu, CEO, AISHELL foundation, China</strong></em></p>
<p>Email: <a class="reference external" href="mailto:buhui%40aishelldata.com">buhui<span>@</span>aishelldata<span>.</span>com</a></p>
<a class="reference internal image-reference" href="_images/buhui.jpeg"><img alt="buhui" src="_images/buhui.jpeg" style="width: 20%;" /></a>
-<p>Hui Bu received his master degree in the Artificial Intelligence Laboratory of Korea University in 2014. He is the founder and the CEO of AISHELL and AISHELL foundation. He participated in the release of AISHELL 1 & 2 & 3 & 4, DMASH and HI-MIA open source database project and is the co-founder of China Kaldi offline Technology Forum.</p>
</section>
diff --git a/docs_m2met2/_build/html/Rules.html b/docs/m2met2/_build/html/Rules.html
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@@ -88,7 +88,7 @@
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="Track_setting_and_evaluation.html">Track & Evaluation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr-main-track">Speaker-Attributed ASR (Main Track)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr">Speaker-Attributed ASR</a></li>
<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#evaluation-metric">Evaluation metric</a></li>
<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#sub-track-arrangement">Sub-track arrangement</a></li>
</ul>
@@ -128,7 +128,6 @@
<ul class="simple">
<li><p>Data augmentation is allowed on the original training dataset, including, but not limited to, adding noise or reverberation, speed perturbation and tone change.</p></li>
<li><p>Participants are permitted to use the Eval set for model training, but it is not allowed to use the Test set for this purpose. Instead, the Test set should only be utilized for parameter tuning and model selection. Any use of the Test-2023 dataset that violates these rules is strictly prohibited, including but not limited to the use of the Test set for fine-tuning or training the model.</p></li>
-<li><p>Multi-system fusion is allowed, but the systems with same structure and different parameters is not encouraged.</p></li>
<li><p>If the cpCER of the two systems on the Test dataset are the same, the system with lower computation complexity will be judged as the superior one.</p></li>
<li><p>If the forced alignment is used to obtain the frame-level classification label, the forced alignment model must be trained on the basis of the data allowed by the corresponding sub-track.</p></li>
<li><p>Shallow fusion is allowed to the end-to-end approaches, e.g., LAS, RNNT and Transformer, but the training data of the shallow fusion language model can only come from the transcripts of the allowed training dataset.</p></li>
diff --git a/docs_m2met2/_build/html/Track_setting_and_evaluation.html b/docs/m2met2/_build/html/Track_setting_and_evaluation.html
similarity index 96%
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@@ -89,7 +89,7 @@
</ul>
</li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">Track & Evaluation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="#speaker-attributed-asr-main-track">Speaker-Attributed ASR (Main Track)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#speaker-attributed-asr">Speaker-Attributed ASR</a></li>
<li class="toctree-l2"><a class="reference internal" href="#evaluation-metric">Evaluation metric</a></li>
<li class="toctree-l2"><a class="reference internal" href="#sub-track-arrangement">Sub-track arrangement</a></li>
</ul>
@@ -125,9 +125,9 @@
<section id="track-evaluation">
<h1>Track & Evaluation<a class="headerlink" href="#track-evaluation" title="Permalink to this heading">露</a></h1>
-<section id="speaker-attributed-asr-main-track">
-<h2>Speaker-Attributed ASR (Main Track)<a class="headerlink" href="#speaker-attributed-asr-main-track" title="Permalink to this heading">露</a></h2>
-<p>The speaker-attributed ASR task poses a unique challenge of transcribing speech from multiple speakers and assigning a speaker label to the transcription. Figure 2 illustrates the difference between the speaker-attributed ASR task and the multi-speaker ASR task. This track allows for the use of the AliMeeting, Aishell4, and Cn-Celeb datasets as constrained data sources during both training and evaluation. The AliMeeting dataset, which was used in the M2MeT challenge, includes Train, Eval, and Test sets. Additionally, a new Test-2023 set, consisting of approximately 10 hours of meeting data recorded in an identical acoustic setting as the AliMeeting corpus, will be released soon for challenge scoring and ranking. It鈥檚 worth noting that the organizers will not provide the near-field audio, transcriptions, or oracle timestamps. Instead, segments containing multiple speakers will be provided on the Test-2023 set, which can be obtained using a simple voice activity detection (VAD) model.</p>
+<section id="speaker-attributed-asr">
+<h2>Speaker-Attributed ASR<a class="headerlink" href="#speaker-attributed-asr" title="Permalink to this heading">露</a></h2>
+<p>The speaker-attributed ASR task poses a unique challenge of transcribing speech from multiple speakers and assigning a speaker label to the transcription. Figure 2 illustrates the difference between the speaker-attributed ASR task and the multi-speaker ASR task. This track allows for the use of the AliMeeting, Aishell4, and Cn-Celeb datasets as constrained data sources during both training and evaluation. The AliMeeting dataset, which was used in the M2MeT challenge, includes Train, Eval, and Test sets. Additionally, a new Test-2023 set, consisting of approximately 10 hours of meeting data recorded in an identical acoustic setting as the AliMeeting corpus, will be released soon for challenge scoring and ranking. It鈥檚 worth noting that the organizers will not provide the near-field audio, transcriptions, or oracle timestamps of the Test-2023 set. Instead, segments containing multiple speakers will be provided, which can be obtained using a simple voice activity detection (VAD) model.</p>
<p><img alt="task difference" src="_images/task_diff.png" /></p>
</section>
<section id="evaluation-metric">
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diff --git a/docs_m2met2/_build/html/_sources/Dataset.md.txt b/docs/m2met2/_build/html/_sources/Dataset.md.txt
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@@ -2,7 +2,7 @@
## Overview of training data
In the fixed training condition, the training dataset is restricted to three publicly available corpora, namely, AliMeeting, AISHELL-4, and CN-Celeb. To evaluate the performance of the models trained on these datasets, we will release a new Test set called Test-2023 for scoring and ranking. We will describe the AliMeeting dataset and the Test-2023 set in detail.
## Detail of AliMeeting corpus
-AliMeeting contains 118.75 hours of speech data in total. The dataset is divided into 104.75 hours for training (Train), 4 hours for evaluation (Eval) and 10 hours as test set (Test) for scoring and ranking. Specifically, the Train and Eval sets contain 212 and 8 sessions, respectively. Each session consists of a 15 to 30-minute discussion by a group of participants. The total number of participants in Train and Eval sets is 456 and 25, respectively, with balanced gender coverage.
+AliMeeting contains 118.75 hours of speech data in total. The dataset is divided into 104.75 hours for training (Train), 4 hours for evaluation (Eval) and 10 hours as test set (Test) for scoring and ranking. Specifically, the Train, Eval and Test sets contain 212, 8 and 20 sessions, respectively. Each session consists of a 15 to 30-minute discussion by a group of participants. The total number of participants in Train, Eval and Test sets is 456, 25 and 60, respectively, with balanced gender coverage.
The dataset is collected in 13 meeting venues, which are categorized into three types: small, medium, and large rooms with sizes ranging from 8 m$^{2}$ to 55 m$^{2}$. Different rooms give us a variety of acoustic properties and layouts. The detailed parameters of each meeting venue will be released together with the Train data. The type of wall material of the meeting venues covers cement, glass, etc. Other furnishings in meeting venues include sofa, TV, blackboard, fan, air conditioner, plants, etc. During recording, the participants of the meeting sit around the microphone array which is placed on the table and conduct a natural conversation. The microphone-speaker distance ranges from 0.3 m to 5.0 m. All participants are native Chinese speakers speaking Mandarin without strong accents. During the meeting, various kinds of indoor noise including but not limited to clicking, keyboard, door opening/closing, fan, bubble noise, etc., are made naturally. For both Train and Eval sets, the participants are required to remain in the same position during recording. There is no speaker overlap between the Train and Eval set. An example of the recording venue from the Train set is shown in Fig 1.
diff --git a/docs_m2met2/Introduction.md b/docs/m2met2/_build/html/_sources/Introduction.md.txt
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+++ b/docs/m2met2/_build/html/_sources/Introduction.md.txt
@@ -9,17 +9,20 @@
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.
## Timeline(AOE Time)
-
-- $ May~5^{th}, 2023: $ Registration deadline, the due date for participants to join the Challenge.
-- $ June~9^{th}, 2023: $ Test data release.
-- $ June~13^{rd}, 2023: $ Final submission deadline.
-- $ June~19^{th}, 2023: $ Evaluation result and ranking release.
-- $ July~3^{rd}, 2023: $ Deadline for paper submission.
-- $ July~10^{th}, 2023: $ Deadline for final paper submission.
-- $ December~12^{nd}\ to\ 16^{th}, 2023: $ ASRU Workshop
+- $ April~29, 2023: $ Challenge and registration open.
+- $ May~8, 2023: $ Baseline release.
+- $ May~15, 2023: $ Registration deadline, the due date for participants to join the Challenge.
+- $ June~9, 2023: $ Test data release and leaderboard open.
+- $ June~13, 2023: $ Final submission deadline.
+- $ June~19, 2023: $ Evaluation result and ranking release.
+- $ July~3, 2023: $ Deadline for paper submission.
+- $ July~10, 2023: $ Deadline for final paper submission.
+- $ December~12\ to\ 16, 2023: $ ASRU Workshop and challenge session
## Guidelines
-Interested participants, whether from academia or industry, must register for the challenge by completing a Google form, which will be available here. The deadline for registration is May 5, 2023.
+Interested participants, whether from academia or industry, must register for the challenge by completing the Google form below. The deadline for registration is May 15, 2023.
+
+[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 three submissions to be included in the ASRU2023 Proceedings.
diff --git a/docs_m2met2/Organizers.md b/docs/m2met2/_build/html/_sources/Organizers.md.txt
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@@ -4,8 +4,6 @@
- Data augmentation is allowed on the original training dataset, including, but not limited to, adding noise or reverberation, speed perturbation and tone change.
- Participants are permitted to use the Eval set for model training, but it is not allowed to use the Test set for this purpose. Instead, the Test set should only be utilized for parameter tuning and model selection. Any use of the Test-2023 dataset that violates these rules is strictly prohibited, including but not limited to the use of the Test set for fine-tuning or training the model.
-
-- Multi-system fusion is allowed, but the systems with same structure and different parameters is not encouraged.
- If the cpCER of the two systems on the Test dataset are the same, the system with lower computation complexity will be judged as the superior one.
diff --git a/docs_m2met2/Track_setting_and_evaluation.md b/docs/m2met2/_build/html/_sources/Track_setting_and_evaluation.md.txt
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+++ b/docs/m2met2/_build/html/_sources/Track_setting_and_evaluation.md.txt
@@ -1,6 +1,6 @@
# Track & Evaluation
-## Speaker-Attributed ASR (Main Track)
-The speaker-attributed ASR task poses a unique challenge of transcribing speech from multiple speakers and assigning a speaker label to the transcription. Figure 2 illustrates the difference between the speaker-attributed ASR task and the multi-speaker ASR task. This track allows for the use of the AliMeeting, Aishell4, and Cn-Celeb datasets as constrained data sources during both training and evaluation. The AliMeeting dataset, which was used in the M2MeT challenge, includes Train, Eval, and Test sets. Additionally, a new Test-2023 set, consisting of approximately 10 hours of meeting data recorded in an identical acoustic setting as the AliMeeting corpus, will be released soon for challenge scoring and ranking. It's worth noting that the organizers will not provide the near-field audio, transcriptions, or oracle timestamps. Instead, segments containing multiple speakers will be provided on the Test-2023 set, which can be obtained using a simple voice activity detection (VAD) model.
+## Speaker-Attributed ASR
+The speaker-attributed ASR task poses a unique challenge of transcribing speech from multiple speakers and assigning a speaker label to the transcription. Figure 2 illustrates the difference between the speaker-attributed ASR task and the multi-speaker ASR task. This track allows for the use of the AliMeeting, Aishell4, and Cn-Celeb datasets as constrained data sources during both training and evaluation. The AliMeeting dataset, which was used in the M2MeT challenge, includes Train, Eval, and Test sets. Additionally, a new Test-2023 set, consisting of approximately 10 hours of meeting data recorded in an identical acoustic setting as the AliMeeting corpus, will be released soon for challenge scoring and ranking. It's worth noting that the organizers will not provide the near-field audio, transcriptions, or oracle timestamps of the Test-2023 set. Instead, segments containing multiple speakers will be provided, which can be obtained using a simple voice activity detection (VAD) model.
diff --git a/docs_m2met2/_build/html/_sources/index.rst.txt b/docs/m2met2/_build/html/_sources/index.rst.txt
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./Rules
./Organizers
./Contact
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
\ No newline at end of file
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@@ -0,0 +1 @@
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"modelscop": [0, 6], "thi": [0, 3, 5, 6], "also": [0, 2, 6], "us": [0, 2, 5, 6], "extract": 0, "embed": 0, "profil": 0, "todo": 0, "fill": 0, "readm": 0, "md": 0, "system": [0, 3, 5, 6, 7], "ar": [0, 2, 3, 5, 6, 7], "tabl": [0, 2], "adopt": 0, "oracl": [0, 6], "dure": [0, 2, 6], "howev": [0, 3, 6], "due": [0, 3], "lack": 0, "label": [0, 5, 6], "evalu": [0, 2, 3, 7], "provid": [0, 2, 6, 7], "addit": [0, 6], "spectral": 0, "cluster": 0, "meanwhil": 0, "eval": [0, 2, 5, 6], "test": [0, 2, 3, 5, 6], "set": [0, 2, 3, 5, 6], "show": 0, "impact": 0, "accuraci": [0, 6], "If": [1, 5, 6], "you": 1, "have": [1, 3], "ani": [1, 5, 6], "question": 1, "about": 1, "m2met2": [1, 3], "0": [1, 2, 3], "challeng": [1, 3, 5, 6], "pleas": 1, "u": [1, 2], "email": [1, 3, 4], "m2met": [1, 3, 6, 7], "alimeet": [1, 6], "gmail": 1, "com": [1, 4], "wechat": 1, "group": [1, 2], "In": [2, 3, 5], "fix": [2, 3, 7], "condit": [2, 3, 7], "restrict": 2, "three": [2, 3, 6], "publicli": [2, 6], "avail": [2, 6], "corpora": 2, "name": 2, "aishel": [2, 4, 6], "4": [2, 6], "cn": [2, 4, 6], "celeb": [2, 6], "To": [2, 3, 7], "perform": [2, 3], "new": [2, 3, 6], "call": 2, "2023": [2, 3, 5, 6], "score": [2, 6], "rank": [2, 3, 6], "describ": 2, "contain": [2, 6], "118": 2, "75": 2, "hour": [2, 3, 6], "speech": [2, 3, 6, 7], "total": [2, 6], "divid": [2, 6], "104": 2, "10": [2, 3, 6], "specif": [2, 6], "212": 2, "8": [2, 3], "20": 2, "session": [2, 3, 6, 7], "respect": 2, "each": [2, 3, 6], "consist": [2, 6], "15": [2, 3], "30": 2, "minut": 2, "discuss": 2, "particip": [2, 5, 6], "number": [2, 3, 6], "456": 2, "25": 2, "60": 2, "balanc": 2, "gender": 2, "coverag": 2, "collect": 2, "13": [2, 3], "meet": [2, 3, 6], "venu": 2, "which": [2, 3, 6], "categor": 2, "type": 2, "small": 2, "medium": 2, "larg": [2, 3], "room": [2, 3], "size": 2, "rang": 2, "m": 2, "2": [2, 6], "55": 2, "differ": [2, 3, 6], "give": 2, "varieti": 2, "acoust": [2, 3, 6], "properti": 2, "layout": 2, "paramet": [2, 5], "togeth": 2, "wall": 2, "materi": 2, "cover": 2, "cement": 2, "glass": 2, "etc": 2, "other": 2, "furnish": 2, "includ": [2, 3, 5, 6], "sofa": 2, "tv": 2, "blackboard": 2, "fan": 2, "air": 2, "condition": 2, "plant": 2, "record": [2, 6], "sit": 2, "around": 2, "microphon": [2, 3], "arrai": [2, 3], "place": 2, "natur": 2, "convers": 2, "distanc": 2, "5": 2, "all": [2, 3, 5, 6], "nativ": 2, "chines": 2, "speak": [2, 3], "mandarin": [2, 3], "without": 2, "strong": 2, "accent": 2, "variou": [2, 3], "kind": 2, "indoor": 2, "nois": [2, 3, 5], "limit": [2, 3, 5], "click": 2, "keyboard": 2, "door": 2, "open": [2, 3, 7], "close": 2, "bubbl": 2, "made": [2, 3], "For": 2, "both": [2, 6], "requir": [2, 3, 6], "remain": [2, 3], "same": [2, 5], "posit": 2, "There": 2, "overlap": [2, 3], "between": [2, 6], "exampl": 2, "fig": 2, "1": 2, "within": [2, 3], "one": [2, 5], "ensur": 2, "ratio": 2, "select": [2, 3, 5, 6], "topic": 2, "medic": 2, "treatment": 2, "educ": 2, "busi": 2, "organ": [2, 3, 5, 6, 7], "manag": 2, "industri": [2, 3], "product": 2, "daili": 2, "routin": 2, "averag": 2, "42": 2, "27": 2, "34": 2, "76": 2, "more": 2, "A": [2, 4], "distribut": 2, "were": 2, "ident": [2, 6], "compris": [2, 3, 7], "therebi": 2, "share": 2, "similar": 2, "configur": 2, "field": [2, 3, 6], "signal": [2, 3], "headset": 2, "onli": [2, 5, 6], "": [2, 6], "own": 2, "transcrib": [2, 3, 6], "It": [2, 6], "worth": [2, 6], "note": [2, 6], "far": [2, 3], "audio": [2, 3, 6], "synchron": 2, "common": 2, "transcript": [2, 3, 5, 6], "prepar": 2, "textgrid": 2, "format": 2, "inform": [2, 3], "durat": 2, "id": 2, "segment": [2, 6], "timestamp": [2, 6], "mention": 2, "abov": 2, "can": [2, 3, 5, 6], "download": 2, "openslr": 2, "via": 2, "follow": [2, 5], "link": 2, "particularli": 2, "baselin": [2, 3, 7], "conveni": 2, "script": 2, "automat": [3, 7], "recognit": [3, 7], "diariz": 3, "signific": 3, "stride": 3, "recent": 3, "year": 3, "result": 3, "surg": 3, "technologi": 3, "applic": 3, "across": 3, "domain": 3, "present": 3, "uniqu": [3, 6], "complex": [3, 5], "divers": 3, "style": 3, "variabl": 3, "confer": 3, "environment": 3, "reverber": [3, 5], "over": 3, "sever": 3, "been": 3, "advanc": [3, 7], "develop": [3, 6], "rich": 3, "comput": [3, 5], "hear": 3, "multisourc": 3, "environ": 3, "chime": 3, "latest": 3, "iter": 3, "ha": 3, "particular": 3, "focu": 3, "distant": 3, "gener": 3, "topologi": 3, "scenario": 3, "while": 3, "progress": 3, "english": 3, "languag": [3, 5], "barrier": 3, "achiev": 3, "compar": 3, "non": 3, "multimod": 3, "base": 3, "process": [3, 6], "misp": 3, "multi": [3, 6], "channel": 3, "parti": [3, 6], "instrument": 3, "seek": 3, "address": 3, "problem": 3, "visual": 3, "everydai": 3, "home": 3, "focus": 3, "tackl": 3, "issu": 3, "offlin": 3, "icassp2022": 3, "two": [3, 5, 7], "main": 3, "task": [3, 6, 7], "former": 3, "involv": [3, 6], "identifi": 3, "who": 3, "spoke": 3, "when": 3, "latter": 3, "aim": 3, "multipl": [3, 6], "simultan": 3, "pose": [3, 6], "technic": 3, "difficulti": 3, "interfer": 3, "build": [3, 6, 7], "success": [3, 7], "previou": 3, "excit": 3, "propos": [3, 7], "asru2023": [3, 7], "special": [3, 5, 7], "origin": [3, 5], "metric": [3, 7], "wa": [3, 6], "independ": 3, "meant": 3, "could": 3, "determin": 3, "correspond": [3, 5], "further": 3, "current": [3, 7], "talker": [3, 7], "toward": 3, "practic": 3, "attribut": [3, 7], "sub": [3, 5, 7], "track": [3, 5, 7], "what": 3, "facilit": [3, 7], "reproduc": [3, 7], "research": [3, 4, 7], "offer": 3, "comprehens": [3, 7], "overview": [3, 7], "dataset": [3, 5, 6, 7], "rule": [3, 7], "furthermor": 3, "carefulli": 3, "curat": 3, "approxim": [3, 6], "design": 3, "enabl": 3, "valid": 3, "state": [3, 6, 7], "art": [3, 7], "area": 3, "april": 3, "29": 3, "registr": 3, "mai": 3, "deadlin": 3, "date": 3, "join": 3, "june": 3, "9": 3, "data": [3, 5, 6], "leaderboard": 3, "final": [3, 5, 6], "submiss": 3, "19": 3, "juli": 3, "paper": [3, 6], "decemb": 3, "12": 3, "16": 3, "asru": 3, "workshop": 3, "interest": 3, "whether": 3, "academia": 3, "must": [3, 5, 6], "regist": 3, "complet": 3, "googl": 3, "form": 3, "below": 3, "work": 3, "dai": 3, "send": 3, "invit": 3, "elig": [3, 5], "team": 3, "qualifi": 3, "adher": [3, 5], "publish": 3, "page": 3, "prior": 3, "submit": 3, "descript": [3, 6], "document": 3, "detail": [3, 6], "approach": [3, 5], "method": 3, "top": 3, "proceed": 3, "lei": 4, "xie": 4, "professor": 4, "northwestern": 4, "polytechn": 4, "univers": 4, "china": 4, "lxie": 4, "nwpu": 4, "edu": 4, "kong": 4, "aik": 4, "lee": 4, "senior": 4, "scientist": 4, "institut": 4, "infocomm": 4, "star": 4, "singapor": 4, "kongaik": 4, "ieee": 4, "org": 4, "zhiji": 4, "yan": 4, "princip": 4, "engin": 4, "alibaba": 4, "yzj": 4, "inc": 4, "shiliang": 4, "zhang": 4, "sly": 4, "zsl": 4, "yanmin": 4, "qian": 4, "shanghai": 4, "jiao": 4, "tong": 4, "yanminqian": 4, "sjtu": 4, "zhuo": 4, "chen": 4, "appli": 4, "microsoft": 4, "usa": 4, "zhuc": 4, "jian": 4, "wu": 4, "wujian": 4, "hui": 4, "bu": 4, "ceo": 4, "foundat": 4, "buhui": 4, "aishelldata": 4, "should": 5, "augment": 5, "allow": [5, 6], "ad": 5, "speed": 5, "perturb": 5, "tone": 5, "chang": 5, "permit": 5, "purpos": 5, "instead": [5, 6], "util": [5, 6], "tune": 5, "violat": 5, "strictli": [5, 6], "prohibit": [5, 6], "fine": 5, "cpcer": [5, 6], "lower": 5, "judg": 5, "superior": 5, "forc": 5, "align": 5, "obtain": [5, 6], "frame": 5, "level": 5, "classif": 5, "basi": 5, "shallow": 5, "fusion": 5, "end": 5, "e": [5, 6], "g": 5, "la": 5, "rnnt": 5, "transform": [5, 6], "come": 5, "right": 5, "interpret": 5, "belong": 5, "case": 5, "circumst": 5, "coordin": 5, "assign": 6, "illustr": 6, "aishell4": 6, "constrain": 6, "sourc": 6, "addition": 6, "corpu": 6, "soon": 6, "simpl": 6, "voic": 6, "activ": 6, "detect": 6, "vad": 6, "concaten": 6, "minimum": 6, "permut": 6, "charact": 6, "error": 6, "rate": 6, "calcul": 6, "step": 6, "firstli": 6, "refer": 6, "hypothesi": 6, "chronolog": 6, "order": 6, "secondli": 6, "cer": 6, "repeat": 6, "possibl": 6, "lowest": 6, "tthe": 6, "insert": 6, "Ins": 6, "substitut": 6, "delet": 6, "del": 6, "output": 6, "text": 6, "frac": 6, "mathcal": 6, "n_": 6, "100": 6, "where": 6, "usag": 6, "third": 6, "hug": 6, "face": 6, "list": 6, "clearli": 6, "privat": 6, "manual": 6, "simul": 6, "thei": 6, "mandatori": 6, "clear": 6, "scheme": 6, "delight": 7, "introduct": 7, "contact": 7}, "objects": {}, "objtypes": {}, "objnames": {}, "titleterms": {"baselin": 0, "overview": [0, 2], "quick": 0, "start": 0, "result": 0, "contact": 1, "dataset": 2, "train": [2, 6], "data": 2, "detail": 2, "alimeet": 2, "corpu": 2, "get": 2, "introduct": 3, "call": 3, "particip": 3, "timelin": 3, "aoe": 3, "time": 3, "guidelin": 3, "organ": 4, "rule": 5, "track": 6, "evalu": 6, "speaker": 6, "attribut": 6, "asr": 6, "metric": 6, "sub": 6, "arrang": 6, "i": 6, "fix": 6, "condit": 6, "ii": 6, "open": 6, "asru": 7, "2023": 7, "multi": 7, "channel": 7, "parti": 7, "meet": 7, "transcript": 7, "challeng": 7, "2": 7, "0": 7, "m2met2": 7, "content": 7}, "envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 8, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx": 57}, "alltitles": {"Baseline": [[0, "baseline"]], "Overview": [[0, "overview"]], "Quick start": [[0, "quick-start"]], "Baseline results": [[0, "baseline-results"]], "Contact": [[1, "contact"]], "Datasets": [[2, "datasets"]], "Overview of training data": [[2, "overview-of-training-data"]], "Detail of AliMeeting corpus": [[2, "detail-of-alimeeting-corpus"]], "Get the data": [[2, "get-the-data"]], "Introduction": [[3, "introduction"]], "Call for participation": [[3, "call-for-participation"]], "Timeline(AOE Time)": [[3, "timeline-aoe-time"]], "Guidelines": [[3, "guidelines"]], "Organizers": [[4, "organizers"]], "Rules": [[5, "rules"]], "Track & Evaluation": [[6, "track-evaluation"]], "Speaker-Attributed ASR": [[6, "speaker-attributed-asr"]], "Evaluation metric": [[6, "evaluation-metric"]], "Sub-track arrangement": [[6, "sub-track-arrangement"]], "Sub-track I (Fixed Training Condition):": [[6, "sub-track-i-fixed-training-condition"]], "Sub-track II (Open Training Condition):": [[6, "sub-track-ii-open-training-condition"]], "ASRU 2023 MULTI-CHANNEL MULTI-PARTY MEETING TRANSCRIPTION CHALLENGE 2.0 (M2MeT2.0)": [[7, "asru-2023-multi-channel-multi-party-meeting-transcription-challenge-2-0-m2met2-0"]], "Contents:": [[7, null]]}, "indexentries": {}})
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@@ -20,10 +20,3 @@
./Rules
./Organizers
./Contact
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
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diff --git a/docs_m2met2_cn/_build/html/_sources/index.rst.txt b/docs/m2met2_cn/_build/html/_sources/index.rst.txt
similarity index 91%
rename from docs_m2met2_cn/_build/html/_sources/index.rst.txt
rename to docs/m2met2_cn/_build/html/_sources/index.rst.txt
index b35ac96..c089b36 100644
--- a/docs_m2met2_cn/_build/html/_sources/index.rst.txt
+++ b/docs/m2met2_cn/_build/html/_sources/index.rst.txt
@@ -20,10 +20,3 @@
./瑙勫垯
./缁勫浼�
./鑱旂郴鏂瑰紡
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
diff --git "a/docs_m2met2_cn/_build/html/_sources/\345\237\272\347\272\277.md.txt" "b/docs/m2met2_cn/_build/html/_sources/\345\237\272\347\272\277.md.txt"
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diff --git "a/docs_m2met2_cn/_build/html/_sources/\346\225\260\346\215\256\351\233\206.md.txt" "b/docs/m2met2_cn/_build/html/_sources/\346\225\260\346\215\256\351\233\206.md.txt"
similarity index 85%
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rename to "docs/m2met2_cn/_build/html/_sources/\346\225\260\346\215\256\351\233\206.md.txt"
index 52965a1..24bfaf3 100644
--- "a/docs_m2met2_cn/_build/html/_sources/\346\225\260\346\215\256\351\233\206.md.txt"
+++ "b/docs/m2met2_cn/_build/html/_sources/\346\225\260\346\215\256\351\233\206.md.txt"
@@ -3,13 +3,13 @@
鍦ㄩ檺瀹氭暟鎹泦鏉′欢涓嬶紝璁粌鏁版嵁闆嗕粎闄愪簬涓変釜鍏紑鐨勮鏂欏簱锛屽嵆AliMeeting銆丄ISHELL-4鍜孋N-Celeb銆備负浜嗚瘎浼板弬璧涜�呮彁浜ょ殑妯″瀷鐨勬�ц兘锛屾垜浠皢鍙戝竷涓�涓柊鐨勬祴璇曢泦锛圱est-2023锛夌敤浜庢墦鍒嗗拰鎺掑悕銆備笅闈㈡垜浠皢璇︾粏鎻忚堪AliMeeting鏁版嵁闆嗗拰Test-2023娴嬭瘯闆嗐��
## Alimeeting鏁版嵁闆嗕粙缁�
-AliMeeting鎬诲叡鍖呭惈118.75灏忔椂鐨勮闊虫暟鎹紝鍖呮嫭104.75灏忔椂鐨勮缁冮泦锛圱rain锛夈��4灏忔椂鐨勯獙璇侀泦锛圗val锛夊拰10灏忔椂鐨勬祴璇曢泦锛圱est锛夈�俆rain闆嗗拰Eval闆嗗垎鍒寘鍚�212鍦哄拰8鍦轰細璁紝鍏朵腑姣忓満浼氳鐢卞涓璇濅汉杩涜15鍒�30鍒嗛挓鐨勮璁恒�俆rain鍜孍val闆嗕腑鍙備笌浼氳鐨勬�讳汉鏁板垎鍒负456浜哄拰25浜猴紝骞朵笖鍙備細鐨勭敺濂虫瘮渚嬩汉鏁板潎琛°��
+AliMeeting鎬诲叡鍖呭惈118.75灏忔椂鐨勮闊虫暟鎹紝鍖呮嫭104.75灏忔椂鐨勮缁冮泦锛圱rain锛夈��4灏忔椂鐨勯獙璇侀泦锛圗val锛夊拰10灏忔椂鐨勬祴璇曢泦锛圱est锛夈�俆rain闆嗭紝Eval闆嗗拰Test闆嗗垎鍒寘鍚�212鍦哄拰8鍦轰細璁紝鍏朵腑姣忓満浼氳鐢卞涓璇濅汉杩涜15鍒�30鍒嗛挓鐨勮璁恒�俆rain锛孍val鍜孴est闆嗕腑鍙備笌浼氳鐨勬�讳汉鏁板垎鍒负456浜哄拰25浜猴紝骞朵笖鍙備細鐨勭敺濂虫瘮渚嬩汉鏁板潎琛°��
璇ユ暟鎹泦鏀堕泦浜�13涓笉鍚岀殑浼氳瀹わ紝鎸夌収澶у皬瑙勬牸鍒嗕负灏忓瀷銆佷腑鍨嬪拰澶у瀷涓夌锛屾埧闂撮潰绉粠8鍒�55骞虫柟绫充笉绛夈�備笉鍚屾埧闂村叿鏈変笉鍚岀殑甯冨眬鍜屽０瀛︾壒鎬э紝姣忎釜鎴块棿鐨勮缁嗗弬鏁颁篃灏嗗彂閫佺粰鍙備笌鑰呫�備細璁満鍦扮殑澧欎綋鏉愭枡绫诲瀷鍖呮嫭姘存偿銆佺幓鐠冪瓑銆備細璁満鍦扮殑瀹跺叿鍖呮嫭娌欏彂銆佺數瑙嗐�侀粦鏉裤�侀鎵囥�佺┖璋冦�佹鐗╃瓑銆傚湪褰曞埗杩囩▼涓紝楹﹀厠椋庨樀鍒楁斁缃簬妗屼笂锛屽涓璇濅汉鍥村潗鍦ㄦ杈硅繘琛岃嚜鐒跺璇濄�傞害鍏嬮闃靛垪绂昏璇濅汉璺濈绾�0.3鍒�5.0绫充箣闂淬�傛墍鏈夎璇濅汉鐨勬瘝璇潎鏄眽璇紝骞朵笖璇寸殑閮芥槸鏅�氳瘽锛屾病鏈夋祿閲嶇殑鍙ｉ煶銆傚湪浼氳褰曞埗鏈熼棿鍙兘浼氫骇鐢熷悇绉嶅鍐呯殑鍣煶锛屽寘鎷敭鐩樺０銆佸紑闂�/鍏抽棬澹般�侀鎵囧０銆佹皵娉″０绛夈�傛墍鏈夎璇濅汉鍦ㄤ細璁殑褰曞埗鏈熼棿鍧囦繚鎸佺浉鍚屼綅缃紝涓嶅彂鐢熻蛋鍔ㄣ�傝缁冮泦鍜岄獙璇侀泦鐨勮璇濅汉娌℃湁閲嶅銆傚浘1灞曠ず浜嗕竴涓細璁鐨勫竷灞�浠ュ強楹﹀厠椋庣殑鎷撴墤缁撴瀯銆�
-姣忓満浼氳鐨勮璇濅汉鏁伴噺浠�2鍒�4浜轰笉绛夈�傚悓鏃朵负浜嗚鐩栧悇绉嶅唴瀹圭殑浼氳鍦烘櫙锛屾垜浠�夋嫨浜嗗绉嶄細璁富棰橈紝鍖呮嫭鍖荤枟銆佹暀鑲层�佸晢涓氥�佺粍缁囩鐞嗐�佸伐涓氱敓浜х瓑涓嶅悓鍐呭鐨勪緥浼氥�俆rain闆嗭紝Eval闆嗗拰Test闆嗙殑骞冲潎璇煶閲嶅彔鐜囧垎鍒负42.27\%鍜�34.76\%銆侫liMeeting Train闆嗭紝Eval闆嗗拰Test闆嗙殑璇︾粏淇℃伅瑙佽〃1銆傝〃2鏄剧ず浜員rain闆�,Eval闆嗗拰Test闆嗕腑涓嶅悓鍙戣█鑰呬汉鏁颁細璁殑璇煶閲嶅彔鐜囧拰浼氳鏁伴噺銆�
+姣忓満浼氳鐨勮璇濅汉鏁伴噺浠�2鍒�4浜轰笉绛夈�傚悓鏃朵负浜嗚鐩栧悇绉嶅唴瀹圭殑浼氳鍦烘櫙锛屾垜浠�夋嫨浜嗗绉嶄細璁富棰橈紝鍖呮嫭鍖荤枟銆佹暀鑲层�佸晢涓氥�佺粍缁囩鐞嗐�佸伐涓氱敓浜х瓑涓嶅悓鍐呭鐨勪緥浼氥�俆rain闆嗭紝Eval闆嗗拰Test闆嗙殑骞冲潎璇煶閲嶅彔鐜囧垎鍒负42.27\%锛�34.76\%鍜�42.8\%銆侫liMeeting Train闆嗭紝Eval闆嗗拰Test闆嗙殑璇︾粏淇℃伅瑙佽〃1銆傝〃2鏄剧ず浜員rain闆�,Eval闆嗗拰Test闆嗕腑涓嶅悓鍙戣█鑰呬汉鏁颁細璁殑璇煶閲嶅彔鐜囧拰浼氳鏁伴噺銆�
Test-2023娴嬭瘯闆嗙敱20鍦轰細璁粍鎴愶紝杩欎簺浼氳鏄湪涓嶢liMeeting鏁版嵁闆嗙浉鍚岀殑澹板鐜涓嬪綍鍒剁殑銆俆est-2023娴嬭瘯闆嗕腑鐨勬瘡涓細璁幆鑺傜敱2鍒�4涓弬涓庤�呯粍鎴愬苟涓斾笌AliMeeting娴嬭瘯闆嗙殑閰嶇疆鐩镐技銆�
diff --git "a/docs_m2met2_cn/_build/html/_sources/\347\256\200\344\273\213.md.txt" "b/docs/m2met2_cn/_build/html/_sources/\347\256\200\344\273\213.md.txt"
similarity index 90%
rename from "docs_m2met2_cn/_build/html/_sources/\347\256\200\344\273\213.md.txt"
rename to "docs/m2met2_cn/_build/html/_sources/\347\256\200\344\273\213.md.txt"
index cf4dfb8..52df97d 100644
--- "a/docs_m2met2_cn/_build/html/_sources/\347\256\200\344\273\213.md.txt"
+++ "b/docs/m2met2_cn/_build/html/_sources/\347\256\200\344\273\213.md.txt"
@@ -13,16 +13,20 @@
## 鏃堕棿瀹夋帓(AOE鏃堕棿)
-- $ 2023.5.5: $ 鍙傝禌鑰呮敞鍐屾埅姝�
+- $ 2023.4.29: $ 寮�鏀炬敞鍐�
+- $ 2023.5.8: $ 鍩虹嚎鍙戝竷
+- $ 2023.5.15: $ 娉ㄥ唽鎴
- $ 2023.6.9: $ 娴嬭瘯闆嗘暟鎹彂甯�
- $ 2023.6.13: $ 鏈�缁堢粨鏋滄彁浜ゆ埅姝�
- $ 2023.6.19: $ 璇勪及缁撴灉鍜屾帓鍚嶅彂甯�
- $ 2023.7.3: $ 璁烘枃鎻愪氦鎴
- $ 2023.7.10: $ 鏈�缁堢増璁烘枃鎻愪氦鎴
-- $ 2023.12.12: $ ASRU Workshop
+- $ 2023.12.12: $ ASRU Workshop & challenge session
## 绔炶禌鎶ュ悕
-鏉ヨ嚜瀛︽湳鐣屽拰宸ヤ笟鐣岀殑鏈夋剰鍚戝弬璧涜�呭潎搴斿湪2023骞�5鏈�5鏃ュ強涔嬪墠濉啓涓嬫柟鐨勮胺姝岃〃鍗曪細
+鏉ヨ嚜瀛︽湳鐣屽拰宸ヤ笟鐣岀殑鏈夋剰鍚戝弬璧涜�呭潎搴斿湪2023骞�5鏈�15鏃ュ強涔嬪墠濉啓涓嬫柟鐨勮胺姝岃〃鍗曪細
+
+[M2MET2.0鎶ュ悕](https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link)
涓诲姙鏂瑰皢鍦�3涓伐浣滄棩鍐呴�氳繃鐢靛瓙閭欢閫氱煡绗﹀悎鏉′欢鐨勫弬璧涘洟闃燂紝鍥㈤槦蹇呴』閬靛畧灏嗗湪鎸戞垬缃戠珯涓婂彂甯冪殑鎸戞垬瑙勫垯銆傚湪鎺掑悕鍙戝竷涔嬪墠锛屾瘡涓弬璧涜�呭繀椤绘彁浜や竴浠界郴缁熸弿杩版枃浠讹紝璇︾粏璇存槑浣跨敤鐨勬柟娉曞拰妯″瀷銆備富鍔炴柟灏嗛�夋嫨鍓嶄笁鍚嶇撼鍏SRU2023璁烘枃闆嗐��
\ No newline at end of file
diff --git "a/docs_m2met2_cn/_build/html/_sources/\347\273\204\345\247\224\344\274\232.md.txt" "b/docs/m2met2_cn/_build/html/_sources/\347\273\204\345\247\224\344\274\232.md.txt"
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rename to "docs/m2met2_cn/_build/html/_sources/\347\273\204\345\247\224\344\274\232.md.txt"
diff --git "a/docs_m2met2_cn/_build/html/_sources/\350\201\224\347\263\273\346\226\271\345\274\217.md.txt" "b/docs/m2met2_cn/_build/html/_sources/\350\201\224\347\263\273\346\226\271\345\274\217.md.txt"
similarity index 100%
rename from "docs_m2met2_cn/_build/html/_sources/\350\201\224\347\263\273\346\226\271\345\274\217.md.txt"
rename to "docs/m2met2_cn/_build/html/_sources/\350\201\224\347\263\273\346\226\271\345\274\217.md.txt"
diff --git "a/docs_m2met2_cn/_build/html/_sources/\350\247\204\345\210\231.md.txt" "b/docs/m2met2_cn/_build/html/_sources/\350\247\204\345\210\231.md.txt"
similarity index 90%
rename from "docs_m2met2_cn/_build/html/_sources/\350\247\204\345\210\231.md.txt"
rename to "docs/m2met2_cn/_build/html/_sources/\350\247\204\345\210\231.md.txt"
index b0928ee..68cb159 100644
--- "a/docs_m2met2_cn/_build/html/_sources/\350\247\204\345\210\231.md.txt"
+++ "b/docs/m2met2_cn/_build/html/_sources/\350\247\204\345\210\231.md.txt"
@@ -4,8 +4,6 @@
- 鍏佽鍦ㄥ師濮嬭缁冩暟鎹泦涓婅繘琛屾暟鎹寮猴紝鍖呮嫭浣嗕笉闄愪簬娣诲姞鍣０鎴栨贩鍝嶃�侀�熷害鎵板姩鍜岄煶璋冨彉鍖栥��
- 鍏佽鍙傝禌鑰呬娇鐢‥val闆嗚繘琛屾ā鍨嬭缁冿紝浣員est闆嗕粎鑳界敤浜庤皟鍙傚拰妯″瀷閫夋嫨锛屼弗绂佷互浠讳綍褰㈠紡浣跨敤Test-2023鏁版嵁闆嗭紝鍖呮嫭浣嗕笉闄愪簬浣跨敤娴嬭瘯鏁版嵁闆嗗井璋冩垨璁粌妯″瀷銆�
-
-- 鍏佽澶氱郴缁熻瀺鍚堬紝浣嗕笉榧撳姳浣跨敤鍏锋湁鐩稿悓缁撴瀯浠呭弬鏁颁笉鍚岀殑瀛愮郴缁熻瀺鍚堛��
- 濡傛灉涓や釜绯荤粺鐨勬祴璇昪pCER鐩稿悓锛屽垯璁＄畻澶嶆潅搴﹁緝浣庣殑绯荤粺灏嗚璁ゅ畾涓烘洿浼樸��
diff --git "a/docs_m2met2_cn/_build/html/_sources/\350\265\233\351\201\223\350\256\276\347\275\256\344\270\216\350\257\204\344\274\260.md.txt" "b/docs/m2met2_cn/_build/html/_sources/\350\265\233\351\201\223\350\256\276\347\275\256\344\270\216\350\257\204\344\274\260.md.txt"
similarity index 86%
rename from "docs_m2met2_cn/_build/html/_sources/\350\265\233\351\201\223\350\256\276\347\275\256\344\270\216\350\257\204\344\274\260.md.txt"
rename to "docs/m2met2_cn/_build/html/_sources/\350\265\233\351\201\223\350\256\276\347\275\256\344\270\216\350\257\204\344\274\260.md.txt"
index a99c1ed..94a6236 100644
--- "a/docs_m2met2_cn/_build/html/_sources/\350\265\233\351\201\223\350\256\276\347\275\256\344\270\216\350\257\204\344\274\260.md.txt"
+++ "b/docs/m2met2_cn/_build/html/_sources/\350\265\233\351\201\223\350\256\276\347\275\256\344\270\216\350\257\204\344\274\260.md.txt"
@@ -1,6 +1,6 @@
# 璧涢亾璁剧疆涓庤瘎浼�
-## 璇磋瘽浜虹浉鍏崇殑璇煶璇嗗埆 (涓昏禌閬�)
-璇磋瘽浜虹浉鍏崇殑ASR浠诲姟闇�瑕佷粠閲嶅彔鐨勮闊充腑璇嗗埆姣忎釜璇磋瘽浜虹殑璇煶锛屽苟涓鸿瘑鍒唴瀹瑰垎閰嶄竴涓璇濅汉鏍囩銆傚浘2灞曠ず浜嗚璇濅汉鐩稿叧璇煶璇嗗埆浠诲姟鍜屽璇磋瘽浜鸿闊宠瘑鍒换鍔＄殑涓昏鍖哄埆銆傚湪鏈绔炶禌涓瑼liMeeting銆丄ishell4鍜孋n-Celeb鏁版嵁闆嗗彲浣滀负鍙楅檺鏁版嵁婧愩�傚湪M2MeT鎸戞垬璧涗腑浣跨敤鐨凙liMeeting鏁版嵁闆嗗寘鍚缁冦�佽瘎浼板拰娴嬭瘯闆嗭紝鍦∕2MET2.0鍙互鍦ㄨ缁冨拰璇勪及涓娇鐢ㄣ�傛澶栵紝涓�涓寘鍚害10灏忔椂浼氳鏁版嵁鐨勬柊鐨凾est-2023闆嗗皢鏍规嵁璧涚▼瀹夋帓鍙戝竷骞剁敤浜庢寫鎴樿禌鐨勮瘎鍒嗗拰鎺掑悕銆傚�煎緱娉ㄦ剰鐨勬槸锛岀粍缁囪�呭皢涓嶆彁渚涜�虫満鐨勮繎鍦洪煶棰戙�佽浆褰曚互鍙婄湡瀹炴椂闂存埑銆備富鍔炴柟灏嗕笉鍐嶆彁渚涙瘡涓璇濅汉鐨勭湡瀹炴椂闂存埑锛岃�屾槸鍦═est-2023闆嗕笂鎻愪緵鍖呭惈澶氫釜璇磋瘽浜虹殑鐗囨銆傝繖浜涚墖娈靛彲浠ラ�氳繃涓�涓畝鍗曠殑vad妯″瀷鑾峰緱銆�
+## 璇磋瘽浜虹浉鍏崇殑璇煶璇嗗埆
+璇磋瘽浜虹浉鍏崇殑ASR浠诲姟闇�瑕佷粠閲嶅彔鐨勮闊充腑璇嗗埆姣忎釜璇磋瘽浜虹殑璇煶锛屽苟涓鸿瘑鍒唴瀹瑰垎閰嶄竴涓璇濅汉鏍囩銆傚浘2灞曠ず浜嗚璇濅汉鐩稿叧璇煶璇嗗埆浠诲姟鍜屽璇磋瘽浜鸿闊宠瘑鍒换鍔＄殑涓昏鍖哄埆銆傚湪鏈绔炶禌涓瑼liMeeting銆丄ishell4鍜孋n-Celeb鏁版嵁闆嗗彲浣滀负鍙楅檺鏁版嵁婧愩�傚湪M2MeT鎸戞垬璧涗腑浣跨敤鐨凙liMeeting鏁版嵁闆嗗寘鍚缁冦�佽瘎浼板拰娴嬭瘯闆嗭紝鍦∕2MET2.0鍙互鍦ㄨ缁冨拰璇勪及涓娇鐢ㄣ�傛澶栵紝涓�涓寘鍚害10灏忔椂浼氳鏁版嵁鐨勬柊鐨凾est-2023闆嗗皢鏍规嵁璧涚▼瀹夋帓鍙戝竷骞剁敤浜庢寫鎴樿禌鐨勮瘎鍒嗗拰鎺掑悕銆傚�煎緱娉ㄦ剰鐨勬槸锛屽浜嶵est-2023娴嬭瘯闆嗭紝涓诲姙鏂瑰皢涓嶅啀鎻愪緵鑰虫満鐨勮繎鍦洪煶棰戙�佽浆褰曚互鍙婄湡瀹炴椂闂存埑銆傝�屾槸鎻愪緵鍙互閫氳繃涓�涓畝鍗曠殑VAD妯″瀷寰楀埌鐨勫寘鍚涓璇濅汉鐨勭墖娈点��
@@ -12,6 +12,6 @@
鍏朵腑 $\mathcal N_{\text{Ins}}$ , $\mathcal N_{\text{Sub}}$ , $\mathcal N_{\text{Del}}$ 鏄笁绉嶉敊璇殑瀛楃鏁�, $\mathcal N_{\text{Total}}$ 鏄瓧绗︽�绘暟.
## 瀛愯禌閬撹缃�
### 瀛愯禌閬撲竴 (闄愬畾璁粌鏁版嵁):
-鍙傝禌鑰呭湪绯荤粺鏋勫缓杩囩▼涓粎鑳戒娇鐢ˋliMeeting銆丄ISHELL-4鍜孋N Celeb锛屼弗绂佷娇鐢ㄩ澶栨暟鎹�傚弬璧涜�呭彲浠ヤ换浣曠涓夋柟寮�婧愮殑棰勮缁冩ā鍨嬶紝濡俒Hugging Face](https://huggingface.co/models)浠ュ強[ModelScope](https://www.modelscope.cn/models)涓婃彁渚涚殑妯″瀷銆傚弬璧涜�呴渶瑕佸湪鏈�缁堢殑绯荤粺鎻忚堪鏂囨。涓缁嗗垪鍑轰娇鐢ㄧ殑棰勮缁冩ā鍨嬪悕绉颁互鍙婇摼鎺ャ��
+鍙傝禌鑰呭湪绯荤粺鏋勫缓杩囩▼涓粎鑳戒娇鐢ˋliMeeting銆丄ISHELL-4鍜孋N-Celeb锛屼弗绂佷娇鐢ㄩ澶栨暟鎹�傚弬璧涜�呭彲浠ヤ换浣曠涓夋柟寮�婧愮殑棰勮缁冩ā鍨嬶紝濡俒Hugging Face](https://huggingface.co/models)浠ュ強[ModelScope](https://www.modelscope.cn/models)涓婃彁渚涚殑妯″瀷銆傚弬璧涜�呴渶瑕佸湪鏈�缁堢殑绯荤粺鎻忚堪鏂囨。涓缁嗗垪鍑轰娇鐢ㄧ殑棰勮缁冩ā鍨嬪悕绉颁互鍙婇摼鎺ャ��
### 瀛愯禌閬撲簩 (寮�鏀捐缁冩暟鎹�):
闄や簡闄愬畾鏁版嵁澶栵紝鍙備笌鑰呭彲浠ヤ娇鐢ㄤ换浣曞叕寮�鍙敤銆佺浜哄綍鍒跺拰妯℃嫙浠跨湡鐨勬暟鎹泦銆備絾鏄紝鍙備笌鑰呭繀椤绘竻妤氬湴鍒楀嚭浣跨敤鐨勬暟鎹�傚悓鏍凤紝鍙傝禌鑰呬篃鍙互浣跨敤浠讳綍绗笁鏂瑰紑婧愮殑棰勮缁冩ā鍨嬶紝浣嗗繀椤诲湪鏈�鍚庣殑绯荤粺鎻忚堪鏂囦欢涓槑纭殑鍒楀嚭鎵�浣跨敤鐨勬暟鎹拰妯″瀷閾炬帴锛屽鏋滀娇鐢ㄦā鎷熶豢鐪熸暟鎹紝璇疯缁嗘弿杩版暟鎹ā鎷熺殑鏂规銆�
\ No newline at end of file
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</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html">璧涢亾璁剧疆涓庤瘎浼�</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">璇磋瘽浜虹浉鍏崇殑璇煶璇嗗埆 (涓昏禌閬�)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">璇磋瘽浜虹浉鍏崇殑璇煶璇嗗埆</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id3">璇勪及鏂规硶</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id4">瀛愯禌閬撹缃�</a></li>
</ul>
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--- a/docs_m2met2_cn/_build/html/index.html
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@@ -85,7 +85,7 @@
</ul>
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-<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">璇磋瘽浜虹浉鍏崇殑璇煶璇嗗埆 (涓昏禌閬�)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">璇磋瘽浜虹浉鍏崇殑璇煶璇嗗埆</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id3">璇勪及鏂规硶</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id4">瀛愯禌閬撹缃�</a></li>
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<li class="toctree-l1"><a class="reference internal" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html">鑱旂郴鏂瑰紡</a></li>
</ul>
</div>
-</section>
-<section id="indices-and-tables">
-<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="姝ゆ爣棰樼殑姘镐箙閾炬帴">露</a></h1>
-<ul class="simple">
-<li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">绱㈠紩</span></a></p></li>
-<li><p><a class="reference internal" href="py-modindex.html"><span class="std std-ref">妯″潡绱㈠紩</span></a></p></li>
-<li><p><a class="reference internal" href="search.html"><span class="std std-ref">鎼滅储椤甸潰</span></a></p></li>
-</ul>
</section>
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rename from docs_m2met2_cn/_build/html/objects.inv
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diff --git a/docs_m2met2_cn/_build/html/search.html b/docs/m2met2_cn/_build/html/search.html
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--- a/docs_m2met2_cn/_build/html/search.html
+++ b/docs/m2met2_cn/_build/html/search.html
@@ -73,7 +73,7 @@
</ul>
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-<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">璇磋瘽浜虹浉鍏崇殑璇煶璇嗗埆 (涓昏禌閬�)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">璇磋瘽浜虹浉鍏崇殑璇煶璇嗗埆</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id3">璇勪及鏂规硶</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id4">瀛愯禌閬撹缃�</a></li>
</ul>
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new file mode 100644
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--- /dev/null
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<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id4">瀛愯禌閬撹缃�</a></li>
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<li class="toctree-l1"><a class="reference internal" href="%E8%A7%84%E5%88%99.html">绔炶禌瑙勫垯</a></li>
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</ul>
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<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id3">璇勪及鏂规硶</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id4">瀛愯禌閬撹缃�</a></li>
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</li>
<li class="toctree-l1"><a class="reference internal" href="%E8%A7%84%E5%88%99.html">绔炶禌瑙勫垯</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html">鑱旂郴鏂瑰紡</a></li>
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</section>
<section id="alimeeting">
<h2>Alimeeting鏁版嵁闆嗕粙缁�<a class="headerlink" href="#alimeeting" title="姝ゆ爣棰樼殑姘镐箙閾炬帴">露</a></h2>
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+<p>AliMeeting鎬诲叡鍖呭惈118.75灏忔椂鐨勮闊虫暟鎹紝鍖呮嫭104.75灏忔椂鐨勮缁冮泦锛圱rain锛夈��4灏忔椂鐨勯獙璇侀泦锛圗val锛夊拰10灏忔椂鐨勬祴璇曢泦锛圱est锛夈�俆rain闆嗭紝Eval闆嗗拰Test闆嗗垎鍒寘鍚�212鍦哄拰8鍦轰細璁紝鍏朵腑姣忓満浼氳鐢卞涓璇濅汉杩涜15鍒�30鍒嗛挓鐨勮璁恒�俆rain锛孍val鍜孴est闆嗕腑鍙備笌浼氳鐨勬�讳汉鏁板垎鍒负456浜哄拰25浜猴紝骞朵笖鍙備細鐨勭敺濂虫瘮渚嬩汉鏁板潎琛°��</p>
<p>璇ユ暟鎹泦鏀堕泦浜�13涓笉鍚岀殑浼氳瀹わ紝鎸夌収澶у皬瑙勬牸鍒嗕负灏忓瀷銆佷腑鍨嬪拰澶у瀷涓夌锛屾埧闂撮潰绉粠8鍒�55骞虫柟绫充笉绛夈�備笉鍚屾埧闂村叿鏈変笉鍚岀殑甯冨眬鍜屽０瀛︾壒鎬э紝姣忎釜鎴块棿鐨勮缁嗗弬鏁颁篃灏嗗彂閫佺粰鍙備笌鑰呫�備細璁満鍦扮殑澧欎綋鏉愭枡绫诲瀷鍖呮嫭姘存偿銆佺幓鐠冪瓑銆備細璁満鍦扮殑瀹跺叿鍖呮嫭娌欏彂銆佺數瑙嗐�侀粦鏉裤�侀鎵囥�佺┖璋冦�佹鐗╃瓑銆傚湪褰曞埗杩囩▼涓紝楹﹀厠椋庨樀鍒楁斁缃簬妗屼笂锛屽涓璇濅汉鍥村潗鍦ㄦ杈硅繘琛岃嚜鐒跺璇濄�傞害鍏嬮闃靛垪绂昏璇濅汉璺濈绾�0.3鍒�5.0绫充箣闂淬�傛墍鏈夎璇濅汉鐨勬瘝璇潎鏄眽璇紝骞朵笖璇寸殑閮芥槸鏅�氳瘽锛屾病鏈夋祿閲嶇殑鍙ｉ煶銆傚湪浼氳褰曞埗鏈熼棿鍙兘浼氫骇鐢熷悇绉嶅鍐呯殑鍣煶锛屽寘鎷敭鐩樺０銆佸紑闂�/鍏抽棬澹般�侀鎵囧０銆佹皵娉″０绛夈�傛墍鏈夎璇濅汉鍦ㄤ細璁殑褰曞埗鏈熼棿鍧囦繚鎸佺浉鍚屼綅缃紝涓嶅彂鐢熻蛋鍔ㄣ�傝缁冮泦鍜岄獙璇侀泦鐨勮璇濅汉娌℃湁閲嶅銆傚浘1灞曠ず浜嗕竴涓細璁鐨勫竷灞�浠ュ強楹﹀厠椋庣殑鎷撴墤缁撴瀯銆�</p>
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<p><img alt="dataset detail" src="_images/dataset_detail.png" />
Test-2023娴嬭瘯闆嗙敱20鍦轰細璁粍鎴愶紝杩欎簺浼氳鏄湪涓嶢liMeeting鏁版嵁闆嗙浉鍚岀殑澹板鐜涓嬪綍鍒剁殑銆俆est-2023娴嬭瘯闆嗕腑鐨勬瘡涓細璁幆鑺傜敱2鍒�4涓弬涓庤�呯粍鎴愬苟涓斾笌AliMeeting娴嬭瘯闆嗙殑閰嶇疆鐩镐技銆�</p>
<p>鎴戜滑杩樹娇鐢ㄨ�虫満楹﹀厠椋庤褰曚簡姣忎釜璇磋瘽浜虹殑杩戝満闊抽淇″彿锛屽苟纭繚鍙浆褰曞搴旇璇濅汉鑷繁鐨勮闊炽�傞渶瑕佹敞鎰忕殑鏄紝楹﹀厠椋庨樀鍒楄褰曠殑杩滃満闊抽鍜岃�虫満楹﹀厠椋庤褰曠殑杩戝満闊抽鍦ㄦ椂闂翠笂鏄悓姝ョ殑銆傛瘡鍦轰細璁殑鎵�鏈夋妱鏈潎浠extGrid鏍煎紡瀛樺偍锛屽唴瀹瑰寘鎷細璁殑鏃堕暱銆佽璇濅汉淇℃伅锛堣璇濅汉鏁伴噺銆佽璇濅汉ID銆佹�у埆绛夛級銆佹瘡涓璇濅汉鐨勭墖娈垫�绘暟銆佹瘡涓墖娈电殑鏃堕棿鎴冲拰杞綍鍐呭銆�</p>
diff --git "a/docs_m2met2_cn/_build/html/\347\256\200\344\273\213.html" "b/docs/m2met2_cn/_build/html/\347\256\200\344\273\213.html"
similarity index 94%
rename from "docs_m2met2_cn/_build/html/\347\256\200\344\273\213.html"
rename to "docs/m2met2_cn/_build/html/\347\256\200\344\273\213.html"
index 00e916c..f1da18e 100644
--- "a/docs_m2met2_cn/_build/html/\347\256\200\344\273\213.html"
+++ "b/docs/m2met2_cn/_build/html/\347\256\200\344\273\213.html"
@@ -90,7 +90,7 @@
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html">璧涢亾璁剧疆涓庤瘎浼�</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">璇磋瘽浜虹浉鍏崇殑璇煶璇嗗埆 (涓昏禌閬�)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">璇磋瘽浜虹浉鍏崇殑璇煶璇嗗埆</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id3">璇勪及鏂规硶</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id4">瀛愯禌閬撹缃�</a></li>
</ul>
@@ -103,7 +103,7 @@
</li>
<li class="toctree-l1"><a class="reference internal" href="%E8%A7%84%E5%88%99.html">绔炶禌瑙勫垯</a></li>
<li class="toctree-l1"><a class="reference internal" href="%E7%BB%84%E5%A7%94%E4%BC%9A.html">缁勫浼�</a></li>
-<li class="toctree-l1"><a class="reference internal" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html">Contact</a></li>
+<li class="toctree-l1"><a class="reference internal" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html">鑱旂郴鏂瑰紡</a></li>
</ul>
@@ -138,18 +138,21 @@
<section id="aoe">
<h2>鏃堕棿瀹夋帓(AOE鏃堕棿)<a class="headerlink" href="#aoe" title="姝ゆ爣棰樼殑姘镐箙閾炬帴">露</a></h2>
<ul class="simple">
-<li><p><span class="math notranslate nohighlight">\( 2023.5.5: \)</span> 鍙傝禌鑰呮敞鍐屾埅姝�</p></li>
+<li><p><span class="math notranslate nohighlight">\( 2023.4.29: \)</span> 寮�鏀炬敞鍐�</p></li>
+<li><p><span class="math notranslate nohighlight">\( 2023.5.8: \)</span> 鍩虹嚎鍙戝竷</p></li>
+<li><p><span class="math notranslate nohighlight">\( 2023.5.15: \)</span> 娉ㄥ唽鎴</p></li>
<li><p><span class="math notranslate nohighlight">\( 2023.6.9: \)</span> 娴嬭瘯闆嗘暟鎹彂甯�</p></li>
<li><p><span class="math notranslate nohighlight">\( 2023.6.13: \)</span> 鏈�缁堢粨鏋滄彁浜ゆ埅姝�</p></li>
<li><p><span class="math notranslate nohighlight">\( 2023.6.19: \)</span> 璇勪及缁撴灉鍜屾帓鍚嶅彂甯�</p></li>
<li><p><span class="math notranslate nohighlight">\( 2023.7.3: \)</span> 璁烘枃鎻愪氦鎴</p></li>
<li><p><span class="math notranslate nohighlight">\( 2023.7.10: \)</span> 鏈�缁堢増璁烘枃鎻愪氦鎴</p></li>
-<li><p><span class="math notranslate nohighlight">\( 2023.12.12: \)</span> ASRU Workshop</p></li>
+<li><p><span class="math notranslate nohighlight">\( 2023.12.12: \)</span> ASRU Workshop & challenge session</p></li>
</ul>
</section>
<section id="id3">
<h2>绔炶禌鎶ュ悕<a class="headerlink" href="#id3" title="姝ゆ爣棰樼殑姘镐箙閾炬帴">露</a></h2>
-<p>鏉ヨ嚜瀛︽湳鐣屽拰宸ヤ笟鐣岀殑鏈夋剰鍚戝弬璧涜�呭潎搴斿湪2023骞�5鏈�5鏃ュ強涔嬪墠濉啓涓嬫柟鐨勮胺姝岃〃鍗曪細</p>
+<p>鏉ヨ嚜瀛︽湳鐣屽拰宸ヤ笟鐣岀殑鏈夋剰鍚戝弬璧涜�呭潎搴斿湪2023骞�5鏈�15鏃ュ強涔嬪墠濉啓涓嬫柟鐨勮胺姝岃〃鍗曪細</p>
+<p><a class="reference external" href="https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link">M2MET2.0鎶ュ悕</a></p>
<p>涓诲姙鏂瑰皢鍦�3涓伐浣滄棩鍐呴�氳繃鐢靛瓙閭欢閫氱煡绗﹀悎鏉′欢鐨勫弬璧涘洟闃燂紝鍥㈤槦蹇呴』閬靛畧灏嗗湪鎸戞垬缃戠珯涓婂彂甯冪殑鎸戞垬瑙勫垯銆傚湪鎺掑悕鍙戝竷涔嬪墠锛屾瘡涓弬璧涜�呭繀椤绘彁浜や竴浠界郴缁熸弿杩版枃浠讹紝璇︾粏璇存槑浣跨敤鐨勬柟娉曞拰妯″瀷銆備富鍔炴柟灏嗛�夋嫨鍓嶄笁鍚嶇撼鍏SRU2023璁烘枃闆嗐��</p>
</section>
</section>
diff --git "a/docs_m2met2_cn/_build/html/\347\273\204\345\247\224\344\274\232.html" "b/docs/m2met2_cn/_build/html/\347\273\204\345\247\224\344\274\232.html"
similarity index 96%
rename from "docs_m2met2_cn/_build/html/\347\273\204\345\247\224\344\274\232.html"
rename to "docs/m2met2_cn/_build/html/\347\273\204\345\247\224\344\274\232.html"
index c295c28..ddf93bb 100644
--- "a/docs_m2met2_cn/_build/html/\347\273\204\345\247\224\344\274\232.html"
+++ "b/docs/m2met2_cn/_build/html/\347\273\204\345\247\224\344\274\232.html"
@@ -27,7 +27,7 @@
<script src="_static/translations.js"></script>
<link rel="index" title="绱㈠紩" href="genindex.html" />
<link rel="search" title="鎼滅储" href="search.html" />
- <link rel="next" title="Contact" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html" />
+ <link rel="next" title="鑱旂郴鏂瑰紡" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html" />
<link rel="prev" title="绔炶禌瑙勫垯" href="%E8%A7%84%E5%88%99.html" />
@@ -40,7 +40,7 @@
<a href="genindex.html" title="鎬荤储寮�"
accesskey="I">绱㈠紩</a></li>
<li class="right" >
- <a href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html" title="Contact"
+ <a href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html" title="鑱旂郴鏂瑰紡"
accesskey="N">涓嬩竴椤�</a> |</li>
<li class="right" >
<a href="%E8%A7%84%E5%88%99.html" title="绔炶禌瑙勫垯"
@@ -89,7 +89,7 @@
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html">璧涢亾璁剧疆涓庤瘎浼�</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">璇磋瘽浜虹浉鍏崇殑璇煶璇嗗埆 (涓昏禌閬�)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">璇磋瘽浜虹浉鍏崇殑璇煶璇嗗埆</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id3">璇勪及鏂规硶</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id4">瀛愯禌閬撹缃�</a></li>
</ul>
@@ -102,7 +102,7 @@
</li>
<li class="toctree-l1"><a class="reference internal" href="%E8%A7%84%E5%88%99.html">绔炶禌瑙勫垯</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">缁勫浼�</a></li>
-<li class="toctree-l1"><a class="reference internal" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html">Contact</a></li>
+<li class="toctree-l1"><a class="reference internal" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html">鑱旂郴鏂瑰紡</a></li>
</ul>
@@ -168,7 +168,7 @@
</div>
<div class="pull-right">
- <a class="btn btn-default" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html" title="涓嬩竴绔� (use the right arrow)">Contact</a>
+ <a class="btn btn-default" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html" title="涓嬩竴绔� (use the right arrow)">鑱旂郴鏂瑰紡</a>
</div>
</div>
<div class="clearer"></div>
@@ -183,7 +183,7 @@
<a href="genindex.html" title="鎬荤储寮�"
>绱㈠紩</a></li>
<li class="right" >
- <a href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html" title="Contact"
+ <a href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html" title="鑱旂郴鏂瑰紡"
>涓嬩竴椤�</a> |</li>
<li class="right" >
<a href="%E8%A7%84%E5%88%99.html" title="绔炶禌瑙勫垯"
diff --git "a/docs_m2met2_cn/_build/html/\350\201\224\347\263\273\346\226\271\345\274\217.html" "b/docs/m2met2_cn/_build/html/\350\201\224\347\263\273\346\226\271\345\274\217.html"
similarity index 99%
rename from "docs_m2met2_cn/_build/html/\350\201\224\347\263\273\346\226\271\345\274\217.html"
rename to "docs/m2met2_cn/_build/html/\350\201\224\347\263\273\346\226\271\345\274\217.html"
index 4486e13..249e5dd 100644
--- "a/docs_m2met2_cn/_build/html/\350\201\224\347\263\273\346\226\271\345\274\217.html"
+++ "b/docs/m2met2_cn/_build/html/\350\201\224\347\263\273\346\226\271\345\274\217.html"
@@ -85,7 +85,7 @@
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html">璧涢亾璁剧疆涓庤瘎浼�</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">璇磋瘽浜虹浉鍏崇殑璇煶璇嗗埆 (涓昏禌閬�)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">璇磋瘽浜虹浉鍏崇殑璇煶璇嗗埆</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id3">璇勪及鏂规硶</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id4">瀛愯禌閬撹缃�</a></li>
</ul>
diff --git "a/docs_m2met2_cn/_build/html/\350\247\204\345\210\231.html" "b/docs/m2met2_cn/_build/html/\350\247\204\345\210\231.html"
similarity index 97%
rename from "docs_m2met2_cn/_build/html/\350\247\204\345\210\231.html"
rename to "docs/m2met2_cn/_build/html/\350\247\204\345\210\231.html"
index b6fae29..5186ebd 100644
--- "a/docs_m2met2_cn/_build/html/\350\247\204\345\210\231.html"
+++ "b/docs/m2met2_cn/_build/html/\350\247\204\345\210\231.html"
@@ -89,7 +89,7 @@
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html">璧涢亾璁剧疆涓庤瘎浼�</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">璇磋瘽浜虹浉鍏崇殑璇煶璇嗗埆 (涓昏禌閬�)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">璇磋瘽浜虹浉鍏崇殑璇煶璇嗗埆</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id3">璇勪及鏂规硶</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id4">瀛愯禌閬撹缃�</a></li>
</ul>
@@ -102,7 +102,7 @@
</li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">绔炶禌瑙勫垯</a></li>
<li class="toctree-l1"><a class="reference internal" href="%E7%BB%84%E5%A7%94%E4%BC%9A.html">缁勫浼�</a></li>
-<li class="toctree-l1"><a class="reference internal" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html">Contact</a></li>
+<li class="toctree-l1"><a class="reference internal" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html">鑱旂郴鏂瑰紡</a></li>
</ul>
@@ -129,7 +129,6 @@
<ul class="simple">
<li><p>鍏佽鍦ㄥ師濮嬭缁冩暟鎹泦涓婅繘琛屾暟鎹寮猴紝鍖呮嫭浣嗕笉闄愪簬娣诲姞鍣０鎴栨贩鍝嶃�侀�熷害鎵板姩鍜岄煶璋冨彉鍖栥��</p></li>
<li><p>鍏佽鍙傝禌鑰呬娇鐢‥val闆嗚繘琛屾ā鍨嬭缁冿紝浣員est闆嗕粎鑳界敤浜庤皟鍙傚拰妯″瀷閫夋嫨锛屼弗绂佷互浠讳綍褰㈠紡浣跨敤Test-2023鏁版嵁闆嗭紝鍖呮嫭浣嗕笉闄愪簬浣跨敤娴嬭瘯鏁版嵁闆嗗井璋冩垨璁粌妯″瀷銆�</p></li>
-<li><p>鍏佽澶氱郴缁熻瀺鍚堬紝浣嗕笉榧撳姳浣跨敤鍏锋湁鐩稿悓缁撴瀯浠呭弬鏁颁笉鍚岀殑瀛愮郴缁熻瀺鍚堛��</p></li>
<li><p>濡傛灉涓や釜绯荤粺鐨勬祴璇昪pCER鐩稿悓锛屽垯璁＄畻澶嶆潅搴﹁緝浣庣殑绯荤粺灏嗚璁ゅ畾涓烘洿浼樸��</p></li>
<li><p>濡傛灉浣跨敤寮哄埗瀵归綈妯″瀷鑾峰緱浜嗛�愬抚鍒嗙被鏍囩锛屽垯蹇呴』浣跨敤鐩稿簲瀛愯禌閬撳厑璁哥殑鏁版嵁瀵瑰己鍒跺榻愭ā鍨嬭繘琛岃缁冦��</p></li>
<li><p>绔埌绔柟娉曚腑鍏佽浣跨敤娴呭眰铻嶅悎璇█妯″瀷锛屾ā鍨嬪彲浠ラ�夋嫨LAS銆丷NNT鍜孴ransformer绛夛紝浣嗘祬灞傝瀺鍚堣瑷�妯″瀷鐨勮缁冩暟鎹彧鑳芥潵鑷簬鍏佽鐨勮缁冩暟鎹泦鐨勮浆褰曟妱鏈��</p></li>
diff --git "a/docs_m2met2_cn/_build/html/\350\265\233\351\201\223\350\256\276\347\275\256\344\270\216\350\257\204\344\274\260.html" "b/docs/m2met2_cn/_build/html/\350\265\233\351\201\223\350\256\276\347\275\256\344\270\216\350\257\204\344\274\260.html"
similarity index 94%
rename from "docs_m2met2_cn/_build/html/\350\265\233\351\201\223\350\256\276\347\275\256\344\270\216\350\257\204\344\274\260.html"
rename to "docs/m2met2_cn/_build/html/\350\265\233\351\201\223\350\256\276\347\275\256\344\270\216\350\257\204\344\274\260.html"
index 7df9889..072ea54 100644
--- "a/docs_m2met2_cn/_build/html/\350\265\233\351\201\223\350\256\276\347\275\256\344\270\216\350\257\204\344\274\260.html"
+++ "b/docs/m2met2_cn/_build/html/\350\265\233\351\201\223\350\256\276\347\275\256\344\270\216\350\257\204\344\274\260.html"
@@ -90,7 +90,7 @@
</ul>
</li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">璧涢亾璁剧疆涓庤瘎浼�</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="#id2">璇磋瘽浜虹浉鍏崇殑璇煶璇嗗埆 (涓昏禌閬�)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#id2">璇磋瘽浜虹浉鍏崇殑璇煶璇嗗埆</a></li>
<li class="toctree-l2"><a class="reference internal" href="#id3">璇勪及鏂规硶</a></li>
<li class="toctree-l2"><a class="reference internal" href="#id4">瀛愯禌閬撹缃�</a></li>
</ul>
@@ -103,7 +103,7 @@
</li>
<li class="toctree-l1"><a class="reference internal" href="%E8%A7%84%E5%88%99.html">绔炶禌瑙勫垯</a></li>
<li class="toctree-l1"><a class="reference internal" href="%E7%BB%84%E5%A7%94%E4%BC%9A.html">缁勫浼�</a></li>
-<li class="toctree-l1"><a class="reference internal" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html">Contact</a></li>
+<li class="toctree-l1"><a class="reference internal" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html">鑱旂郴鏂瑰紡</a></li>
</ul>
@@ -127,8 +127,8 @@
<section id="id1">
<h1>璧涢亾璁剧疆涓庤瘎浼�<a class="headerlink" href="#id1" title="姝ゆ爣棰樼殑姘镐箙閾炬帴">露</a></h1>
<section id="id2">
-<h2>璇磋瘽浜虹浉鍏崇殑璇煶璇嗗埆 (涓昏禌閬�)<a class="headerlink" href="#id2" title="姝ゆ爣棰樼殑姘镐箙閾炬帴">露</a></h2>
-<p>璇磋瘽浜虹浉鍏崇殑ASR浠诲姟闇�瑕佷粠閲嶅彔鐨勮闊充腑璇嗗埆姣忎釜璇磋瘽浜虹殑璇煶锛屽苟涓鸿瘑鍒唴瀹瑰垎閰嶄竴涓璇濅汉鏍囩銆傚浘2灞曠ず浜嗚璇濅汉鐩稿叧璇煶璇嗗埆浠诲姟鍜屽璇磋瘽浜鸿闊宠瘑鍒换鍔＄殑涓昏鍖哄埆銆傚湪鏈绔炶禌涓瑼liMeeting銆丄ishell4鍜孋n-Celeb鏁版嵁闆嗗彲浣滀负鍙楅檺鏁版嵁婧愩�傚湪M2MeT鎸戞垬璧涗腑浣跨敤鐨凙liMeeting鏁版嵁闆嗗寘鍚缁冦�佽瘎浼板拰娴嬭瘯闆嗭紝鍦∕2MET2.0鍙互鍦ㄨ缁冨拰璇勪及涓娇鐢ㄣ�傛澶栵紝涓�涓寘鍚害10灏忔椂浼氳鏁版嵁鐨勬柊鐨凾est-2023闆嗗皢鏍规嵁璧涚▼瀹夋帓鍙戝竷骞剁敤浜庢寫鎴樿禌鐨勮瘎鍒嗗拰鎺掑悕銆傚�煎緱娉ㄦ剰鐨勬槸锛岀粍缁囪�呭皢涓嶆彁渚涜�虫満鐨勮繎鍦洪煶棰戙�佽浆褰曚互鍙婄湡瀹炴椂闂存埑銆備富鍔炴柟灏嗕笉鍐嶆彁渚涙瘡涓璇濅汉鐨勭湡瀹炴椂闂存埑锛岃�屾槸鍦═est-2023闆嗕笂鎻愪緵鍖呭惈澶氫釜璇磋瘽浜虹殑鐗囨銆傝繖浜涚墖娈靛彲浠ラ�氳繃涓�涓畝鍗曠殑vad妯″瀷鑾峰緱銆�</p>
+<h2>璇磋瘽浜虹浉鍏崇殑璇煶璇嗗埆<a class="headerlink" href="#id2" title="姝ゆ爣棰樼殑姘镐箙閾炬帴">露</a></h2>
+<p>璇磋瘽浜虹浉鍏崇殑ASR浠诲姟闇�瑕佷粠閲嶅彔鐨勮闊充腑璇嗗埆姣忎釜璇磋瘽浜虹殑璇煶锛屽苟涓鸿瘑鍒唴瀹瑰垎閰嶄竴涓璇濅汉鏍囩銆傚浘2灞曠ず浜嗚璇濅汉鐩稿叧璇煶璇嗗埆浠诲姟鍜屽璇磋瘽浜鸿闊宠瘑鍒换鍔＄殑涓昏鍖哄埆銆傚湪鏈绔炶禌涓瑼liMeeting銆丄ishell4鍜孋n-Celeb鏁版嵁闆嗗彲浣滀负鍙楅檺鏁版嵁婧愩�傚湪M2MeT鎸戞垬璧涗腑浣跨敤鐨凙liMeeting鏁版嵁闆嗗寘鍚缁冦�佽瘎浼板拰娴嬭瘯闆嗭紝鍦∕2MET2.0鍙互鍦ㄨ缁冨拰璇勪及涓娇鐢ㄣ�傛澶栵紝涓�涓寘鍚害10灏忔椂浼氳鏁版嵁鐨勬柊鐨凾est-2023闆嗗皢鏍规嵁璧涚▼瀹夋帓鍙戝竷骞剁敤浜庢寫鎴樿禌鐨勮瘎鍒嗗拰鎺掑悕銆傚�煎緱娉ㄦ剰鐨勬槸锛屽浜嶵est-2023娴嬭瘯闆嗭紝涓诲姙鏂瑰皢涓嶅啀鎻愪緵鑰虫満鐨勮繎鍦洪煶棰戙�佽浆褰曚互鍙婄湡瀹炴椂闂存埑銆傝�屾槸鎻愪緵鍙互閫氳繃涓�涓畝鍗曠殑VAD妯″瀷寰楀埌鐨勫寘鍚涓璇濅汉鐨勭墖娈点��</p>
<p><img alt="task difference" src="_images/task_diff.png" /></p>
</section>
<section id="id3">
@@ -142,7 +142,7 @@
<h2>瀛愯禌閬撹缃�<a class="headerlink" href="#id4" title="姝ゆ爣棰樼殑姘镐箙閾炬帴">露</a></h2>
<section id="id5">
<h3>瀛愯禌閬撲竴 (闄愬畾璁粌鏁版嵁):<a class="headerlink" href="#id5" title="姝ゆ爣棰樼殑姘镐箙閾炬帴">露</a></h3>
-<p>鍙傝禌鑰呭湪绯荤粺鏋勫缓杩囩▼涓粎鑳戒娇鐢ˋliMeeting銆丄ISHELL-4鍜孋N Celeb锛屼弗绂佷娇鐢ㄩ澶栨暟鎹�傚弬璧涜�呭彲浠ヤ换浣曠涓夋柟寮�婧愮殑棰勮缁冩ā鍨嬶紝濡�<a class="reference external" href="https://huggingface.co/models">Hugging Face</a>浠ュ強<a class="reference external" href="https://www.modelscope.cn/models">ModelScope</a>涓婃彁渚涚殑妯″瀷銆傚弬璧涜�呴渶瑕佸湪鏈�缁堢殑绯荤粺鎻忚堪鏂囨。涓缁嗗垪鍑轰娇鐢ㄧ殑棰勮缁冩ā鍨嬪悕绉颁互鍙婇摼鎺ャ��</p>
+<p>鍙傝禌鑰呭湪绯荤粺鏋勫缓杩囩▼涓粎鑳戒娇鐢ˋliMeeting銆丄ISHELL-4鍜孋N-Celeb锛屼弗绂佷娇鐢ㄩ澶栨暟鎹�傚弬璧涜�呭彲浠ヤ换浣曠涓夋柟寮�婧愮殑棰勮缁冩ā鍨嬶紝濡�<a class="reference external" href="https://huggingface.co/models">Hugging Face</a>浠ュ強<a class="reference external" href="https://www.modelscope.cn/models">ModelScope</a>涓婃彁渚涚殑妯″瀷銆傚弬璧涜�呴渶瑕佸湪鏈�缁堢殑绯荤粺鎻忚堪鏂囨。涓缁嗗垪鍑轰娇鐢ㄧ殑棰勮缁冩ā鍨嬪悕绉颁互鍙婇摼鎺ャ��</p>
</section>
<section id="id6">
<h3>瀛愯禌閬撲簩 (寮�鏀捐缁冩暟鎹�):<a class="headerlink" href="#id6" title="姝ゆ爣棰樼殑姘镐箙閾炬帴">露</a></h3>
diff --git a/docs_m2met2_cn/conf.py b/docs/m2met2_cn/conf.py
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diff --git a/docs_m2met2_cn/index.rst b/docs/m2met2_cn/index.rst
similarity index 91%
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rename to docs/m2met2_cn/index.rst
index b35ac96..c089b36 100644
--- a/docs_m2met2_cn/index.rst
+++ b/docs/m2met2_cn/index.rst
@@ -20,10 +20,3 @@
./瑙勫垯
./缁勫浼�
./鑱旂郴鏂瑰紡
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
diff --git a/docs_m2met2_cn/make.bat b/docs/m2met2_cn/make.bat
similarity index 100%
rename from docs_m2met2_cn/make.bat
rename to docs/m2met2_cn/make.bat
diff --git "a/docs_m2met2_cn/\345\237\272\347\272\277.md" "b/docs/m2met2_cn/\345\237\272\347\272\277.md"
similarity index 100%
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diff --git "a/docs_m2met2_cn/\346\225\260\346\215\256\351\233\206.md" "b/docs/m2met2_cn/\346\225\260\346\215\256\351\233\206.md"
similarity index 85%
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index 52965a1..24bfaf3 100644
--- "a/docs_m2met2_cn/\346\225\260\346\215\256\351\233\206.md"
+++ "b/docs/m2met2_cn/\346\225\260\346\215\256\351\233\206.md"
@@ -3,13 +3,13 @@
鍦ㄩ檺瀹氭暟鎹泦鏉′欢涓嬶紝璁粌鏁版嵁闆嗕粎闄愪簬涓変釜鍏紑鐨勮鏂欏簱锛屽嵆AliMeeting銆丄ISHELL-4鍜孋N-Celeb銆備负浜嗚瘎浼板弬璧涜�呮彁浜ょ殑妯″瀷鐨勬�ц兘锛屾垜浠皢鍙戝竷涓�涓柊鐨勬祴璇曢泦锛圱est-2023锛夌敤浜庢墦鍒嗗拰鎺掑悕銆備笅闈㈡垜浠皢璇︾粏鎻忚堪AliMeeting鏁版嵁闆嗗拰Test-2023娴嬭瘯闆嗐��
## Alimeeting鏁版嵁闆嗕粙缁�
-AliMeeting鎬诲叡鍖呭惈118.75灏忔椂鐨勮闊虫暟鎹紝鍖呮嫭104.75灏忔椂鐨勮缁冮泦锛圱rain锛夈��4灏忔椂鐨勯獙璇侀泦锛圗val锛夊拰10灏忔椂鐨勬祴璇曢泦锛圱est锛夈�俆rain闆嗗拰Eval闆嗗垎鍒寘鍚�212鍦哄拰8鍦轰細璁紝鍏朵腑姣忓満浼氳鐢卞涓璇濅汉杩涜15鍒�30鍒嗛挓鐨勮璁恒�俆rain鍜孍val闆嗕腑鍙備笌浼氳鐨勬�讳汉鏁板垎鍒负456浜哄拰25浜猴紝骞朵笖鍙備細鐨勭敺濂虫瘮渚嬩汉鏁板潎琛°��
+AliMeeting鎬诲叡鍖呭惈118.75灏忔椂鐨勮闊虫暟鎹紝鍖呮嫭104.75灏忔椂鐨勮缁冮泦锛圱rain锛夈��4灏忔椂鐨勯獙璇侀泦锛圗val锛夊拰10灏忔椂鐨勬祴璇曢泦锛圱est锛夈�俆rain闆嗭紝Eval闆嗗拰Test闆嗗垎鍒寘鍚�212鍦哄拰8鍦轰細璁紝鍏朵腑姣忓満浼氳鐢卞涓璇濅汉杩涜15鍒�30鍒嗛挓鐨勮璁恒�俆rain锛孍val鍜孴est闆嗕腑鍙備笌浼氳鐨勬�讳汉鏁板垎鍒负456浜哄拰25浜猴紝骞朵笖鍙備細鐨勭敺濂虫瘮渚嬩汉鏁板潎琛°��
璇ユ暟鎹泦鏀堕泦浜�13涓笉鍚岀殑浼氳瀹わ紝鎸夌収澶у皬瑙勬牸鍒嗕负灏忓瀷銆佷腑鍨嬪拰澶у瀷涓夌锛屾埧闂撮潰绉粠8鍒�55骞虫柟绫充笉绛夈�備笉鍚屾埧闂村叿鏈変笉鍚岀殑甯冨眬鍜屽０瀛︾壒鎬э紝姣忎釜鎴块棿鐨勮缁嗗弬鏁颁篃灏嗗彂閫佺粰鍙備笌鑰呫�備細璁満鍦扮殑澧欎綋鏉愭枡绫诲瀷鍖呮嫭姘存偿銆佺幓鐠冪瓑銆備細璁満鍦扮殑瀹跺叿鍖呮嫭娌欏彂銆佺數瑙嗐�侀粦鏉裤�侀鎵囥�佺┖璋冦�佹鐗╃瓑銆傚湪褰曞埗杩囩▼涓紝楹﹀厠椋庨樀鍒楁斁缃簬妗屼笂锛屽涓璇濅汉鍥村潗鍦ㄦ杈硅繘琛岃嚜鐒跺璇濄�傞害鍏嬮闃靛垪绂昏璇濅汉璺濈绾�0.3鍒�5.0绫充箣闂淬�傛墍鏈夎璇濅汉鐨勬瘝璇潎鏄眽璇紝骞朵笖璇寸殑閮芥槸鏅�氳瘽锛屾病鏈夋祿閲嶇殑鍙ｉ煶銆傚湪浼氳褰曞埗鏈熼棿鍙兘浼氫骇鐢熷悇绉嶅鍐呯殑鍣煶锛屽寘鎷敭鐩樺０銆佸紑闂�/鍏抽棬澹般�侀鎵囧０銆佹皵娉″０绛夈�傛墍鏈夎璇濅汉鍦ㄤ細璁殑褰曞埗鏈熼棿鍧囦繚鎸佺浉鍚屼綅缃紝涓嶅彂鐢熻蛋鍔ㄣ�傝缁冮泦鍜岄獙璇侀泦鐨勮璇濅汉娌℃湁閲嶅銆傚浘1灞曠ず浜嗕竴涓細璁鐨勫竷灞�浠ュ強楹﹀厠椋庣殑鎷撴墤缁撴瀯銆�
-姣忓満浼氳鐨勮璇濅汉鏁伴噺浠�2鍒�4浜轰笉绛夈�傚悓鏃朵负浜嗚鐩栧悇绉嶅唴瀹圭殑浼氳鍦烘櫙锛屾垜浠�夋嫨浜嗗绉嶄細璁富棰橈紝鍖呮嫭鍖荤枟銆佹暀鑲层�佸晢涓氥�佺粍缁囩鐞嗐�佸伐涓氱敓浜х瓑涓嶅悓鍐呭鐨勪緥浼氥�俆rain闆嗭紝Eval闆嗗拰Test闆嗙殑骞冲潎璇煶閲嶅彔鐜囧垎鍒负42.27\%鍜�34.76\%銆侫liMeeting Train闆嗭紝Eval闆嗗拰Test闆嗙殑璇︾粏淇℃伅瑙佽〃1銆傝〃2鏄剧ず浜員rain闆�,Eval闆嗗拰Test闆嗕腑涓嶅悓鍙戣█鑰呬汉鏁颁細璁殑璇煶閲嶅彔鐜囧拰浼氳鏁伴噺銆�
+姣忓満浼氳鐨勮璇濅汉鏁伴噺浠�2鍒�4浜轰笉绛夈�傚悓鏃朵负浜嗚鐩栧悇绉嶅唴瀹圭殑浼氳鍦烘櫙锛屾垜浠�夋嫨浜嗗绉嶄細璁富棰橈紝鍖呮嫭鍖荤枟銆佹暀鑲层�佸晢涓氥�佺粍缁囩鐞嗐�佸伐涓氱敓浜х瓑涓嶅悓鍐呭鐨勪緥浼氥�俆rain闆嗭紝Eval闆嗗拰Test闆嗙殑骞冲潎璇煶閲嶅彔鐜囧垎鍒负42.27\%锛�34.76\%鍜�42.8\%銆侫liMeeting Train闆嗭紝Eval闆嗗拰Test闆嗙殑璇︾粏淇℃伅瑙佽〃1銆傝〃2鏄剧ず浜員rain闆�,Eval闆嗗拰Test闆嗕腑涓嶅悓鍙戣█鑰呬汉鏁颁細璁殑璇煶閲嶅彔鐜囧拰浼氳鏁伴噺銆�
Test-2023娴嬭瘯闆嗙敱20鍦轰細璁粍鎴愶紝杩欎簺浼氳鏄湪涓嶢liMeeting鏁版嵁闆嗙浉鍚岀殑澹板鐜涓嬪綍鍒剁殑銆俆est-2023娴嬭瘯闆嗕腑鐨勬瘡涓細璁幆鑺傜敱2鍒�4涓弬涓庤�呯粍鎴愬苟涓斾笌AliMeeting娴嬭瘯闆嗙殑閰嶇疆鐩镐技銆�
diff --git "a/docs_m2met2_cn/\347\256\200\344\273\213.md" "b/docs/m2met2_cn/\347\256\200\344\273\213.md"
similarity index 90%
rename from "docs_m2met2_cn/\347\256\200\344\273\213.md"
rename to "docs/m2met2_cn/\347\256\200\344\273\213.md"
index cf4dfb8..52df97d 100644
--- "a/docs_m2met2_cn/\347\256\200\344\273\213.md"
+++ "b/docs/m2met2_cn/\347\256\200\344\273\213.md"
@@ -13,16 +13,20 @@
## 鏃堕棿瀹夋帓(AOE鏃堕棿)
-- $ 2023.5.5: $ 鍙傝禌鑰呮敞鍐屾埅姝�
+- $ 2023.4.29: $ 寮�鏀炬敞鍐�
+- $ 2023.5.8: $ 鍩虹嚎鍙戝竷
+- $ 2023.5.15: $ 娉ㄥ唽鎴
- $ 2023.6.9: $ 娴嬭瘯闆嗘暟鎹彂甯�
- $ 2023.6.13: $ 鏈�缁堢粨鏋滄彁浜ゆ埅姝�
- $ 2023.6.19: $ 璇勪及缁撴灉鍜屾帓鍚嶅彂甯�
- $ 2023.7.3: $ 璁烘枃鎻愪氦鎴
- $ 2023.7.10: $ 鏈�缁堢増璁烘枃鎻愪氦鎴
-- $ 2023.12.12: $ ASRU Workshop
+- $ 2023.12.12: $ ASRU Workshop & challenge session
## 绔炶禌鎶ュ悕
-鏉ヨ嚜瀛︽湳鐣屽拰宸ヤ笟鐣岀殑鏈夋剰鍚戝弬璧涜�呭潎搴斿湪2023骞�5鏈�5鏃ュ強涔嬪墠濉啓涓嬫柟鐨勮胺姝岃〃鍗曪細
+鏉ヨ嚜瀛︽湳鐣屽拰宸ヤ笟鐣岀殑鏈夋剰鍚戝弬璧涜�呭潎搴斿湪2023骞�5鏈�15鏃ュ強涔嬪墠濉啓涓嬫柟鐨勮胺姝岃〃鍗曪細
+
+[M2MET2.0鎶ュ悕](https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link)
涓诲姙鏂瑰皢鍦�3涓伐浣滄棩鍐呴�氳繃鐢靛瓙閭欢閫氱煡绗﹀悎鏉′欢鐨勫弬璧涘洟闃燂紝鍥㈤槦蹇呴』閬靛畧灏嗗湪鎸戞垬缃戠珯涓婂彂甯冪殑鎸戞垬瑙勫垯銆傚湪鎺掑悕鍙戝竷涔嬪墠锛屾瘡涓弬璧涜�呭繀椤绘彁浜や竴浠界郴缁熸弿杩版枃浠讹紝璇︾粏璇存槑浣跨敤鐨勬柟娉曞拰妯″瀷銆備富鍔炴柟灏嗛�夋嫨鍓嶄笁鍚嶇撼鍏SRU2023璁烘枃闆嗐��
\ No newline at end of file
diff --git "a/docs_m2met2_cn/\347\273\204\345\247\224\344\274\232.md" "b/docs/m2met2_cn/\347\273\204\345\247\224\344\274\232.md"
similarity index 100%
rename from "docs_m2met2_cn/\347\273\204\345\247\224\344\274\232.md"
rename to "docs/m2met2_cn/\347\273\204\345\247\224\344\274\232.md"
diff --git "a/docs_m2met2_cn/\350\201\224\347\263\273\346\226\271\345\274\217.md" "b/docs/m2met2_cn/\350\201\224\347\263\273\346\226\271\345\274\217.md"
similarity index 100%
rename from "docs_m2met2_cn/\350\201\224\347\263\273\346\226\271\345\274\217.md"
rename to "docs/m2met2_cn/\350\201\224\347\263\273\346\226\271\345\274\217.md"
diff --git "a/docs_m2met2_cn/\350\247\204\345\210\231.md" "b/docs/m2met2_cn/\350\247\204\345\210\231.md"
similarity index 90%
rename from "docs_m2met2_cn/\350\247\204\345\210\231.md"
rename to "docs/m2met2_cn/\350\247\204\345\210\231.md"
index b0928ee..68cb159 100644
--- "a/docs_m2met2_cn/\350\247\204\345\210\231.md"
+++ "b/docs/m2met2_cn/\350\247\204\345\210\231.md"
@@ -4,8 +4,6 @@
- 鍏佽鍦ㄥ師濮嬭缁冩暟鎹泦涓婅繘琛屾暟鎹寮猴紝鍖呮嫭浣嗕笉闄愪簬娣诲姞鍣０鎴栨贩鍝嶃�侀�熷害鎵板姩鍜岄煶璋冨彉鍖栥��
- 鍏佽鍙傝禌鑰呬娇鐢‥val闆嗚繘琛屾ā鍨嬭缁冿紝浣員est闆嗕粎鑳界敤浜庤皟鍙傚拰妯″瀷閫夋嫨锛屼弗绂佷互浠讳綍褰㈠紡浣跨敤Test-2023鏁版嵁闆嗭紝鍖呮嫭浣嗕笉闄愪簬浣跨敤娴嬭瘯鏁版嵁闆嗗井璋冩垨璁粌妯″瀷銆�
-
-- 鍏佽澶氱郴缁熻瀺鍚堬紝浣嗕笉榧撳姳浣跨敤鍏锋湁鐩稿悓缁撴瀯浠呭弬鏁颁笉鍚岀殑瀛愮郴缁熻瀺鍚堛��
- 濡傛灉涓や釜绯荤粺鐨勬祴璇昪pCER鐩稿悓锛屽垯璁＄畻澶嶆潅搴﹁緝浣庣殑绯荤粺灏嗚璁ゅ畾涓烘洿浼樸��
diff --git "a/docs_m2met2_cn/\350\265\233\351\201\223\350\256\276\347\275\256\344\270\216\350\257\204\344\274\260.md" "b/docs/m2met2_cn/\350\265\233\351\201\223\350\256\276\347\275\256\344\270\216\350\257\204\344\274\260.md"
similarity index 86%
rename from "docs_m2met2_cn/\350\265\233\351\201\223\350\256\276\347\275\256\344\270\216\350\257\204\344\274\260.md"
rename to "docs/m2met2_cn/\350\265\233\351\201\223\350\256\276\347\275\256\344\270\216\350\257\204\344\274\260.md"
index a99c1ed..94a6236 100644
--- "a/docs_m2met2_cn/\350\265\233\351\201\223\350\256\276\347\275\256\344\270\216\350\257\204\344\274\260.md"
+++ "b/docs/m2met2_cn/\350\265\233\351\201\223\350\256\276\347\275\256\344\270\216\350\257\204\344\274\260.md"
@@ -1,6 +1,6 @@
# 璧涢亾璁剧疆涓庤瘎浼�
-## 璇磋瘽浜虹浉鍏崇殑璇煶璇嗗埆 (涓昏禌閬�)
-璇磋瘽浜虹浉鍏崇殑ASR浠诲姟闇�瑕佷粠閲嶅彔鐨勮闊充腑璇嗗埆姣忎釜璇磋瘽浜虹殑璇煶锛屽苟涓鸿瘑鍒唴瀹瑰垎閰嶄竴涓璇濅汉鏍囩銆傚浘2灞曠ず浜嗚璇濅汉鐩稿叧璇煶璇嗗埆浠诲姟鍜屽璇磋瘽浜鸿闊宠瘑鍒换鍔＄殑涓昏鍖哄埆銆傚湪鏈绔炶禌涓瑼liMeeting銆丄ishell4鍜孋n-Celeb鏁版嵁闆嗗彲浣滀负鍙楅檺鏁版嵁婧愩�傚湪M2MeT鎸戞垬璧涗腑浣跨敤鐨凙liMeeting鏁版嵁闆嗗寘鍚缁冦�佽瘎浼板拰娴嬭瘯闆嗭紝鍦∕2MET2.0鍙互鍦ㄨ缁冨拰璇勪及涓娇鐢ㄣ�傛澶栵紝涓�涓寘鍚害10灏忔椂浼氳鏁版嵁鐨勬柊鐨凾est-2023闆嗗皢鏍规嵁璧涚▼瀹夋帓鍙戝竷骞剁敤浜庢寫鎴樿禌鐨勮瘎鍒嗗拰鎺掑悕銆傚�煎緱娉ㄦ剰鐨勬槸锛岀粍缁囪�呭皢涓嶆彁渚涜�虫満鐨勮繎鍦洪煶棰戙�佽浆褰曚互鍙婄湡瀹炴椂闂存埑銆備富鍔炴柟灏嗕笉鍐嶆彁渚涙瘡涓璇濅汉鐨勭湡瀹炴椂闂存埑锛岃�屾槸鍦═est-2023闆嗕笂鎻愪緵鍖呭惈澶氫釜璇磋瘽浜虹殑鐗囨銆傝繖浜涚墖娈靛彲浠ラ�氳繃涓�涓畝鍗曠殑vad妯″瀷鑾峰緱銆�
+## 璇磋瘽浜虹浉鍏崇殑璇煶璇嗗埆
+璇磋瘽浜虹浉鍏崇殑ASR浠诲姟闇�瑕佷粠閲嶅彔鐨勮闊充腑璇嗗埆姣忎釜璇磋瘽浜虹殑璇煶锛屽苟涓鸿瘑鍒唴瀹瑰垎閰嶄竴涓璇濅汉鏍囩銆傚浘2灞曠ず浜嗚璇濅汉鐩稿叧璇煶璇嗗埆浠诲姟鍜屽璇磋瘽浜鸿闊宠瘑鍒换鍔＄殑涓昏鍖哄埆銆傚湪鏈绔炶禌涓瑼liMeeting銆丄ishell4鍜孋n-Celeb鏁版嵁闆嗗彲浣滀负鍙楅檺鏁版嵁婧愩�傚湪M2MeT鎸戞垬璧涗腑浣跨敤鐨凙liMeeting鏁版嵁闆嗗寘鍚缁冦�佽瘎浼板拰娴嬭瘯闆嗭紝鍦∕2MET2.0鍙互鍦ㄨ缁冨拰璇勪及涓娇鐢ㄣ�傛澶栵紝涓�涓寘鍚害10灏忔椂浼氳鏁版嵁鐨勬柊鐨凾est-2023闆嗗皢鏍规嵁璧涚▼瀹夋帓鍙戝竷骞剁敤浜庢寫鎴樿禌鐨勮瘎鍒嗗拰鎺掑悕銆傚�煎緱娉ㄦ剰鐨勬槸锛屽浜嶵est-2023娴嬭瘯闆嗭紝涓诲姙鏂瑰皢涓嶅啀鎻愪緵鑰虫満鐨勮繎鍦洪煶棰戙�佽浆褰曚互鍙婄湡瀹炴椂闂存埑銆傝�屾槸鎻愪緵鍙互閫氳繃涓�涓畝鍗曠殑VAD妯″瀷寰楀埌鐨勫寘鍚涓璇濅汉鐨勭墖娈点��
@@ -12,6 +12,6 @@
鍏朵腑 $\mathcal N_{\text{Ins}}$ , $\mathcal N_{\text{Sub}}$ , $\mathcal N_{\text{Del}}$ 鏄笁绉嶉敊璇殑瀛楃鏁�, $\mathcal N_{\text{Total}}$ 鏄瓧绗︽�绘暟.
## 瀛愯禌閬撹缃�
### 瀛愯禌閬撲竴 (闄愬畾璁粌鏁版嵁):
-鍙傝禌鑰呭湪绯荤粺鏋勫缓杩囩▼涓粎鑳戒娇鐢ˋliMeeting銆丄ISHELL-4鍜孋N Celeb锛屼弗绂佷娇鐢ㄩ澶栨暟鎹�傚弬璧涜�呭彲浠ヤ换浣曠涓夋柟寮�婧愮殑棰勮缁冩ā鍨嬶紝濡俒Hugging Face](https://huggingface.co/models)浠ュ強[ModelScope](https://www.modelscope.cn/models)涓婃彁渚涚殑妯″瀷銆傚弬璧涜�呴渶瑕佸湪鏈�缁堢殑绯荤粺鎻忚堪鏂囨。涓缁嗗垪鍑轰娇鐢ㄧ殑棰勮缁冩ā鍨嬪悕绉颁互鍙婇摼鎺ャ��
+鍙傝禌鑰呭湪绯荤粺鏋勫缓杩囩▼涓粎鑳戒娇鐢ˋliMeeting銆丄ISHELL-4鍜孋N-Celeb锛屼弗绂佷娇鐢ㄩ澶栨暟鎹�傚弬璧涜�呭彲浠ヤ换浣曠涓夋柟寮�婧愮殑棰勮缁冩ā鍨嬶紝濡俒Hugging Face](https://huggingface.co/models)浠ュ強[ModelScope](https://www.modelscope.cn/models)涓婃彁渚涚殑妯″瀷銆傚弬璧涜�呴渶瑕佸湪鏈�缁堢殑绯荤粺鎻忚堪鏂囨。涓缁嗗垪鍑轰娇鐢ㄧ殑棰勮缁冩ā鍨嬪悕绉颁互鍙婇摼鎺ャ��
### 瀛愯禌閬撲簩 (寮�鏀捐缁冩暟鎹�):
闄や簡闄愬畾鏁版嵁澶栵紝鍙備笌鑰呭彲浠ヤ娇鐢ㄤ换浣曞叕寮�鍙敤銆佺浜哄綍鍒跺拰妯℃嫙浠跨湡鐨勬暟鎹泦銆備絾鏄紝鍙備笌鑰呭繀椤绘竻妤氬湴鍒楀嚭浣跨敤鐨勬暟鎹�傚悓鏍凤紝鍙傝禌鑰呬篃鍙互浣跨敤浠讳綍绗笁鏂瑰紑婧愮殑棰勮缁冩ā鍨嬶紝浣嗗繀椤诲湪鏈�鍚庣殑绯荤粺鎻忚堪鏂囦欢涓槑纭殑鍒楀嚭鎵�浣跨敤鐨勬暟鎹拰妯″瀷閾炬帴锛屽鏋滀娇鐢ㄦā鎷熶豢鐪熸暟鎹紝璇疯缁嗘弿杩版暟鎹ā鎷熺殑鏂规銆�
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diff --git a/docs_m2met2/_build/html/_sources/Baseline.md.txt b/docs_m2met2/_build/html/_sources/Baseline.md.txt
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-# Baseline
-## Overview
-We will release an E2E SA-ASR~\cite{kanda21b_interspeech} baseline conducted on [FunASR](https://github.com/alibaba-damo-academy/FunASR) at the time according to the timeline. The model architecture is shown in Figure 3. The SpeakerEncoder is initialized with a pre-trained speaker verification model from ModelScope. This speaker verification model is also be used to extract the speaker embedding in the speaker profile.
-
-
-
-## Quick start
-#TODO: fill with the README.md of the baseline
-
-## Baseline results
-The results of the baseline system are shown in Table 3. The speaker profile adopts the oracle speaker embedding during training. However, due to the lack of oracle speaker label during evaluation, the speaker profile provided by an additional spectral clustering is used. Meanwhile, the results of using the oracle speaker profile on Eval and Test Set are also provided to show the impact of speaker profile accuracy.
-
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diff --git a/docs_m2met2/_build/html/_sources/Introduction.md.txt b/docs_m2met2/_build/html/_sources/Introduction.md.txt
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-# Introduction
-## Call for participation
-Automatic speech recognition (ASR) and speaker diarization have made significant strides in recent years, resulting in a surge of speech technology applications across various domains. However, meetings present unique challenges to speech technologies due to their complex acoustic conditions and diverse speaking styles, including overlapping speech, variable numbers of speakers, far-field signals in large conference rooms, and environmental noise and reverberation.
-
-Over the years, several challenges have been organized to advance the development of meeting transcription, including the Rich Transcription evaluation and Computational Hearing in Multisource Environments (CHIME) challenges. The latest iteration of the CHIME challenge has a particular focus on distant automatic speech recognition (ASR) and developing systems that can generalize across various array topologies and application scenarios. However, while progress has been made in English meeting transcription, language differences remain a significant barrier to achieving comparable results in non-English languages, such as Mandarin.
-
-The Multimodal Information Based Speech Processing (MISP) and Multi-Channel Multi-Party Meeting Transcription (M2MeT) challenges have been instrumental in advancing Mandarin meeting transcription. The MISP challenge seeks to address the problem of audio-visual distant multi-microphone signal processing in everyday home environments, while the M2MeT challenge focuses on tackling the speech overlap issue in offline meeting rooms.
-
-The ICASSP2022 M2MeT challenge focuses on meeting scenarios, and it comprises two main tasks: speaker diarization and multi-speaker automatic speech recognition (ASR). 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. By attributing speech to specific speakers, this task aims to improve the accuracy and applicability of multi-talker ASR systems in real-world settings. The challenge provides detailed datasets, rules, evaluation methods, and baseline systems to facilitate reproducible research in this field. 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)
-
-- $ May~5^{th}, 2023: $ Registration deadline, the due date for participants to join the Challenge.
-- $ June~9^{th}, 2023: $ Test data release.
-- $ June~13^{rd}, 2023: $ Final submission deadline.
-- $ June~19^{th}, 2023: $ Evaluation result and ranking release.
-- $ July~3^{rd}, 2023: $ Deadline for paper submission.
-- $ July~10^{th}, 2023: $ Deadline for final paper submission.
-- $ December~12^{nd}\ to\ 16^{th}, 2023: $ ASRU Workshop
-
-## Guidelines
-
-Possible improved version: Interested participants, whether from academia or industry, must register for the challenge by completing a Google form, which will be available here. The deadline for registration is May 5, 2023.
-
-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 three submissions to be included in the ASRU2023 Proceedings.
diff --git a/docs_m2met2/_build/html/_sources/Organizers.md.txt b/docs_m2met2/_build/html/_sources/Organizers.md.txt
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-# Organizers
-***Lei Xie, Professor, Northwestern Polytechnical University, China***
-
-Email: [lxie@nwpu.edu.cn](mailto:lxie@nwpu.edu.cn)
-
-<img src="images/lxie.jpeg" alt="lxie" width="20%">
-
-Lei Xie received the Ph.D. degree in computer science from Northwestern Polytechnical University, Xi'an, China, in 2004. From 2001 to 2002, he was with the Department of Electronics and Information Processing, Vrije Universiteit Brussel (VUB), Brussels, Belgium, as a Visiting Scientist. From 2004 to 2006, he was a Senior Research Associate with the Center for Media Technology, School of Creative Media, City University of Hong Kong, Hong Kong, China. From 2006 to 2007, he was a Postdoctoral Fellow with the Human-Computer Communications Laboratory (HCCL), The Chinese University of Hong Kong, Hong Kong, China. He is currently a Professor with School of Computer Science, Northwestern Polytechnical University, Xian, China and leads the Audio, Speech and Language Processing Group (ASLP@NPU). He has published over 200 papers in referred journals and conferences, such as IEEE/ACM Transactions on Audio, Speech and Language Processing, IEEE Transactions on Multimedia, Interspeech, ICASSP, ASRU, ACL and ACM Multimedia. He has achieved several best paper awards in flagship conferences. His current research interests include general topics in speech and language processing, multimedia, and human-computer interaction. Dr. Xie is currently an associate editor (AE) of IEEE/ACM Trans. on Audio, Speech and language Processing. He has actively served as Chairs in many conferences and technical committees. He serves as an IEEE Speech and Language Processing
-Technical Committee Member.
-
-***Kong Aik Lee, Senior Scientist at Institute for Infocomm Research, A\*Star, Singapore***
-
-Email: [kongaik.lee@ieee.org](mailto:kongaik.lee@ieee.org)
-
-<img src="images/kong.png" alt="kong" width="20%">
-
-Kong Aik Lee started off him career as a researcher, then a team leader and a strategic planning manager, at the Institute Infocomm Research, A*STAR, Singapore, working on speaker and language recognition research. From 2018 to 2020, he spent two and a half years in NEC Corporation, Japan, focusing very much on voice biometrics and multi-modal biometrics products. He is proud to work with a great team on voice biometrics featured on NEC Bio-Idiom platform. He returned to Singapore in July 2020, and now leading the speech and audio analytics research at the Institute for Infocomm Research, as a Senior Scientist and PI. He also serve as an Editor for Elsevier Computer Speech and Language (since 2016), and was an Associate Editor for IEEE/ACM Transactions on Audio, Speech and Language Processing (2017 - 2021), and am an elected member of IEEE Speech and Language Technical Committee (2019 - 2021).
-
-***Zhijie Yan, Principal Engineer at Alibaba, China***
-Email: [zhijie.yzj@alibaba-inc.com](mailto:zhijie.yzj@alibaba-inc.com)
-
-<img src="images/zhijie.jpg" alt="zhijie" width="20%">
-
-Zhijie Yan holds a PhD from the University of Science and Technology of China, and is a senior member of the Institute of Electrical and Electronics Engineers (IEEE). He is also an expert reviewer of top academic conferences and journals in the speech field. His research fields include speech recognition, speech synthesis, voiceprints, and speech interaction. His research results are applied in speech services provided by Alibaba Group and Ant Financial. He was awarded the title of "One of the Top 100 Grassroots Scientists" by the China Association for Science and Technology.
-
-
-***Shiliang Zhang, Senior Engineer at Alibaba, China***
-Email: [sly.zsl@alibaba-inc.com](mailto:sly.zsl@alibaba-inc.com)
-
-<img src="images/zsl.JPG" alt="zsl" width="20%">
-
-Shiliang Zhang graduated with a Ph.D. from the University of Science and Technology of China in 2017. His research areas mainly include speech recognition, natural language understanding, and machine learning. Currently, he has published over 40 papers in mainstream academic journals and conferences in the fields of speech and machine learning, and has applied for dozens of patents. After obtaining his doctorate degree, he joined the Alibaba Intelligent Speech team. He is currently leading the direction of speech recognition and fundamental technology at DAMO Academy's speech laboratory.
-
-
-
-***Yanmin Qian, Professor, Shanghai Jiao Tong University, China***
-
-Email: [yanminqian@sjtu.edu.cn](mailto:yanminqian@sjtu.edu.cn)
-
-<img src="images/qian.jpeg" alt="qian" width="20%">
-
-Yanmin Qian received the B.S. degree from the Department of Electronic and Information Engineering,Huazhong University of Science and Technology, Wuhan, China, in 2007, and the Ph.D. degree from the Department of Electronic Engineering, Tsinghua University, Beijing, China, in 2012. Since 2013, he has been with the Department of Computer Science and Engineering, Shanghai Jiao Tong University (SJTU), Shanghai, China, where he is currently an Associate Professor. From 2015 to 2016, he also worked as an Associate Research in the Speech Group, Cambridge University Engineering Department, Cambridge, U.K. He is a senior member of IEEE and a member of ISCA, and one of the founding members of Kaldi Speech Recognition Toolkit. He has published more than 110 papers on speech and language processing with 4000+ citations, including the top conference: ICASSP, INTERSPEECH and ASRU. His current research interests include the acoustic and language modeling in speech recognition, speaker and language recognition, key word spotting, and multimedia signal processing.
-
-
-
-***Zhuo Chen, Applied Scientist in Microsoft, USA***
-
-Email: [zhuc@microsoft.com](mailto:zhuc@microsoft.com)
-
-<img src="images/chenzhuo.jpg" alt="chenzhuo" width="20%">
-
-Zhuo Chen received the Ph.D. degree from Columbia University, New York, NY, USA, in 2017. He is currently a Principal Applied Data Scientist with Microsoft. He has authored or coauthored more than 80 papers in peer-reviewed journals and conferences with around 6000 citations. He is a reviewer or technical committee member for more than ten journals and conferences. His research interests include automatic conversation recognition, speech separation, diarisation, and speaker information extraction. He actively participated in the academic events and challenges, and won several awards. Meanwhile, he contributed to open-sourced datasets, such as WSJ0-2mix, LibriCSS, and AISHELL-4, that have been main benchmark datasets for multi-speaker processing research. In 2020, he was the Team Leader in 2020 Jelinek workshop, leading more than 30 researchers and students to push the state of the art in conversation transcription.
-
-***Jian Wu, Applied Scientist in Microsoft, USA***
-
-Email: [wujian@microsoft.com](mailto:wujian@microsoft.com)
-
-<img src="images/wujian.jpg" alt="wujian" width="20%">
-
-Jian Wu received a master degree from Northwestern Polytechnical University, Xi'an, China, in 2020 and currently he is a Applied Scientist in Microsoft, USA. His research interests cover multi-channel signal processing, robust and multi-talker speech recognition, speech enhancement, dereverberation and separation. He has around 30 conference publications with a total citation over 1200. He participated in several challenges such as CHiME5, DNS 2020 and FFSVC 2020 and contributed to the open-sourced datasets including LibriCSS and AISHELL-4. He is also a reviewer for several journals and conferences such as ICASSP, SLT, TASLP and SPL.
-
-***Hui Bu, CEO, AISHELL foundation, China***
-
-Email: [buhui@aishelldata.com](mailto:buhui@aishelldata.com)
-
-<img src="images/buhui.jpeg" alt="buhui" width="20%">
-
-Hui Bu received his master degree in the Artificial Intelligence Laboratory of Korea University in 2014. He is the founder and the CEO of AISHELL and AISHELL foundation. He participated in the release of AISHELL 1 \& 2 \& 3 \& 4, DMASH and HI-MIA open source database project and is the co-founder of China Kaldi offline Technology Forum.
diff --git a/docs_m2met2/_build/html/_sources/Track_setting_and_evaluation.md.txt b/docs_m2met2/_build/html/_sources/Track_setting_and_evaluation.md.txt
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-# Track & Evaluation
-## Speaker-Attributed ASR (Main Track)
-The speaker-attributed ASR task poses a unique challenge of transcribing speech from multiple speakers and assigning a speaker label to the transcription. Figure 2 illustrates the difference between the speaker-attributed ASR task and the multi-speaker ASR task. This track allows for the use of the AliMeeting, Aishell4, and Cn-Celeb datasets as constrained data sources during both training and evaluation. The AliMeeting dataset, which was used in the M2MeT challenge, includes Train, Eval, and Test sets. Additionally, a new Test-2023 set, consisting of approximately 10 hours of meeting data recorded in an identical acoustic setting as the AliMeeting corpus, will be released soon for challenge scoring and ranking. It's worth noting that the organizers will not provide the near-field audio, transcriptions, or oracle timestamps. Instead, segments containing multiple speakers will be provided on the Test-2023 set, which can be obtained using a simple voice activity detection (VAD) model.
-
-
-
-## Evaluation metric
-The accuracy of a speaker-attributed ASR system is evaluated using the concatenated minimum permutation character error rate (cpCER) metric. The calculation of cpCER involves three steps. Firstly, the reference and hypothesis transcriptions from each speaker in a session are concatenated in chronological order. Secondly, the character error rate (CER) is calculated between the concatenated reference and hypothesis transcriptions, and this process is repeated for all possible speaker permutations. Finally, the permutation with the lowest CER is selected as the cpCER for that session. TThe CER is obtained by dividing the total number of insertions (Ins), substitutions (Sub), and deletions(Del) of characters required to transform the ASR output into the reference transcript by the total number of characters in the reference transcript. Specifically, CER is calculated by:
-
-$$\text{CER} = \frac {\mathcal N_{\text{Ins}} + \mathcal N_{\text{Sub}} + \mathcal N_{\text{Del}} }{\mathcal N_{\text{Total}}} \times 100\%,$$
-
-where $\mathcal N_{\text{Ins}}$, $\mathcal N_{\text{Sub}}$, $\mathcal N_{\text{Del}}$ are the character number of the three errors, and $\mathcal N_{\text{Total}}$ is the total number of characters.
-## Sub-track arrangement
-### Sub-track I (Fixed Training Condition):
-Participants are required to use only the fixed-constrained data (i.e., AliMeeting, Aishell-4, and CN-Celeb) for system development. The usage of any additional data is strictly prohibited. However, participants can use open-source pre-trained models from third-party sources, such as [Hugging Face](https://huggingface.co/models) and [ModelScope](https://www.modelscope.cn/models), provided that the list of utilized models is clearly stated in the final system description paper.
-### Sub-track II (Open Training Condition):
-Participants are allowed to use any publicly available data set, privately recorded data, and manual simulation to build their systems in addition to the fixed-constrained data. They can also utilize any open-source pre-trained models, but it is mandatory to provide a clear list of the data and models used in the final system description paper. If manually simulated data is used, a detailed description of the data simulation scheme must be provided.
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diff --git a/docs_m2met2/_build/html/searchindex.js b/docs_m2met2/_build/html/searchindex.js
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-Search.setIndex({"docnames": ["Baseline", "Contact", "Dataset", "Introduction", "Organizers", "Rules", "Track_setting_and_evaluation", "index"], "filenames": ["Baseline.md", "Contact.md", "Dataset.md", "Introduction.md", "Organizers.md", "Rules.md", "Track_setting_and_evaluation.md", "index.rst"], "titles": ["Baseline", "Contact", "Datasets", "Introduction", "Organizers", "Rules", "Track & Evaluation", "ASRU 2023 MULTI-CHANNEL MULTI-PARTY MEETING TRANSCRIPTION CHALLENGE 2.0 (M2MeT2.0)"], "terms": {"we": [0, 2, 3, 7], "releas": [0, 2, 3, 4, 6], "an": [0, 2, 3, 4, 6], "e2": 0, "sa": 0, "asr": [0, 3, 7], "cite": 0, "kanda21b_interspeech": 0, "conduct": [0, 2], "funasr": 0, "time": [0, 6], "accord": [0, 3], "timelin": [0, 2], "The": [0, 2, 3, 4, 5, 6], "model": [0, 2, 3, 4, 5, 6], "architectur": 0, "i": [0, 2, 3, 4, 5], "shown": [0, 2], "figur": [0, 6], "3": [0, 2, 3, 4], "speakerencod": 0, "initi": 0, "pre": [0, 6], "train": [0, 3, 5, 7], "speaker": [0, 2, 3, 4, 7], "verif": 0, "from": 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"Sub-track arrangement": [[6, "sub-track-arrangement"]], "Sub-track I (Fixed Training Condition):": [[6, "sub-track-i-fixed-training-condition"]], "Sub-track II (Open Training Condition):": [[6, "sub-track-ii-open-training-condition"]], "ASRU 2023 MULTI-CHANNEL MULTI-PARTY MEETING TRANSCRIPTION CHALLENGE 2.0 (M2MeT2.0)": [[7, "asru-2023-multi-channel-multi-party-meeting-transcription-challenge-2-0-m2met2-0"]], "Contents:": [[7, null]], "Indices and tables": [[7, "indices-and-tables"]]}, "indexentries": {}})
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-.. m2met2 documentation master file, created by
- sphinx-quickstart on Tue Apr 11 14:18:55 2023.
- You can adapt this file completely to your liking, but it should at least
- contain the root `toctree` directive.
-
-ASRU 2023 MULTI-CHANNEL MULTI-PARTY MEETING TRANSCRIPTION CHALLENGE 2.0 (M2MeT2.0)
-==================================================================================
-Building on the success of the M2MeT challenge, we are delighted to propose the M2MeT2.0 challenge as a special session at ASRU2023.
-To advance the current state-of-the-art in multi-talker automatic speech recognition, the M2MeT2.0 challenge proposes a speaker-attributed ASR task, comprising two sub-tracks: fixed and open training conditions.
-To facilitate reproducible research, we provide a comprehensive overview of the dataset, challenge rules, evaluation metrics, and baseline systems.
-
-.. toctree::
- :maxdepth: 1
- :caption: Contents:
-
- ./Introduction
- ./Dataset
- ./Track_setting_and_evaluation
- ./Baseline
- ./Rules
- ./Organizers
- ./Contact
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
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+++ /dev/null
@@ -1 +0,0 @@
-Search.setIndex({"docnames": ["index", "\u57fa\u7ebf", "\u6570\u636e\u96c6", "\u7b80\u4ecb", "\u7ec4\u59d4\u4f1a", "\u8054\u7cfb\u65b9\u5f0f", "\u89c4\u5219", "\u8d5b\u9053\u8bbe\u7f6e\u4e0e\u8bc4\u4f30"], "filenames": ["index.rst", "\u57fa\u7ebf.md", "\u6570\u636e\u96c6.md", "\u7b80\u4ecb.md", "\u7ec4\u59d4\u4f1a.md", "\u8054\u7cfb\u65b9\u5f0f.md", "\u89c4\u5219.md", "\u8d5b\u9053\u8bbe\u7f6e\u4e0e\u8bc4\u4f30.md"], "titles": ["ASRU 2023 \u591a\u901a\u9053\u591a\u65b9\u4f1a\u8bae\u8f6c\u5f55\u6311\u6218 2.0", "\u57fa\u7ebf", "\u6570\u636e\u96c6", "\u7b80\u4ecb", "\u7ec4\u59d4\u4f1a", "\u8054\u7cfb\u65b9\u5f0f", "\u7ade\u8d5b\u89c4\u5219", "\u8d5b\u9053\u8bbe\u7f6e\u4e0e\u8bc4\u4f30"], "terms": {"m2met": [0, 3, 5, 7], "asru2023": [0, 3], "m2met2": [0, 3, 5, 7], "contact": [], "funasr": 1, "sa": 1, "asr": [1, 3, 7], "speakerencod": 1, "modelscop": [1, 7], "todo": 1, "fill": 1, "with": 1, "the": 1, "readm": 1, "md": 1, "of": 1, "baselin": [1, 2], "aishel": [2, 7], "cn": [2, 4, 7], "celeb": [2, 7], "test": [2, 6, 7], "2023": [2, 3, 6, 7], "118": 2, "75": 2, "104": 2, "train": 2, "eval": [2, 6], "10": [2, 3, 7], "212": 2, "15": 2, "30": 2, "456": 2, "25": 2, "13": [2, 3], "55": 2, "42": 2, "27": 2, "34": 2, "76": 2, "20": 2, "textgrid": 2, "id": 2, "openslr": 2, "automat": 3, "speech": 3, "recognit": 3, "speaker": 3, "diariz": 3, "rich": 3, "transcript": 3, "evalu": 3, "chime": 3, "comput": 3, "hear": 3, "in": 3, "multisourc": 3, "environ": 3, "misp": 3, "multimod": 3, "inform": 3, "base": 3, "process": 3, "multi": 3, "channel": 3, "parti": 3, "meet": 3, "assp2022": 3, "19": 3, "12": 3, "asru": 3, "workshop": 3, "lxie": 4, "nwpu": 4, "edu": 4, "kong": 4, "aik": 4, "lee": 4, "star": 4, "kongaik": 4, "ieee": 4, "org": 4, "zhiji": 4, "yzj": 4, "alibaba": 4, "inc": 4, "com": [4, 5], "sli": 4, "zsl": 4, "yanminqian": 4, "sjtu": 4, "zhuc": 4, "microsoft": 4, "wujian": 4, "ceo": 4, "buhui": 4, "aishelldata": 4, "alimeet": [5, 7], "gmail": 5, "cpcer": [6, 7], "las": 6, "rnnt": 6, "transform": 6, "aishell4": 7, "vad": 7, "cer": 7, "ins": 7, "sub": 7, "del": 7, "text": 7, "frac": 7, "mathcal": 7, "n_": 7, "total": 7, "time": 7, "100": 7, "hug": 7, "face": 7}, "objects": {}, "objtypes": {}, "objnames": {}, "titleterms": {"asru": 0, "2023": 0, "indic": 0, "and": 0, "tabl": 0, "alimeet": 2, "aoe": 3, "contact": []}, "envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 8, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx": 57}, "alltitles": {"ASRU 2023 \u591a\u901a\u9053\u591a\u65b9\u4f1a\u8bae\u8f6c\u5f55\u6311\u6218 2.0": [[0, "asru-2023-2-0"]], "\u76ee\u5f55:": [[0, null]], "Indices and tables": [[0, "indices-and-tables"]], "\u57fa\u7ebf": [[1, "id1"]], "\u57fa\u7ebf\u6982\u8ff0": [[1, "id2"]], "\u5feb\u901f\u5f00\u59cb": [[1, "id3"]], "\u57fa\u7ebf\u7ed3\u679c": [[1, "id4"]], "\u6570\u636e\u96c6": [[2, "id1"]], "\u6570\u636e\u96c6\u6982\u8ff0": [[2, "id2"]], "Alimeeting\u6570\u636e\u96c6\u4ecb\u7ecd": [[2, "alimeeting"]], "\u83b7\u53d6\u6570\u636e": [[2, "id3"]], "\u7b80\u4ecb": [[3, "id1"]], "\u7ade\u8d5b\u4ecb\u7ecd": [[3, "id2"]], "\u65f6\u95f4\u5b89\u6392(AOE\u65f6\u95f4)": [[3, "aoe"]], "\u7ade\u8d5b\u62a5\u540d": [[3, "id3"]], "\u7ade\u8d5b\u89c4\u5219": [[6, "id1"]], "\u8d5b\u9053\u8bbe\u7f6e\u4e0e\u8bc4\u4f30": [[7, "id1"]], "\u8bf4\u8bdd\u4eba\u76f8\u5173\u7684\u8bed\u97f3\u8bc6\u522b (\u4e3b\u8d5b\u9053)": [[7, "id2"]], "\u8bc4\u4f30\u65b9\u6cd5": [[7, "id3"]], "\u5b50\u8d5b\u9053\u8bbe\u7f6e": [[7, "id4"]], "\u5b50\u8d5b\u9053\u4e00 (\u9650\u5b9a\u8bad\u7ec3\u6570\u636e):": [[7, "id5"]], "\u5b50\u8d5b\u9053\u4e8c (\u5f00\u653e\u8bad\u7ec3\u6570\u636e):": [[7, "id6"]], "\u7ec4\u59d4\u4f1a": [[4, "id1"]], "\u8054\u7cfb\u65b9\u5f0f": [[5, "id1"]]}, "indexentries": {}})
\ No newline at end of file
diff --git a/egs_modelscope/asr/TEMPLATE/README.md b/egs_modelscope/asr/TEMPLATE/README.md
index a064302..83c462d 100644
--- a/egs_modelscope/asr/TEMPLATE/README.md
+++ b/egs_modelscope/asr/TEMPLATE/README.md
@@ -58,6 +58,22 @@
#### [RNN-T-online model]()
Undo
+#### [MFCCA Model](https://www.modelscope.cn/models/NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/summary)
+For more model detailes, please refer to [docs](https://www.modelscope.cn/models/NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/summary)
+```python
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+inference_pipeline = pipeline(
+ task=Tasks.auto_speech_recognition,
+ model='NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950',
+ model_revision='v3.0.0'
+)
+
+rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
+print(rec_result)
+```
+
#### API-reference
##### Define pipeline
- `task`: `Tasks.auto_speech_recognition`
@@ -94,6 +110,8 @@
- `njob`: only used for CPU inference (`gpu_inference`=`false`), `64` (Default), the number of jobs for CPU decoding
- `checkpoint_dir`: only used for infer finetuned models, the path dir of finetuned models
- `checkpoint_name`: only used for infer finetuned models, `valid.cer_ctc.ave.pb` (Default), which checkpoint is used to infer
+ - `decoding_mode`: `normal` (Default), decoding mode for UniASR model(fast銆乶ormal銆乷ffline)
+ - `hotword_txt`: `None` (Default), hotword file for contextual paraformer model(the hotword file name ends with .txt")
- Decode with multi GPUs:
```shell
diff --git a/egs_modelscope/asr/TEMPLATE/infer.py b/egs_modelscope/asr/TEMPLATE/infer.py
index 9f280d5..5bc205c 100644
--- a/egs_modelscope/asr/TEMPLATE/infer.py
+++ b/egs_modelscope/asr/TEMPLATE/infer.py
@@ -11,6 +11,7 @@
model=args.model,
output_dir=args.output_dir,
batch_size=args.batch_size,
+ param_dict={"decoding_model": args.decoding_mode, "hotword": args.hotword_txt}
)
inference_pipeline(audio_in=args.audio_in)
@@ -19,7 +20,9 @@
parser.add_argument('--model', type=str, default="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch")
parser.add_argument('--audio_in', type=str, default="./data/test/wav.scp")
parser.add_argument('--output_dir', type=str, default="./results/")
+ parser.add_argument('--decoding_mode', type=str, default="normal")
+ parser.add_argument('--hotword_txt', type=str, default=None)
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--gpuid', type=str, default="0")
args = parser.parse_args()
- modelscope_infer(args)
\ No newline at end of file
+ modelscope_infer(args)
diff --git a/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/README.md b/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/README.md
deleted file mode 100644
index 16aeada..0000000
--- a/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/README.md
+++ /dev/null
@@ -1,53 +0,0 @@
-# ModelScope Model
-
-## How to finetune and infer using a pretrained Paraformer-large Model
-
-### Finetune
-
-- Modify finetune training related parameters in `finetune.py`
- - <strong>output_dir:</strong> # result dir
- - <strong>data_dir:</strong> # the dataset dir needs to include files: `train/wav.scp`, `train/text`; `validation/wav.scp`, `validation/text`
- - <strong>dataset_type:</strong> # for dataset larger than 1000 hours, set as `large`, otherwise set as `small`
- - <strong>batch_bins:</strong> # batch size. For dataset_type is `small`, `batch_bins` indicates the feature frames. For dataset_type is `large`, `batch_bins` indicates the duration in ms
- - <strong>max_epoch:</strong> # number of training epoch
- - <strong>lr:</strong> # learning rate
-
-- Then you can run the pipeline to finetune with:
-```python
- python finetune.py
-```
-
-### Inference
-
-Or you can use the finetuned model for inference directly.
-
-- Setting parameters in `infer.py`
- - <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed
- - <strong>output_dir:</strong> # result dir
- - <strong>ngpu:</strong> # the number of GPUs for decoding
- - <strong>njob:</strong> # the number of jobs for each GPU
-
-- Then you can run the pipeline to infer with:
-```python
- python infer.py
-```
-
-- Results
-
-The decoding results can be found in `$output_dir/1best_recog/text.sp.cer` and `$output_dir/1best_recog/text.nosp.cer`, which includes recognition results with or without separating character (src) of each sample and the CER metric of the whole test set.
-
-### Inference using local finetuned model
-
-- Modify inference related parameters in `infer_after_finetune.py`
- - <strong>output_dir:</strong> # result dir
- - <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed
- - <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pb`
-
-- Then you can run the pipeline to finetune with:
-```python
- python infer_after_finetune.py
-```
-
-- Results
-
-The decoding results can be found in `$output_dir/1best_recog/text.sp.cer` and `$output_dir/1best_recog/text.nosp.cer`, which includes recognition results with or without separating character (src) of each sample and the CER metric of the whole test set.
diff --git a/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/README.md b/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/README.md
new file mode 120000
index 0000000..bb55ab5
--- /dev/null
+++ b/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/README.md
@@ -0,0 +1 @@
+../../TEMPLATE/README.md
\ No newline at end of file
diff --git a/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/infer.py b/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/infer.py
index 8abadd7..12ec2ac 100755
--- a/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/infer.py
+++ b/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/infer.py
@@ -1,102 +1,27 @@
import os
import shutil
-from multiprocessing import Pool
-
+import argparse
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
-from funasr.utils.compute_wer import compute_wer
-
-def modelscope_infer_core(output_dir, split_dir, njob, idx):
- output_dir_job = os.path.join(output_dir, "output.{}".format(idx))
- gpu_id = (int(idx) - 1) // njob
- if "CUDA_VISIBLE_DEVICES" in os.environ.keys():
- gpu_list = os.environ['CUDA_VISIBLE_DEVICES'].split(",")
- os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_list[gpu_id])
- else:
- os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)
- inference_pipline = pipeline(
+def modelscope_infer(args):
+ os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpuid)
+ inference_pipeline = pipeline(
task=Tasks.auto_speech_recognition,
- model='NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950',
- model_revision='v3.0.0',
- output_dir=output_dir_job,
- batch_size=1,
+ model=args.model,
+ model_revision=args.model_revision,
+ output_dir=args.output_dir,
+ batch_size=args.batch_size,
)
- audio_in = os.path.join(split_dir, "wav.{}.scp".format(idx))
- inference_pipline(audio_in=audio_in)
-
-
-def modelscope_infer(params):
- # prepare for multi-GPU decoding
- ngpu = params["ngpu"]
- njob = params["njob"]
- output_dir = params["output_dir"]
- if os.path.exists(output_dir):
- shutil.rmtree(output_dir)
- os.mkdir(output_dir)
- split_dir = os.path.join(output_dir, "split")
- os.mkdir(split_dir)
- nj = ngpu * njob
- wav_scp_file = os.path.join(params["data_dir"], "wav.scp")
- with open(wav_scp_file) as f:
- lines = f.readlines()
- num_lines = len(lines)
- num_job_lines = num_lines // nj
- start = 0
- for i in range(nj):
- end = start + num_job_lines
- file = os.path.join(split_dir, "wav.{}.scp".format(str(i + 1)))
- with open(file, "w") as f:
- if i == nj - 1:
- f.writelines(lines[start:])
- else:
- f.writelines(lines[start:end])
- start = end
- p = Pool(nj)
- for i in range(nj):
- p.apply_async(modelscope_infer_core,
- args=(output_dir, split_dir, njob, str(i + 1)))
- p.close()
- p.join()
-
- # combine decoding results
- best_recog_path = os.path.join(output_dir, "1best_recog")
- os.mkdir(best_recog_path)
- files = ["text", "token", "score"]
- for file in files:
- with open(os.path.join(best_recog_path, file), "w") as f:
- for i in range(nj):
- job_file = os.path.join(output_dir, "output.{}/1best_recog".format(str(i + 1)), file)
- with open(job_file) as f_job:
- lines = f_job.readlines()
- f.writelines(lines)
-
- # If text exists, compute CER
- text_in = os.path.join(params["data_dir"], "text")
- if os.path.exists(text_in):
- text_proc_file = os.path.join(best_recog_path, "token")
- text_proc_file2 = os.path.join(best_recog_path, "token_nosep")
- with open(text_proc_file, 'r') as hyp_reader:
- with open(text_proc_file2, 'w') as hyp_writer:
- for line in hyp_reader:
- new_context = line.strip().replace("src","").replace(" "," ").replace(" "," ").strip()
- hyp_writer.write(new_context+'\n')
- text_in2 = os.path.join(best_recog_path, "ref_text_nosep")
- with open(text_in, 'r') as ref_reader:
- with open(text_in2, 'w') as ref_writer:
- for line in ref_reader:
- new_context = line.strip().replace("src","").replace(" "," ").replace(" "," ").strip()
- ref_writer.write(new_context+'\n')
-
-
- compute_wer(text_in, text_proc_file, os.path.join(best_recog_path, "text.sp.cer"))
- compute_wer(text_in2, text_proc_file2, os.path.join(best_recog_path, "text.nosp.cer"))
-
+ inference_pipeline(audio_in=args.audio_in)
if __name__ == "__main__":
- params = {}
- params["data_dir"] = "./example_data/validation"
- params["output_dir"] = "./output_dir"
- params["ngpu"] = 1
- params["njob"] = 1
- modelscope_infer(params)
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--model', type=str, default="NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950")
+ parser.add_argument('--model_revision', type=str, default="v3.0.0")
+ parser.add_argument('--audio_in', type=str, default="./data/test/wav.scp")
+ parser.add_argument('--output_dir', type=str, default="./results/")
+ parser.add_argument('--batch_size', type=int, default=1)
+ parser.add_argument('--gpuid', type=str, default="0")
+ args = parser.parse_args()
+ modelscope_infer(args)
diff --git a/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/infer.sh b/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/infer.sh
new file mode 100755
index 0000000..51a4968
--- /dev/null
+++ b/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/infer.sh
@@ -0,0 +1,70 @@
+#!/usr/bin/env bash
+
+set -e
+set -u
+set -o pipefail
+
+stage=1
+stop_stage=3
+model="NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950"
+data_dir="./data/test"
+output_dir="./results_pl_gpu"
+batch_size=1
+gpu_inference=true # whether to perform gpu decoding
+gpuid_list="3,4" # set gpus, e.g., gpuid_list="0,1"
+njob=4 # the number of jobs for CPU decoding, if gpu_inference=false, use CPU decoding, please set njob
+
+. utils/parse_options.sh || exit 1;
+
+if ${gpu_inference} == "true"; then
+ nj=$(echo $gpuid_list | awk -F "," '{print NF}')
+else
+ nj=$njob
+ batch_size=1
+ gpuid_list=""
+ for JOB in $(seq ${nj}); do
+ gpuid_list=$gpuid_list"-1,"
+ done
+fi
+
+mkdir -p $output_dir/split
+split_scps=""
+for JOB in $(seq ${nj}); do
+ split_scps="$split_scps $output_dir/split/wav.$JOB.scp"
+done
+perl utils/split_scp.pl ${data_dir}/wav.scp ${split_scps}
+
+if [ $stage -le 1 ] && [ $stop_stage -ge 1 ];then
+ echo "Decoding ..."
+ gpuid_list_array=(${gpuid_list//,/ })
+ ./utils/run.pl JOB=1:${nj} ${output_dir}/log/infer.JOB.log \
+ python infer.py \
+ --model ${model} \
+ --audio_in ${output_dir}/split/wav.JOB.scp \
+ --output_dir ${output_dir}/output.JOB \
+ --batch_size ${batch_size} \
+ --gpuid ${gpuid_list_array[JOB-1]}
+
+ mkdir -p ${output_dir}/1best_recog
+ for f in token score text; do
+ if [ -f "${output_dir}/output.1/1best_recog/${f}" ]; then
+ for i in $(seq "${nj}"); do
+ cat "${output_dir}/output.${i}/1best_recog/${f}"
+ done | sort -k1 >"${output_dir}/1best_recog/${f}"
+ fi
+ done
+fi
+
+if [ $stage -le 2 ] && [ $stop_stage -ge 2 ];then
+ echo "Computing WER ..."
+ cp ${output_dir}/1best_recog/token ${output_dir}/1best_recog/text.proc
+ cp ${data_dir}/text ${output_dir}/1best_recog/text.ref
+ sed -e 's/src//g' ${output_dir}/1best_recog/text.proc | sed -e 's/ \+/ /g' > ${output_dir}/1best_recog/text_nosp.proc
+ sed -e 's/src//g' ${output_dir}/1best_recog/text.ref | sed -e 's/ \+/ /g' > ${output_dir}/1best_recog/text_nosp.ref
+
+ python utils/compute_wer.py ${output_dir}/1best_recog/text.ref ${output_dir}/1best_recog/text.proc ${output_dir}/1best_recog/text.sp.cer
+ tail -n 3 ${output_dir}/1best_recog/text.sp.cer
+ python utils/compute_wer.py ${output_dir}/1best_recog/text_nosp.ref ${output_dir}/1best_recog/text_nosp.proc ${output_dir}/1best_recog/text.nosp.cer
+ tail -n 3 ${output_dir}/1best_recog/text.nosp.cer
+fi
+
diff --git a/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/utils b/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/utils
new file mode 120000
index 0000000..2ac163f
--- /dev/null
+++ b/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/utils
@@ -0,0 +1 @@
+../../../../egs/aishell/transformer/utils
\ No newline at end of file
diff --git a/egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/demo.py b/egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/demo.py
new file mode 100644
index 0000000..bec6f05
--- /dev/null
+++ b/egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/demo.py
@@ -0,0 +1,12 @@
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+param_dict = dict()
+param_dict['hotword'] = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/hotword.txt"
+inference_pipeline = pipeline(
+ task=Tasks.auto_speech_recognition,
+ model="damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404",
+ param_dict=param_dict)
+
+rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_hotword.wav')
+print(rec_result)
diff --git a/egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/infer.py b/egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/infer.py
deleted file mode 100644
index 16a57e9..0000000
--- a/egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/infer.py
+++ /dev/null
@@ -1,21 +0,0 @@
-from modelscope.pipelines import pipeline
-from modelscope.utils.constant import Tasks
-
-
-if __name__ == '__main__':
- param_dict = dict()
- param_dict['hotword'] = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/hotword.txt"
-
- audio_in = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_hotword.wav"
- output_dir = None
- batch_size = 1
-
- inference_pipeline = pipeline(
- task=Tasks.auto_speech_recognition,
- model="damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404",
- output_dir=output_dir,
- batch_size=batch_size,
- param_dict=param_dict)
-
- rec_result = inference_pipeline(audio_in=audio_in)
- print(rec_result)
diff --git a/egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/infer.py b/egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/infer.py
new file mode 120000
index 0000000..128fc31
--- /dev/null
+++ b/egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/infer.py
@@ -0,0 +1 @@
+../../TEMPLATE/infer.py
\ No newline at end of file
diff --git a/egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/infer.sh b/egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/infer.sh
new file mode 100644
index 0000000..e60f6d9
--- /dev/null
+++ b/egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/infer.sh
@@ -0,0 +1,105 @@
+#!/usr/bin/env bash
+
+set -e
+set -u
+set -o pipefail
+
+stage=1
+stop_stage=2
+model="damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404"
+data_dir="./data/test"
+output_dir="./results"
+batch_size=64
+gpu_inference=true # whether to perform gpu decoding
+gpuid_list="0,1" # set gpus, e.g., gpuid_list="0,1"
+njob=64 # the number of jobs for CPU decoding, if gpu_inference=false, use CPU decoding, please set njob
+checkpoint_dir=
+checkpoint_name="valid.cer_ctc.ave.pb"
+hotword_txt=None
+
+. utils/parse_options.sh || exit 1;
+
+if ${gpu_inference} == "true"; then
+ nj=$(echo $gpuid_list | awk -F "," '{print NF}')
+else
+ nj=$njob
+ batch_size=1
+ gpuid_list=""
+ for JOB in $(seq ${nj}); do
+ gpuid_list=$gpuid_list"-1,"
+ done
+fi
+
+mkdir -p $output_dir/split
+split_scps=""
+for JOB in $(seq ${nj}); do
+ split_scps="$split_scps $output_dir/split/wav.$JOB.scp"
+done
+perl utils/split_scp.pl ${data_dir}/wav.scp ${split_scps}
+
+if [ -n "${checkpoint_dir}" ]; then
+ python utils/prepare_checkpoint.py ${model} ${checkpoint_dir} ${checkpoint_name}
+ model=${checkpoint_dir}/${model}
+fi
+
+if [ $stage -le 1 ] && [ $stop_stage -ge 1 ];then
+ echo "Decoding ..."
+ gpuid_list_array=(${gpuid_list//,/ })
+ for JOB in $(seq ${nj}); do
+ {
+ id=$((JOB-1))
+ gpuid=${gpuid_list_array[$id]}
+ mkdir -p ${output_dir}/output.$JOB
+ python infer.py \
+ --model ${model} \
+ --audio_in ${output_dir}/split/wav.$JOB.scp \
+ --output_dir ${output_dir}/output.$JOB \
+ --batch_size ${batch_size} \
+ --gpuid ${gpuid} \
+ --hotword_txt ${hotword_txt}
+ }&
+ done
+ wait
+
+ mkdir -p ${output_dir}/1best_recog
+ for f in token score text; do
+ if [ -f "${output_dir}/output.1/1best_recog/${f}" ]; then
+ for i in $(seq "${nj}"); do
+ cat "${output_dir}/output.${i}/1best_recog/${f}"
+ done | sort -k1 >"${output_dir}/1best_recog/${f}"
+ fi
+ done
+fi
+
+if [ $stage -le 2 ] && [ $stop_stage -ge 2 ];then
+ echo "Computing WER ..."
+ cp ${output_dir}/1best_recog/text ${output_dir}/1best_recog/text.proc
+ cp ${data_dir}/text ${output_dir}/1best_recog/text.ref
+ python utils/compute_wer.py ${output_dir}/1best_recog/text.ref ${output_dir}/1best_recog/text.proc ${output_dir}/1best_recog/text.cer
+ tail -n 3 ${output_dir}/1best_recog/text.cer
+fi
+
+if [ $stage -le 3 ] && [ $stop_stage -ge 3 ];then
+ echo "SpeechIO TIOBE textnorm"
+ echo "$0 --> Normalizing REF text ..."
+ ./utils/textnorm_zh.py \
+ --has_key --to_upper \
+ ${data_dir}/text \
+ ${output_dir}/1best_recog/ref.txt
+
+ echo "$0 --> Normalizing HYP text ..."
+ ./utils/textnorm_zh.py \
+ --has_key --to_upper \
+ ${output_dir}/1best_recog/text.proc \
+ ${output_dir}/1best_recog/rec.txt
+ grep -v $'\t$' ${output_dir}/1best_recog/rec.txt > ${output_dir}/1best_recog/rec_non_empty.txt
+
+ echo "$0 --> computing WER/CER and alignment ..."
+ ./utils/error_rate_zh \
+ --tokenizer char \
+ --ref ${output_dir}/1best_recog/ref.txt \
+ --hyp ${output_dir}/1best_recog/rec_non_empty.txt \
+ ${output_dir}/1best_recog/DETAILS.txt | tee ${output_dir}/1best_recog/RESULTS.txt
+ rm -rf ${output_dir}/1best_recog/rec.txt ${output_dir}/1best_recog/rec_non_empty.txt
+fi
+
diff --git a/egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/utils b/egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/utils
new file mode 120000
index 0000000..2ac163f
--- /dev/null
+++ b/egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/utils
@@ -0,0 +1 @@
+../../../../egs/aishell/transformer/utils
\ No newline at end of file
diff --git a/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/demo.py b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/demo.py
new file mode 100644
index 0000000..edc3a05
--- /dev/null
+++ b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/demo.py
@@ -0,0 +1,9 @@
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+inference_pipeline = pipeline(
+ task=Tasks.auto_speech_recognition,
+ model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch')
+
+rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
+print(rec_result)
diff --git a/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer.py b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer.py
deleted file mode 100644
index 9f280d5..0000000
--- a/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import os
-import shutil
-import argparse
-from modelscope.pipelines import pipeline
-from modelscope.utils.constant import Tasks
-
-def modelscope_infer(args):
- os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpuid)
- inference_pipeline = pipeline(
- task=Tasks.auto_speech_recognition,
- model=args.model,
- output_dir=args.output_dir,
- batch_size=args.batch_size,
- )
- inference_pipeline(audio_in=args.audio_in)
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument('--model', type=str, default="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch")
- parser.add_argument('--audio_in', type=str, default="./data/test/wav.scp")
- parser.add_argument('--output_dir', type=str, default="./results/")
- parser.add_argument('--batch_size', type=int, default=64)
- parser.add_argument('--gpuid', type=str, default="0")
- args = parser.parse_args()
- modelscope_infer(args)
\ No newline at end of file
diff --git a/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer.py b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer.py
new file mode 120000
index 0000000..128fc31
--- /dev/null
+++ b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer.py
@@ -0,0 +1 @@
+../../TEMPLATE/infer.py
\ No newline at end of file
diff --git a/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer.sh b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer.sh
index b8b011c..ef49d7a 100644
--- a/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer.sh
+++ b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer.sh
@@ -12,7 +12,9 @@
batch_size=64
gpu_inference=true # whether to perform gpu decoding
gpuid_list="0,1" # set gpus, e.g., gpuid_list="0,1"
-njob=4 # the number of jobs for CPU decoding, if gpu_inference=false, use CPU decoding, please set njob
+njob=64 # the number of jobs for CPU decoding, if gpu_inference=false, use CPU decoding, please set njob
+checkpoint_dir=
+checkpoint_name="valid.cer_ctc.ave.pb"
. utils/parse_options.sh || exit 1;
@@ -34,6 +36,11 @@
done
perl utils/split_scp.pl ${data_dir}/wav.scp ${split_scps}
+if [ -n "${checkpoint_dir}" ]; then
+ python utils/prepare_checkpoint.py ${model} ${checkpoint_dir} ${checkpoint_name}
+ model=${checkpoint_dir}/${model}
+fi
+
if [ $stage -le 1 ] && [ $stop_stage -ge 1 ];then
echo "Decoding ..."
gpuid_list_array=(${gpuid_list//,/ })
diff --git a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/demo.py b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/demo.py
new file mode 100644
index 0000000..570f910
--- /dev/null
+++ b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/demo.py
@@ -0,0 +1,12 @@
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+decoding_mode="normal" #fast, normal, offline
+inference_pipeline = pipeline(
+ task=Tasks.auto_speech_recognition,
+ model='damo/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825',
+ param_dict={"decoding_model": decoding_mode}
+)
+
+rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
+print(rec_result)
diff --git a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/infer.py b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/infer.py
deleted file mode 100644
index d28395b..0000000
--- a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/infer.py
+++ /dev/null
@@ -1,89 +0,0 @@
-import os
-import shutil
-from multiprocessing import Pool
-
-from modelscope.pipelines import pipeline
-from modelscope.utils.constant import Tasks
-
-from funasr.utils.compute_wer import compute_wer
-
-
-def modelscope_infer_core(output_dir, split_dir, njob, idx):
- output_dir_job = os.path.join(output_dir, "output.{}".format(idx))
- gpu_id = (int(idx) - 1) // njob
- if "CUDA_VISIBLE_DEVICES" in os.environ.keys():
- gpu_list = os.environ['CUDA_VISIBLE_DEVICES'].split(",")
- os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_list[gpu_id])
- else:
- os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)
- inference_pipline = pipeline(
- task=Tasks.auto_speech_recognition,
- model="damo/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825",
- output_dir=output_dir_job,
- batch_size=1
- )
- audio_in = os.path.join(split_dir, "wav.{}.scp".format(idx))
- inference_pipline(audio_in=audio_in, param_dict={"decoding_model": "normal"})
-
-
-def modelscope_infer(params):
- # prepare for multi-GPU decoding
- ngpu = params["ngpu"]
- njob = params["njob"]
- output_dir = params["output_dir"]
- if os.path.exists(output_dir):
- shutil.rmtree(output_dir)
- os.mkdir(output_dir)
- split_dir = os.path.join(output_dir, "split")
- os.mkdir(split_dir)
- nj = ngpu * njob
- wav_scp_file = os.path.join(params["data_dir"], "wav.scp")
- with open(wav_scp_file) as f:
- lines = f.readlines()
- num_lines = len(lines)
- num_job_lines = num_lines // nj
- start = 0
- for i in range(nj):
- end = start + num_job_lines
- file = os.path.join(split_dir, "wav.{}.scp".format(str(i + 1)))
- with open(file, "w") as f:
- if i == nj - 1:
- f.writelines(lines[start:])
- else:
- f.writelines(lines[start:end])
- start = end
-
- p = Pool(nj)
- for i in range(nj):
- p.apply_async(modelscope_infer_core,
- args=(output_dir, split_dir, njob, str(i + 1)))
- p.close()
- p.join()
-
- # combine decoding results
- best_recog_path = os.path.join(output_dir, "1best_recog")
- os.mkdir(best_recog_path)
- files = ["text", "token", "score"]
- for file in files:
- with open(os.path.join(best_recog_path, file), "w") as f:
- for i in range(nj):
- job_file = os.path.join(output_dir, "output.{}/1best_recog".format(str(i + 1)), file)
- with open(job_file) as f_job:
- lines = f_job.readlines()
- f.writelines(lines)
-
- # If text exists, compute CER
- text_in = os.path.join(params["data_dir"], "text")
- if os.path.exists(text_in):
- text_proc_file = os.path.join(best_recog_path, "token")
- compute_wer(text_in, text_proc_file, os.path.join(best_recog_path, "text.cer"))
- os.system("tail -n 3 {}".format(os.path.join(best_recog_path, "text.cer")))
-
-
-if __name__ == "__main__":
- params = {}
- params["data_dir"] = "./data/test"
- params["output_dir"] = "./results"
- params["ngpu"] = 1
- params["njob"] = 8
- modelscope_infer(params)
diff --git a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/infer.py b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/infer.py
new file mode 120000
index 0000000..128fc31
--- /dev/null
+++ b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/infer.py
@@ -0,0 +1 @@
+../../TEMPLATE/infer.py
\ No newline at end of file
diff --git a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/infer.sh b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/infer.sh
new file mode 100644
index 0000000..b44be9f
--- /dev/null
+++ b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/infer.sh
@@ -0,0 +1,105 @@
+#!/usr/bin/env bash
+
+set -e
+set -u
+set -o pipefail
+
+stage=1
+stop_stage=2
+model="damo/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825"
+data_dir="./data/test"
+output_dir="./results"
+batch_size=1
+gpu_inference=true # whether to perform gpu decoding
+gpuid_list="0,1" # set gpus, e.g., gpuid_list="0,1"
+njob=64 # the number of jobs for CPU decoding, if gpu_inference=false, use CPU decoding, please set njob
+checkpoint_dir=
+checkpoint_name="valid.cer_ctc.ave.pb"
+decoding_mode="normal"
+
+. utils/parse_options.sh || exit 1;
+
+if ${gpu_inference} == "true"; then
+ nj=$(echo $gpuid_list | awk -F "," '{print NF}')
+else
+ nj=$njob
+ batch_size=1
+ gpuid_list=""
+ for JOB in $(seq ${nj}); do
+ gpuid_list=$gpuid_list"-1,"
+ done
+fi
+
+mkdir -p $output_dir/split
+split_scps=""
+for JOB in $(seq ${nj}); do
+ split_scps="$split_scps $output_dir/split/wav.$JOB.scp"
+done
+perl utils/split_scp.pl ${data_dir}/wav.scp ${split_scps}
+
+if [ -n "${checkpoint_dir}" ]; then
+ python utils/prepare_checkpoint.py ${model} ${checkpoint_dir} ${checkpoint_name}
+ model=${checkpoint_dir}/${model}
+fi
+
+if [ $stage -le 1 ] && [ $stop_stage -ge 1 ];then
+ echo "Decoding ..."
+ gpuid_list_array=(${gpuid_list//,/ })
+ for JOB in $(seq ${nj}); do
+ {
+ id=$((JOB-1))
+ gpuid=${gpuid_list_array[$id]}
+ mkdir -p ${output_dir}/output.$JOB
+ python infer.py \
+ --model ${model} \
+ --audio_in ${output_dir}/split/wav.$JOB.scp \
+ --output_dir ${output_dir}/output.$JOB \
+ --batch_size ${batch_size} \
+ --gpuid ${gpuid} \
+ --decoding_mode ${decoding_mode}
+ }&
+ done
+ wait
+
+ mkdir -p ${output_dir}/1best_recog
+ for f in token score text; do
+ if [ -f "${output_dir}/output.1/1best_recog/${f}" ]; then
+ for i in $(seq "${nj}"); do
+ cat "${output_dir}/output.${i}/1best_recog/${f}"
+ done | sort -k1 >"${output_dir}/1best_recog/${f}"
+ fi
+ done
+fi
+
+if [ $stage -le 2 ] && [ $stop_stage -ge 2 ];then
+ echo "Computing WER ..."
+ cp ${output_dir}/1best_recog/text ${output_dir}/1best_recog/text.proc
+ cp ${data_dir}/text ${output_dir}/1best_recog/text.ref
+ python utils/compute_wer.py ${output_dir}/1best_recog/text.ref ${output_dir}/1best_recog/text.proc ${output_dir}/1best_recog/text.cer
+ tail -n 3 ${output_dir}/1best_recog/text.cer
+fi
+
+if [ $stage -le 3 ] && [ $stop_stage -ge 3 ];then
+ echo "SpeechIO TIOBE textnorm"
+ echo "$0 --> Normalizing REF text ..."
+ ./utils/textnorm_zh.py \
+ --has_key --to_upper \
+ ${data_dir}/text \
+ ${output_dir}/1best_recog/ref.txt
+
+ echo "$0 --> Normalizing HYP text ..."
+ ./utils/textnorm_zh.py \
+ --has_key --to_upper \
+ ${output_dir}/1best_recog/text.proc \
+ ${output_dir}/1best_recog/rec.txt
+ grep -v $'\t$' ${output_dir}/1best_recog/rec.txt > ${output_dir}/1best_recog/rec_non_empty.txt
+
+ echo "$0 --> computing WER/CER and alignment ..."
+ ./utils/error_rate_zh \
+ --tokenizer char \
+ --ref ${output_dir}/1best_recog/ref.txt \
+ --hyp ${output_dir}/1best_recog/rec_non_empty.txt \
+ ${output_dir}/1best_recog/DETAILS.txt | tee ${output_dir}/1best_recog/RESULTS.txt
+ rm -rf ${output_dir}/1best_recog/rec.txt ${output_dir}/1best_recog/rec_non_empty.txt
+fi
+
diff --git a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/utils b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/utils
new file mode 120000
index 0000000..2ac163f
--- /dev/null
+++ b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/utils
@@ -0,0 +1 @@
+../../../../egs/aishell/transformer/utils
\ No newline at end of file
diff --git a/funasr/datasets/large_datasets/utils/tokenize.py b/funasr/datasets/large_datasets/utils/tokenize.py
index 022d321..0d2fd84 100644
--- a/funasr/datasets/large_datasets/utils/tokenize.py
+++ b/funasr/datasets/large_datasets/utils/tokenize.py
@@ -17,13 +17,21 @@
return word_list
def seg_tokenize(txt, seg_dict):
+ pattern = re.compile(r'^[\u4E00-\u9FA50-9]+$')
out_txt = ""
for word in txt:
word = word.lower()
if word in seg_dict:
out_txt += seg_dict[word] + " "
else:
- out_txt += "<unk>" + " "
+ if pattern.match(word):
+ for char in word:
+ if char in seg_dict:
+ out_txt += seg_dict[char] + " "
+ else:
+ out_txt += "<unk>" + " "
+ else:
+ out_txt += "<unk>" + " "
return out_txt.strip().split()
def tokenize(data,
diff --git a/funasr/datasets/preprocessor.py b/funasr/datasets/preprocessor.py
index 20a3831..758c750 100644
--- a/funasr/datasets/preprocessor.py
+++ b/funasr/datasets/preprocessor.py
@@ -44,15 +44,22 @@
i += len(longest_word)
return word_list
-
def seg_tokenize(txt, seg_dict):
+ pattern = re.compile(r'^[\u4E00-\u9FA50-9]+$')
out_txt = ""
for word in txt:
word = word.lower()
if word in seg_dict:
out_txt += seg_dict[word] + " "
else:
- out_txt += "<unk>" + " "
+ if pattern.match(word):
+ for char in word:
+ if char in seg_dict:
+ out_txt += seg_dict[char] + " "
+ else:
+ out_txt += "<unk>" + " "
+ else:
+ out_txt += "<unk>" + " "
return out_txt.strip().split()
def seg_tokenize_wo_pattern(txt, seg_dict):
--
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