From 580b11b57ac4b62f7e2acda73813a4e10e8e4cd3 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 10 十月 2023 17:17:29 +0800
Subject: [PATCH] v0.8.0
---
README.md | 24 ++++++++++++++++--------
1 files changed, 16 insertions(+), 8 deletions(-)
diff --git a/README.md b/README.md
index 8abe03a..0a8ee83 100644
--- a/README.md
+++ b/README.md
@@ -28,9 +28,11 @@
<a name="whats-new"></a>
## What's new:
-
+- 2023/10/07: [FunCodec](https://github.com/alibaba-damo-academy/FunCodec): A Fundamental, Reproducible and Integrable Open-source Toolkit for Neural Speech Codec.
+- 2023/09/01: The offline file transcription service 2.0 (CPU) of Mandarin has been released, with added support for ffmpeg, timestamp, and hotword models. For more details, please refer to ([Deployment documentation](funasr/runtime/docs/SDK_tutorial.md)).
+- 2023/08/07: The real-time transcription service (CPU) of Mandarin has been released. For more details, please refer to ([Deployment documentation](funasr/runtime/docs/SDK_tutorial_online.md)).
- 2023/07/17: BAT is released, which is a low-latency and low-memory-consumption RNN-T model. For more details, please refer to ([BAT](egs/aishell/bat)).
-- 2023/07/03: The CPU version of the Chinese offline file transcription service has been released. For more details, please refer to ([Deployment documentation](funasr/runtime/docs/SDK_tutorial.md)).
+- 2023/07/03: The offline file transcription service (CPU) of Mandarin has been released. For more details, please refer to ([Deployment documentation](funasr/runtime/docs/SDK_tutorial.md)).
- 2023/06/26: ASRU2023 Multi-Channel Multi-Party Meeting Transcription Challenge 2.0 completed the competition and announced the results. For more details, please refer to ([M2MeT2.0](https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html)).
@@ -46,7 +48,7 @@
<a name="quick-start"></a>
## Quick Start
-Quick start for new users锛圼tutorial](https://alibaba-damo-academy.github.io/FunASR/en/funasr/quick_start_zh.html)锛�
+Quick start for new users锛圼tutorial](https://alibaba-damo-academy.github.io/FunASR/en/funasr/quick_start.html)锛�
FunASR supports inference and fine-tuning of models trained on industrial datasets of tens of thousands of hours. For more details, please refer to ([modelscope_egs](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html)). It also supports training and fine-tuning of models on academic standard datasets. For more details, please refer to([egs](https://alibaba-damo-academy.github.io/FunASR/en/academic_recipe/asr_recipe.html)). The models include speech recognition (ASR), speech activity detection (VAD), punctuation recovery, language model, speaker verification, speaker separation, and multi-party conversation speech recognition. For a detailed list of models, please refer to the [Model Zoo](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md):
@@ -59,18 +61,18 @@
|DingTalk group | WeChat group |
|:---:|:-----------------------------------------------------:|
-|<div align="left"><img src="docs/images/dingding.jpg" width="250"/> | <img src="docs/images/wechat.png" width="232"/></div> |
+|<div align="left"><img src="docs/images/dingding.jpg" width="250"/> | <img src="docs/images/wechat.png" width="215"/></div> |
## Contributors
-| <div align="left"><img src="docs/images/damo.png" width="180"/> | <div align="left"><img src="docs/images/nwpu.png" width="260"/> | <img src="docs/images/China_Telecom.png" width="200"/> </div> | <img src="docs/images/RapidAI.png" width="200"/> </div> | <img src="docs/images/aihealthx.png" width="200"/> </div> |
-|:---------------------------------------------------------------:|:---------------------------------------------------------------:|:--------------------------------------------------------------:|:-------------------------------------------------------:|:-----------------------------------------------------------:|
+| <div align="left"><img src="docs/images/damo.png" width="180"/> | <div align="left"><img src="docs/images/nwpu.png" width="260"/> | <img src="docs/images/China_Telecom.png" width="200"/> </div> | <img src="docs/images/RapidAI.png" width="200"/> </div> | <img src="docs/images/aihealthx.png" width="200"/> </div> | <img src="docs/images/XVERSE.png" width="250"/> </div> |
+|:---------------------------------------------------------------:|:---------------------------------------------------------------:|:--------------------------------------------------------------:|:-------------------------------------------------------:|:-----------------------------------------------------------:|:------------------------------------------------------:|
-The contributors can be found in [contributors list]((./Acknowledge))
+The contributors can be found in [contributors list](./Acknowledge.md)
## License
This project is licensed under the [The MIT License](https://opensource.org/licenses/MIT). FunASR also contains various third-party components and some code modified from other repos under other open source licenses.
-The use of pretraining model is subject to [model licencs](./MODEL_LICENSE)
+The use of pretraining model is subject to [model license](./MODEL_LICENSE)
## Citations
@@ -87,6 +89,12 @@
year={2023},
booktitle={INTERSPEECH},
}
+@inproceedings{wang2023told,
+ author={Jiaming Wang and Zhihao Du and Shiliang Zhang},
+ title={{TOLD:} {A} Novel Two-Stage Overlap-Aware Framework for Speaker Diarization},
+ year={2023},
+ booktitle={ICASSP},
+}
@inproceedings{gao22b_interspeech,
author={Zhifu Gao and ShiLiang Zhang and Ian McLoughlin and Zhijie Yan},
title={{Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition}},
--
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