Commit @25881f6ab70d849fc0f6e5ddc0e9d9906ac3f45b - yjyoon/whisper_streaming

Dominik Macháček 2023-11-29

bibtex and proceedings link

@25881f6ab70d849fc0f6e5ddc0e9d9906ac3f45b

0a50eec

25881f6

README.md

--- README.md

+++ README.md


 Abstract:    Whisper is one of the recent state-of-the-art multilingual speech recognition and translation models, however, it is not designed for real time transcription. In this paper, we build on top of Whisper and create Whisper-Streaming, an implementation of real-time speech transcription and translation of Whisper-like models. Whisper-Streaming uses local agreement policy with self-adaptive latency to enable streaming transcription. We show that Whisper-Streaming achieves high quality and 3.3 seconds latency on unsegmented long-form speech transcription test set, and we demonstrate its robustness and practical usability as a component in live transcription service at a multilingual conference. 
 
 
-Pre-print: https://arxiv.org/abs/2307.14743
+Paper in proceedings: http://www.afnlp.org/conferences/ijcnlp2023/proceedings/main-demo/cdrom/pdf/2023.ijcnlp-demo.3.pdf
 
 Demo video: https://player.vimeo.com/video/840442741
 
 [Slides](http://ufallab.ms.mff.cuni.cz/~machacek/pre-prints/AACL23-2.11.2023-Turning-Whisper-oral.pdf) -- 15 minutes oral presentation at IJCNLP-AACL 2023
 
+Please, cite us. [Bibtex citation](http://www.afnlp.org/conferences/ijcnlp2023/proceedings/main-demo/cdrom/bib/2023.ijcnlp-demo.3.bib):
+
+```
+@InProceedings{machacek-dabre-bojar:2023:ijcnlp,
+  author    = {Macháček, Dominik  and  Dabre, Raj  and  Bojar, Ondřej},
+  title     = {Turning Whisper into Real-Time Transcription System},
+  booktitle      = {System Demonstrations},
+  month          = {November},
+  year           = {2023},
+  address        = {Bali, Indonesia},
+  publisher      = {Asian Federation of Natural Language Processing},
+  pages     = {17--24},
+}
+```
+
 ## Installation
 
 1) ``pip install librosa`` -- audio processing library

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...	...	@@ -8,12 +8,27 @@
8	8	Abstract: Whisper is one of the recent state-of-the-art multilingual speech recognition and translation models, however, it is not designed for real time transcription. In this paper, we build on top of Whisper and create Whisper-Streaming, an implementation of real-time speech transcription and translation of Whisper-like models. Whisper-Streaming uses local agreement policy with self-adaptive latency to enable streaming transcription. We show that Whisper-Streaming achieves high quality and 3.3 seconds latency on unsegmented long-form speech transcription test set, and we demonstrate its robustness and practical usability as a component in live transcription service at a multilingual conference.
9	9
10	10
11		-Pre-print: https://arxiv.org/abs/2307.14743
	11	+Paper in proceedings: http://www.afnlp.org/conferences/ijcnlp2023/proceedings/main-demo/cdrom/pdf/2023.ijcnlp-demo.3.pdf
12	12
13	13	Demo video: https://player.vimeo.com/video/840442741
14	14
15	15	[Slides](http://ufallab.ms.mff.cuni.cz/~machacek/pre-prints/AACL23-2.11.2023-Turning-Whisper-oral.pdf) -- 15 minutes oral presentation at IJCNLP-AACL 2023
16	16
	17	+Please, cite us. [Bibtex citation](http://www.afnlp.org/conferences/ijcnlp2023/proceedings/main-demo/cdrom/bib/2023.ijcnlp-demo.3.bib):
	18	+
	19	+```
	20	+@InProceedings{machacek-dabre-bojar:2023:ijcnlp,
	21	+ author = {Macháček, Dominik and Dabre, Raj and Bojar, Ondřej},
	22	+ title = {Turning Whisper into Real-Time Transcription System},
	23	+ booktitle = {System Demonstrations},
	24	+ month = {November},
	25	+ year = {2023},
	26	+ address = {Bali, Indonesia},
	27	+ publisher = {Asian Federation of Natural Language Processing},
	28	+ pages = {17--24},
	29	+}
	30	+```
	31	+
17	32	## Installation
18	33
19	34	1) ``pip install librosa`` -- audio processing library

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