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""" |
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The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals |
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synchronized to a common timeline. These include close-talking and far-field microphones, individual and |
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room-view video cameras, and output from a slide projector and an electronic whiteboard. During the meetings, |
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the participants also have unsynchronized pens available to them that record what is written. The meetings |
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were recorded in English using three different rooms with different acoustic properties, and include mostly |
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non-native speakers. |
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""" |
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|
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import os |
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|
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import datasets |
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|
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_CITATION = """\ |
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@inproceedings{10.1007/11677482_3, |
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author = {Carletta, Jean and Ashby, Simone and Bourban, Sebastien and Flynn, Mike and Guillemot, Mael and Hain, Thomas and Kadlec, Jaroslav and Karaiskos, Vasilis and Kraaij, Wessel and Kronenthal, Melissa and Lathoud, Guillaume and Lincoln, Mike and Lisowska, Agnes and McCowan, Iain and Post, Wilfried and Reidsma, Dennis and Wellner, Pierre}, |
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title = {The AMI Meeting Corpus: A Pre-Announcement}, |
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year = {2005}, |
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isbn = {3540325492}, |
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publisher = {Springer-Verlag}, |
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address = {Berlin, Heidelberg}, |
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url = {https://doi.org/10.1007/11677482_3}, |
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doi = {10.1007/11677482_3}, |
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abstract = {The AMI Meeting Corpus is a multi-modal data set consisting of 100 hours of meeting |
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recordings. It is being created in the context of a project that is developing meeting |
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browsing technology and will eventually be released publicly. Some of the meetings |
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it contains are naturally occurring, and some are elicited, particularly using a scenario |
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in which the participants play different roles in a design team, taking a design project |
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from kick-off to completion over the course of a day. The corpus is being recorded |
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using a wide range of devices including close-talking and far-field microphones, individual |
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and room-view video cameras, projection, a whiteboard, and individual pens, all of |
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which produce output signals that are synchronized with each other. It is also being |
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hand-annotated for many different phenomena, including orthographic transcription, |
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discourse properties such as named entities and dialogue acts, summaries, emotions, |
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and some head and hand gestures. We describe the data set, including the rationale |
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behind using elicited material, and explain how the material is being recorded, transcribed |
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and annotated.}, |
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booktitle = {Proceedings of the Second International Conference on Machine Learning for Multimodal Interaction}, |
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pages = {28–39}, |
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numpages = {12}, |
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location = {Edinburgh, UK}, |
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series = {MLMI'05} |
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} |
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""" |
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|
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_DESCRIPTION = """\ |
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The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals |
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synchronized to a common timeline. These include close-talking and far-field microphones, individual and |
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room-view video cameras, and output from a slide projector and an electronic whiteboard. During the meetings, |
|
the participants also have unsynchronized pens available to them that record what is written. The meetings |
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were recorded in English using three different rooms with different acoustic properties, and include mostly |
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non-native speakers. \n |
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""" |
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|
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_HOMEPAGE = "https://groups.inf.ed.ac.uk/ami/corpus/" |
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|
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_LICENSE = "CC BY 4.0" |
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|
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_TRAIN_SAMPLE_IDS = [ |
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"EN2001a", |
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"EN2001b", |
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"EN2001d", |
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"EN2001e", |
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"EN2003a", |
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"EN2004a", |
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"EN2005a", |
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"EN2006a", |
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"EN2006b", |
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"EN2009b", |
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"EN2009c", |
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"EN2009d", |
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"ES2002a", |
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"ES2002b", |
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"ES2002c", |
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"ES2002d", |
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"ES2003a", |
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"ES2003b", |
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"ES2003c", |
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"ES2003d", |
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"ES2005a", |
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"ES2005b", |
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"ES2005c", |
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"ES2005d", |
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"ES2006a", |
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"ES2006b", |
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"ES2006c", |
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"ES2006d", |
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"ES2007a", |
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"ES2007b", |
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"ES2007c", |
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"ES2007d", |
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"ES2008a", |
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"ES2008b", |
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"ES2008c", |
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"ES2008d", |
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"ES2009a", |
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"ES2009b", |
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"ES2009c", |
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"ES2009d", |
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"ES2010a", |
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"ES2010b", |
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"ES2010c", |
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"ES2010d", |
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"ES2012a", |
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"ES2012b", |
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"ES2012c", |
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"ES2012d", |
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"ES2013a", |
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"ES2013b", |
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"ES2013c", |
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"ES2013d", |
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"ES2014a", |
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"ES2014b", |
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"ES2014c", |
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"ES2014d", |
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"ES2015a", |
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"ES2015b", |
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"ES2015c", |
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"ES2015d", |
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"ES2016a", |
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"ES2016b", |
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"ES2016c", |
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"ES2016d", |
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"IB4005", |
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"IN1001", |
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"IN1002", |
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"IN1005", |
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"IN1007", |
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"IN1008", |
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"IN1009", |
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"IN1012", |
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"IN1013", |
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"IN1014", |
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"IN1016", |
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"IS1000a", |
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"IS1000b", |
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"IS1000c", |
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"IS1000d", |
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"IS1001a", |
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"IS1001b", |
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"IS1001c", |
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"IS1001d", |
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"IS1002b", |
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"IS1002c", |
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"IS1002d", |
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"IS1003a", |
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"IS1003b", |
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"IS1003c", |
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"IS1003d", |
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"IS1004a", |
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"IS1004b", |
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"IS1004c", |
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"IS1004d", |
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"IS1005a", |
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"IS1005b", |
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"IS1005c", |
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"IS1006a", |
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"IS1006b", |
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"IS1006c", |
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"IS1006d", |
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"IS1007a", |
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"IS1007b", |
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"IS1007c", |
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"IS1007d", |
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"TS3005a", |
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"TS3005b", |
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"TS3005c", |
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"TS3005d", |
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"TS3006a", |
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"TS3006b", |
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"TS3006c", |
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"TS3006d", |
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"TS3007a", |
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"TS3007b", |
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"TS3007c", |
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"TS3007d", |
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"TS3008a", |
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"TS3008b", |
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"TS3008c", |
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"TS3008d", |
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"TS3009a", |
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"TS3009b", |
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"TS3009c", |
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"TS3009d", |
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"TS3010a", |
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"TS3010b", |
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"TS3010c", |
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"TS3010d", |
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"TS3011a", |
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"TS3011b", |
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"TS3011c", |
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"TS3011d", |
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"TS3012a", |
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"TS3012b", |
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"TS3012c", |
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"TS3012d", |
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] |
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|
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_VALIDATION_SAMPLE_IDS = [ |
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"ES2011a", |
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"ES2011c", |
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"IB4001", |
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"IB4003", |
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"IB4010", |
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"IS1008a", |
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"IS1008c", |
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"TS3004a", |
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"TS3004c", |
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"ES2011b", |
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"ES2011d", |
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"IB4002", |
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"IB4004", |
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"IB4011", |
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"IS1008b", |
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"IS1008d", |
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"TS3004b", |
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"TS3004d", |
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] |
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|
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_EVAL_SAMPLE_IDS = [ |
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"EN2002a", |
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"EN2002b", |
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"EN2002c", |
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"EN2002d", |
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"ES2004a", |
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"ES2004b", |
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"ES2004c", |
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"ES2004d", |
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"IS1009a", |
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"IS1009b", |
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"IS1009c", |
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"IS1009d", |
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"TS3003a", |
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"TS3003b", |
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"TS3003c", |
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"TS3003d", |
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] |
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|
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_SAMPLE_IDS = { |
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"train": _TRAIN_SAMPLE_IDS, |
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"dev": _VALIDATION_SAMPLE_IDS, |
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"eval": _EVAL_SAMPLE_IDS, |
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} |
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|
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_SUBSETS = ("ihm",) |
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|
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_BASE_DATA_URL = "https://huggingface.co/datasets/edinburghcstr/ami/resolve/main/" |
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|
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_AUDIO_ARCHIVE_URL = _BASE_DATA_URL + "audio/{subset}/{split}/{_id}.tar.gz" |
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_ANNOTATIONS_ARCHIVE_URL = _BASE_DATA_URL + "annotations/{split}/text" |
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|
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logger = datasets.utils.logging.get_logger(__name__) |
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|
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|
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class AMIConfig(datasets.BuilderConfig): |
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"""BuilderConfig for AMI.""" |
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|
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def __init__(self, name, *args, **kwargs): |
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"""BuilderConfig for AMI""" |
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super().__init__(name=name, *args, **kwargs) |
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|
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|
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class AMI(datasets.GeneratorBasedBuilder): |
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|
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VERSION = datasets.Version("1.0.0") |
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|
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BUILDER_CONFIGS = [ |
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AMIConfig(name=subset) for subset in _SUBSETS |
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] |
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DEFAULT_WRITER_BATCH_SIZE = 128 |
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|
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def _info(self): |
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features = datasets.Features( |
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{ |
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"meeting_id": datasets.Value("string"), |
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"audio_id": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"audio": datasets.Audio(sampling_rate=16_000), |
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"begin_time": datasets.Value("float32"), |
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"end_time": datasets.Value("float32"), |
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"microphone_id": datasets.Value("string"), |
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"speaker_id": datasets.Value("string"), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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|
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def _split_generators(self, dl_manager): |
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splits = ["train", "dev", "eval"] |
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|
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audio_archives_urls = {} |
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for split in splits: |
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audio_archives_urls[split] = [ |
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_AUDIO_ARCHIVE_URL.format(subset=self.config.name, split=split, _id=m) for m in _SAMPLE_IDS[split] |
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] |
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|
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audio_archives = dl_manager.download(audio_archives_urls) |
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local_extracted_archives_paths = dl_manager.extract(audio_archives) if not dl_manager.is_streaming else { |
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split: [None] * len(audio_archives[split]) for split in splits |
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} |
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|
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annotations_urls = {split: _ANNOTATIONS_ARCHIVE_URL.format(split=split) for split in splits} |
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annotations = dl_manager.download(annotations_urls) |
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|
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
|
gen_kwargs={ |
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"audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["train"]], |
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"local_extracted_archives_paths": local_extracted_archives_paths["train"], |
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"annotation": annotations["train"], |
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"split": "train" |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["dev"]], |
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"local_extracted_archives_paths": local_extracted_archives_paths["dev"], |
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"annotation": annotations["dev"], |
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"split": "dev" |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["eval"]], |
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"local_extracted_archives_paths": local_extracted_archives_paths["eval"], |
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"annotation": annotations["eval"], |
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"split": "eval" |
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}, |
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), |
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] |
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|
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def _generate_examples(self, audio_archives, local_extracted_archives_paths, annotation, split): |
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|
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assert len(audio_archives) == len(local_extracted_archives_paths) |
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|
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with open(annotation, "r", encoding="utf-8") as f: |
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transcriptions = {} |
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for line in f.readlines(): |
|
line_items = line.strip().split() |
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_id = line_items[0] |
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text = " ".join(line_items[1:]) |
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_, meeting_id, microphone_id, speaker_id, begin_time, end_time = _id.split("_") |
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audio_filename = "_".join([split, _id.lower()]) + ".wav" |
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|
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transcriptions[audio_filename] = { |
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"audio_id": _id, |
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"meeting_id": meeting_id, |
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"text": text, |
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"begin_time": int(begin_time) / 100, |
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"end_time": int(end_time) / 100, |
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"microphone_id": microphone_id, |
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"speaker_id": speaker_id, |
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} |
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|
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features = ["meeting_id", "audio_id", "text", "begin_time", "end_time", "microphone_id", "speaker_id"] |
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for archive, local_archive_path in zip(audio_archives, local_extracted_archives_paths): |
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for audio_path, audio_file in archive: |
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|
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audio_meta = transcriptions[audio_path.split("/")[-1]] |
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|
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yield audio_path, { |
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"audio": { |
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"path": os.path.join(local_archive_path, audio_path) if local_archive_path else audio_path, |
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"bytes": audio_file.read(), |
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}, |
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**{feature: audio_meta[feature] for feature in features} |
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} |
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|
