23.10 release

README.md

@@ -1,20 +1,22 @@
----
-license: cc0-1.0
-language:
-- cy
-tags:
-- verbatim transcriptions
-- speech recognition
-pretty_name: 'Banc Trawsgrifiadau Bangor '
-size_categories:
-- 10K<n<100K
----
+
+ ---
+ license: cc0-1.0
+ language:
+ - cy
+ tags:
+ - verbatim transcriptions
+ - speech recognition
+ pretty_name: 'Banc Trawsgrifiadau Bangor'
+ size_categories:
+ - 10K<n<100K
+ ---
+ 
 
 [See below for English](#bangor-transcription-bank)
 
 # Banc Trawsgrifiadau Bangor
 
-Dyma fanc o 25 awr 34 munud a 24 eiliad o segmentau o leferydd naturiol dros hanner cant o gyfranwyr ar ffurf ffeiliau mp3, ynghyd â thrawsgrifiadau 'verbatim' cyfatebol o’r lleferydd ar ffurf ffeil .tsv. Mae'r mwyafrif o'r lleferydd yn leferydd digymell, naturiol. Dosbarthwn y deunydd hwn o dan drwydded agored CC0.
+Dyma fanc o 30 awr 20 munud a 41 eiliad o segmentau o leferydd naturiol dros hanner cant o gyfranwyr ar ffurf ffeiliau mp3, ynghyd â thrawsgrifiadau 'verbatim' cyfatebol o’r lleferydd ar ffurf ffeil .tsv. Mae'r mwyafrif o'r lleferydd yn leferydd digymell, naturiol. Dosbarthwn y deunydd hwn o dan drwydded agored CC0.
 
 ## Pwrpas
 Pwrpas y trawsgrifiadau hyn yw gweithredu fel data hyfforddi ar gyfer modelau adnabod lleferydd, gan gynnwys [ein modelau wav2vec](https://github.com/techiaith/docker-wav2vec2-cy). Ar gyfer y diben hwnnw, mae gofyn am drawsgrifiadau mwy verbatim o'r hyn a ddywedwyd na'r hyn a welir mewn trawsgrifiadau traddodiadol ac mewn isdeitlau, felly datblygwyd confensiwn arbennig ar gyfer y gwaith trawsgrifio ([gweler isod](#confensiynau_trawsgrifio)). Gydag ein modelau wav2vec, caiff cydran ychwnaegol, sef 'model iaith' ei defnyddio ar ôl y model adnabod lleferydd i safoni mwy ar allbwn y model iaith i fod yn debycach i drawsgrifiadau traddodiadol ac isdeitlau.
@@ -71,6 +73,7 @@ Ni ddefnyddiwyd collnodau i marcio pob un llythyren a hepgorwyd gan siaradwyr. E
 Yn hytrach, defnyddiwyd collnodau i wahaniaethu rhwng gwahanol eiriau oedd yn cael eu sillafu'r union yr un fath fel arall. Er enghraifft rydym yn defnyddio collnod o flaen _’ma_ (sef _yma_) i wahaniaethu rhyngddo â _ma’_ (sef _mae_), _gor’o’_ i wahaniaethu rhwng _gorfod_ a ffurf trydydd person unigol amser dibynnol presennol _gori_, a _pwysa’_ i wahaniaethu rhwng ffurf luosog _pwys_ a nifer o ffurfiau berfol posib _pwyso_.
 
 Fodd bynnag, ceir eithriad i’r rheol hon, a hynny pan fo sillafu gair heb gollnod yn newid sŵn y llythyren cyn neu ar ôl y collnod, ac felly _Cymra’g_ sy’n gywir, nid _Cymrag_. 
+
 ### Tagiau
 Wrth drawsgrifio, defnyddiwyd y tagiau hyn i recordio elfennau oedd y tu hwnt i leferydd yr unigolion:
 
@@ -79,10 +82,12 @@ Wrth drawsgrifio, defnyddiwyd y tagiau hyn i recordio elfennau oedd y tu hwnt i
 * \<cerddoriaeth>
 * \<chwerthin>
 * \<chwythu allan>
+* \<clirio gwddf>
 * \<distawrwydd>
 * \<ochneidio>
 * \<PII>
 * \<peswch>
+* \<sniffian>
 * \<twtian>
 
 Rhagwelwn y bydd y rhestr hon yn chwyddo wrth i ni drawsgrifio mwy o leferydd ac wrth i ni daro ar draws mwy o elfennau sydd y tu hwnt i leferydd unigolion.
@@ -147,17 +152,21 @@ Trawsgrifiwyd rhifau fel geiriau yn hytrach na digidau, hynny yw hyn sy’n gywi
 **ac nid:**
 
 > Y flwyddyn 2020
+
 ### Gorffen gair ar ei hanner
 Marciwyd gair oedd wedi ei orffen ar ei hanner gyda `-`. Er enghraifft:
 > Ma’n rhaid i mi **ca-** cael diod.
+
 ### Gorffen brawddeg ar ei hanner/ailddechrau brawddeg
 Marciwyd brawddeg oedd wedi ei gorffen ar ei hanner gyda `...`. Er enghraifft:
 > Ma’n rhaid i mi ca’l... Ma’ rhaid i mi brynu diod.
+
 ### Siaradwr yn torri ar draws siaradwr arall
 Ceir yn y data llawer o enghreifftiau o siaradwr yn torri ar draws y prif leferydd gan ddefnyddio  synau nad ydynt yn eiriol, geiriau neu ymadroddion (megis _m-hm_, _ie_, _ydi_, _yn union_ ac ati). Pan oedd y ddau siaradwr i'w clywed yn glir ag ar wahân, rhoddwyd `...` ar ddiwedd rhan gyntaf y lleferydd toredig, a `...` arall ar ddechrau ail ran y lleferydd toredig, fel yn yr enghraifft ganlynol:
 > Ond y peth yw... M-hm. ...mae’r ddau yn wir
 
 Pan nad oedd y ddau siaradwyr i'w clywed yn glir ag ar wahân, fe hepgorwyd y lleferydd o’r data.
+
 ### Rhegfeydd
 Dylid nodi ein bod ni heb hepgor rhegfeydd wrth drawsgrifio.
  
@@ -170,9 +179,12 @@ Er mwyn parchu'r cyfrannwyr, wrth lwytho'r data hwn i lawr rydych yn cytuno i be
 ## Diolchiadau
 Diolchwn i'r cyfrannwyr am eu caniatâd i ddefnyddio'u lleferydd. Rydym hefyd yn ddiolchgar i Lywodraeth Cymru am ariannu’r gwaith hwn fel rhan o broject Technoleg Testun, Lleferydd a Chyfieithu ar gyfer yr Iaith Gymraeg. 
 
+---
+
 # Bangor Transcription Bank
 
-This resource is a bank of 25 hours 34 minutes and 24 seconds of segments of natural speech from over 50 contributors in mp3 file format, together with corresponding 'verbatim' transcripts of the speech in .tsv file format. The majority of the speech is spontaneous, natural speech. We distribute this material under a CC0 open license.
+This resource is a bank of 30 hours 20 minutes and 41 seconds of segments of natural speech from over 50 contributors in mp3 file format, together with corresponding 'verbatim' transcripts of the speech in .tsv file format. The majority of the speech is spontaneous, natural speech. We distribute this material under a CC0 open license.
+
 ## Purpose
 The purpose of these transcripts is to act as training data for speech recognition models, including [our wav2vec models](https://github.com/techiaith/docker-wav2vec2-cy). For that purpose, transcriptions are more verbatim than what is seen in traditional transcriptions and than what is required for subtitling purposes, thus a bespoke set of conventions has been developed for the transcription work ([see below](#transcription_conventions) ). Our wav2vec models use an auxiliary component, namely a 'language model',  to further standardize the speech recognition model’s  output in order that it be more similar to traditional transcriptions and subtitles.
 
@@ -194,10 +206,12 @@ Here is the information about the columns.
 | `audio_filesize` | The size of the file |
 | `transcript` | Transcript |
 | `duration` | Duration of the clip in milliseconds. |
+
 ## The Process of Creating the Resource
 
 The audio files were mainly collected from Welsh podcasts, after having gained the consent of the podcast owners and individual contributors to do so. We are extremely grateful to those people. In addition, some scripts were created which mimicked the pattern of news items and articles. These scripts were then read by Language Technologies Unit researchers in order to ensure that content of that type was included in the bank.
 The audio files were run through our in-house automated transcriber to segment the audio and create raw transcripts. Using Elan 6.4 (available from https://archive.mpi.nl/tla/elan), experienced transcribers listened to and corrected the raw transcript.
+
 ## A Note About Content Anonymization 
 Out of respect to the contributors, we have anonymised all transcripts. It was decided to anonymize not only the names of individual people, but also any other Personally Identifiable Information (PII) including, but not limited to:
 * Phone number
@@ -211,15 +225,18 @@ When transcribing, all segments containing PII were marked with the \<PII> tag,
 We have also randomized the order of the segments so that they are not published in the order they appeared in the original audio files.
 
 <a name="transcription_conventions"></a>
+
 ## Transcription Conventions
 These transcription conventions were developed to ensure that the transcriptions were not only verbatim but also consistent. They were developed by referring to conventions used by the Unit in the past, conventions such as those used in the CorCenCC, Siarad, CIG1 and CIG2 corpora, and also through a process of ongoing development as the team undertook the task of transcription.
 **NOTE** - as we have partially developed the conventions at the same time as undertaking the task of transcription the early transcriptions may not follow the latest principles faithfully. We intend to check the transcripts after we have refined the conventions.
+
 ### Apostrophes
 Apostrophes were not used to mark every single letter omitted by speakers. For example, _gwitho_ (which is a pronunciation of _gweithio_) is correct, not _gw’ith'o_.
 
 Rather, apostrophes were used to distinguish between different words that were otherwise spelled identically. For example we use an apostrophe in front of _'ma_ (a pronunciation of _yma_) to distinguish it from _ma'_ (a pronunciation of _mae_), _gor'o'_ to distinguish between _gorfod_ and the third person singular form of the present dependent tense _gori_, and _pwysa'_ to distinguish between the plural form of _pwys_ and a number of possible verb forms of _pwyso_.
 
 However, there is an exception to this rule, that being when spelling a word without an apostrophe would change the sound of the letter before or after the apostrophe, thus  _Cymra'g_ is correct, not _Cymrag_.
+
 ### Tags
 When transcribing, these tags were used to record elements that were external to the speech of the individuals:
 * \<anadlu>
@@ -227,13 +244,16 @@ When transcribing, these tags were used to record elements that were external to
 * \<cerddoriaeth>
 * \<chwerthin>
 * \<chwythu allan>
+* \<clirio gwddf>
 * \<distawrwydd>
 * \<ochneidio>
 * \<PII>
 * \<peswch>
+* \<sniffian>
 * \<twtian>
 
 We anticipate that this list will grow as we transcribe more speech and as we come across more elements that are external to the speech of individuals.
+
 ### Non-verbal sounds
 Efforts were made to transcribe non-verbal sounds consistently. For example, _yy_ was always used (rather than _yrr_, _yr_ or _err_, or a mixture of those) to represent or reflect the sound made when a speaker was trying to think or paused in speaking.
 
@@ -244,12 +264,14 @@ The following were used in transcription:
 * m-hm
 
 Again, we anticipate that this list will grow as we transcribe more speech and as we encounter more non-verbal sounds.
+
 ### English words
 We have surrounded each English word or phrase with asterixis, for example:
 > Dwi’n deall **\*sort of\***.
 
 ### Adapting English words as Welsh language infinitives
 When speakers use English words as infinitives (by adding _io_ at the end of the word for example) we have endeavoured to spell the word using Welsh spelling conventions rather than adding _io_ to the English spelling of the word. For example we have transcribed _heitio_ instead of _hateio_, and _lyfio_ instead of _loveio_.
+
 ### Correction of mis-pronunciations
 To ensure that we adhere to the principles of verbatim transcription it was decided that we should not correct speakers' mis-pronunciations. For example, in the following sentence:
 > enfawr fel y diffyg o fwyd yym **efallu** cam-drin
@@ -265,6 +287,7 @@ We have surrounded all quoted words or phrases with _”_, for example:
 
 ### A note about our use of commas
 As a comma is essentially a convention used for written text, commas were not used prolifically in transcription. Using a comma where one would expected to see it in a written text during transcription would not necessarily have reflected the individual's speech. This should be borne in mind when reading the transcripts.
+
 ### Individual letters
 Individual letters were spelled out rather than being transcribed as individual letters.
 
@@ -288,18 +311,22 @@ Numbers were transcribed as words rather than digits, thus this is correct:
 **rather than:**
 
 > Y flwyddyn 2020
+
 ### Half-finished words
 Half-finished words are marked with a `-`. For example:
 > Ma’n rhaid i mi **ca-** cael diod.
+
 ### Half-finished/restarted sentences
 Half-finished sentences are marked with a `...`. For example:
 > Ma’n rhaid i mi ca’l... Ma’ rhaid i mi brynu diod.
+
 ### Speaker interruptions
 There are many examples of a speaker interrupting another speaker by using non-verbal sounds, words or phrases (such as _m-hm_, _ie_, _ydi_, _yn union_ etc.) in the data. When the two speakers could be heard clearly and distinctly, a `...` was placed at the end of the first part of the broken speech, and another `...` at the beginning of the second part of the broken speech, as in the following example:
 
 > Ond y peth yw... M-hm. ...mae’r ddau yn wir
 
 When the two speakers could not be heard clearly and distinctly, the speech was omitted from the data.
+
 ### Swearwords
 It should be noted that we have not omitted swearwords when transcribing.
  

clips.zip

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+size 656947605