Updated README with markdown formatting and some links

README.md

@@ -1,7 +1,7 @@
-Europarl-ASR
-v1.0
-2 April 2021
-www.mllp.upv.es/europarl-asr/
+# Europarl-ASR
+v1.0<br />
+2 April 2021<br />
+www.mllp.upv.es/europarl-asr
 
 A large English-language speech and text corpus of parliamentary debates for
 streaming ASR benchmarking and speech data filtering/verbatimization.
@@ -18,21 +18,21 @@ Universitat Politècnica de València.
 README CONTENTS
 ---------------
 
-- Overview
-- Corpus structure and contents
-- Additional Europarl-ASR materials
-- Extended description
-- Acknowledgements
-- Legal disclaimers
-- Licence
+- [Overview](#overview)
+- [Corpus structure and contents](#contents)
+- [Additional Europarl-ASR materials](#additional)
+- [Extended description](#description)
+- [Acknowledgements](#ack)
+- [Legal disclaimers](#legal)
+- [Licence](#licence)
 
 
-OVERVIEW
+<a id="overview"></a>OVERVIEW
 --------
 
 Europarl-ASR (EN) includes:
 
-*Speech data:
+#### Speech data
 
 - 1.3K hours of English-language annotated speech data.
 - 18 hours of speech data with both manually revised verbatim transcriptions
@@ -43,11 +43,11 @@ Europarl-ASR (EN) includes:
   (training partition): official non-verbatim transcriptions, automatically
   noise-filtered transcriptions and automatically verbatimized transcriptions.
 
-*Text data:
+#### Text data
 
 - 70M tokens of English-language text data.
 
-*Pretrained language models:
+#### Pretrained language models
 
 - The Europarl-ASR English-language n-gram language model and vocabulary.
 
@@ -58,7 +58,7 @@ tokens, Europarl-ASR also includes tools to add all English-language text from
 the DCEP Digital Corpus of the European Parliament.
 
 
-CORPUS STRUCTURE AND CONTENTS
+<a id="contents"></a>CORPUS STRUCTURE AND CONTENTS
 -----------------------------
 
 Total size: 20 GB
@@ -72,6 +72,7 @@ data (for language modelling).
 Here we can see more completely the corpus structure, with additional
 subdirectories:
 
+```
   Europarl-ASR
   └── en
       ├── dev
@@ -116,18 +117,19 @@ subdirectories:
               └── internal
                   ├── prepro
                   └── raw
+```
 
-*Speech data ("original_audio" directories):
+#### Speech data ("original_audio" directories)
 
 In the cases of "dev" and "test", they are subdivided in directories "spk-dep"
 and "spk-indep". Thus, for speech data, we have 2 train-dev-test partitions
 for 2 different ASR tasks, as follows:
 
-  1) ASR with known speakers (MEP):
-  train ; dev/original_audio/spk-dep ; test/original_audio/spk-dep
-  
-  2) ASR with unknown speakers (Guest):
-  train ; dev/original_audio/spk-indep ; test/original_audio/spk-indep
+1. ASR with known speakers (MEP):<br />
+   train ; dev/original_audio/spk-dep ; test/original_audio/spk-dep
+   
+1. ASR with unknown speakers (Guest):<br />
+   train ; dev/original_audio/spk-indep ; test/original_audio/spk-indep
 
 Each of these partition directories contains 3 to 4 subdirectories (depending
 on whether it is the train set or the dev/test sets): "lists", "metadata",
@@ -140,40 +142,40 @@ speeches per speaker.
 corresponding set (as csv and json files). For each speech we will find these
 metadata (as reflected in speeches.headers.csv):
 
-  term;session_date;speech_id;speaker_type;speaker_id;raw_dur;
-  aligned-speech_dur;filtered-speech_dur;cer;ar;path;agenda_item_title
+&nbsp;&nbsp;&nbsp;&nbsp;term;session_date;speech_id;speaker_type;speaker_id;raw_dur;<br />
+&nbsp;&nbsp;&nbsp;&nbsp;aligned-speech_dur;filtered-speech_dur;cer;ar;path;agenda_item_title
 
 And for each speaker (as reflected in speakers.headers.csv):
 
-  type;id;name;gender;url
+&nbsp;&nbsp;&nbsp;&nbsp;type;id;name;gender;url
 
 "speeches" contains a subdirectory for each speech in the corresponding set,
 according to this subdirectory structure:
 
-  speeches/<term>/<session_date>/<speech_id>/
+&nbsp;&nbsp;&nbsp;&nbsp;`speeches/<term>/<session_date>/<speech_id>/`
 
 For each speech, we will find some of the following files (depending on
 whether it is in the train set or in the dev/test sets):
 
-  ep-asr.en.orig.<term>.<session_date>.<speech_id>.m4a
-  [In all sets] Audio of the speech.
+&nbsp;&nbsp;&nbsp;&nbsp;`ep-asr.en.orig.<term>.<session_date>.<speech_id>.m4a`<br />
+&nbsp;&nbsp;&nbsp;&nbsp;[In all sets] Audio of the speech.
 
-  ep-asr.en.orig.<term>.<session_date>.<speech_id>.tr.orig.{dfxp,json,srt,txt}
-  [In all sets] Official non-verbatim transcription of the speech, as a txt
+&nbsp;&nbsp;&nbsp;&nbsp;`ep-asr.en.orig.<term>.<session_date>.<speech_id>.tr.orig.{dfxp,json,srt,txt}`<br />
+&nbsp;&nbsp;&nbsp;&nbsp;[In all sets] Official non-verbatim transcription of the speech, as a txt
   raw transcription file, as dfxp or srt force-aligned timed subtitle files,
   and its json metadata.
 
-  ep-asr.en.orig.<term>.<session_date>.<speech_id>.tr.filt.{dfxp,json,srt}
-  [In train set] Automatically filtered transcription of the speech, as dfxp
+&nbsp;&nbsp;&nbsp;&nbsp;`ep-asr.en.orig.<term>.<session_date>.<speech_id>.tr.filt.{dfxp,json,srt}`<br />
+&nbsp;&nbsp;&nbsp;&nbsp;[In train set] Automatically filtered transcription of the speech, as dfxp
   or srt force-aligned timed subtitle files, and its json metadata.
 
-  ep-asr.en.orig.<term>.<session_date>.<speech_id>.tr.verb.{dfxp,json,srt,txt}
-  [In train set] Automatically verbatimized transcription of the speech, as
+&nbsp;&nbsp;&nbsp;&nbsp;`ep-asr.en.orig.<term>.<session_date>.<speech_id>.tr.verb.{dfxp,json,srt,txt}`<br />
+&nbsp;&nbsp;&nbsp;&nbsp;[In train set] Automatically verbatimized transcription of the speech, as
   a txt transcription file, as dfxp or srt force-aligned timed subtitle files,
   and its json metadata.
 
-  ep-asr.en.orig.<term>.<session_date>.<speech_id>.tr.rev.{dfxp,json,srt,txt}
-  [In dev/test sets] Manually revised verbatim transcription of the speech,
+&nbsp;&nbsp;&nbsp;&nbsp;`ep-asr.en.orig.<term>.<session_date>.<speech_id>.tr.rev.{dfxp,json,srt,txt}`<br />
+&nbsp;&nbsp;&nbsp;&nbsp;[In dev/test sets] Manually revised verbatim transcription of the speech,
   as a txt transcription file, as dfxp or srt force-aligned timed subtitle
   files, and its json metadata.
 
@@ -185,7 +187,7 @@ transcriptions (*.ref) and as segment time marked files (*.stm). In all 4
 cases, the text is presented preprocessed for evaluation (tokenized,
 lowercased, punctuation removed...).
 
-*Text data ("text" directories):
+#### Text data ("text" directories)
 
 In the case of "train", they are subdivided in directories "external" and
 "internal". "internal" contains all the official non-verbatim transcriptions
@@ -197,24 +199,24 @@ Each "text" directory contains 2 subdirectories: "raw" (except in
 "train/external"), "prepro" (in all sets), or "scripts" (only in
 "train/external").
 
-  "raw" contains the raw text data for the corresponding set (*.txt.gz), and
+&nbsp;&nbsp;&nbsp;&nbsp;"raw" contains the raw text data for the corresponding set (*.txt.gz), and
   its metadata (*.csv). In the cases of "dev" and "test", both the official
   non-verbatim transcriptions (*.orig.*) and the manually revised verbatim
   transcriptions (*.rev.*) are included.
 
-  "prepro" contains the text data for the corresponding set, preprocessed for
+&nbsp;&nbsp;&nbsp;&nbsp;"prepro" contains the text data for the corresponding set, preprocessed for
   training or evaluation (tokenized, lowercased, punctuation removed...). This
   data is released to facilitate the reproducibility of our experiments.
 
-  Finally, "scripts" (only in "train/text/external") contains the script
+&nbsp;&nbsp;&nbsp;&nbsp;Finally, "scripts" (only in "train/text/external") contains the script
   get_DCEP.sh, which can be used to download the DCEP corpus from its original
   website and save it in compressed plain text (.txt.gz).
 
 
-ADDITIONAL Europarl-ASR MATERIALS
+<a id="additional-materials"></a>ADDITIONAL Europarl-ASR MATERIALS
 ---------------------------------
 
-https://www.mllp.upv.es/europarl-asr/Europarl-ASR_v1.0_ngram_lm_and_vocab.tar.gz
+https://www.mllp.upv.es/europarl-asr/Europarl-ASR_v1.0_ngram_lm_and_vocab.tar.gz<br />
 https://www.mllp.upv.es/europarl-asr/Europarl-ASR_transcription_guidelines.pdf
 
 In addition to the speech and text data included in the main release and
@@ -229,7 +231,7 @@ materials to facilitate the reproducibility of our experiments:
   dev and test sets.
 
 
-EXTENDED DESCRIPTION
+<a id="description"></a>EXTENDED DESCRIPTION
 --------------------
 
 Europarl-ASR (EN) is a large English-language speech and text corpus of
@@ -244,7 +246,7 @@ Intel·ligència Artificial, Universitat Politècnica de València
 
 Europarl-ASR (EN) includes:
 
-*Speech data:
+#### Speech data
 
 - 1.3K hours of English-language annotated speech data (33K speeches, 1K
   speakers).
@@ -256,11 +258,11 @@ Europarl-ASR (EN) includes:
   (training partition): official non-verbatim transcriptions, automatically
   noise-filtered transcriptions and automatically verbatimized transcriptions.
 
-*Text data:
+#### Text data
 
 - 70M tokens of English-language text data.
 
-*Language models:
+#### Language models
 
 - The Europarl-ASR English-language n-gram language model and vocabulary.
 
@@ -283,7 +285,7 @@ Detailed dates of the EP speech and text data gathered:
 - DCEP (does not include any EP reports of proceedings): 2001 to 2012.
 
 
-ACKNOWLEDGEMENTS
+<a id="ack"></a>ACKNOWLEDGEMENTS
 ---------------
 
 The authors would like to acknowledge:
@@ -298,15 +300,15 @@ The authors would like to acknowledge:
   ( https://www.statmt.org/europarl/ ).
 
 This work has received funding from the EU's H2020 research and innovation
-programme under grant agreements 761758 (X5gon) and 952215 (TAILOR); the
-Government of Spain's research project Multisub (RTI2018-094879-B-I00,
+programme under grant agreements 761758 ([X5gon](https://cordis.europa.eu/project/id/761758)) and 952215 ([TAILOR](https://cordis.europa.eu/project/id/952215)); the
+Government of Spain's research project [Multisub](https://www.mllp.upv.es/projects/multisub/) (RTI2018-094879-B-I00,
 MCIU/AEI/FEDER,EU) and FPU scholarships FPU14/03981 and FPU18/04135; the
-Generalitat Valenciana's research project Classroom Activity Recognition
+Generalitat Valenciana's research project [Classroom Activity Recognition](https://aplicat.upv.es/exploraupv/ficha-proyecto/proyecto/20190714)
 (PROMETEO/2019/111) and predoctoral research scholarship ACIF/2017/055; and
 the Universitat Politècnica de València's PAID-01-17 R&D support programme.
 
 
-LEGAL DISCLAIMERS
+<a id="legal"></a>LEGAL DISCLAIMERS
 -----------------
 
 - Speech and text data from the European Parliament website (audio, official
@@ -319,7 +321,7 @@ LEGAL DISCLAIMERS
   update: 11 March 2015).
 
 
-LICENCE
+<a id="licence"></a>LICENCE
 -------
 
 - Speech and text data from the European Parliament website (audio, official
@@ -344,4 +346,4 @@ LICENCE
   Silvestre-Cerdà are licenced under CC BY 4.0. To view a copy of this
   licence, visit http://creativecommons.org/licenses/by/4.0/
 
-See the file LICENSE for the full licence texts.
+See the [LICENSE](https://mllp.upv.es/git-pub/ggarces/Europarl-ASR/src/master/LICENSE) file for the full licence texts.