Initialized Europarl-ASR README

README.md

@@ -1,8 +1,347 @@
-# Europarl-ASR
-
-Europarl-ASR: A Large Speech+Text Corpus of Parliamentary Debates for Streaming ASR Benchmarking and Speech Data Filtering/Verbatimization.
-
-- 1300 hours EN transcribed speech data.
-- 18 hours EN speech data w/ revised verbatim and official non-verbatim transcriptions, split in 2 dev/test partitions for 2 realistic ASR tasks.
-- 3 full sets of timed transcriptions for the training data: official non-verbatim, automatically noise-filtered, and automatically verbatimized.
-- 70 million tokens EN text data.
+Europarl-ASR
+v1.0
+2 April 2021
+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.
+
+Keywords: automatic speech recognition; speech corpus; speech data filtering;
+speech data verbatimization.
+
+CONTACT:
+Gonçal V. Garcés Díaz-Munío (gogardia@vrain.upv.es),
+Joan Albert Silvestre-Cerdà (jsilvestre@vrain.upv.es).
+Universitat Politècnica de València.
+
+
+README CONTENTS
+---------------
+
+- Overview
+- Corpus structure and contents
+- Additional Europarl-ASR materials
+- Extended description
+- Acknowledgements
+- Legal disclaimers
+- Licence
+
+
+OVERVIEW
+--------
+
+Europarl-ASR (EN) includes:
+
+*Speech data:
+
+- 1.3K hours of English-language annotated speech data.
+- 18 hours of speech data with both manually revised verbatim transcriptions
+  and official non-verbatim transcriptions, split in 2 independent validation-
+  evaluation partitions for 2 realistic ASR tasks (with vs. without previous
+  knowledge of the speaker).
+- 3 full sets of timed transcriptions for the rest of the speech data
+  (training partition): official non-verbatim transcriptions, automatically
+  noise-filtered transcriptions and automatically verbatimized transcriptions.
+
+*Text data:
+
+- 70M tokens of English-language text data.
+
+*Pretrained language models:
+
+- The Europarl-ASR English-language n-gram language model and vocabulary.
+
+This data comprises most of the European Parliament's English-language debate
+recordings, transcriptions and translations available from the Parliament's
+website from 1996 to 2020. Additionally, to increase text data up to 170M
+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
+-----------------------------
+
+Total size: 20 GB
+
+The data is organized in 3 main directories: "train" (training data), "dev"
+(validation data) and "test" (evaluation data). Each directory contains the
+subdirectories "original_audio" and "text", the first one containing speech
+data with annotations (for acoustic modelling), the second one containing text
+data (for language modelling).
+
+Here we can see more completely the corpus structure, with additional
+subdirectories:
+
+  Europarl-ASR
+  └── en
+      ├── dev
+      │   ├── original_audio
+      │   │   ├── spk-dep
+      │   │   │   ├── lists
+      │   │   │   ├── metadata
+      │   │   │   ├── refs
+      │   │   │   └── speeches
+      │   │   └── spk-indep
+      │   │       ├── lists
+      │   │       ├── metadata
+      │   │       ├── refs
+      │   │       └── speeches
+      │   └── text
+      │       ├── prepro
+      │       └── raw
+      ├── test
+      │   ├── original_audio
+      │   │   ├── spk-dep
+      │   │   │   ├── lists
+      │   │   │   ├── metadata
+      │   │   │   ├── refs
+      │   │   │   └── speeches
+      │   │   └── spk-indep
+      │   │       ├── lists
+      │   │       ├── metadata
+      │   │       ├── refs
+      │   │       └── speeches
+      │   └── text
+      │       ├── prepro
+      │       └── raw
+      └── train
+          ├── original_audio
+          │   ├── lists
+          │   ├── metadata
+          │   └── speeches
+          └── text
+              ├── external
+              │   ├── prepro
+              │   └── scripts
+              └── internal
+                  ├── prepro
+                  └── raw
+
+*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
+
+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",
+"refs" (only in "dev" and "test") and "speeches".
+
+"lists" contains lists of all the speeches in "speeches", as well as lists of
+speeches per speaker.
+
+"metadata" contains metadata for each speech and for each speaker in the
+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
+
+And for each speaker (as reflected in speakers.headers.csv):
+
+  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>/
+
+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.
+
+  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
+  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
+  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
+  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,
+  as a txt transcription file, as dfxp or srt force-aligned timed subtitle
+  files, and its json metadata.
+
+Finally, in "refs" (only in "dev" and "test") each file contains every speech
+in the corresponding dev or test set, that is, the full reference for that
+set. In each case, we will find 4 files, containing the official non-verbatim
+reference (*.orig.*) and the manually revised verbatim reference (*.rev.*), as
+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):
+
+In the case of "train", they are subdivided in directories "external" and
+"internal". "internal" contains all the official non-verbatim transcriptions
+and translations in the train set, together with the selected non-overlapping
+Europarl v10 transcriptions and translations; "external" contains the files to
+make use of the external DCEP: Digital Corpus of the European Parliament.
+
+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
+  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
+  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
+  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
+---------------------------------
+
+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_transcription_guidelines.pdf
+
+In addition to the speech and text data included in the main release and
+described in this document, we are making available for download the following
+materials to facilitate the reproducibility of our experiments:
+
+- The pretrained Europarl-ASR English-language n-gram language model, together
+  with its vocabulary file.
+
+- The Europarl-ASR English-language verbatim transcription guidelines, which
+  were applied to produce the manually revised verbatim transcriptions for the
+  dev and test sets.
+
+
+EXTENDED DESCRIPTION
+--------------------
+
+Europarl-ASR (EN) is a large English-language speech and text corpus of
+parliamentary debates for (streaming) ASR benchmarking and speech data
+filtering/verbatimization, including 1300 hours of annotated English-language
+speeches from European Parliament sessions held in the period 1996-2020.
+
+It was compiled and released by the Machine Learning and Language Processing
+(MLLP) research group of VRAIN Institut Valencià d'Investigació en
+Intel·ligència Artificial, Universitat Politècnica de València
+( www.mllp.upv.es ).
+
+Europarl-ASR (EN) includes:
+
+*Speech data:
+
+- 1.3K hours of English-language annotated speech data (33K speeches, 1K
+  speakers).
+- 18 hours of speech data with both manually revised verbatim transcriptions
+  and official non-verbatim transcriptions, split in 2 independent validation-
+  evaluation partitions for 2 realistic ASR tasks (with vs. without previous
+  knowledge of the speaker).
+- 3 full sets of timed transcriptions for the rest of the speech data
+  (training partition): official non-verbatim transcriptions, automatically
+  noise-filtered transcriptions and automatically verbatimized transcriptions.
+
+*Text data:
+
+- 70M tokens of English-language text data.
+
+*Language models:
+
+- The Europarl-ASR English-language n-gram language model and vocabulary.
+
+This data comprises most of the European Parliament's English-language debate
+recordings, transcriptions and translations available from the Parliament's
+website from 1999 to 2020 (recordings being only available from 2008). This is
+complemented by including all English-language transcriptions and translations
+from the Europarl v10 text corpus for the period 1996-1999.
+
+Additionally, to increase text data for language modelling up to 170M tokens,
+Europarl-ASR also includes tools to add all English-language text from the
+DCEP Digital Corpus of the European Parliament.
+
+Detailed dates of the EP speech and text data gathered:
+
+- English speech: 2008-09-01 to 2020-05-27.
+- English transcriptions: 1999-07-20 to 2020-05-27.
+- Translations into English: 1999-07-20 to 2012-11-30.
+- Europarl v10 (selected to avoid overlapping): 1996-04-15 to 1999-07-19.
+- DCEP (does not include any EP reports of proceedings): 2001 to 2012.
+
+
+ACKNOWLEDGEMENTS
+---------------
+
+The authors would like to acknowledge:
+
+- The European Parliament, the European Commission and other EU organizations,
+  for making available a wealth of multilingual speech and text data, both in
+  their websites and as ready-made corpora such as the DCEP Digital Corpus of
+  the European Parliament
+  ( https://ec.europa.eu/jrc/en/language-technologies ).
+
+- Philipp Koehn for compiling the Europarl corpus
+  ( 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,
+MCIU/AEI/FEDER,EU) and FPU scholarships FPU14/03981 and FPU18/04135; the
+Generalitat Valenciana's research project Classroom Activity Recognition
+(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
+-----------------
+
+- Speech and text data from the European Parliament website (audio, official
+  transcriptions and translations) were sourced from
+  https://www.europarl.europa.eu/plenary/en/debates-video.html
+
+- Text data from the DCEP Digital Corpus of the European Parliament are the
+  exclusive property of the European Parliament. These data were sourced from
+  https://ec.europa.eu/jrc/en/language-technologies/dcep (date of the latest
+  update: 11 March 2015).
+
+
+LICENCE
+-------
+
+- Speech and text data from the European Parliament website (audio, official
+  transcriptions and translations) are the exclusive property of the European
+  Union represented by the European Parliament. These data are reused here
+  under the conditions stated in the European Parliament website's Legal
+  notice ( https://www.europarl.europa.eu/legal-notice ).
+
+- Text data from the DCEP Digital Corpus of the European Parliament are the
+  exclusive property of the European Parliament. The European Parliament
+  retains ownership of the data. These data are reused here under the usage
+  conditions of the DCEP Digital Corpus of the European Parliament (
+  https://ec.europa.eu/jrc/en/language-technologies/dcep#Usage%20Conditions ).
+
+- Text data from the Europarl v10 corpus are reused here under the Europarl
+  corpus terms of use ( https://www.statmt.org/europarl/ ).
+
+- Europarl-ASR data and code not covered by the previously mentioned licences
+  © 2021 by Pau Baquero-Arnal, Jorge Civera, Gonçal V. Garcés Dı́az-Munı́o,
+  Adrià Giménez, Javier Iranzo-Sánchez, Javier Jorge, Alfons Juan, Alejandro
+  Pérez-González-de-Martos, Nahuel Roselló, Albert Sanchis and Joan Albert
+  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.