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dc.contributor.author Hernández Mena, Carlos Daniel
dc.date.accessioned 2023-01-03T09:44:08Z
dc.date.available 2023-01-03T09:44:08Z
dc.date.issued 2022-12-08
dc.identifier.uri http://hdl.handle.net/20.500.12537/305
dc.description - ENGLISH The "Kaldi Recipe for Faroese" is a code recipe intended to show how to use the corpus "Ravnursson Faroese Speech and Transcripts" [1] to create automatic speech recognition systems using the Kaldi toolkit [2]. - ÍSLENSLA "Kaldi Forskrift fyrir færeysku" er forskrift af því hvernig má nota gagnasafnið "Ravnursson Faroese Speech and Transcripts" [1] til að búa til talgreini í verkfærakistunni Kaldi [2]. [1] Hernández Mena, Carlos Daniel; Simonsen, Annika. "Ravnursson Faroese Speech and Transcripts". Web Downloading: http://hdl.handle.net/20.500.12537/276 [2] Povey, D., Ghoshal, A., Boulianne, G., Burget, L., Glembek, O., Goel, N., ... & Vesely, K. (2011). The Kaldi speech recognition toolkit. In IEEE 2011 workshop on automatic speech recognition and understanding (No. CONF). IEEE Signal Processing Society.
dc.language.iso fao
dc.publisher Reykjavík University
dc.rights Creative Commons - Attribution 4.0 International (CC BY 4.0)
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.rights.label PUB
dc.source.uri https://github.com/CarlosDanielMena/Kaldi_Recipe_for_Faroese
dc.subject faroese
dc.subject kaldi
dc.subject ASR
dc.subject Speech Recognition
dc.subject Acoustic Model
dc.title Kaldi Recipe for Faroese
dc.type toolService
metashare.ResourceInfo#ContentInfo.detailedType other
metashare.ResourceInfo#ResourceComponentType#ToolServiceInfo.languageDependent true
has.files yes
branding Clarin IS Repository
contact.person Carlos Daniel Hernández Mena carlos.mena@ciempiess.org Reykjavík University
files.size 106244360
files.count 2


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-------------------------------------------------------------------------------
                           Kaldi Recipe for Faroese
-------------------------------------------------------------------------------

Author                : Carlos Daniel Hernández Mena.

Programming Languages : Kaldi, Python3, Bash

Recommended use       : speech recognition.

-------------------------------------------------------------------------------
Description
-------------------------------------------------------------------------------

The "Kaldi Recipe for Faroese" is a code recipe intended to show how to use
the corpus "Ravnursson Faroese Speech and Transcripts" [1] to create automatic 
speech recognition systems using the Kaldi toolkit [2].

In order to set the scripts up, it is necessary to install minimum 
requirements and to specify some paths; all of these indicated in the 
"run.sh" script of the recipe.

-------------------------------------------------------------------------------
Abou . . .
                                            
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  • Kaldi_Recipe_for_Faroese
    • README.txt-1 B
    • ravnursson
      • s5
        • cmd.sh-1 B
        • utils18 B
        • steps18 B
        • conf
          • online_cmvn.conf-1 B
          • decode.config-1 B
          • mfcc_hires.conf-1 B
          • mfcc.conf-1 B
        • run.sh-1 B
        • local
          • Prefabricated_Files
            • silence_phones.txt-1 B
            • extra_questions.txt-1 B
            • optional_silence.txt-1 B
            • nonsilence_phones.txt-1 B
          • etc
            • 3GRAM_ARPA_MODEL_PRUNED.lm-1 B
            • FAROESE_ASR.dic-1 B
            • 4GRAM_ARPA_MODEL.lm-1 B
          • create_lexicon.py-1 B
          • chain
            • run_tdnn_lstm_ADAPTED.sh29 B
            • compare_wer_general.sh-1 B
            • tuning
              • run_lstm_1c.sh-1 B
              • run_tdnn_lstm_1r.sh-1 B
              • run_tdnn_1f.sh-1 B
              • run_tdnn_lstm_1a.sh-1 B
              • run_tdnn_lstm_1f.sh-1 B
              • run_tdnn_lstm_1k.sh-1 B
              • run_lstm_1e_ADAPTED.sh-1 B
              • run_lstm_1a.sh-1 B
              • run_tdnn_1d.sh-1 B
              • run_tdnn_lstm_attention_bs_1a.sh-1 B
              • run_tdnn_lstm_1u.sh-1 B
              • run_tdnn_lstm_1d.sh-1 B
              • run_tdnn_lstm_1i.sh-1 B
              • run_tdnn_lstm_1n.sh-1 B
              • run_tdnn_1b.sh-1 B
              • run_blstm_1a.sh-1 B
              • run_lstm_1d.sh-1 B
              • run_tdnn_lstm_1s.sh-1 B
              • run_tdnn_1g.sh-1 B
              • run_tdnn_lstm_1b.sh-1 B
              • run_tdnn_lstm_1g.sh-1 B
              • run_tdnn_lstm_attention_1a.sh-1 B
              • run_tdnn_lstm_1e_disc.sh-1 B
              • run_tdnn_lstm_1l.sh-1 B
              • run_lstm_1b.sh-1 B
              • run_tdnn_1e.sh-1 B
              • run_tdnn_lstm_attention_bs_1b.sh-1 B
              • run_tdnn_lstm_1v.sh-1 B
              • run_tdnn_lstm_1e.sh-1 B
              • run_tdnn_lstm_1j.sh-1 B
              • run_tdnn_lstm_1o.sh-1 B
              • run_tdnn_1c.sh-1 B
              • run_lstm_1e.sh-1 B
              • run_tdnn_lstm_1t.sh-1 B
              • run_tdnn_lstm_1c.sh-1 B
              • run_tdnn_lstm_1h.sh-1 B
              • run_tdnn_lstm_1m.sh-1 B
              • run_tdnn_1a.sh-1 B
          • nnet3
            • run_tdnn_lstm.sh26 B
            • run_tdnn_lstm_disc.sh31 B
            • run_tdnn_lstm_lfr.sh30 B
            • run_ivector_common_ADAPTED.sh-1 B
            • run_tdnn.sh21 B
            • run_lstm.sh21 B
            • tuning
              • run_tdnn_lstm_1c.sh-1 B
              • run_lstm_1a.sh-1 B
              • run_tdnn_1b.sh-1 B
              • run_tdnn_lstm_1b.sh-1 B
              • run_tdnn_1a.sh-1 B
              • run_tdnn_lstm_1a_disc.sh-1 B
              • run_tdnn_lstm_1a.sh-1 B
              • run_tdnn_lfr_1a.sh-1 B
              • run_tdnn_lstm_1b_disc.sh-1 B
              • run_tdnn_1c.sh-1 B
              • run_tdnn_lstm_lfr_1a.sh-1 B
            • run_blstm.sh-1 B
            • compare_wer.sh-1 B
            • run_ivector_common.sh-1 B
          • score.sh23 B
          • corpus_faroese_data_prep.py-1 B
        • path.sh-1 B
        • RESULTS-1 B

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