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- Name
- README.txt
- Size
- 5.38 KB
- Format
- Text file
- Description
- Unknown
- MD5
- d064579c2e4a0b08fe130b947b5f0ce6
-------------------------------------------------------------------------------
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 . . .
- Name
- Kaldi_Recipe_for_Faroese.zip
- Size
- 101.32 MB
- Format
- application/zip
- Description
- Unknown
- MD5
- 28ba7ae492920ce1bbe84e631e6e49aa
- 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
- Prefabricated_Files
- path.sh-1 B
- RESULTS-1 B
- s5