Speech

Python scripts to compute audio and language models from voxforge.org speech data. Models that can be built include:

• CMU Sphinx audio model
• various Kaldi audio models
• cmuclmtk language model
• srilm language model
• sequitur g2p model

Important: Please note that these scripts form in no way a complete application ready for end-user consumption. However, if you are a developer interested in natural language processing you may find some of them useful. Contributions, patches and pull requests are very welcome.

At the time of this writing, the scripts here are focused on building the german VoxForge model. However, there is no reason why they couldn't be used to build other language models as well, in fact I am planning to add support for the english language und audio models soon.

Links

• Data / Models

• Code

Requirements

Note: very incomplete.

• Python 2.7 with nltk, numpy, ...
• CMU Sphinx, cmuclmtk
• srilm
• kaldi

Setup Notes

Just some rough notes on the environment needed to get these scripts to run. This is in no way a complete set of instructions, just some hints to get you started.

~/.speechrc:

[speech]
vf_login            = <your voxforge login>

vf_audiodir_de      = /home/bofh/projects/ai/data/speech/de/voxforge/audio
vf_contribdir_de    = /home/bofh/projects/ai/data/speech/de/voxforge/audio-contrib
extrasdir_de        = /home/bofh/projects/ai/data/speech/de/kitchen
gspv2_dir           = /home/bofh/projects/ai/data/speech/de/gspv2

kaldi_root          = /apps/kaldi

wav16_dir_de        = /home/bofh/projects/ai/data/speech/de/16kHz
wav16_dir_en        = /home/bofh/projects/ai/data/speech/en/16kHz

europarl_de         = /home/bofh/projects/ai/data/corpora/de/europarl-v7.de-en.de
parole_de           = /home/bofh/projects/ai/data/corpora/de/German Parole Corpus/DE_Parole/

[tts]
host      = dagobert
port      = 8300

Language Model

extract sentences from corpuses:

./speech_sentences.py

train language model using SRILM for use in both sphinx and kaldi builds:

./speech_build_lm.py

voxforge

download latest audio data from voxforge, add them to submissions:

./speech_pull_voxforge.sh
./speech_audio_scan.py

Submission Review and Transcription

The main tool used for submission review, transcription and lexicon expansion is:

./speech_editor.py

Lexicon

The lexicon used here (data/src/speech/de/dict.ipa) is my own creation, i.e. entries have been manually checked and added using my speech_editor / lex_editor tools. For new entries, I usually let MaryTTS, espeak and sequitur generate phonemes, listen to them using MaryTTS and pick the best one. Quite frequently I will still make manual adjustments (typically I will add or move stress markers, syllable boundaries, change vocal lengths, ...), often using additional sources like wiktionary which has IPA transcriptions for many words.

In general it is recommended to use the speech_editor.py tool (see above) which ensures all lexicon entries are actually covered by audio submissions. However, there are tools which work on the lexicon directly:

I also tend to review lexicon entries randomly from time to time. For that I have a small script which will pick 20 random entries where sequitur disagrees with the current transcription in the lexicon:

./speech_lex_edit.py `./speech_lex_review.py`

Also, I sometimes use this command to add missing words from transcripts in batch mode:

./speech_lex_edit.py `./speech_lex_missing.py`

CMU Sphinx Model

To build the CMU Sphinx model:

./speech_sphinx_export.py
cd data/dst/speech/de/cmusphinx/
./sphinx-run.sh

Running pocketsphinx

just a sample invocation for live audio from mic:

pocketsphinx_continuous \
    -hmm model_parameters/voxforge.cd_cont_3000 \
    -lw 10 -feat 1s_c_d_dd -beam 1e-80 -wbeam 1e-40 \
    -dict etc/voxforge.dic \
    -lm etc/voxforge.lm.DMP \
    -wip 0.2 \
    -agc none -varnorm no -cmn current

Kaldi Models

HMM Models

To build the kaldi models:

./speech_kaldi_export.py
cd data/dst/speech/de/kaldi/
./run-lm.sh
./run-am.sh

Once this is finished, you can find various models in the exp/ subdirectory. A few notes on what all those models are supposed to be:

exp              tool                  training set       lm        based on        # comment
---------------------------------------------------------------------------------------------------------------------------------
mono             train_mono.sh         train              lang                      # Train monophone models
mono_ali         align_si.sh           train              lang      mono            # Get alignments from monophone system.
tri1             train_deltas.sh       train              lang      mono_ali        # train tri1 [first triphone pass]
tri1_ali         align_si.sh           train              lang      tri1            
tri2a            train_deltas.sh       train              lang      tri1_ali        # Train tri2a, which is deltas+delta+deltas
tri2b            train_lda_mllt.sh     train              lang      tri1_ali        # tri2b [LDA+MLLT]
tri2b_ali        align_si.sh           train              lang      tri2b           # Align all data with LDA+MLLT system (tri2b)
tri2b_denlats    make_denlats.sh       train              lang      tri2b           # Do MMI on top of LDA+MLLT.
tri2b_mmi        train_mmi.sh          train              lang      tri2b_denlats   
tri2b_mmi_b0.05  train_mmi.sh --boost  train              lang      tri2b_denlats 
tri2b_mpe        train_mpe.sh          train              lang      tri2b_denlats   # Do MPE.

tri3b            train_sat.sh          train              lang      tri2b_ali       # LDA + MLLT + SAT.
tri3b_ali        align_fmllr.sh        train              lang      tri3b           # align all data.

tri3b_denlats    make_denlats.sh       train              lang      tri3b           # Do MMI on top of LDA+MLLT+SAT
tri3b_mmi        train_mmi.sh          train              lang      tri3b_denlats   
tri3b_mmi_b0.05  train_mmi.sh --boost  train              lang      tri3b_denlats 
tri3b_mpe        train_mpe.sh          train              lang      tri3b_denlats   # Do MPE.

ubm5a            train_ubm.sh          train              lang      tri3b_ali       # SGMM (subspace gaussian mixture model)
sgmm_5a          train_sgmm2.sh        train              lang      ubm5a
sgmm_5a_denlats  make_denlats_sgmm2.sh train              lang      sgmm_5a_ali 
sgmm_5a_mmi_b0.1 train_mmi_sgmm2.sh    train              lang      sgmm_5a_denlats 

NNet3 Models

To build the kaldi models:

./speech_kaldi_export.py
cd data/dst/speech/de/kaldi/
./run-lm.sh
./run-nnet3.sh

License

My own scripts as well as the data I create (i.e. lexicon and transcripts) is LGPLv3 licensed unless otherwise noted in the script's copyright headers.

Some scripts and files are based on works of others, in those cases it is my intention to keep the original license intact. Please make sure to check the copyright headers inside for more information.

Author

Guenter Bartsch guenter@zamia.org