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kaldi_de.yaml
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# You have to download Librispeech "online nnet2" models in order to use this sample
# Run download-librispeech-nnet2.sh in 'test/models' to download them.
use-nnet2: True
decoder:
# All the properties nested here correspond to the kaldinnet2onlinedecoder GStreamer plugin properties.
# Use gst-inspect-1.0 ./libgstkaldionline2.so kaldinnet2onlinedecoder to discover the available properties
nnet-mode: 3
use-threaded-decoder: True
model : kaldi_de/final.mdl
word-syms : kaldi_de/words.txt
fst : kaldi_de/HCLG.fst
# feature-type : "mfcc"
mfcc-config : kaldi_de/conf/mfcc_hires.conf
frame-subsampling-factor: 3
ivector-extraction-config : kaldi_de/ivector_extractor/ivector_extractor.conf
# max-active: 10000
# beam: 10.0
max-active: 10000
beam: 5.0
lattice-beam: 5.0
acoustic-scale: 1.0 #0.083
do-endpointing : true
endpoint-silence-phones : "1:2:3:4:5:6"
traceback-period-in-secs: 0.25
chunk-length-in-secs: 0.25
num-nbest: 10
#Additional functionality that you can play with:
#lm-fst: test/models/english/librispeech_nnet_a_online/G.fst
#big-lm-const-arpa: test/models/english/librispeech_nnet_a_online/G.carpa
phone-syms: kaldi_de/phones.txt
#word-boundary-file: de_350k_nnet3chain_tdnn1f_1024_sp_bi/phones/word_boundary.int
#do-phone-alignment: true
out-dir: tmp
use-vad: False
silence-timeout: 180
# Just a sample post-processor that appends "." to the hypothesis
post-processor: perl -npe 'BEGIN {use IO::Handle; STDOUT->autoflush(1);} s/(.*)/\1./;'
# A sample full post processor that add a confidence score to 1-best hyp and deletes other n-best hyps
#full-post-processor: ./sample_full_post_processor.py
logging:
version : 1
disable_existing_loggers: False
formatters:
simpleFormater:
format: '%(asctime)s - %(levelname)7s: %(name)10s: %(message)s'
datefmt: '%Y-%m-%d %H:%M:%S'
handlers:
console:
class: logging.StreamHandler
formatter: simpleFormater
level: DEBUG
root:
level: DEBUG
handlers: [console]