HARK FORUM › Use kaldi trained model in mobvoihotwords
Tagged: kaldi; mobvoihotwords;
- This topic has 1 reply, 2 voices, and was last updated 2 years, 2 months ago by Masayuki Takigahira.
March 10, 2021 at 11:30 am #2271shipleyxieParticipant
I have trained a model in kaldi(mobvoihotwords), and i want use it in kaldidecoder(kaldidecoder-hark_3.1.0-openblas.tar.xz).
Following the instruction of kaldidecoder, I run the below cmd, but got some cnfused *ERROR*, Could you help me to solve this?
kaldidecoder --filename-features-list=wav.scp --nnet-type=3 --filename-words=../tree/graph_online/words.txt --filename-align-lexicon=../tree/graph_online/phones/align_lexicon.int --filename-mdl=final.mdl --filename-fst=../tree/graph_online/HCLG.fst
The log messages are posted below;
[[ Configuration of UI-Registered options ]] acoustic-scale = 0.1 add-pitch = false beam = 16 beam-delta = 0.5 computation.debug = false config = '' debug-computation = false delta = 0.000976562 determinize-lattice = true enable-debug = false extra-left-context-initial = 0 fbank-config = '' feature-type = 'mfcc' filename-align-lexicon = '../tree/graph_online/phones/align_lexicon.int' filename-class-frame-counts = '' filename-feature-transform = '' filename-features-list = 'wav.scp' filename-fst = '../tree/graph_online/HCLG.fst' filename-mdl = 'final.mdl' filename-nnet = '' filename-phones = '' filename-words = '../tree/graph_online/words.txt' frame-subsampling-factor = 1 frames-per-chunk = 20 hash-ratio = 2 help = false host-mfcnet = 'localhost' host-result = 'localhost' ivector-extraction-config = '' ivector-silence-weighting.max-state-duration = -1 ivector-silence-weighting.silence-phones = '' ivector-silence-weighting.silence-weight = 1 lattice-beam = 10 lm-name = '' max-active = 2147483647 max-mem = 50000000 max-tasks = 4 mfcc-config = '' min-active = 200 minimize = false nbest_count = 1 nnet-type = 3 online-pitch-config = '' optimization.allocate-from-other = true optimization.allow-left-merge = true optimization.allow-right-merge = true optimization.backprop-in-place = true optimization.consolidate-model-update = true optimization.convert-addition = true optimization.extend-matrices = true optimization.initialize-undefined = true optimization.max-deriv-time = 2147483647 optimization.max-deriv-time-relative = 2147483647 optimization.memory-compression-level = 1 optimization.min-deriv-time = -2147483648 optimization.move-sizing-commands = true optimization.optimize = true optimization.optimize-row-ops = true optimization.propagate-in-place = true optimization.remove-assignments = true optimization.snip-row-ops = true optimization.split-row-ops = true phone-determinize = true plp-config = '' port-mfcnet = 5530 port-result = 10500 print-args = true print-kaldi-license = false print-openblas-license = false print-openfst-license = false progress-output-interval = 0 prune-interval = 25 splice = 3 verbose = 0 word-determinize = true [ERROR] Expected token "<Nnet3>", got instead "<DIMENSION>". terminate called after throwing an instance of 'std::runtime_error' what():  48813 abort (core dumped) ~/project/kaldidecoder3/build/src/kaldidecoder --nnet-type=3
March 15, 2021 at 1:09 pm #2295Masayuki TakigahiraModerator
- This topic was modified 2 years, 3 months ago by shipleyxie.
Thank you for your inquiry.
[ERROR] Expected token "<Nnet3>", got instead "<DIMENSION>".
According to the content of the error message, it seems that a file that is not a model in nnet3 format was entered. First, please check the your recipe used for learning.
From the content of the error message, it looks like you’ve probably given a model in nnet1 format.
Is there a file called final.nnet other than final.mdl? If final.nnet exists, you should specify –nnet-type=1 instead of –nnet-type=3 because it is a learning recipe for nnet1.
For details, refer to the following items in HARK-Document.
HARK Support Team.
- This reply was modified 2 years, 2 months ago by Masayuki Takigahira.
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