Use kaldi trained model in mobvoihotwords

HARK FORUM Use kaldi trained model in mobvoihotwords

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  • #2271
    Avatarshipleyxie
    Participant

    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():
    [1]    48813 abort (core dumped)  ~/project/kaldidecoder3/build/src/kaldidecoder  --nnet-type=3
    
    • This topic was modified 1 month, 1 week ago by Avatarshipleyxie.
    #2295

    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.
    https://www.hark.jp/document/hark-document-en/subsec-KaldiDecoder.html

    Best regards,
    HARK Support Team.

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