Use kaldi trained model in mobvoihotwords

HARK FORUM Use kaldi trained model in mobvoihotwords

Viewing 2 posts - 1 through 2 (of 2 total)
  • Author
    Posts
  • #2271
    shipleyxie
    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 3 years, 2 months ago by shipleyxie.
      #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.

    Viewing 2 posts - 1 through 2 (of 2 total)
    • You must be logged in to reply to this topic.