Comments (3)
You should use 320 labels
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thank you...
it works. my network is two layer with 320 neurons, but the convergence is very slow.
what could be the reasons
- my inputs are binary images. whether cmvn is required ?
- due to frame skipping. if so, kindly say where to change the parameter
- any parameter is specifically set for speech in eesen
from eesen.
the problem was with parameters...
now it is working
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Related Issues (20)
- Clean up v2 for swb
- DeepBiLSTM HOT 2
- Missing label.counts HOT 3
- Query on LibriSpeech Character Error Rate HOT 2
- difference in output labels HOT 1
- Memory Leak HOT 1
- failed: Dim() == v.Dim() HOT 2
- Potential overflow when calculating exp
- Clarification Regarding Using WFST decoding HOT 1
- Installing error HOT 8
- Training Error when run tedlium recipe HOT 2
- LatticeFasterDecoder failed with "link_extra_cost == link_extra_cost" HOT 1
- Cannot install openfst-1.4.1 HOT 2
- Read failure in ReadBasicType, file position is -1, next char is -1
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- Librispeech - Training starting error HOT 3
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