hii @nmstoker I am not able to analyze loss
e.g
Epoch 1 | Training | Elapsed Time: 1 day, 23:54:37 | Steps: 2528 | Loss: 100.6082
and starting loss is around 178
how to calculate loss ?
and another question is:
I am train a model but not give --epochs parameter so,is any default value for epochs
Thank you
I followed these instructions, but I am noticing some strange behavior (it sort of makes sense, but I want to mitigate it). I am trying to use deepspeech with a very small number of commands. I created a corpus that uses these command phrases which include sequences of numbers and then created the custom trie and lm.binary files.
The LM works and increases the accuracy of the model for my use case. The strange behavior is that the model becomes very bad at ignoring OOV words. Instead of classifying it as an , it seems to be forcing things into a bucket.
For example, I created a LM that focuses mostly on numbers and then as a smoke test, I passed it audio from LibriSpeech which may include numbers sometimes; it is mostly other words.
The output of that is:
two nine one two
one one ten one
five ten four seven
three one
Is there a way to check confidences to manually ignore, or can I set this up differently to better ignore them by means of the language model? Thanks!