Comments (5)
Yes great point. In the committed code the ground truth labels are being used for the transducer. I have some code to do this properly using the inferred output but I haven't had a chance to clean it up and commit it yet. That's why I haven't posted "transducer" results in the README yet.
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And yes, I will continue to support this project though any collaborators would be very welcome!!
from speech.
@awni Have you made any progress on cleaning up the decoder?
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Just for reference, here is my inference code.
https://gist.github.com/yjchun/aae0b86f89ffb46a54c28e7747124ee4
May not be useful as it is because it uses my model class's internal logic.
The problem is inference is awfully slow. Takes 3 seconds for 10 seconds of audio with 400 hidden layers. 161 chars = 161 prediction+joint network calls at minimum.
Had to read Grave's paper in and out for a few days for this piece of code.
And thank you @awni for sharing your transducer loss implementation which was very helpful.
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Here is my implement for greed decoder.
you may need some change for your lstm decoder network.
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Related Issues (20)
- ctc decoder with language model
- Pytest FAILURES
- Word Level LM at Line 96
- WARNING: Forward backward likelihood mismatch 0.000050 HOT 1
- make error from torch.utils.ffi import create_extension HOT 1
- your paper link
- Error in seq2seq.py of Method collate HOT 3
- Sample generation
- Make error HOT 1
- 请问有成功训练出中文汉语模型的么 HOT 1
- make error : system cannot find the path specified
- Out of memory Error HOT 1
- Multi Gpu Training support
- Loss is decrease but SER is increase
- Transducer: zip object is not subscriptable HOT 1
- Could you share your WER on librispeech?
- Make Requires Cuda
- TIMIT PER HOT 2
- Can this model convert Chinese audio to text?
- [CONTRIBUTION] Speech Dataset Generator
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