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License: MIT License
An implementation of deep speaker from baidu
License: MIT License
Is it possible to take weights for deeps speaker from https://github.com/a-nagrani/VGGVox and load them into your code and run? And even if no, how can I train your model and test it after? I've looked through the deep-speaker/deep_speaker/train/ folder but I still can not get how to path dataset for training
Thanks in advance)
The model used on deep-speaker is ResNet. This is the first model that must be tested.
Test performance with Inception model.
An MFCC library is needed to extract features. It should be implemented in rust, with python bindings via cbindgen and cffi. some crates are availabe:
we must test them to see if they can provide the same result than the default tensorflow one, and allow for more configuration.
Otherwise a new library can be written, preferably on top of an existing dct library. One is available here:
If no serious option exists, then fftw can be used as a base of a new mfcc rust library:
The training application must be implemented with cyclotron, so that is is functional and reactive. This should ease experimenting with multiple models and multiple settings. It should also allow some performance optimizations on the training.
Hard triplet loss is needed. Tensorflow contains a contrib with a semi-hard triplet loss. This contrib code can be derived to implement hard triplet loss.
https://www.tensorflow.org/api_docs/python/tf/contrib/losses/metric_learning/triplet_semihard_loss
Cosine similarity is needed to compute triplet loss. This is the distance function used in deep-speaker.
Test performances with mobilenet v2 model.
Implement feature extraction independently from the training part: Since this part takes some time, it should be done once so that multiple trainings can be done without computing them each time.
input is the voxceleb2 dataset.
output is a mfcc binary dataset (MFCC storage format to define.).
configuration parameters:
Utterances longer than the one used for features are split in several utterance. This allows to increase the dataset.
Notes:
No background sound is added and no volume adjustment is done because the voxceleb2 dataset already contains various backgrounds.
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