Deep Speaker: an End-to-End Neural Speaker Embedding System https://arxiv.org/pdf/1705.02304.pdf
Work accomplished so far:
- Triplet loss
- Triplet loss test
- Model implementation
- Data pipeline implementation. We're going to use the LibriSpeech dataset with 2300+ different speakers.
- Train the models
Visualization of a possible triplet (Anchor, Positive, Negative) in the cosine similarity space
Please message me if you want to contribute. I'll be happy to hear your ideas. There are a lot of undisclosed things in the paper, such as:
- Input size to the network? Which inputs exactly?
- How many filter banks do we use?
- Sample Rate?
Available here: http://www.openslr.org/12/
List of possible other datasets: http://kaldi-asr.org/doc/examples.html
Extract of this dataset:
filenames chapter_id speaker_id dataset_id
0 /Volumes/Transcend/data-set/LibriSpeech/dev-clean/1272/128104/1272-128104-0000.wav 128104 1272 dev-clean
1 /Volumes/Transcend/data-set/LibriSpeech/dev-clean/1272/128104/1272-128104-0001.wav 128104 1272 dev-clean
2 /Volumes/Transcend/data-set/LibriSpeech/dev-clean/1272/128104/1272-128104-0002.wav 128104 1272 dev-clean
3 /Volumes/Transcend/data-set/LibriSpeech/dev-clean/1272/128104/1272-128104-0003.wav 128104 1272 dev-clean
4 /Volumes/Transcend/data-set/LibriSpeech/dev-clean/1272/128104/1272-128104-0004.wav 128104 1272 dev-clean
5 /Volumes/Transcend/data-set/LibriSpeech/dev-clean/1272/128104/1272-128104-0005.wav 128104 1272 dev-clean
6 /Volumes/Transcend/data-set/LibriSpeech/dev-clean/1272/128104/1272-128104-0006.wav 128104 1272 dev-clean
7 /Volumes/Transcend/data-set/LibriSpeech/dev-clean/1272/128104/1272-128104-0007.wav 128104 1272 dev-clean
8 /Volumes/Transcend/data-set/LibriSpeech/dev-clean/1272/128104/1272-128104-0008.wav 128104 1272 dev-clean
9 /Volumes/Transcend/data-set/LibriSpeech/dev-clean/1272/128104/1272-128104-0009.wav 128104 1272 dev-clean
