Deep Speaker: an End-to-End Neural Speaker Embedding System https://arxiv.org/pdf/1705.02304.pdf
This code is not functional yet! I'm making a call for contributors to help make a great implementation! The basics stuffs are already there. Thanks!
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
Simply run those commands:
git clone https://github.com/philipperemy/deep-speaker.git
cd deep-speaker
pip3 install -r requirements.txt
cd audio/
./convert_flac_2_wav.sh # make sure ffmpeg is installed!
cd ..
python3 models_train.py
Preconditions:
- Installed tensorflow: https://www.tensorflow.org/install/install_linux
sudo apt-get install python3-tk ffmpeg- ~ 6 GB memory
- install ffmpeg (and add to PATH)
- use git bash for:
cd audio; ./convert_flac_2_wav.sh - other steps analogous to above
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
Training on the GPU.
Visualization of a possible triplet (Anchor, Positive, Negative) in the cosine similarity space
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