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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Deep Speaker: an End-to-End Neural Speaker Embedding System.
| Date | Stars |
|---|---|
| 2026-07-24 | 941 |
| 2026-07-25 | 941 |
| 2026-07-28 | 941 |
| 2026-07-30 | 941 |
| 2026-08-06 | 941 |
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## Deep Speaker: An End-to-End Neural Speaker Embedding System. Unofficial Tensorflow/Keras implementation of Deep Speaker | [Paper](https://arxiv.org/pdf/1705.02304.pdf) | [Pretrained Models](https://drive.google.com/open?id=18h2bmsAWrqoUMsh_FQHDDxp7ioGpcNBa). Tested with Tensorflow 2.3, 2.4, 2.5 and 2.6. ### Sample Results Models were trained on clean speech data. Keep in mind that the performance will be lower on noisy data. It is advised to remove silence and background noise before computing the embeddings (by using Sox for example). There is a discussion on the topic: [Silence / Background Noise similarity](https://github.com/philipperemy/deep-speaker/issues/62). *Model name* | *Testing dataset* | *Num speakers* | *F* | *TPR* | *ACC* | *EER* | Training Logs | Download model | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | ResCNN Softmax trained | [LibriSpeech](http://www.openslr.org/12/) all(*) | 2484 | 0.789 | 0.733 | 0.996 | 0.043 | [Click](https://docs.google.com/document/d/1ZZjBk5TgFgaY9GgOcHaieOpiyB9lB6oTRSdk6g8FPRs) | [Click](https://drive.google.com/open?id=1SJBmHpnaW1VcbFWP6JfvbT3wWP9PsqxS) ResCNN Softmax+Triplet trained | [LibriSpeech](http://www.openslr.org/12/) all(*) | 2484 | 0.843 | 0.825 | 0.997 | 0.025 | [Click](https://docs.google.com/document/d/1mL0Jb8IpA7DOzFci71RT1OYTq7Kkw2DjTkI4BRpEzKc) | [Click](https://drive.google.com/file/d/1F9NvdrarWZNktdX9KlRYWWHDwRkip_aP) (*) all includes: dev-clean, dev-other, test-clean, test-other, train-clean-100, train-clean-360, train-other-500. The Softmax+Triplet checkpoint is also available on the [Chinese cloud - WeiYun](https://share.weiyun.com/V2suEUVh). ### Overview Deep Speaker is a neural speaker embedding system that maps utterances to a hypersphere where speaker similarity is measured by cosine similarity. The embeddings generated by Deep Speaker can be used for many tasks, including speaker identification, verification, and clustering. ## Getting started ### Install dependencies #### Requirements - tensorflow>=2.0 - keras>=2.3.1 - python>=3.6 ```bash pip install -r requirements.txt ``` If you see this error: `libsndfile not found`, run this: `sudo apt-get install libsndfile-dev`. ### Training The code for training is available in this repository. It takes a bit less than a week with a GTX1070 to train the models. System requirements for a complete training are: - At least 300GB of free disk space on a fast SSD (250GB just for all the uncompressed + processed data) - 32GB of memory and at least 32GB of swap (can create swap with SSD space). - A NVIDIA GPU such as the 1080Ti. ```bash pip uninstall -y tensorflow && pip install tensorflow-gpu ./deep-speaker download_librispeech # if the download is too slow, consider replacing [wget] by [axel -n 10 -a] in download_librispeech.sh. ./deep-speaker build_mfcc # will build MFCC for softmax pre-training and triplet training. ./deep-speaker build_model_inputs # will build inputs for softmax pre-training. ./deep-speaker train_softmax # takes ~3 days. ./deep-speaker train_triplet # takes ~3 days. ``` NOTE: If you want to use your own dataset, make sure you follow the directory structure of librispeech. Audio files have to be in `.flac`. format. If you have `.wav`, you can use `ffmpeg` to make the conversion. Both formats are flawless (FLAC is compressed WAV). ### Test instruction using pretrained model - Download the trained models *Model name* | *Used datasets for training* | *Num speakers* | *Model Link* | | :--- | :--- | :--- | :--- | ResCNN Softmax trained | [LibriSpeech](http://www.openslr.org/12/) train-clean-360 | 921 | [Click](https://drive.google.com/open?id=1SJBmHpnaW1VcbFWP6JfvbT3wWP9PsqxS) ResCNN Softmax+Triplet trained | [LibriSpeech](http://www.openslr.org/12/) all | 2484 | [Click](https://drive.google.com/file/d/1F9NvdrarWZNktdX9KlRYWWHDwRkip_aP) Note: the pre-training was performed on a subset of all the speakers w
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Philippe Rémy · Imperial College London
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a18f47ab5af8bc19, topic:deep-learning, topic:tensorflow, readme:pre-training