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https://github.com/HarryVolek/PyTorch_Speaker_Verification
PyTorch implementation of "Generalized End-to-End Loss for Speaker Verification" by Wan, Li et al.
https://github.com/HarryVolek/PyTorch_Speaker_Verification
pytorch speaker-identification speaker-verification
Last synced: 26 days ago
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PyTorch implementation of "Generalized End-to-End Loss for Speaker Verification" by Wan, Li et al.
- Host: GitHub
- URL: https://github.com/HarryVolek/PyTorch_Speaker_Verification
- Owner: HarryVolek
- License: bsd-3-clause
- Created: 2018-09-20T23:25:08.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2022-01-20T11:54:08.000Z (over 2 years ago)
- Last Synced: 2024-02-21T05:35:12.376Z (4 months ago)
- Topics: pytorch, speaker-identification, speaker-verification
- Language: Python
- Homepage:
- Size: 54.7 KB
- Stars: 566
- Watchers: 18
- Forks: 165
- Open Issues: 31
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-diarization - PyTorch_Speaker_Verification - vector | Python & PyTorch | PyTorch implementation of "Generalized End-to-End Loss for Speaker Verification" by Wan, Li et al. With UIS-RNN integration. | (Software / Speaker embedding)
- my-awesome-stars - HarryVolek/PyTorch_Speaker_Verification - PyTorch implementation of "Generalized End-to-End Loss for Speaker Verification" by Wan, Li et al. (Python)
- awesome-speaker-embedding - GE2E
- awesome-asv-antispoofing - PyTorch_Speaker_Verification - vector | Python & PyTorch | PyTorch implementation of "Generalized End-to-End Loss for Speaker Verification" by Wan, Li et al. With UIS-RNN integration. | (Software / Speaker embedding)
README
# PyTorch_Speaker_Verification
PyTorch implementation of speech embedding net and loss described here: https://arxiv.org/pdf/1710.10467.pdf.
Also contains code to create embeddings compatible as input for the speaker diarization model found at https://github.com/google/uis-rnn
The TIMIT speech corpus was used to train the model, found here: https://catalog.ldc.upenn.edu/LDC93S1,
or here, https://github.com/philipperemy/timit
# Dependencies
* PyTorch 0.4.1
* python 3.5+
* numpy 1.15.4
* librosa 0.6.1
The python WebRTC VAD found at https://github.com/wiseman/py-webrtcvad is required to create run dvector_create.py, but not to train the neural network.
# Preprocessing
Change the following config.yaml key to a regex containing all .WAV files in your downloaded TIMIT dataset. The TIMIT .WAV files must be converted to the standard format (RIFF) for the dvector_create.py script, but not for training the neural network.
```yaml
unprocessed_data: './TIMIT/*/*/*/*.wav'
```
Run the preprocessing script:
```
./data_preprocess.py
```
Two folders will be created, train_tisv and test_tisv, containing .npy files containing numpy ndarrays of speaker utterances with a 90%/10% training/testing split.
# Training
To train the speaker verification model, run:
```
./train_speech_embedder.py
```
with the following config.yaml key set to true:
```yaml
training: !!bool "true"
```
for testing, set the key value to:
```yaml
training: !!bool "false"
```
The log file and checkpoint save locations are controlled by the following values:
```yaml
log_file: './speech_id_checkpoint/Stats'
checkpoint_dir: './speech_id_checkpoint'
```
Only TI-SV is implemented.
# Performance
```
EER across 10 epochs: 0.0377
```
# D vector embedding creation
After training and testing the model, run dvector_create.py to create the numpy files train_sequence.npy, train_cluster_ids.npy, test_sequence.npy, and test_cluster_ids.npy.
These files can be loaded and used to train the uis-rnn model found at https://github.com/google/uis-rnn