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: 3 months 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 (about 6 years ago)
• Default Branch: master
• Last Pushed: 2022-01-20T11:54:08.000Z (almost 3 years ago)
• Last Synced: 2024-06-05T13:46:13.911Z (5 months ago)
• Topics: pytorch, speaker-identification, speaker-verification
• Language: Python
• Homepage:
• Size: 54.7 KB
• Stars: 573
• Watchers: 18
• Forks: 165
• Open Issues: 31
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-speaker-embedding - GE2E

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

![training loss](https://github.com/HarryVolek/PyTorch_Speaker_Verification/blob/master/Results/Loss.png)

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