| --- |
| tags: |
| - pytorch_model_hub_mixin |
| - model_hub_mixin |
| - gender-classification |
| - VoxCeleb |
| license: mit |
| datasets: |
| - ProgramComputer/voxceleb |
| pipeline_tag: audio-classification |
| --- |
| |
| ## Usage |
| ``` |
| import torch |
| from model import ECAPA_gender |
| # You could directly download the model from the huggingface model hub |
| model = ECAPA_gender.from_pretrained("JaesungHuh/voice-gender-classifier") |
| model.eval() |
| # If you are using gpu .... |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
| model.to(device) |
| # Load the audio file and use predict function to directly get the output |
| example_file = "data/00001.wav" |
| with torch.no_grad(): |
| output = model.predict(example_file, device=device) |
| print("Gender : ", output) |
| ``` |
|
|
| ## Pretrained weights |
| For those who need pretrained weights, please download it in [here](https://drive.google.com/file/d/1ojtaa6VyUhEM49F7uEyvsLSVN3T8bbPI/view?usp=sharing) |
|
|
| ## Training details |
| State-of-the-art speaker verification model already produces good representation of the speaker's gender. |
|
|
| I used the pretrained ECAPA-TDNN from [TaoRuijie's](https://github.com/TaoRuijie/ECAPA-TDNN) repository, added one linear layer to make two-class classifier, and finetuned the model with the VoxCeleb2 dev set. |
|
|
| The model achieved **98.7%** accuracy on the VoxCeleb1 identification test split. |
|
|
| ## Caveat |
| I would like to note the training dataset I've used for this model (VoxCeleb) may not represent the global human population. Please be careful of unintended biases when using this model. |
|
|
| ## Reference |
| - [Original github repository](https://github.com/JaesungHuh/voice-gender-classifier) |
| - I modified the model architecture from [TaoRuijie's](https://github.com/TaoRuijie/ECAPA-TDNN) repository. |
| - For more details about ECAPA-TDNN, check the [paper](https://arxiv.org/abs/2005.07143). |
