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@@ -9,6 +9,55 @@ datasets:
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  - ProgramComputer/voxceleb
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  ---
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- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
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- - Library: [More Information Needed]
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- - Docs: [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - ProgramComputer/voxceleb
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  ---
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+ # Voice gender classifier
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+ - This repo contains the inference code to use pretrained human voice gender classifier.
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+ - You could also try 🤗[Huggingface online demo](https://huggingface.co/spaces/JaesungHuh/voice-gender-classifier).
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+
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+ ## Installation
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+ First, clone the original [github repository](https://github.com/JaesungHuh/voice-gender-classifier)
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+ ```
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+ git clone https://github.com/JaesungHuh/voice-gender-classifier.git
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+ ```
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+
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+ and install the packages via pip.
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+
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+ ```
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+ cd voice-gender-classifier
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+ pip install -r requirements.txt
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+ ```
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+
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+ ## Usage
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+ ```
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+ import torch
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+
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+ from model import ECAPA_gender
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+
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+ # You could directly download the model from the huggingface model hub
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+ model = ECAPA_gender.from_pretrained("JaesungHuh/ecapa-gender")
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+ model.eval()
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+
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+ # If you are using gpu ....
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+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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+ model.to(device)
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+
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+ # Load the audio file and use predict function to directly get the output
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+ example_file = "data/00001.wav"
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+ with torch.no_grad():
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+ output = model.predict(example_file, device=device)
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+ print("Gender : ", output)
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+ ```
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+
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+ ## Pretrained weights
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+ For those who need pretrained weights, please download it in [here](https://drive.google.com/file/d/1ojtaa6VyUhEM49F7uEyvsLSVN3T8bbPI/view?usp=sharing)
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+
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+ ## Training details
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+ State-of-the-art speaker verification model already produces good representation of the speaker's gender.
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+
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+ 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.
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+
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+ The model achieved **98.7%** accuracy on the VoxCeleb1 identification test split.
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+
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+ ## Reference
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+ - [Original github repository](https://github.com/JaesungHuh/voice-gender-classifier)
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+ - I modified the model architecture from [TaoRuijie's](https://github.com/TaoRuijie/ECAPA-TDNN) repository.
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+ - For more details about ECAPA-TDNN, check the [paper](https://arxiv.org/abs/2005.07143).