Duplicate from JaesungHuh/voice-gender-classifier

Co-authored-by: Huh <JaesungHuh@users.noreply.huggingface.co>

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.gitignore

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+.DS_Store

README.md

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+---
+tags:
+- pytorch_model_hub_mixin
+- model_hub_mixin
+- gender-classification
+- VoxCeleb
+license: mit
+datasets:
+- ProgramComputer/voxceleb
+pipeline_tag: audio-classification
+---
+
+# Voice gender classifier 
+- This repo contains the inference code to use pretrained human voice gender classifier.
+- You could also try 🤗[Huggingface online demo](https://huggingface.co/spaces/JaesungHuh/voice-gender-classifier).
+
+## Installation
+First, clone the original [github repository](https://github.com/JaesungHuh/voice-gender-classifier)
+```
+git clone https://github.com/JaesungHuh/voice-gender-classifier.git
+```
+
+and install the packages via pip.
+
+```
+cd voice-gender-classifier
+pip install -r requirements.txt
+```
+
+## 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).

config.json

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+{
+  "C": 1024
+}

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