Rapid Training of Cat and Dog Sound Classification Model
This paper introduces how to quickly perform sound classification training and inference using PyTorch and the macls library. First, create a Python 3.11 virtual environment via Anaconda and install the PyTorch 2.5.1 GPU version along with the macls library. Next, prepare the dataset, with provided download links or support for custom formats. The training part can be completed with just three lines of code for model training, optimization, and saving. The inference phase loads the pre-trained model for prediction. The framework supports multiple sound classification models, facilitating different scenario requirements.
Read MoreSound Classification Based on PyTorch
This code is mainly based on the PaddlePaddle framework and is used to implement a speech recognition system based on acoustic features. The project structure is clear, including functional modules such as training, evaluation, and prediction, and provides detailed command-line parameter configuration files. The following is a detailed analysis and usage instructions for the project: ### 1. Project Structure ``` . ├── configs # Configuration files directory │ └── bi_lstm.yml ├── infer.py # Acoustic model inference code ├── recor ``` (Note: The original Chinese text was cut off at "recor" in the last line, so the translation reflects the visible content.)
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