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 MoreQuick Deployment of Speech Recognition Framework Using MASR V3
This framework appears to be very comprehensive and user-friendly, covering multiple stages from data preparation to model training and inference. To help readers better understand and utilize this framework, I will provide detailed explanations for each part along with some sample code. ### 1. Environment Setup First, you need to install the necessary dependency packages. Assuming you have already created and activated a virtual environment: ```sh pip install paddlepaddle==2.4.0 -i https://mirror.baidu.com/pypi/ ```
Read MoreQuick Deployment of Speech Recognition Framework Using PPASR V3
This detailed introduction demonstrates the process of developing and deploying speech recognition tasks using the PaddleSpeech framework. Below are some supplements and suggestions to the information you provided: 1. **Installation Environment**: Ensure your environment has installed the necessary dependencies, including libraries such as PaddlePaddle and PaddleSpeech. These libraries can be installed via the pip command. 2. **Data Preprocessing**: - You may need to perform preprocessing steps on the raw audio, such as sample rate adjustment and noise removal.
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