Stream and Non-Stream Speech Recognition Implemented with PyTorch
### Project Overview This project is a speech recognition system implemented based on PyTorch. By utilizing pretrained models and custom configurations, it can recognize input audio files and output corresponding text results. ### Install Dependencies First, necessary libraries need to be installed. Run the following command in the terminal or command line: ```bash pip install torch torchaudio numpy librosa ``` If the speech synthesis module is required, additionally install `gTTS` and
Read MoreHandwritten Tibetan Character Recognition with PaddlePaddle
This code uses the PaddlePaddle framework for training, prediction, and plotting of a Tibetan MNIST handwritten digit recognition model. The entire process can be divided into the following steps: 1. **Dataset Loading**: First, the dataset is downloaded from the Kesci platform and decompressed, then the original images are converted into normalized grayscale images. 2. **Model Definition and Training**: - A simple CNN network structure is defined. - The optimizer, loss function, and accuracy calculation method are set. - Using Padd
Read More"PaddlePaddle from Beginner to 'Alchemy' (Refined Version)" Part 4 - Convolutional Neural Networks
This tutorial provides a detailed introduction to training and predicting a handwritten digit recognition model using the PaddlePaddle framework. Below is a summary and further explanation of the key steps: ### 1. Preparing the Dataset First, the MNIST dataset is obtained from PaddlePaddle using the `fetch MNIST data` command. It is a widely used dataset for training machine learning models. ```python import paddle.v2 as paddle from paddle.v2.da ``` （Note: The code snippet appears truncated in the original input. The translation assumes the standard MNIST loading syntax in PaddlePaddle v2, though the full code may require additional imports or dataset initialization steps not visible in the provided snippet.)
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