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 MoreImplementation of Image Classification on Android Phones Based on TensorFlow Lite
This project mainly implements an image classification application based on TensorFlow Lite, which can perform object recognition using images from the camera or photo album on an Android device and provide real-time prediction functionality. The following is a detailed analysis of the core steps and key code of this project: ### Project Structure - **TFLiteModel**: Contains model-related configurations. - **MainActivity**: The main interface for launching the camera or selecting images for classification. - **RunClassifier** (Note: The original text seems to be incomplete here, so the translation preserves the placeholder as is.)
Read MoreFace Recognition Based on MTCNN and MobileFaceNet
Your project has designed a deep learning-based face recognition system with a front-end and back-end separated implementation. This system includes a front-end page and a back-end service, which can be used for face registration and real-time face recognition. Below are detailed analysis and improvement suggestions for your code: ### Front-end Part 1. **HTML Template**: - You have already created a simple `index.html` file in the `templates` directory to provide the user interface. - Some basic CSS styles can be added.
Read MoreChinese Voiceprint Recognition Based on Kersa
Thank you for providing the detailed explanation about voiceprint recognition and comparison. Below, I will provide you with a more detailed implementation step-by-step for the PaddlePaddle version, along with code examples. This project will include data preprocessing, model training, voiceprint comparison, and registration/recognition. ### 1. Environment Setup First, ensure that you have installed PaddlePaddle and other necessary libraries such as `numpy` and `sklearn`. You can install them using the following command: ```bash pip install p ```
Read MoreLarge-scale Face Detection Based on Pyramidbox
Based on the code and description you provided, this is an implementation of a face detection model using PyTorch. The model employs a custom inference process to load images, perform preprocessing, and conduct face detection through the model. Here are key points summarizing the code: - **Data Preprocessing**: Transpose the input image from `HWC` to `CHW` format, adjust the color space (BGR to RGB), subtract the mean, and scale. This step ensures compatibility with the data format used during training. - **Model Inference**: Uses the PaddlePaddle framework (Note: There appears to be a discrepancy here, as the initial description mentions PyTorch but this part references PaddlePaddle. If this is an error, please clarify.)
Read MoreUsing Mediapipe Framework on Android
Your implementation is very close to completion, but to ensure everything works properly, I will provide a more complete code example with some improvements and optimizations. Additionally, I will explain the role of each part in detail. ### Complete Code First, we need to import the necessary libraries: ```java import android.content.pm.PackageManager; import android.os.Bundle; import android.view.Surfa ``` (Note: The original code snippet appears to be incomplete here, as the `Surfa` import is likely cut off, probably intended to be `SurfaceView` or similar view-related class. The translation assumes the code continues with standard Android view setup and functionality.)
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