Run Large Language Model Service with One Click and Build a Chat Application
This article introduces a method to build a local large language model chat service based on the Qwen-7B-Int4 model. First, you need to install the GPU version of PyTorch and other dependency libraries. Then, execute `server.py` in the terminal to start the service. The service supports Windows and Linux systems and can run smoothly with a low VRAM requirement (8G graphics card). In addition, an Android application source code is also provided. By modifying the service address and opening the `AndroidClient` file with Android Studio...
Read MoreEasily and Quickly Set Up a Local Speech Synthesis Service
This article introduces a method to quickly set up a local speech synthesis service using the VITS model architecture. First, you need to install the PyTorch environment and related dependency libraries. To start the service, simply run the `server.py` program. Additionally, the source code for an Android application is provided, which requires modifying the server address to connect to your local service. At the end of the article, a QR code is provided to join a knowledge planet and obtain the complete source code. The entire process is simple and efficient, and the service can run without an internet connection.
Read MoreReal-time Speech Recognition Service with Remarkably High Recognition Accuracy
This article introduces the installation, configuration, and application deployment of the FunASR speech recognition framework. First, PyTorch and related dependency libraries need to be installed. For the CPU version, it can be completed using the command `conda install pytorch torchvision torchaudio cpuonly -c pytorch`; for the GPU version, use `conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c p` (Note: The original command may be truncated, and the complete command should be checked for accuracy).
Read MoreFunASR Speech Recognition GUI Application
This paper introduces a speech recognition GUI application developed based on FunASR, which supports recognition of local audio and video files as well as audio recording recognition. The application includes short audio recognition, long audio recognition (with and without timestamps), and audio file playback. The installation environment requires dependencies such as PyTorch (CPU/GPU), FFmpeg, and pyaudio. To use the application, execute `main.py`. The interface provides four options: short speech recognition, long speech recognition, recording recognition, and playback functionality. Among them, long speech recognition is divided into two models: one for concatenated output and another for explicit
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