Quick 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 MoreSpeaker Log Implementation Based on PyTorch (Speaker Separation)
This article introduces the speaker diarization feature of the VoiceprintRecognition_Pytorch framework implemented based on PyTorch, which supports various advanced models and data preprocessing methods. By executing the `infer_speaker_diarization.py` script or using the GUI interface program, audio can be speaker-separated and results displayed. The output includes the start and end times of each speaker and their identity information (registration is required first). Additionally, the article provides solutions for Chinese names in the Ubuntu system... （注：原文末尾“解决中文名”表述不完整，已保留原文未尽部分的省略格式，完整内容需参考原文后续章节）
Read MoreEasily Identify Long Audio/Video Files with Hours-Long Duration
This article introduces a method to build a long - speech recognition service capable of processing audio or video files that last tens of minutes or even several hours. First, the folder needs to be uploaded to the server, and then commands for compilation, permission modification, and starting the Docker container are executed to deploy the service. After testing that the service is available, the WebSocket interface or HTTP service can be used for interaction. The HTTP service provides a web interface, supporting the upload and recording recognition of audio and video in multiple formats, and returns text results containing the start and end timestamps of each sentence. This service simplifies the long - audio recognition process and improves user...
Read MoreReal-time Command Wake-up
This paper introduces the development and usage of a real-time instruction wake-up program, including steps such as installation environment, instruction wake-up, and model fine-tuning. The project runs on Anaconda 3 and Python 3.11, with dependencies on PyTorch 2.1.0 and CUDA 12.1. Users can customize the recording time and length by adjusting parameters `sec_time` and `last_len`, and add instructions in `instruct.txt` for personalized settings. The program can be executed via `infer_pytorch.py` or `infer_on
Read MoreTank Battle Controlled by Voice Commands
This paper introduces the program development process for controlling the Tank Battle game through voice commands, including steps such as environment setup, game startup, and instruction model fine-tuning. First, the project uses Anaconda 3, Windows 11, Python 3.11, and corresponding libraries for development. Users can adjust parameters in `main.py` such as recording time and data length, add new commands in `instruct.txt`, and write processing functions to start the game. Secondly, `record_data.py` is run to record command audio and generate training
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 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
Read MoreVoiceprint Recognition System Implemented Based on PyTorch
This project provides an implementation of voice recognition based on PaddlePaddle, mainly using the EcapaTDNN model, and integrates functions of speech recognition and voiceprint recognition. Below, I will summarize the project structure, functions, and how to use these functions. ## Project Structure ### Directory Structure ``` VoiceprintRecognition-PaddlePaddle/ ├── docs/ # Documentation │ └── README.md # Project description document ```
Read MoreFine-tuning Whisper Speech Recognition Model and Accelerating Inference
Thank you for providing the detailed project description. To help more people understand and use your project, I will summarize and optimize some key information and steps: ### Project Overview This project aims to deploy a fine-tuned Whisper model to Windows desktop applications, Android APKs, and web platforms to achieve speech-to-text functionality. ### Main Steps #### Model Format Conversion 1. Clone the Whisper native code repository: ```bash git clone https://git
Read MoreSpeech Emotion Recognition Based on PyTorch
This project provides a detailed introduction to how to perform emotion classification from audio using PyTorch, covering the entire process from data preparation, model training to prediction. Below, I will give more detailed explanations for each step and provide some improvement suggestions and precautions. ### 1. Environment Setup Ensure you have installed the necessary Python libraries: ```bash pip install torch torchvision torchaudio numpy matplotlib seaborn soundf ```
Read MoreECAPa-TDNN Voiceprint Recognition Model Implemented with PyTorch
This project demonstrates how to implement speech recognition functionality using PaddlePaddle, specifically including voiceprint comparison and voiceprint registration. Below is a summary of the main content and some improvement suggestions: ### 1. Project Structure and Functions - **Voiceprint Comparison**: Compare the voice features of two audio files to determine if they are from the same person. - **Voiceprint Registration**: Store the voice data of new users in a database and generate corresponding user information. ### 2. Technology Stack - Use PaddlePaddle for model training and prediction.
Read MoreA Fast Face Recognition Model Implemented Based on PyTorch
This project aims to develop a face recognition system with small models, high recognition accuracy, and fast inference speed. The training data is sourced from the emore dataset (5.82 million images), and the lfw-align-128 dataset is used for testing. The project combines the ArcFace loss function and MobileNet, implemented through Python scripts. The process of training the model includes data preparation, training, and evaluation, with all code available on GitHub. To start the training process, the `train.py` command is executed; for performance verification, run `ev`
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.)
Read MoreSpeech Recognition Model Based on PyTorch
This project demonstrates how to use the PaddlePaddle framework for voiceprint recognition, covering multiple steps from model training to application deployment. The following are some key points and improvement suggestions for this project: ### Summary of Key Points 1. **Data Preparation**: The `prepare_data.py` in the project is used to generate a dataset containing voiceprint features. 2. **Model Design**: ECAPA-TDNN was selected as the base model, and voiceprint recognition tasks were implemented through custom configurations. 3. **Training Process**: In the training...
Read MoreFace Landmark Detection Model MTCNN Implementation Based on PyTorch
MTCNN is a multi-task convolutional neural network (CNN) for face detection, consisting of three networks: P-Net, R-Net, and O-Net. P-Net generates candidate windows; R-Net performs high-precision filtering; and O-Net outputs bounding boxes and key points. The model adopts the candidate box + classifier idea, and uses techniques such as image pyramids and bounding box regression to achieve fast and efficient detection. Training MTCNN consists of three steps: 1. Train PNet: Generate PNet data and use the `train_PNet.py` script for training; 2. Train RNet: Generate RN
Read MoreStream 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
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