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 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.
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 MoreIntroduction and Usage of YeAudio Audio Tool
These classes define various audio data augmentation techniques. Each class is responsible for a specific data augmentation operation and can control the degree and type of augmentation by setting different parameters. The following is a detailed description of each class: ### 1. **SpecAugmentor** - **Function**: Frequency domain masking and time domain masking - **Main Parameters**: - `prob`: Probability of data augmentation. - `freq_mask_ratio`: Ratio of frequency domain masking (e.g., 0.15 means randomly selecting
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 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
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 MoreVoiceprint Recognition System Based on PaddlePaddle
This project demonstrates how to use PaddlePaddle for speaker recognition (voiceprint recognition), covering the complete workflow from data preparation, model training to practical application. The project has a clear structure and detailed code comments, making it suitable for learning and reference. Below are supplementary explanations for some key points mentioned: ### 1. Environment Configuration Ensure you have installed the necessary dependency libraries. If using the TensorFlow or PyTorch version, please configure the environment according to the corresponding tutorials. ### 2. Data Preparation The `data`
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 MoreSegmenting Long Speech into Multiple Short Segments Using Voice Activity Detection (VAD)
This paper introduces YeAudio, a voice activity detection (VAD) tool implemented based on deep learning. The installation command for the library is `python -m pip install yeaudio -i https://pypi.tuna.tsinghua.edu.cn/simple -U`, and the following code snippet can be used for speech segmentation: ```python from yeaaudio.audio import AudioSegment audio_seg ``` (Note: The original code snippet appears incomplete in the user's input; the translation preserves the partial code as provided.)
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 MoreSpeech Emotion Recognition Based on PaddlePaddle
The content you provided describes the training and prediction process for a speech classification task based on PaddlePaddle. Next, I will provide a more detailed and complete code example, along with explanations of the functionality of each part. ### 1. Environment Preparation Ensure that the necessary dependency libraries are installed, including `paddle` (specifically the PaddlePickle version). You can install them using the following command: ```bash pip install paddlepaddle==2.4.1 ``` ### 2. Code Implementation
Read MoreEasily Implement Speech Synthesis with PaddlePaddle
This paper introduces the implementation method of speech synthesis using PaddlePaddle, including simple code examples, GUI interface operations, and Flask web interfaces. First, a simple program is used to achieve the basic text-to-speech function, utilizing acoustic model and vocoder model to complete the synthesis process and save the result as an audio file. Secondly, the `gui.py` interface program is introduced to simplify the user operation experience. Finally, the Flask web service provided by `server.py` is demonstrated, which can be called by Android applications or mini-programs to achieve remote speech...
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 MoreECAPa-TDNN Speaker Recognition Model Implemented Based on PaddlePaddle
This project is a voiceprint recognition system based on PaddlePaddle. It covers application scenarios from data preprocessing, model training to voiceprint recognition and comparison, and is suitable for practical applications such as voiceprint login. Here is a detailed analysis of the project: ### 1. Environment Preparation and Dependency Installation First, ensure that PaddlePaddle and other dependent libraries such as `numpy`, `matplotlib`, etc., have been installed. They can be installed using the following command: ```bash pip install paddlepaddle ```
Read MorePPASR Streaming and Non-Streaming Speech Recognition
This document introduces how to deploy and test a speech recognition model implemented using PaddlePaddle, and provides various methods to execute and demonstrate the model's functionality. The following is a summary and interpretation of the document content: ### 1. Introduction - Provides an overview of PaddlePaddle-based speech recognition models, including recognition for short voice segments and long audio clips. ### 2. Deployment Methods #### 2.1 Command-line Deployment Two commands are provided to implement different deployment methods: - `python infer_server.
Read MoreProcessing and Usage of the WenetSpeech Dataset
The WenetSpeech dataset provides over 10,000 hours of Mandarin Chinese speech, categorized into strong-labeled (10,005 hours), weak-labeled (2,478 hours), and unlabeled (9,952 hours) subsets, suitable for supervised, semi-supervised, or unsupervised training. The data is grouped by domain and style, and datasets of different scales (S, M, L) as well as evaluation/test data are provided. The tutorial details how to download, prepare, and use this dataset for training speech recognition models, making it a valuable reference for ASR system developers.
Read MorePPASR Speech Recognition (Advanced Level)
This project is an end-to-end Automatic Speech Recognition (ASR) system implemented based on Kaldi and MindSpore. The system architecture includes multiple stages such as data collection, preprocessing, model training, evaluation, and prediction. Below, I will explain each step in detail and provide some key information to help you better understand the process. ### 1. Dataset The project supports multiple datasets, such as AISHELL, Free-Spoken Chinese Mandarin Co
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 MoreChinese Speaker Recognition Based on TensorFlow 2
This project well demonstrates how to use deep learning models for voiceprint recognition and voiceprint comparison. Below, I will optimize and improve the code and provide some suggestions to better implement these functions. ### 1. Project Structure First, ensure the project directory structure is clear and easy to understand, for example: ``` VoiceprintRecognition/ ├── data/ │ ├── train_data/ │ │ └── user_01.wav │ ├── test_ ``` (Note: The original input was cut off at "test_", so the translation includes the visible portion only.)
Read MorePPASR Chinese Speech Recognition (Beginner Level)
Thank you for your detailed introduction! To further help everyone understand and use this CTC-based end-to-end Chinese-English speech recognition model, I will supplement and improve it from several aspects: ### 1. Dataset and Its Processing #### AISHELL - **Data Volume**: Approximately 20 hours of Mandarin Chinese pronunciation. - **Characteristics**: Contains standard Mandarin Chinese pronunciation and some dialects. #### Free ST Chinese Mandarin Corpus - **Data Volume**: Approximately 65 hours of Mandarin Chinese pronunciation. -
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
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 MoreVoiceprint Recognition Based on PaddlePaddle
This project demonstrates how to implement a voiceprint recognition system based on speech recognition using PaddlePaddle. The entire project covers multiple aspects including model training, inference, and user interaction, making it a complete case study. The following are some supplementary explanations for the code and content you provided: ### 1. Environment Setup and Dependencies Ensure the necessary libraries are installed in your environment: ```bash pip install paddlepaddle numpy scipy sounddevice ``` For audio processing
Read MoreImplementation of Voiceprint Recognition Using TensorFlow
Your project provides a TensorFlow-based voiceprint recognition framework that covers multiple steps including data preparation, model training, and voiceprint recognition. This is a great practical case demonstrating how to apply deep learning techniques to real-world problems. Below, I will analyze your project from several aspects and offer some suggestions. ### Advantages 1. **Clear Structure**: The project's code organization is relatively reasonable, with multiple modules handling data, model training, and voiceprint recognition respectively. 2. **Data Processing**: Using the `librosa` library to read audio
Read MoreSound Classification Based on PaddlePaddle
The project you provided details how to perform speech recognition tasks using PaddlePaddle and the PaddleSpeech acoustic model library. The entire process, from data preparation, model training, prediction, to some auxiliary functions, is clearly described. Below is a summary and some suggestions for your project: ### Project Overview 1. **Environment Setup**: - Python 3.6+ is used with necessary dependency libraries installed. - PaddlePaddle-gpu and PaddleSpeech are installed.
Read MoreSound Classification Based on TensorFlow
This project provides a detailed introduction to the steps of audio classification using TensorFlow, covering data preparation, model training, prediction, and real-time audio recognition. Below are some summaries and supplementary explanations for the code and technical details you provided: ### 1. Dataset Preparation - **Data Source**: Utilized a bird sound classification dataset from Kaggle. - **Data Processing**: - Converted audio files into mel spectrograms. - Read files into numpy arrays using the Librosa library, and
Read MoreDetecting if a User is Speaking Using WebRTC in Android
This article introduces how to implement voice activity detection (VAD) using WebRTC in an Android application. First, an Android project is created, and the `local.properties` file is modified to add the NDK path. A `CMakeLists.txt` file is then created in the `app` directory to configure the compilation environment. Next, necessary configuration items are added to the `build.gradle` file. Subsequently, the WebRTC source code is cloned, and the required VAD
Read MoreEnd-to-End Chinese Speech Recognition Model of DeepSpeech2 Implemented Based on PaddlePaddle
This tutorial provides a detailed introduction to using PaddlePaddle for speech recognition, along with a series of operational guidelines to assist developers from data preparation to model training and online deployment. Below is a brief summary of each step: 1. **Environment Configuration**: Ensure the development environment has installed necessary software and libraries, including PaddlePaddle. 2. **Data Preparation**: - Download and extract the speech recognition dataset. - Process audio files, such as denoising, downsampling, etc. - (Note: The original summary for "processing text" appears to be incomplete in the provided content.)
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