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 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 MoreHarmonyOS Application Development - Recording Audio and Implementing Real - time Speech Recognition with WebSocket
Your code implements a complete example of real-time speech recognition using WebSocket. The following are some supplementary and optimization suggestions for the entire project to ensure robustness and maintainability. ### 1. Permission Check and Prompt When requesting permissions, more detailed prompt information can be provided, and reasonable operational suggestions can be given after the user refuses authorization, or guide the user to go to the settings page for manual authorization. ```javascript reqPermissionsAndRecord(permissions: Ar ```
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 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 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 MoreTraining a Chinese Punctuation Model Based on PaddlePaddle
This project provides a complete process to train and use a model for adding punctuation marks to Chinese text. Below is a summary of the entire process: 1. **Environment Preparation**: - Ensure necessary libraries are installed, such as `paddlepaddle-gpu` and `PaddleNLP`. - Configure the training dataset. 2. **Data Processing and Preprocessing**: - Tokenize the input text and label the punctuation marks. - Create splits for training, validation, and test sets. 3.
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 MoreAdding Punctuation Marks to Speech Recognition Text
This paper introduces a method for adding punctuation marks to speech recognition text according to grammar, mainly divided into four steps: downloading and decompressing the model, installing PaddleNLP and PPASR tools, importing the PunctuationPredictor class, and using this class to automatically add punctuation marks to the text. The specific steps are as follows: 1. Download the model and decompress it into the `models/` directory. 2. Install the relevant libraries of PaddleNLP and PPASR. 3. Instantiate the predictor using the `PunctuationPredictor` class and pass in the pre
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 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 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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