Fine-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 MoreBuilding an Animal Recognition System with PaddlePaddle to Identify Thousands of Animal Species
This paper introduces a project for animal recognition using PaddlePaddle. Firstly, the animal recognition task can be completed with just a few lines of code. Secondly, a GUI interface is provided to facilitate users in uploading images for recognition. Finally, a Flask web interface is supported for Android calls, enabling cross - platform application. The project includes details such as model path, image reading, and prediction result output, and running screenshots are attached to demonstrate the implementation effect.
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 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 MoreFast Face Recognition Model Implemented with PaddlePaddle
This project develops a small and efficient face recognition system based on the ArcFace and PP-OCRv2 models. The training dataset is emore (containing 85,742 individuals and 5,822,653 images), and the lfw-align-128 dataset is used for testing. The project provides complete code and preprocessing scripts. The `create_dataset.py` script is executed to organize raw data into binary file format, improving training efficiency. Model training and evaluation are controlled by `train.py` and `eval.py` respectively. The prediction function supports
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 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 MoreMy New Book, "Introduction to and Practical Guide of PaddlePaddle Fluid Deep Learning" Has Been Published!
This book provides a detailed introduction to deep learning development using PaddlePaddle, covering the entire process from environment setup to practical project applications. The content includes environment setup, quick start, linear regression algorithm, practical cases of convolutional neural networks and recurrent neural networks, generative adversarial networks, reinforcement learning, etc. Additionally, it explains model saving and usage, transfer learning, and the application of the mobile framework Paddle-Lite. This book is suitable for beginners to get started and can help solve practical problems such as flower species recognition and news headline classification projects. All the code in the book has been tested, and there are supporting resources.
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 MoreAge and Gender Recognition Based on MXNET
This project is a deep learning-based face age and gender recognition system. It uses OpenCV and MTCNN (Multi-Task Cascaded Convolutional Network) for face detection, along with a pretrained model for age and gender prediction. Below, I will briefly introduce how to run and understand these scripts. ### 1. Environment Preparation Ensure you have installed the necessary Python libraries: ```bash pip install numpy opencv-python dlib mtcnn ```
Read MoreCRNN Text Recognition Model Implemented with PaddlePaddle 2.0 Dynamic Graph
This document introduces a CRNN text recognition model implemented using PaddlePaddle 2.0 dynamic graph. The model extracts features through CNN, performs sequence prediction via RNN, and uses CTC Loss for loss calculation, making it suitable for input images of irregular lengths. **Training and Data Preparation:** 1. **Environment Configuration**: PaddlePaddle 2.0.1 and Python 3.7 need to be installed. 2. **Dataset Generation**: - Use the `create_image.py` script to automatically generate validation
Read MoreEnd-to-End Recognition of Captchas Based on PaddlePaddle 2.0
Your code has covered most aspects of the CAPTCHA recognition project, including data processing, model training, and inference. Below are some suggestions for improvements and enhancements to your provided code: ### 1. Data Preprocessing Ensure the image dimensions are consistent (27x72), as this is the input size used during training. ### 2. Model Definition Your `Model` class has already encapsulated the network structure well. You can further optimize it and add more comments to facilitate understanding. ### 3. Training Process During the training process, ensure that when using multi-GPU training,
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 MoreImplementing Image Classification on Android Phones Based on TNN
This project is mainly an image classifier based on TensorFlow Lite, which can achieve real-time image recognition on Android devices. Its main functions and implementation steps are as follows: ### Project Structure - **MainActivity.java**: Implements gallery image selection and real-time camera prediction on the main interface. - **MNNClassification.java**: Integrates and encapsulates MNN model-related operations. ### Implementation Ideas 1. **Initialization**:
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