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mozilla/DeepSpeechArchived

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DeepSpeech

DeepSpeech 是一个开源的语音转文字框架和机器学习引擎,旨在本地设备上将口语音频转换为书面文本。它提供无需连接外部服务器即可运行的设备端语音识别。

该系统支持跨多种硬件平台的实时语音转录,范围从单板计算机和边缘设备到 GPU 服务器。这允许直接在本地硬件上进行音频分析和处理。

Features

  • Local Speech-to-Text - Provides a machine learning engine for the on-device conversion of spoken audio into text without internet access.
  • Real-Time Transcription - Provides instantaneous conversion of live audio streams into text transcripts across various hardware platforms.
  • On-Device Inference Engines - Ships a runtime optimized for executing speech recognition models locally on edge hardware to ensure privacy and low latency.
  • Speech Recognition - Implements tools and models for converting spoken language into text locally on a device.
  • Speech-to-Text Engines - Implements a high-performance engine for converting spoken audio into written text using local machine learning models.
  • Speech-to-Text Modeling Toolkits - Provides a toolkit for training and deploying models that convert audio signals into written text.
  • Speech-to-Text Frameworks - Offers an open-source framework for building and deploying embedded voice recognition models on diverse hardware.
  • Embedded Voice Processing - Enables the integration of speech-to-text capabilities directly into edge devices like Raspberry Pi.
  • Natural Language Processing - TensorFlow implementation of DeepSpeech architecture.
  • Speech and Audio Models - Open-source speech-to-text engine for mobile and edge.
  • Speech Processing - Pretrained automatic speech recognition engine.
  • Acoustic User Interface - Open-source speech-to-text engine using machine learning.
  • 声学用户界面 - 基于深度学习的开源语音转文字(STT)引擎。
  • Audio Processing - Embedded speech-to-text engine using deep learning.

Star 历史

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DeepSpeech 的开源替代方案

相似的开源项目,按与 DeepSpeech 的功能重合度排序。
  • alphacep/vosk-apialphacep 的头像

    alphacep/vosk-api

    14,853在 GitHub 上查看↗

    Vosk is an offline speech-to-text engine and API that converts spoken audio into text locally on a device. It provides a cross-platform speech toolkit with language bindings for integrating voice recognition into server environments, Android, iOS, and Raspberry Pi. The project includes a speaker identification tool to distinguish between different voices and an acoustic model trainer for building custom neural network models. These training tools enable speech feature extraction and model accuracy evaluation to improve recognition for specialized domains. The system supports real-time audio

    Jupyter Notebookandroidasrdeep-learning
    在 GitHub 上查看↗14,853
  • k2-fsa/sherpa-onnxk2-fsa 的头像

    k2-fsa/sherpa-onnx

    13,017在 GitHub 上查看↗

    Sherpa-ONNX is an ONNX-based speech processing toolkit that provides a local speech recognition engine, an on-device voice synthesis tool, and a speaker identification framework. It is designed as a cross-platform speech API that enables speech-to-text, text-to-speech, and speaker verification tasks to be executed locally on a device without requiring network access. The project is distinguished by its ability to perform zero-shot voice cloning and speaker diarization on-device. It supports a wide range of hardware accelerations, including GPU and various NPU architectures, and provides a Web

    C++aarch64androidarm32
    在 GitHub 上查看↗13,017
  • pipecat-ai/pipecatpipecat-ai 的头像

    pipecat-ai/pipecat

    12,846在 GitHub 上查看↗

    Pipecat is a framework and software development kit for building real-time multimodal AI agents and speech-to-speech systems. It utilizes a frame-based data pipeline to route audio, video, and text through a modular sequence of processors, enabling the orchestration of low-latency conversational AI. The project is distinguished by its ability to coordinate complex multimodal services, including speech-to-text, language models, and text-to-speech, within a single pipeline. It features semantic voice activity detection for natural turn-taking, state-machine conversation flows for dialogue manag

    Pythonaichatbot-frameworkchatbots
    在 GitHub 上查看↗12,846
  • facebookresearch/wav2letterfacebookresearch 的头像

    facebookresearch/wav2letter

    6,444在 GitHub 上查看↗

    wav2letter is an automatic speech recognition toolkit and deep learning framework designed to convert audio speech signals into written text. It functions as a distributed training system and an inference engine for building and deploying neural network architectures. The system enables the training of large-scale speech models across multiple compute nodes using custom architecture files and structured recipes. It includes an inference engine that allows these trained models to be executed within Python workflows to transform audio sequences into text. The framework covers the full speech r

    C++
    在 GitHub 上查看↗6,444
查看 DeepSpeech 的所有 30 个替代方案→

常见问题解答

mozilla/deepspeech 是做什么的?

DeepSpeech 是一个开源的语音转文字框架和机器学习引擎,旨在本地设备上将口语音频转换为书面文本。它提供无需连接外部服务器即可运行的设备端语音识别。

mozilla/deepspeech 的主要功能有哪些?

mozilla/deepspeech 的主要功能包括:Local Speech-to-Text, Real-Time Transcription, On-Device Inference Engines, Speech Recognition, Speech-to-Text Engines, Speech-to-Text Modeling Toolkits, Speech-to-Text Frameworks, Embedded Voice Processing。

mozilla/deepspeech 有哪些开源替代品?

mozilla/deepspeech 的开源替代品包括: alphacep/vosk-api — Vosk is an offline speech-to-text engine and API that converts spoken audio into text locally on a device. It provides… k2-fsa/sherpa-onnx — Sherpa-ONNX is an ONNX-based speech processing toolkit that provides a local speech recognition engine, an on-device… pipecat-ai/pipecat — Pipecat is a framework and software development kit for building real-time multimodal AI agents and speech-to-speech… facebookresearch/wav2letter — wav2letter is an automatic speech recognition toolkit and deep learning framework designed to convert audio speech… sevask/ecoute — Ecoute is a live transcription tool that provides real-time transcripts for both the user's microphone input (You) and… koljab/realtimestt — RealtimeSTT is a local speech-to-text engine and real-time automatic speech recognition server. It utilizes…