uberi/speech_recognition

Speech recognition module for Python, supporting several engines and APIs, online and offline.

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Summary Information

Updated 42 minutes ago
Added to GitGenius on September 7th, 2026
Created on April 23rd, 2014
Open Issues & Pull Requests: 312 (+0)
GitHub issues: Enabled
Number of forks: 2,418
Total Stargazers: 8,986 (+0)
Total Subscribers: 268 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 3.7 days
Mean response time: 120.9 days
90th percentile: 270.4 days
Tracked items: 74

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 85% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Only 5% of issues opened in the past year have been closed. Three people close 87% of everything that gets resolved.

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Issue Activity (beta)

Open issues: 26
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 1,183 days
Stale 30+ days: 26
Stale 90+ days: 25

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • needinfo (19)
  • stale (18)
  • help wanted (5)
  • duplicate (4)
  • enhancement (4)
  • bug (3)
  • pocketsphinx (2)
  • question (2)

Most active issues this week

No issue events were indexed in the last 7 days.

Detailed Description

SpeechRecognition is a Python library that provides speech recognition capabilities with support for multiple engines and APIs, both online and offline.

The library solves the problem of integrating speech-to-text functionality into Python applications by offering a unified interface across diverse recognition backends. Rather than requiring developers to learn separate APIs for each service, the tool abstracts away the differences between engines like CMU Sphinx, Google Speech Recognition, Microsoft Azure Speech, IBM Speech to Text, Vosk, OpenAI Whisper, and others. This approach lets developers switch between providers or combine multiple engines without rewriting application code.

Developers should choose this library if they need flexible speech recognition that can work both with cloud services and offline engines. It suits projects ranging from simple voice command applications to more complex transcription systems. The tool is particularly valuable for applications that require fallback options or the ability to choose between online APIs and offline models depending on deployment constraints or privacy requirements. The library supports both real-time audio input and file-based processing.

The project maintains active integration testing across supported Python versions. Development activity shows ongoing expansion of supported engines and APIs, with recent additions including Groq Whisper API and Cohere Transcribe API alongside established providers. The codebase receives regular updates to maintain compatibility with evolving third-party services and Python language changes.