canopyai/orpheus-tts

Towards Human-Sounding Speech

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Summary Information

Updated 20 minutes ago
Added to GitGenius on September 10th, 2026
Created on March 8th, 2025
Open Issues & Pull Requests: 126 (+0)
GitHub issues: Enabled
Number of forks: 535
Total Stargazers: 6,335 (+0)
Total Subscribers: 73 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 7.6 hours
Mean response time: 6.1 days
90th percentile: 8.3 days
Tracked items: 197

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 100% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Only 3% of issues opened in the past year have been closed. Three people close 59% of everything that gets resolved.

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Issue Activity (beta)

Open issues: 118
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 445 days
Stale 30+ days: 118
Stale 90+ days: 113

Recent activity

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

Top labels

  • good first issue (1)

Most active issues this week

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Detailed Description

Orpheus TTS is a text-to-speech system designed to produce human-sounding speech output. The project addresses the challenge of generating natural-sounding audio from text by implementing a real-time capable architecture. Its approach focuses on creating speech synthesis that prioritizes naturalness and human-like qualities in the generated output.

The tool is built for developers and applications requiring speech generation with an emphasis on quality and real-time performance. It integrates with large language model workflows, making it suitable for projects that combine natural language processing with audio synthesis. The real-time capability means it can generate speech with minimal latency, which is valuable for interactive applications, conversational AI systems, and live streaming scenarios where immediate audio output is necessary.

Adoption considerations center on use cases where speech naturalness is a primary concern. The project suits applications ranging from voice assistants to content creation tools where the quality of synthesized speech directly impacts user experience. Teams building LLM-based systems that need audio output will find the real-time processing particularly relevant, as it enables seamless integration without significant delays between text generation and speech playback.

The project shows active development with regular code contributions and ongoing refinement of the speech synthesis capabilities. The codebase receives consistent updates addressing both core functionality and performance optimization. Documentation and examples are maintained to support developer adoption and integration into existing systems.