by agituts · Agent Tool · ★ 114
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gemini-2-podcast Setup Guide A Python-based tool that generates engaging podcast conversations using Google's Gemini 2.0 Flash Experimental model for script generation and text-to-speech conversion. Now with multi-language support for generating podcasts in various languages. Features Converts content from multiple source formats (PDF, URL, TXT, Markdown) into natural conversational scripts. Generates high-quality audio using Google's text-to-speech capabilities. Supports multiple languages for podcast generation. Provides two distinct voices for dynamic conversations. Handles error recovery and retries for robust audio generation. Progress tracking with visual feedback during generation. Prerequisites Microsoft C++ Build Tools Download Microsoft C++ Build Tools from Visual Studio Installer. Run the installer and select: Desktop development with C++ workload. Optional MSVC build tools (, , ) under Installation details. After installation: Reboot your computer. Add MSBuild to system environment variables: System Dependencies For Ubuntu/Debian:
| Stars | 114 |
| Forks | 17 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 76.3344650041945/100 |
| Open Issues | 2 |
| Last Updated | 2024-12-30 |
| Created | 2024-12-22 |
| Platforms | gemini, python |
| Est. Tokens | ~3k |
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gemini-2-podcast is A Python-based tool that generates engaging podcast conversations using Google's Gemini 2.0 Flash Experimental model for script generation and text-to-speech conversion.. It is categorized as a Agent Tool with 114 GitHub stars.
gemini-2-podcast is primarily written in Python. It covers topics such as gemini-2, gemini-2-0-flash-exp, podcast.
You can find installation instructions and usage details in the gemini-2-podcast GitHub repository at github.com/agituts/gemini-2-podcast. The project has 114 stars and 17 forks, indicating an active community.
The top alternatives to gemini-2-podcast on Agent Skills Hub include personalized-podcast, SpotifyScraper, podcast-llm. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.