k2-fsa/sherpa-onnx
Feb 2026 – Feb 2026
1 Month active
Languages Used
CC++DartGoJavaScriptPython
Technical Skills
Audio ProcessingC programmingC++C++ developmentConcurrencyDart development
Simon Law
PROFILE
Simon Law
During February 2026, Ramishi developed a voice embeddings caching and seed configuration feature for the k2-fsa/sherpa-onnx repository, focusing on optimizing text-to-speech runtime performance. Ramishi implemented a thread-safe LRU cache in C++ to avoid redundant Mimi encoder runs, reducing per-utterance latency in audio processing workflows. The work also introduced seed support for deterministic noise generation, ensuring reproducible results across TTS models and APIs. By extending cache and seed configuration to the C API, Go wrapper, and Node.js/WASM interfaces, Ramishi improved both configurability and observability. The solution demonstrated depth in concurrency, cross-language bindings, and efficient data structure design.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
1
Lines of code
406
Activity Months1
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Work History
February 2026
4 Commits • 1 Features
Feb 1, 2026February 2026 performance summary for k2-fsa/sherpa-onnx: Implemented Voice Embeddings Caching and Seed Configuration for TTS to optimize runtime performance and ensure reproducible results. Introduced a thread-safe LRU cache for voice embeddings to skip redundant Mimi encoder runs; added seed support for deterministic noise generation across Offline TTS Pocket Model, TTS configurations, and PocketTTS Node.js/WASM APIs; exposed cache capacity and seed parameters via model config, C API, Go wrapper, and CLI (pocket-voice-embedding-cache-capacity). Extended bindings across Dart, Flutter, Go, and C API with examples; integrated cache & seed support into Node.js Addon and WASM APIs. This work reduces redundant computations, lowers latency, and improves configurability and reproducibility for voice embedding handling in TTS.
4 Commits • 1 Features
Feb 1, 2026February 2026 performance summary for k2-fsa/sherpa-onnx: Implemented Voice Embeddings Caching and Seed Configuration for TTS to optimize runtime performance and ensure reproducible results. Introduced a thread-safe LRU cache for voice embeddings to skip redundant Mimi encoder runs; added seed support for deterministic noise generation across Offline TTS Pocket Model, TTS configurations, and PocketTTS Node.js/WASM APIs; exposed cache capacity and seed parameters via model config, C API, Go wrapper, and CLI (pocket-voice-embedding-cache-capacity). Extended bindings across Dart, Flutter, Go, and C API with examples; integrated cache & seed support into Node.js Addon and WASM APIs. This work reduces redundant computations, lowers latency, and improves configurability and reproducibility for voice embedding handling in TTS.
February 2026
Activity
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Quality Metrics
Correctness90.0%
Maintainability85.0%
Architecture90.0%
Performance85.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
CC++DartGoJavaScriptPython
Technical Skills
Audio ProcessingC programmingC++C++ developmentConcurrencyDart developmentData StructuresGo developmentMachine LearningNode.jsPython developmentSpeech ProcessingText-to-SpeechWASMaudio processing
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