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Termux-STT

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디바이스 리소스를 활용한 통합 온디바이스 음성인식(STT) 및 순수 Python 128d X-Vector 화자 분리 프레임워크
Unified On-Device Speech-to-Text Utilizing Device Resources & Pure Python 128d X-Vector Speaker Diarization


Architecture & Overview

Whisper.cpp, Vosk, Sherpa-ONNX 3대 네이티브 엔진을 통합하고, 닫힌 형태 순수 Python 128차원 X-Vector 클러스터링을 결합하여 80MB 미만의 초경량 메모리로 100% 온디바이스 실시간 음성인식과 화자 분리를 구현합니다.

Integrates Whisper.cpp, Vosk, and Sherpa-ONNX with a closed-form pure-Python 128-dimensional X-Vector clustering algorithm that operates in under 80MB RAM with zero cloud egress.


Installation & Quickstart

Python (PyPI)

pip install termux-stt
from termux_stt import create_engine

# 1. Initialize Engine with Hybrid GPU-Encoder / CPU-Decoder Acceleration
engine = create_engine("whisper", model="small", lang="en", threads=4, split_mode=True)

# 2. Transcribe Audio directly into Subtitles
result = engine.transcribe("samples/jfk_1min.wav")
print("Transcript:\n", result.text)
print("SRT Subtitles:\n", result.to_srt())

# 3. 2-Speaker Diarization without PyTorch
hybrid = create_engine("hybrid", lang="en", num_speakers=2)
diar_result = hybrid.diarize("samples/jfk_1min.wav")
for seg in diar_result.segments:
    print(f"[{seg.speaker}] ({seg.start:.1f}s -> {seg.end:.1f}s): {seg.text}")

Node.js / TypeScript (npm)

npm install termux-stt
const { createEngine } = require("termux-stt");

async function main() {
  // 1. Initialize Whisper Engine
  const engine = createEngine("whisper", { model: "tiny", lang: "en", threads: 4 });

  // 2. Transcribe Audio
  const result = await engine.transcribe("samples/jfk_1min.wav");
  console.log("Transcript:", result.text);
  console.log("SRT Subtitles:\n", result.toSrt());
}
main();

Official Documentation & Benchmarks


License

Licensed under the Apache-2.0 License. Copyright (c) 2026 Eunho Kim (@uno-km).

Metadata

Release files for termux-stt 1.3.3

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Source distribution for termux-stt 1.3.3
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termux_stt-1.3.3-py3-none-any.whl Python 3 none any Details

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