Termux-STT

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();

Distributed Clustering & Memory Pooling (AMEVA-Cluster)

Termux-STT natively integrates with AMEVA-Cluster (pip install ameva-cluster) for distributed large-model speech recognition (e.g. whisper-large-v3-turbo) across interconnected mobile fleets.

1. Install Cluster Runtime

pip install ameva-cluster
# or Node.js:
npm install @ameva/cluster

2. Launch Worker Node on Remote Phone

# On remote worker device (e.g. Galaxy A53):
ameva-cluster worker --port 50052

3. Distributed Transcription via Master Node

# Master node sharding Whisper-Large layers across remote phone RAM:
termux-stt transcribe meeting.wav \
  --model whisper-large-v3-turbo.bin \
  --rpc 192.0.2.10:50052,192.0.2.11:50052

from termux_stt import create_engine

# Python SDK Distributed STT
engine = create_engine(
    "whisper",
    model="large-v3-turbo",
    cluster_rpc_servers="192.0.2.10:50052,192.0.2.11:50052"
)
result = engine.transcribe("meeting.wav")
print(result.text)

Official Documentation & Benchmarks

License

termux stt speech-to-text whisper whisper-cpp vosk sherpa-onnx diarization speaker-diarization voice-recognition audio-transcription on-device-ai edge-ai mobile-ai vulkan vulkan-compute gpu-acceleration real-time-factor low-latency arm64 android snapdragon adreno exynos arm-mali zero-compilation vad voice-activity-detection silero-vad x-vector k-means clustering srt-export vtt-export rttm microphone-streaming headless-audio pulseaudio offline-speech privacy-first termux-aichain termux-tts termux-llamacpp termux-diffusion ameva-runtime ggml quantization bionic-libc autonomous-agents voice-assistant

Metadata

Release files for termux-stt 1.5.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for termux-stt 1.5.0
File	Size	Uploaded
termux_stt-1.5.0.tar.gz	1.7 MB	Oct 6, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for termux-stt 1.5.0
File	Interpreter	ABI	Platform	Reset
termux_stt-1.5.0-py3-none-any.whl 89.3 kB Oct 6, 2026	Python 3	none	any	Details

Total release size: 1.8 MB

Release files / termux_stt-1.5.0.tar.gz

Download URL	termux_stt-1.5.0.tar.gz
Size	1.7 MB
Tags	Source
SHA-256 checksum How to use checksums	`233f6dd7d6358758210006c4954854390a11ae8673ef62a8a2fb15b8e52ca516`
BLAKE2b-256 checksum How to use checksums	`725bf61ba5a0586e7c348924dfb1a3e2974d0dbc6f8c916528c4f432b39e21d4`
Upload date	Oct 6, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.11.16`

Release files / termux_stt-1.5.0-py3-none-any.whl

Download URL	termux_stt-1.5.0-py3-none-any.whl
Size	89.3 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`d9de25dd02f9576bc294fcaec753ac262c301406b2552cdc1e6aea2d45dddc29`
BLAKE2b-256 checksum How to use checksums	`62557abf4d9326b14f1f597a1fac1c07877997f81808e2108e0dd3000c0842c2`
Upload date	Oct 6, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.11.16`