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The Power of Real-Time Translation to Accelerate Global Innovation: VoicePing × Plug and Play Japan
AI Translation

The Power of Real-Time Translation to Accelerate Global Innovation: VoicePing × Plug and Play Japan

As a global accelerator that promotes N-to-N collaboration between startups and major corporations, Plug and Play Japan holds its annual "Plug and Play Japan Summit," which typically attracts around 2,200 participants. With many international speakers and attendees, language barriers posed a major challenge. This is why the real-time translation tool "VoicePing" was implemented.

VoicePing Editorial
8 min
Evaluating Speaker Diarization Models: A Practical Comparison
Speaker Diarization NeMo

Evaluating Speaker Diarization Models: A Practical Comparison

Technical comparison of NeMo MSDD and Pyannote 3.1 across 6 real-world test scenarios

Ashar Mirza - VoicePing
4 min
Global Collaboration Transcending Language Barriers: Kepple Group’s VoicePing Implementation Case Study
AI Translation

Global Collaboration Transcending Language Barriers: Kepple Group’s VoicePing Implementation Case Study

Global Collaboration Transcending Language Barriers: Kepple Group’s VoicePing Implementation Case Study

VoicePing Editorial
9 min
Breaking Language Barriers to Drive Global Innovation from Kobe: VoicePing Implementation at Kobe City Economic and Tourism Bureau, New Industry Development Division
AI Translation

Breaking Language Barriers to Drive Global Innovation from Kobe: VoicePing Implementation at Kobe City Economic and Tourism Bureau, New Industry Development Division

Breaking Language Barriers to Drive Global Innovation from Kobe: VoicePing Implementation at Kobe City Economic and Tourism Bureau, New Industry Development Division

VoicePing Editorial
9 min
How a Go Rewrite Made Our WebSocket Proxy 100x More Efficient
Go WebSocket

How a Go Rewrite Made Our WebSocket Proxy 100x More Efficient

Rewriting a Python WebSocket proxy in Go with lock-free connection pooling and event-driven reconciliation

Akira Noda - VoicePing
16 min
Part 2: Scaling Translation Inference: +82% Throughput
vLLM Translation

Part 2: Scaling Translation Inference: +82% Throughput

How we improved vLLM inference throughput by 82% using AsyncLLMEngine and right-sized continuous batching

Ashar Mirza - VoicePing
5 min

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