Speech-to-Text & Text-to-Speech

“I’m Michael, an AI audio engineer transforming speech into text and voices into lifelike AI narration. Janction gives me the power to process speech faster, cheaper, and at scale.”

🎙️ I’m Michael Chen, a 33-year-old AI audio engineer based in Toronto. At VoxMedia, I work on automated speech processing for videos, podcasts, and AI-powered customer service assistants. Whether it’s creating subtitles for YouTube, generating AI voiceovers, or real-time transcriptions, I need high-speed AI inference to keep up with fast-paced media production.

💻 My problem?

Speech-to-text (STT) and text-to-speech (TTS) models need serious GPU power. Whisper, Tacotron, and WaveNet work well, but my RTX 6000 struggles with large-scale processing. Real-time AI dubbing and multilingual voice synthesis require low latency, and using cloud-based services like AWS Polly or Google Speech API gets too expensive when handling bulk workloads.

🚀 That’s why I use Janction.

Janction’s on-demand GPU pool gives me access to enterprise-grade GPUs for real-time speech processing, whether I’m automating video subtitles, fine-tuning an AI voice, or transcribing an entire podcast series. Instead of waiting for slow local processing or paying premium cloud prices, I can scale up instantly and process speech at lightning speed.

💡 What I love about Janction:

✅ Faster speech processing – I can transcribe and synthesize AI voices in real time.

✅ Low-latency TTS generation – My AI-generated voices sound natural without delays.

✅ Scalability for bulk workloads – When I have large media projects, I just add more GPUs.

✅ Cost-effective AI inference – No more expensive cloud API fees.

✅ API-friendly automation – Seamlessly integrates with editing and production workflows.

🎧 Now, I can focus on delivering high-quality AI-driven speech solutions without bottlenecks. Thanks to Janction, my media team processes speech faster, scales seamlessly, and saves costs on AI-driven audio workflows.