Building a Speech-to-Text App: Architecture Overview for Developers

If you're planning to build a transcription application, understanding the architecture before writing code saves time and prevents costly rewrites. This article is part of our developer's guide to speech-to-text integration, focusing specifically on system design and component decisions.

Whether you're building a meeting transcriber, a podcast search tool, or a voice-enabled feature for your SaaS product, the architectural patterns remain similar. Let's walk through the key decisions.

Core Components of a Transcription App

A functional speech-to-text application typically consists of four layers:

1. Audio Capture Layer

This is where audio enters your system. Options include:

• Browser microphone access via the Web Audio API
• File upload endpoints accepting audio/video formats
• Stream ingestion from platforms like Zoom or YouTube
• Mobile SDKs for native apps

The capture layer handles format conversion, sample rate normalization, and buffering. For real-time apps, this layer also manages chunking—splitting continuous audio into processable segments.

2. Processing Backend

Your backend receives audio and coordinates transcription. Common tech stacks in 2026 include:

• Node.js + Express/Fastify for real-time WebSocket connections
• Python + FastAPI for async processing and ML model integration
• Go for high-throughput batch processing systems

The backend's job isn't to transcribe—it's to orchestrate. It sends audio to the transcription engine, handles retries on failures, manages job queues, and stores results.

3. Transcription Engine

This is the core speech-to-text service. You have two architectural choices:

• Cloud APIs (Google Cloud Speech, AssemblyAI, Deepgram, ElevenLabs): Send audio, receive text. The provider handles model hosting, scaling, and accuracy improvements.
• Self-hosted models (Whisper, faster-whisper, Vosk): Run transcription on your infrastructure. Offers privacy control but requires GPU resources and operational overhead.

For most applications, cloud APIs make sense. They handle the ML complexity while you focus on your product. Self-hosting becomes relevant when you need complete data control or have specialized accuracy requirements.

To understand how these APIs work internally, see our article on how speech-to-text APIs work under the hood.

4. Data Storage and Retrieval

Transcripts need a home. Your storage layer typically includes:

• Object storage (S3, GCS, R2) for original audio files
• Document database (MongoDB, PostgreSQL with JSONB) for transcripts with metadata
• Search index (Elasticsearch, Typesense) if you're building transcript search functionality

Consider transcript structure carefully. Storing word-level timestamps and speaker labels enables features like click-to-play and speaker filtering later.

Real-Time vs Batch Architecture

The biggest architectural fork is whether you need real-time transcription.

Real-Time Architecture

Real-time systems process audio as it's spoken. The typical flow:

1. Client captures audio in chunks (100-500ms)
2. WebSocket connection streams chunks to backend
3. Backend forwards to real-time transcription API
4. Partial results stream back to client
5. Final transcript consolidates when audio ends

Latency matters here. Modern real-time APIs achieve 200-400ms latency—fast enough that text appears while someone is still speaking. Your architecture must minimize additional delays from network hops and processing overhead.

When to use: Live captioning, voice assistants, real-time meeting notes.

For a deeper comparison, read batch vs real-time transcription: which one do you actually need.

Batch Architecture

Batch systems process complete audio files asynchronously. The flow:

1. Client uploads file to your backend
2. Backend stores file and creates a processing job
3. Worker picks up job, sends to transcription API
4. Results stored in database
5. Client polls or receives webhook notification

Batch is simpler to implement and more cost-effective for processing existing recordings. The trade-off is users wait for results—anywhere from seconds to minutes depending on file length.

When to use: Podcast transcription, video subtitle generation, interview processing.

Adding Speaker Diarization

If your app involves multiple speakers (meetings, interviews, podcasts), you'll need speaker diarization—identifying who said what.

Diarization adds complexity:

• Architectural impact: Transcription and diarization are often separate processes. Some APIs combine them; others require post-processing.
• Accuracy considerations: Diarization accuracy varies significantly between providers and depends heavily on audio quality.
• Storage requirements: You'll need to store speaker segments alongside text, increasing data complexity.

When evaluating transcription providers, check whether diarization is built-in or requires additional integration.

Scaling Considerations

As your app grows, architecture decisions compound:

Job Queues

For batch processing, implement proper queuing (Redis, RabbitMQ, SQS). Queues let you:

• Handle traffic spikes without dropping jobs
• Retry failed transcriptions automatically
• Process files in parallel across multiple workers

Caching

Transcription is expensive. Cache aggressively:

• Store transcripts with content hashes to avoid re-processing identical audio
• Cache API responses for repeated requests
• Consider transcript de-duplication for platforms with shared content

Cost Management

Transcription costs scale with audio minutes. For strategies on managing costs at scale, see cost optimization tips for speech-to-text.

Practical Tech Stack Recommendation

For a typical transcription app MVP in 2026:

Component	Recommendation	Why
Frontend	React or Next.js	Mature ecosystem, good WebSocket support
Backend	Node.js/Express or Python/FastAPI	Async-friendly, extensive STT SDK support
Queue	Redis with BullMQ	Simple setup, reliable for most scales
Database	PostgreSQL or MongoDB	Both handle transcript JSON well
Storage	S3-compatible (AWS S3, Cloudflare R2)	Cost-effective, widely supported
Transcription	Cloud API (ElevenLabs, AssemblyAI, Deepgram)	Handles ML complexity for you

This stack balances development speed with production readiness. You can always swap components as requirements evolve.

What About the Frontend?

The frontend varies significantly by use case, but common patterns include:

• Audio player integration: Sync transcript with playback position
• Search interface: Full-text search across transcripts
• Export options: SRT subtitles, plain text, structured JSON
• Speaker visualization: Color-coded segments, speaker timelines

Keep your frontend simple initially. A basic transcript view with timestamps covers most MVP needs.

Conclusion

Building a speech-to-text application comes down to four decisions:

1. Real-time or batch? Choose based on your users' needs, not technical preference.
2. Cloud API or self-hosted? Cloud APIs for most cases; self-host only with clear privacy or accuracy requirements.
3. With or without diarization? If multiple speakers are involved, factor this in from the start.
4. Scaling strategy: Implement queues and caching early—they're easier to add before you have traffic.

If you're looking for a straightforward way to add transcription to your workflow without building infrastructure, Scriby handles the complexity—upload audio, get transcripts with speaker labels, pay only for what you use.