When you send an audio file to a speech-to-text API, a lot happens before you get your transcript back. Understanding these internal mechanics helps you build more reliable integrations, debug issues faster, and optimize for cost and performance. This article is part of our developer's guide to speech-to-text integration.
Audio Preprocessing: What Happens First
Before any speech recognition begins, the API must prepare your audio for processing. This preprocessing stage is critical for accuracy.
Format Conversion and Normalization
Most APIs accept common formats like MP3, WAV, FLAC, and M4A. Internally, they convert everything to a standardized format—typically 16-bit PCM audio at 16kHz. This normalization ensures the model receives consistent input regardless of what you upload.
Loudness normalization also happens at this stage. The API adjusts audio levels so quiet recordings and loud ones produce comparable results. Some services apply noise reduction filters to minimize background interference.
Chunking Strategies
APIs don't process your entire file as a single unit. Instead, they split audio into manageable chunks. This chunking serves multiple purposes:
- Memory efficiency: Processing a 2-hour podcast as one piece would require enormous memory
- Parallelization: Chunks can be processed simultaneously across multiple servers
- Streaming support: For real-time transcription, chunks enable continuous output
OpenAI's API, for example, limits uploads to 25 MB. Larger files must be split client-side. Other APIs like Deepgram and AssemblyAI handle chunking automatically using voice activity detection (VAD) to find natural break points between speech segments.
Feature Extraction: Turning Sound Into Data
Raw audio is just variations in air pressure over time. Before the speech recognition model can work with it, the audio must be converted into numerical features.
From Waveform to Features
The audio waveform gets transformed into spectral features that capture pitch, intensity, and frequency characteristics. Common representations include Mel-frequency cepstral coefficients (MFCCs) or mel spectrograms.
These features represent the audio as a series of snapshots—typically every 10-25 milliseconds. Each snapshot captures the frequency content at that moment, creating a visual fingerprint of the speech.
Why This Matters for Timestamps
Timestamp accuracy depends directly on this feature extraction step. Most APIs offer timestamps with 100-millisecond granularity, which corresponds to how the audio was segmented during preprocessing.
Google's Speech-to-Text API provides time offsets showing where each word begins and ends relative to the start of the audio. OpenAI offers configurable granularity—you can request timestamps at the segment level, word level, or both.
Synchronous vs Asynchronous Processing
APIs offer different processing modes depending on your latency requirements and audio length.
Synchronous (Real-Time) Mode
For short audio clips or live streaming, synchronous processing returns results immediately. You send audio, the API processes it, and you receive the transcript in the same request.
This mode works well for:
- Voice commands and dictation
- Live captioning
- Short audio clips under a few minutes
The tradeoff is that longer files block your application while waiting for completion.
Asynchronous (Batch) Mode
For longer recordings, async processing makes more sense. You submit audio and receive a job ID. Your application polls for results or receives a webhook callback when processing completes.
Google calls this "Long Running Operations." AssemblyAI calls it batch transcription. The pattern is similar: submit once, check back later.
Async mode enables:
- Processing files of any length
- Parallel submission of multiple files
- Background processing without blocking your application
Error Handling and Retry Logic
Robust integrations need proper error handling. Here's what to account for.
Common Error Types
Speech-to-text APIs can fail for various reasons:
- Rate limits: Submitting too many requests too quickly
- File size limits: Audio exceeding maximum allowed duration or bytes
- Format errors: Unsupported audio codecs or corrupted files
- Network timeouts: Connection interrupted during upload or processing
- Recognition failures: Audio too noisy or unclear to transcribe
Retry Strategies
Not every error deserves a retry. Rate limits (HTTP 429) and server errors (HTTP 503) are temporary—retrying makes sense. Client errors like invalid format (HTTP 400) won't improve with retries.
For recoverable errors, use exponential backoff. Start with a short delay (100-500ms), then double it with each retry attempt. Add random jitter to prevent multiple clients from retrying simultaneously and overwhelming the service.
Most APIs recommend a maximum of 3-5 retry attempts before giving up and alerting your application.
Optimizing API Usage
Understanding internals helps you optimize for both cost and performance.
Reduce Preprocessing Overhead
Upload audio in formats close to what the API uses internally. WAV or FLAC at 16kHz mono typically requires less server-side conversion than compressed formats.
Trim Silence
You're usually charged per audio minute. Long silences cost money without adding value. Trim silence before uploading, or use APIs that support smart chunking with VAD.
Choose the Right Mode
Don't use real-time streaming for batch processing—it's more expensive and less accurate. Reserve synchronous mode for latency-sensitive use cases. Batch everything else.
Handle Partial Results
Streaming APIs often return interim (partial) results before finalizing. These help with responsiveness but may change. Only persist final results.
Conclusion
Speech-to-text APIs handle significant complexity behind simple endpoints: format conversion, intelligent chunking, feature extraction, and parallel processing. Understanding these mechanics helps you build more reliable integrations, debug problems effectively, and optimize costs.
If you're building an application that needs straightforward transcription without managing this complexity yourself, Scriby offers a simple, pay-as-you-go API with speaker diarization and support for 100+ languages. No subscriptions, no minimum commitments—just upload and transcribe.