Estimate sustained task and media throughput for an AI transcription pipeline. Instead of treating model speed as the only constraint, the calculation includes average media length, per-task overhead, parallel workers, planned utilization, and operating hours. Use the output to compare deployment configurations and check whether daily volume targets are realistic.
Processing seconds per task = audio minutes × 60 × real-time factor + overhead. Tasks/hour = workers × 3,600 ÷ processing seconds × utilization. Daily output = tasks/hour × operating hours.
The main result is a planning estimate for one transcription workload or its available capacity. It is only as accurate as the pricing, model-speed, token-density, and workload assumptions entered. Use measured production values whenever possible and run high- and low-case scenarios before committing capacity or a customer price.
This planning estimate does not guarantee vendor billing, model performance, transcription accuracy, translation quality, or service-level objectives.
With 12 workers, 20-minute audio, 0.22 RTF, 15 seconds overhead, and 75% utilization, processing time is 279 seconds and throughput is about 116.1 tasks/hour.
Use representative production values or a weighted average from recent tasks. Peak values are better when testing capacity risk.
Not automatically. Increase workload, overhead, tokens, or utilization headroom to model retries and operational variance.
Yes. Enter each option’s measured speed, infrastructure assumptions, and labor or usage prices as separate scenarios.
Headroom helps absorb variable media length, traffic bursts, startup time, retries, and queueing without immediately missing targets.
Update it after pricing, models, hardware, workflow stages, average task size, or concurrency patterns change.
| Variable | Meaning |
|---|---|
| Workload | Media length, source volume, or concurrent demand entered above. |
| Efficiency | Measured model rate, token density, batching gain, or utilization assumption. |
| Overhead | Workflow work outside the primary model calculation. |
| Result | Estimated requirement or capacity before unmodeled operational variance. |