Speech-to-text
Whisper base requirements, license & speed
By OpenAI · released 2022-09-26 · 73M parameters
Commercial useYes
Min memory~1 GB VRAM
Runs on CPUYes
StreamingNo
Key facts
- License
- apache-2.0 source
- Commercial use
- Yes sourceApache-2.0 on HF card; MIT on GitHub
- Parameters
- 73M sourcesafetensors parameter count
- Minimum memory
- ~1 GB VRAM source
- Runs on CPU
- Yes sourceWhisper models run on CPU via faster-whisper int8 or whisper.cpp; measured here
- Streaming
- No source30-second window model; streaming only via wrappers
- Voice cloning
- n/a (speech-to-text)
- Languages
- en, zh, de, es, ru, ko, fr, ja, pt, tr, pl, ca, nl, ar, sv, it, id, hi, fi, vi, he, uk, el, ms, cs, ro, da, hu, ta, no, th, ur, hr, bg, lt, la, mi, ml, cy, sk, te, fa, lv, bn, sr, az, sl, kn, et, mk, br, eu, is, hy, ne, mn, bs, kk, sq, sw, gl, mr, pa, si, km, sn, yo, so, af, oc, ka, be, tg, sd, gu, am, yi, lo, uz, fo, ht, ps, tk, nn, mt, sa, lb, my, bo, tl, mg, as, tt, haw, ln, ha, ba, jw, su source
- Released
- 2022-09-26 sourceHugging Face repo creation date (proxy for release date)
- Gated on HF
- No
Measured speed & memory
| Hardware | Runtime / settings | RTF | First-segment latency | Peak RAM | WER* | Date |
|---|---|---|---|---|---|---|
| Cloud x86 VM, 8 vCPU Intel Xeon, 16 GB RAM, no GPU | faster-whisper base (int8, beam 5) | 0.0410 (24× real time) | 0.413 s | 443.0 MB | 6.5% | 2026-10-21 |
| Cloud x86 VM, 8 vCPU Intel Xeon, 16 GB RAM, no GPU | whisper.cpp base (ggml fp16, beam 5, 8 threads, pywhispercpp) | 0.0786 (13× real time) | 0.793 s | 334.7 MB | 3.2% | 2026-10-21 |
Median of 5 runs after one warm-up, ~10 s English passage. Hardware is labelled exactly: a cloud x86 VM is not an N100 or a Pi. Full method on the CPU only page. Peak RAM is whole-process RSS. *WER on clean synthetic speech, a sanity check, not a real-world accuracy figure. Methodology
Accuracy
We don't run accuracy evals. For word error rate, check the Hugging Face Open ASR Leaderboard.
Comparisons: faster-whisper vs whisper.cpp
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