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Speech to TextBenchmarks

Pulse benchmarks

Every public and internal benchmark Pulse and Pulse Pro are measured on, one page each.
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Smallest STT models are evaluated against three open-source datasets, FLEURS, ESB, and WildASR, plus an internal English perturbation suite. Word Error Rate (WER) by language. Lower is better. NA = not available or not supported by that provider.

This page covers both models:

  • Pulse Pro (English only) sits in the leaderboard-accuracy band. Benchmarks are on the Open ASR Leaderboard ESB suite and FLEURS English.
  • Pulse (21 streaming + 12 pre-recorded languages) is evaluated on FLEURS, ESB, WildASR, and our internal perturbation suite below.

Benchmarks

Open ASR Leaderboard

Pulse Pro against the leaderboard top three, FLEURS English and throughput.

Latency

Time to first transcript on Pulse streaming across concurrency levels.

FLEURS

Pulse word error rate on FLEURS, streaming and pre-recorded, per language.

ESB English

Pulse streaming word error rate across the eight ESB datasets.

Hindi

Pulse streaming Hindi accuracy across public datasets.

East Asian languages

Pulse streaming accuracy on Mandarin, Cantonese, Japanese and Korean.

WildASR robustness

Pulse accuracy on noisy, accented, real-world audio.

Contact center calls

Pulse on real English and Hindi contact-center recordings.

Perturbation robustness

Internal English and Hindi perturbation suites: noise, speed, pitch, codecs.

Diarization

Speaker diarization error rate and speed against other STT APIs.

Optimization Tips

  • Use 16 kHz sample rate for an optimal balance of quality and latency.
  • Choose linear16 encoding for the lowest latency.
  • Enable only the features your use case requires; each optional feature adds work.
  • Batch process when latency is not critical.

Next Steps