Speech-to-Text Benchmark Results — Word Error Rate (WER %, lower is better)

Methodology: benchmarks run on the same framework as the Solaria-1 campaign. Solaria-3 and Solaria-1 compared against 9 STT providers. Each provider tested on identical audio files via production APIs with default settings. Real customer audio = Gladia internal production dataset, annotated by humans. Soniox and Pipecat STT Benchmark excluded on some datasets pending data availability. Transcriptions normalized with OpenAI Whisper text normalizer before WER computation. Diarization Error Rate (DER) measured on DIHARD III using standard protocols. 74+ hours of audio across 8 evaluation datasets. Full open benchmark methodology: https://github.com/gladiaio/normalization

Columns: WER = Word Error Rate (%, lower is better) · Perfect = number of files with 0% WER · High WER = files with WER above 50% · RTFx = Real-Time Factor multiplier (higher = faster than real-time)

Real Customer Audio — English (Gladia Internal Dataset)

Switchboard — Conversational Speech

Common Voice 24 — Clean Audio (Multilingual)

VoxPopuli — Formal Discourse (European Parliament)

Earnings22 Full — Financial Calls (Long-form, single file)

Earnings22 Cleaned AA — Financial Calls (Curated by Artificial Analysis)

Multilingual LibriSpeech — Audiobooks (5 languages, average)

Multilingual LibriSpeech — WER by Language (%)

Pipecat STT Benchmark — Real-Time Streaming

Speaker Diarization Benchmark — DIHARD III — Diarization Error Rate (DER %, lower is better)