api.lokutor.com), on public test sets, on 3 and 4 November 2026. Raw per-utterance outputs and the scripts are kept; ask us for them. We show the weak results next to the strong ones.
Speech to text: Conv 2.0
Word error rate (WER, lower is better) with the Open ASR Leaderboard code and normaliser, throughPOST /v1/speech-to-text (ElevenLabs-compatible). Our submission to the leaderboard is pull request #267.
English, short-form
The Open ASR average over its nine standard sets (URGENT clean excluded) is 7.56%; over the seven classic sets (AMI, Earnings22, GigaSpeech, both LibriSpeech sets, SPGISpeech, VoxPopuli) it is 6.70%. Throughput as the leaderboard defines RTFx: 13.6 on average, measured per request over the internet with 8 requests in flight, so it is a per-request figure, not server capacity.
Other languages
Basque and Spanish (HiTZ test sets, Whisper basic normaliser, so comparable with HiTZ’s model cards): Basque 10.1% (4.4 h; Common Voice 8.3, parliament 6.4, OpenSLR 21.7) and Spanish 9.0% (7.5 h; parliament 5.0, MLS 6.5, VoxPopuli 10.0, Common Voice 11.4, OpenSLR 13.5).
Details that matter: Common Voice rows are random 5,000-clip samples (seed 42) of the full test sets; three Gigaspeech and 34 AMI predictions came back empty and are counted as deletions; the model writes numerals where some references spell numbers out.
Text to speech: Versa 2.0
Seed-TTS-Eval, English (1,088 Common Voice utterances, WER only, Whisper-large-v3 as the recogniser): 1.82% with voice F1 and 1.90% with voice M1. We report WER only: the benchmark’s speaker-similarity score compares the output to a prompt speaker, and our stock voices are not conditioned on that clip, so the number would not mean anything. This is also not the benchmark’s zero-shot cloning task. The scoring path was re-implemented for English (the official script needs CUDA); normalisation follows the repository’srun_wer.py.
Speed and capacity are measured separately and published in the models page: about 90 ms to first audio under load.