Independent publisher
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guruawe
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This profile connects the 5 model and dataset records shown below to their upstream cards and sources. The library counts describe SAVRN's coverage, rather than this publisher's entire output.
Published tasks: Speech recognition.
Reported licenses: other. Open a record to review its specific license and conditions.
Compare model specifications, source dates, and hardware estimates on the individual records. Publisher identity verification is separate from a model's quality, deployment readiness, or suitability for your work.
Models
Fine-tuned omniASRLLM1Bv2 (run 3, step 20,000) blended with the base model at α = 0.85 (WiSE-FT weight interpolation). Use this model for monolingual Indian-language speech. English, Hinglish / code-mixed speech and all other languages are served by v1 (same run, α = 0.5). The α = 0.7 version of this file is in the repo history. α 1.0 was rejected: FLEURS dev (63 languages) 13.63 vs 13.51 for α 0.5, 38 languages worse, and the worst Hinglish / Indian-English scores. Paired bootstrap, α 0.85 vs α 0.7 on IndicVoices dev: −2.00 [−3.12, −1.15], P(better) = 1.00. \ Same-domain: training included IndicVoices train audio. Full α 0.85 test halves and FLEURS-Indic are scored after the current GPU…