An encoder-only, CTC-based open speech foundation model from ESPnet/CMU that reproduces Whisper-style multilingual ASR, speech translation and language identification using fully public data. Trained on 320k hours of cleaned YODAS + prior OWSM data across 75 languages.
A solid 1B-parameter dense audio model from ESPnet. Treat the modality benchmarks above as the leading indicator of fit — composite scoring across modalities is still maturing.
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OWSM (Open Whisper-style Speech Model) is a community effort led by CMU's WAVLab and the ESPnet team to build fully reproducible, openly trained alternatives to OpenAI Whisper. The v4 CTC variant is an encoder-only model using hierarchical multi-task self-conditioned CTC with an E-Branchformer encoder.
Reproducible research, multilingual transcription/translation across 75 languages, forced alignment (CTC segmentation), and as a base for further fine-tuning where training transparency is required.
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