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ESPnet E-Branchformer (no LM)

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ESPnet E-Branchformer (no LM) is an open-weights speech-to-text model from ESPnet, released January 2023. Its published size is 0.14892B parameters. Licence: cc-by-4.0.

Catalogue identity notes

Exact LibriSpeech 960h recipe run, README lines 264–318. Published checkpoint linked by recipe authors; a compatible local command has not yet been verified. Decoder and checkpoint averaging are recorded per result. Architecture names do not denote a generic pretrained model.

Model information sources

· Parameter count ↗

README lines 264-265 (identity note cites 264-318); model link matches the row's model_repo. ASR model only; LM not included.

46.1±3.9#116 of 162 speech-to-text modelsfrom 2 benchmark results across 1 source* thinly tested, treat the rank as provisional

Score within the Open ASR English short-form WER (March 2026) comparison set only.

$ how it compares

The speech-to-text models ranked either side of ESPnet E-Branchformer (no LM), out of 176 with a score, then more from ESPnet.

#modelscorewhereRAMreleased
127GigaAM Multilingual Large (CTC 600M) Sber47±7local~2.3GB2026-07
128STT En Conformer-Transducer Large NVIDIA46±4local~1.1GB2022-09
129ESPnet E-Branchformer (no LM) ESPnet46±4local~0.8GB2023-01
130ESPnet Multiconvformer (no LM) ESPnet46±4local~0.8GB2024-03
131STT En FastConformer-Transducer Large NVIDIA46±3local~1.1GB2023-06
more from ESPnet
112ESPnet HuBERT Large + Conformer + Transformer LM ESPnet50±4local~8GB2022-01
113ESPnet WavLM Large + Conformer + Transformer LM ESPnet50±4local~8GB2022-01
135ESPnet wav2vec 2.0 Large + Conformer + Transformer LM ESPnet45±4local~8GB2021-12

see all speech-to-text models · find the ones that fit your machine

$ what it's good at

other tests2 not in a task · 94% of its score
testresultvs the others in its rundifficultycountswhy
LibriSpeech test-other WER lab4.6% ✎█▃▃·▃▅·▅···▅ beats 9/11avg 3.7%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 73%typical
LibriSpeech test-clean WER lab2.2% ✎█··▃▃▅▅▃···▅ beats 7/11avg 1.9%▇▇▇▇▇▇▇▇ 21%blunt

$ where to run it

Other ways to run it

  1. Run it as published · needs ~0.81 GB · from github.com/espnet (official)
    uv run --python 3.12 --with espnet==202610.post2 --with espnet_model_zoo==0.1.11 --with librosa python -c "import librosa; from espnet2.bin.asr_inference import Speech2Text; asr=Speech2Text.from_pretrained('asapp/e_branchformer_librispeech@f50914447c48b091738b3e020023ac69dbde9ea9', beam_size=60, ctc_weight=0.3, lm_weight=0); speech,_=librosa.load('audio.wav', sr=16000, mono=True); print(asr(speech)[0][0])"

Weights and documentation: huggingface.co/asapp ↗

$ how the score was worked out · ESPnet LibriSpeech ASR recipe… 94%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
ESPnet LibriSpeech ASR recipe results (pinned September 2026) · lab-reported2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 94%Apache-2.0
prior · pull toward the middle▇▇ 6%
weighted average= 46.1

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json