Model reference · open weights
reazonspeech-nemo is an open-weight audio or speech model from reazon-research.
For self-hosting, compare this build's requirements below with the GPU you plan to rent.
Summary of the reazon-research/reazonspeech-nemo-v2 model card, 2026-10-01
What it is
| Released by | reazon-research |
|---|---|
| Released | 2024-01-30 |
From the model card
reazonspeech-nemo-v2 is an automatic speech recognition model trained
on ReazonSpeech v2.0 corpus.
This model supports inference of long-form Japanese audio clips up to several hours.
The model features an improved Conformer architecture from Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition.
Subword-based RNN-T model. The total parameter count is 619M.
Encoder uses Longformer attention with local context size of 256, and has a single global token.
Decoder has a vocabulary space of 3000 tokens constructed by SentencePiece unigram tokenizer.
We trained this model for 1 million steps using AdamW optimizer following Noam annealing schedule.
We recommend to use this model through our reazonspeech library.
from reazonspeech.nemo.asr import load_model, transcribe, audio_from_path
audio = audio_from_path("speech.wav")
model = load_model()
ret = transcribe(model, audio)
print(ret.text)
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.
How it works