Model reference · open weights
tedlium2_streaming_transformer is an open-weight audio or speech model from espnet. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | espnet |
|---|---|
| Type | Audio & music |
| Task | Speech→text |
| Runs with | espnet |
| Released | 2026-09-20 |
| Popularity | 21 downloads / month |
| Licence | Open weights |
About
espnet/tedlium2_streaming_transformerA streaming Transformer: it decodes while the audio is still arriving, a block
at a time, rather than waiting for the end of the utterance. Trained on
TED-LIUM 2 with the egs2/tedlium2/asr1 recipe and
conf/train_asr_streaming_transformer.yaml,
with a BPE-500 language model beside it.
Trained and originally published by Keqi Deng,
as
D-Keqi/espnet_asr_train_asr_streaming_transformer_raw_en_bpe500_sp_valid.acc.ave.
This copy is here so that the demo notebooks and the documentation point at a
model the organization keeps alive; the weights are the same ones.
import soundfile as sf
from espnet2.bin.asr_inference_streaming import Speech2TextStreaming
s2t = Speech2TextStreaming.from_pretrained(
"espnet/tedlium2_streaming_transformer",
device="cpu",
beam_size=20,
ctc_weight=0.5,
penalty=0.0,
nbest=1,
disable_repetition_detection=True,
)
speech, rate = sf.read("audio.wav", dtype="float32") # 16 kHz, one channel
# 640 samples is 40 ms: the hypothesis comes back after each slice, and grows
chunk = 640
slices = len(speech) // chunk
for i in range(slices):
results = s2t(speech=speech[i * chunk : (i + 1) * chunk], is_final=False)
if results:
print(results[0][0])
results = s2t(speech=speech[slices * chunk :], is_final=True)
print(results[0][0])
There is a notebook that runs this end to end:
Demos/asr_streaming_demo.ipynb.
For non-streaming recognition, OWSM-CTC is far more accurate and covers 150+ languages.
TED-LIUM 2, decoded with the language model in this repository.
| dataset | WER | CER |
|---|---|---|
| dev | 11.4 | 5.4 |
| test | 10.8 | 5.3 |
The full tables are in
exp/asr_train_asr_streaming_transformer_raw_en_bpe500_sp/RESULTS.md.
Trained with espnet 0.9.8 and pytorch 1.5.1, in November 2021, and loaded by
current ESPnet through the meta.yaml in this repository.
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
From the published model card. Full card on the HuggingFace links in the sidebar.
How it works
Using it via the API
Once AxForge deploys tedlium2-streaming-transformer for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (tedlium2-streaming-transformer below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/audio/transcriptions \ -H "Authorization: Bearer $AXFORGE_API_KEY" \ -F model="tedlium2-streaming-transformer" -F file=@audio.mp3
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.