Model reference · open weights

sepformer-dns4-16k-enhancement

Available as managed deployment Audio speechbrain Audio→audio 1 variants 528 dl/mo

sepformer-dns4-16k-enhancement is an open-weight audio or speech model from speechbrain. 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 byspeechbrain
TypeAudio & music
TaskAudio→audio
Runs withspeechbrain
Released2023-08-06
Popularity528 downloads / month
LicenceOpen weights

About

What sepformer-dns4-16k-enhancement is

This repository provides all the necessary tools to perform speech enhancement (denoising) with a SepFormer model, implemented with SpeechBrain. The model is trained on 1300HRS of Microsoft-DNS 4 dataset with 16k sampling frequency. For a better experience we encourage you to learn more about SpeechBrain. Evaluation on DNS4 2022 baseline dev set using DNSMOS are-

ReleaseSIGBAKOVRL
08-01-232.9993.0762.437

DNSMOS - deep noise suppression (DNS)- mean opinion score (MOS) is a non-intrusive evaluation metric. It computes 3 scores– SIG (speech quality), BAK (background noise quality), and OVRL (overall quality) each on a scale of 1 to 5, with 5 being the best quality.

Read the full model card

Install SpeechBrain

First of all, please install SpeechBrain with the following command:

pip install speechbrain

Please notice that we encourage you to read our tutorials and learn more about SpeechBrain.

Perform speech enhancement on your own audio file

from speechbrain.inference.separation import SepformerSeparation as separator
import torchaudio

model = separator.from_hparams(source="speechbrain/sepformer-dns4-16k-enhancement", savedir='pretrained_models/sepformer-dns4-16k-enhancement')

# for custom file, change path
est_sources = model.separate_file(path='speechbrain/sepformer-dns4-16k-enhancement/example_dns4-16k.wav')

torchaudio.save("enhanced_dns4-16k.wav", est_sources[:, :, 0].detach().cpu(), 16000)

Inference on GPU

To perform inference on the GPU, add run_opts={"device":"cuda"} when calling the from_hparams method.

You can find our training results (models, logs, etc) here.

Limitations

The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.

Referencing SpeechBrain

@misc{speechbrain,
  title={{SpeechBrain}: A General-Purpose Speech Toolkit},
  author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
  year={2021},
  eprint={2106.04624},
  archivePrefix={arXiv},
  primaryClass={eess.AS},
  note={arXiv:2106.04624}
}

Referencing SepFormer

@inproceedings{subakan2021attention,
      title={Attention is All You Need in Speech Separation},
      author={Cem Subakan and Mirco Ravanelli and Samuele Cornell and Mirko Bronzi and Jianyuan Zhong},
      year={2021},
      booktitle={ICASSP 2021}
}

Referencing ICASSP 2022 Deep Noise Suppression Challenge

@inproceedings{dubey2022icassp,
  title={ICASSP 2022 Deep Noise Suppression Challenge},
  author={Dubey, Harishchandra and Gopal, Vishak and Cutler, Ross and Matusevych, Sergiy and Braun, Sebastian and Eskimez, Emre Sefik and Thakker, Manthan and Yoshioka, Takuya and Gamper, Hannes and Aichner, Robert},
  booktitle={ICASSP},
  year={2022}
}

About SpeechBrain

  • Website: https://speechbrain.github.io/
  • Code: https://github.com/speechbrain/speechbrain/
  • HuggingFace: https://huggingface.co/speechbrain/

From the published model card. Full card on the HuggingFace links in the sidebar.

How it works

How audio & music work

Audio or textinputAudio modelrecognise / synthesiseText or audiooutputSpeech-to-text turns audio into text; text-to-speech and music models turn text into audio.

Benchmarks

Reported results

As published on the model card — the maker's own numbers, not measured by AxForge.

TaskDatasetMetricScore
Speech EnhancementDNS-4DNSMOS SIG
Speech EnhancementDNS-4DNSMOS BAK
Speech EnhancementDNS-4DNSMOS OVRL

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys sepformer-dns4-16k-enhancement for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (sepformer-dns4-16k-enhancement 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="sepformer-dns4-16k-enhancement" -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.

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