Skip to content
ModelListedExternal
Download

Vctk Dns2020 Bsrnn Small Noncausal

This model was trained by wyz based on the universalsev1 recipe in espnet. More information can be found at

KindOtherVersionRevision 0fe515a

Model source

Kind
Other
File size
18.5 MB
Version
vRevision 0fe515a
Updated
Sep 19, 2026
Downloads
1
Tasks
Audio
Source
Hugging Face

Source excerpt

This model was trained by wyz based on the universalsev1 recipe in espnet. More information can be found at

Sources

1 source
Hugging FacePrimaryVersion 0fe515a20565d2a808a7a78931febfc026b51a6b
huggingface.co/wyz/vctk_dns2020_bsrnn_small_noncausal/tree/0fe515a20565d2a808a7a78931febfc026b51a6b

Verified Sep 19

Model artifacts

1 artifact

exp_vctk_dns20/enh_train_enh_bsrnn_small_noncausal_raw/94epoch.pth

pth · 18.5 MB · SHA-256 0283472bc386…a577 · Hugging Face

Download

Source excerpts

2 excerpts

--- tags: - espnet - audio - audio-to-audio language: en datasets: - VCTK_DEMAND - DNS2020 license: cc-by-4.0 --- ## ESPnet2 ENH model ### `wyz/vctk_dns2020_bsrnn_small_noncausal` This model was trained by wyz based on the universal_se_v1 recipe in [espnet](https://github.com/espnet/espnet/). More information can be found at https://github.com/Emrys365/se-scaling. ### Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. To use the model in the Python interface, you could use the following code: ```python import soundfile as sf from espnet2.bin.enh_inference import SeparateSpeech # For model downloading + loading model = SeparateSpeech.from_pretrained( model_tag="wyz/vctk_dns2020_bsrnn_small_noncausal", normalize_output_wav=True, device="cuda", ) # For loading a downloaded model # model = SeparateSpeech( # train_config="exp_vctk_dns20/enh_train_enh_bsrnn_small_noncausal_raw/config.yaml", # model_file="exp_vctk_dns20/enh_train_enh_bsrnn_small_noncausal_raw/xxxx.pth", # normalize_output_wav=True, # device="cuda", # ) audio, fs = sf.read("/path/to/noisy/utt1.flac") enhanced = model(audio[None, :], fs=fs)[0] ``` <!-- Generated by ./scripts/utils/show_enh_score.sh --> # RESULTS ## Environments - date: `Tue Feb 27 22:32:36 EST 2024` - python version: `3.8.16 (default, Mar 2 2023, 03:21:46) [GCC 11.2.0]` - espnet version: `espnet 202304` - pytorch version: `pytorch 2.0.1+cu118` - Git hash: `443028662106472c60fe8bd892cb277e5b488651` - Commit date: `Thu May 11 03:32:59 2023 +0000` ## enhanced_test_16k |dataset|PESQ_WB|STOI|SAR|SDR|SIR|SI_SNR|OVRL|SIG|BAK|P808_MOS| |---|---|---|---|---|---|---|---|---|---|---| |chime4_et05_real_isolated_6ch_track|1.17|53.40|-2.41|-2.41|0.00|-30.92|3.06|3.37|3.93|3.74| |chime4_et05_simu_isolated_6ch_track|1.53|83.93|9.20|9.20|0.00|2.74|2.96|3.24|3.97|3.40| |dns20_tt_synthetic_no_reverb|3.22|97.78|19.73|19.73|0.00|19.70|3.34|3.58|4.12|4.03| |reverb_et_real_8ch_multich|1.88|78.41|10.33|10.33|0.00|6.27|2.62|3.13|3.38|3.39| |reverb_et_simu_8ch_multich|1.62|85.35|9.19|9.19|0.00|-10.73|2.61|3.13|3.23|3.53| |whamr_tt_mix_single_reverb_max_16k|1.53|84.88|8.30|8.30|0.00|2.76|2.98|3.33|3.85|3.69| ## enhanced_test_48k |dataset|STOI|SAR|SDR|SIR|SI_SNR|OVRL|SIG|BAK|P808_MOS| |---|---|---|---|---|---|---|---|---|---| |vctk_no...

Source context: 1 downloads · 0 likes · Pipeline audio-to-audio · Library espnet · Repo wyz/vctk_dns2020_bsrnn_small_noncausal