Skip to content
ModelListedExternal
Download

Vctk Dns2020 Whamr Bsrnn Xtiny Noncausal

This model was trained by Emrys365 based on the universalsev1 recipe in espnet. More information can be found at

KindOtherVersionRevision 716d700

Model source

Kind
Other
File size
2.18 MB
Version
vRevision 716d700
Updated
Sep 18, 2026
Downloads
2
Tasks
Audio
Source
Hugging Face

Source excerpt

This model was trained by Emrys365 based on the universalsev1 recipe in espnet. More information can be found at

Sources

1 source
Hugging FacePrimaryVersion 716d700ce93e8e9cbf3e712fbce17a94af764635
huggingface.co/wyz/vctk_dns2020_whamr_bsrnn_xtiny_noncausal/tree/716d700ce93e8e9cbf3e712fbce17a94af764635

Verified Sep 18

Model artifacts

1 artifact

exp_vctk_dns20_whamr/enh_train_enh_bsrnn_xtiny_noncausal_raw/97epoch.pth

pth · 2.18 MB · SHA-256 af031167f2e3…232e · Hugging Face

Download file

Source excerpts

2 excerpts

--- tags: - espnet - audio - audio-to-audio datasets: - VCTK_DEMAND - DNS2020 - WHAMR language: en license: cc-by-4.0 --- ## ESPnet2 ENH model ### `wyz/vctk_dns2020_whamr_bsrnn_xtiny_noncausal` This model was trained by Emrys365 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_whamr_bsrnn_xtiny_noncausal", normalize_output_wav=True, device="cuda", ) # For loading a downloaded model # model = SeparateSpeech( # train_config="exp_vctk_dns20_whamr/enh_train_enh_bsrnn_xtiny_noncausal_raw/config.yaml", # model_file="exp_vctk_dns20_whamr/enh_train_enh_bsrnn_xtiny_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 20:16:27 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|52.75|-2.98|-2.98|0.00|-31.60|2.73|3.02|3.85|3.44| |chime4_et05_simu_isolated_6ch_track|1.38|81.26|7.60|7.60|0.00|1.73|2.66|2.95|3.82|3.11| |dns20_tt_synthetic_no_reverb|2.80|96.68|16.87|16.87|0.00|16.84|3.23|3.52|4.00|3.91| |reverb_et_real_8ch_multich|1.19|72.22|3.92|3.92|0.00|1.63|2.87|3.25|3.72|3.67| |reverb_et_simu_8ch_multich|1.97|91.96|10.05|10.05|0.00|-9.30|3.01|3.36|3.83|3.75| |whamr_tt_mix_single_reverb_max_16k|1.76|89.17|8.10|8.10|0.00|4.86|2.96|3.24|3.95|3.53| ## enhanced_test_48k |dataset|STOI|SAR|SDR|SIR|SI_SNR|OVRL|SIG|BAK|P808_MOS| |---|---|---|-...

Source context: 2 downloads · 0 likes · Pipeline audio-to-audio · Library espnet · Repo wyz/vctk_dns2020_whamr_bsrnn_xtiny_noncausal