Skip to content
ModelListedExternal
Download

Vctk Dns2020 Whamr Conv Tasnet Small

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

KindOtherVersionRevision 59dcca7

Model source

Kind
Other
File size
11 B–4.39 MB
Version
vRevision 59dcca7
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 59dcca7bd18a0be0d3aaac5486da546654f5d0b9
huggingface.co/wyz/vctk_dns2020_whamr_conv_tasnet_small/tree/59dcca7bd18a0be0d3aaac5486da546654f5d0b9

Verified Sep 19

Model artifacts

2 artifacts

exp_vctk_dns20_whamr/enh_train_enh_conv_tasnet_small_raw/44epoch.pth

pth · 4.39 MB · SHA-256 c6941f087fee…0393 · Hugging Face

Download

exp_vctk_dns20_whamr/enh_train_enh_conv_tasnet_small_raw/valid.loss.best.pth

pth · 11 B · Hugging Face

Source excerpts

2 excerpts

--- tags: - espnet - audio - audio-to-audio language: en datasets: - VCTK_DEMAND - DNS2020 - WHAMR license: cc-by-4.0 --- ## ESPnet2 ENH model ### `wyz/vctk_dns2020_whamr_conv_tasnet_small` 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_whamr_conv_tasnet_small", normalize_output_wav=True, device="cuda", ) # For loading a downloaded model # model = SeparateSpeech( # train_config="exp_vctk_dns20_whamr/enh_train_enh_conv_tasnet_small_raw/config.yaml", # model_file="exp_vctk_dns20_whamr/enh_train_enh_conv_tasnet_small_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: `Thu Feb 29 00:21:34 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|1.21|53.13|-2.72|-2.72|0.00|-31.10|2.85|3.22|3.71|3.24| |chime4_et05_simu_isolated_6ch_track|1.41|82.10|7.96|7.96|0.00|1.39|2.80|3.12|3.84|3.04| |dns20_tt_synthetic_no_reverb|2.51|95.75|15.75|15.75|0.00|15.74|3.19|3.49|3.96|3.76| |reverb_et_real_8ch_multich|1.22|70.41|3.82|3.82|0.00|1.50|2.85|3.24|3.70|3.42| |reverb_et_simu_8ch_multich|1.64|87.62|9.39|9.39|0.00|-9.93|2.86|3.30|3.64|3.58| |whamr_tt_mix_single_reverb_max_16k|1.74|89.96|9.31|9.31|0.00|6.20|3.12|3.39|4.02|3.41| ## enhanced_test_48k |dataset|STOI|SAR|SDR|SIR|SI_SNR|OVRL|SIG|BAK|P808_MOS| |---|---|---|---|---|---|---|---|---|--...

Source context: 1 downloads · 0 likes · Pipeline audio-to-audio · Library espnet · Repo wyz/vctk_dns2020_whamr_conv_tasnet_small