Vctk Dns2020 Whamr Conv Tasnet Medium
This model was trained by wyz based on the universalsev1 recipe in espnet. More information can be found at
Model source
- Kind
- Other
- File size
- 11 B–54.5 MB
- Version
- vRevision aa66bd9
- Updated
- Sep 18, 2026
- Downloads
- 2
- 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
Sources
1 sourceVerified Sep 18
Model artifacts
2 artifacts
Model artifacts
2 artifactsexp_vctk_dns20_whamr/enh_train_enh_conv_tasnet_medium_raw/97epoch.pth
pth · 54.5 MB · SHA-256 55fbb73e6eb3…60ac · Hugging Face
Downloadexp_vctk_dns20_whamr/enh_train_enh_conv_tasnet_medium_raw/valid.loss.best.pth
pth · 11 B · Hugging Face
Source excerpts
2 excerpts
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_medium` 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_medium", normalize_output_wav=True, device="cuda", ) # For loading a downloaded model # model = SeparateSpeech( # train_config="exp_vctk_dns20_whamr/enh_train_enh_conv_tasnet_medium_raw/config.yaml", # model_file="exp_vctk_dns20_whamr/enh_train_enh_conv_tasnet_medium_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 00:23:44 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|1.23|54.02|-2.52|-2.52|0.00|-31.02|2.96|3.32|3.77|3.36| |chime4_et05_simu_isolated_6ch_track|1.45|83.16|8.17|8.17|0.00|1.11|2.91|3.22|3.87|3.15| |dns20_tt_synthetic_no_reverb|2.75|96.62|17.28|17.28|0.00|17.26|3.25|3.53|4.02|3.85| |reverb_et_real_8ch_multich|1.15|66.72|0.89|0.89|0.00|-1.55|2.97|3.35|3.75|3.49| |reverb_et_simu_8ch_multich|1.89|90.80|9.06|9.06|0.00|-9.60|3.02|3.40|3.78|3.65| |whamr_tt_mix_single_reverb_max_16k|1.93|91.89|9.91|9.91|0.00|7.44|3.19|3.45|4.06|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_conv_tasnet_medium
