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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

TypeOtherVersionRevision aa66bd9

Source du modèle

Type
Other
Taille du fichier
11 B–54,5 MB
Version
vRevision aa66bd9
Mis à jour
18 sept. 2026
Téléchargements
2
Tâches
Audio
Source
Hugging Face

Extrait de la source

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

Sources

1 source
Hugging FacePrincipalVersion aa66bd950eaba46fbaf0cbb498d1ebbc9d104eb1
huggingface.co/wyz/vctk_dns2020_whamr_conv_tasnet_medium/tree/aa66bd950eaba46fbaf0cbb498d1ebbc9d104eb1

Vérifié 18 sept.

Artefacts du modèle

2 artefacts

exp_vctk_dns20_whamr/enh_train_enh_conv_tasnet_medium_raw/97epoch.pth

pth · 54,5 MB · SHA-256 55fbb73e6eb3…60ac · Hugging Face

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exp_vctk_dns20_whamr/enh_train_enh_conv_tasnet_medium_raw/valid.loss.best.pth

pth · 11 B · Hugging Face

Extraits de sources

2 extraits

--- 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