Vctk Bsrnn Small Causal
This model was trained by wyz based on the universalsev1 recipe in espnet. More information can be found at
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- Other
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- 16,0 MB
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- vRevision 8a95807
- Atualizado
- 12 de set. de 2026
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- 1
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- Hugging Face
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This model was trained by wyz based on the universalsev1 recipe in espnet. More information can be found at
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1 artefatoexp_vctk/enh_train_enh_bsrnn_small_raw/58epoch.pth
pth · 16,0 MB · SHA-256 42618963b26f…adf4 · Hugging Face
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2 trechos--- tags: - espnet - audio - audio-to-audio language: en datasets: - VCTK_DEMAND license: cc-by-4.0 --- ## ESPnet2 ENH model ### `wyz/vctk_bsrnn_small_causal` 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_bsrnn_small_causal", normalize_output_wav=True, device="cuda", ) # For loading a downloaded model # model = SeparateSpeech( # train_config="exp_vctk/enh_train_enh_bsrnn_small_raw/config.yaml", # model_file="exp_vctk/enh_train_enh_bsrnn_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: `Wed Feb 28 16:59:40 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|1.10|44.96|-3.99|-3.99|0.00|-31.45|2.32|2.66|3.43|3.12| |chime4_et05_simu_isolated_6ch_track|1.14|61.04|3.75|3.75|0.00|-2.40|2.12|2.40|3.45|2.76| |dns20_tt_synthetic_no_reverb|1.84|88.70|10.81|10.81|0.00|9.79|2.97|3.38|3.69|3.69| |reverb_et_real_8ch_multich|1.08|51.66|2.32|2.32|0.00|-1.53|2.05|2.34|3.42|3.10| |reverb_et_simu_8ch_multich|1.49|78.03|8.14|8.14|0.00|-11.17|2.68|3.05|3.64|3.52| |whamr_tt_mix_single_reverb_max_16k|1.22|73.23|5.17|5.17|0.00|0.77|2.31|2.61|3.60|3.22| ## enhanced_test_48k |dataset|STOI|SAR|SDR|SIR|SI_SNR|OVRL|SIG|BAK|P808_MOS| |---|---|---|---|---|---|---|---|---|---| |vctk_noisy_tt_2spk|94.53|19.67|19.67|0.00|18.63|3.09|3.42|3.92|3.47| #...
Source context: 1 downloads · 0 likes · Pipeline audio-to-audio · Library espnet · Repo wyz/vctk_bsrnn_small_causal
