This model was trained by wyz based on the universalsev1 recipe in espnet. More information can be found at
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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 fuenteVerificado 12 sept
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1 artefactoexp_vctk/enh_train_enh_bsrnn_tiny_raw/58epoch.pth
pth · 5,23 MB · SHA-256 1e91d4507c1b…0e47 · Hugging Face
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2 extractos--- tags: - espnet - audio - audio-to-audio language: en datasets: - VCTK_DEMAND license: cc-by-4.0 --- ## ESPnet2 ENH model ### `wyz/vctk_bsrnn_tiny_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_tiny_causal", normalize_output_wav=True, device="cuda", ) # For loading a downloaded model # model = SeparateSpeech( # train_config="exp_vctk/enh_train_enh_bsrnn_tiny_raw/config.yaml", # model_file="exp_vctk/enh_train_enh_bsrnn_tiny_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 16:59:38 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|1.11|45.80|-3.86|-3.86|0.00|-31.52|2.23|2.58|3.40|3.06| |chime4_et05_simu_isolated_6ch_track|1.16|66.45|4.51|4.51|0.00|-1.05|2.08|2.38|3.44|2.71| |dns20_tt_synthetic_no_reverb|1.97|91.38|12.59|12.59|0.00|11.95|3.02|3.42|3.73|3.68| |reverb_et_real_8ch_multich|1.10|61.04|4.02|4.02|0.00|0.48|2.08|2.41|3.37|3.13| |reverb_et_simu_8ch_multich|1.51|80.09|8.35|8.35|0.00|-11.06|2.66|3.05|3.61|3.52| |whamr_tt_mix_single_reverb_max_16k|1.21|73.29|4.55|4.55|0.00|0.26|2.14|2.44|3.49|3.08| ## enhanced_test_48k |dataset|STOI|SAR|SDR|SIR|SI_SNR|OVRL|SIG|BAK|P808_MOS| |---|---|---|---|---|---|---|---|---|---| |vctk_noisy_tt_2spk|94.16|19.62|19.62|0.00|18.47|3.07|3.39|3.93|3.44| ## EN...
Source context: 1 downloads · 0 likes · Pipeline audio-to-audio · Library espnet · Repo wyz/vctk_bsrnn_tiny_causal