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import os |
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import torch |
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import shutil |
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import logging |
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import gradio as gr |
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from audio_separator.separator import Separator |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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use_autocast = device == "cuda" |
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ROFORMER_MODELS = { |
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'BS-Roformer-Viperx-1297.ckpt': 'model_bs_roformer_ep_317_sdr_12.9755.ckpt', |
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'BS-Roformer-Viperx-1296.ckpt': 'model_bs_roformer_ep_368_sdr_12.9628.ckpt', |
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'BS-Roformer-Viperx-1053.ckpt': 'model_bs_roformer_ep_937_sdr_10.5309.ckpt', |
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'BS-Roformer-De-Reverb.ckpt': 'deverb_bs_roformer_8_384dim_10depth.ckpt', |
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'Mel-Roformer-Viperx-1143.ckpt': 'model_mel_band_roformer_ep_3005_sdr_11.4360.ckpt', |
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'Mel-Roformer-Crowd-Aufr33-Viperx.ckpt': 'mel_band_roformer_crowd_aufr33_viperx_sdr_8.7144.ckpt', |
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'Mel-Roformer-Karaoke-Aufr33-Viperx.ckpt': 'mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt', |
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'Mel-Roformer-Denoise-Aufr33': 'denoise_mel_band_roformer_aufr33_sdr_27.9959.ckpt', |
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'Mel-Roformer-Denoise-Aufr33-Aggr': 'denoise_mel_band_roformer_aufr33_aggr_sdr_27.9768.ckpt', |
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'MelBand Roformer Kim | Inst V1 by Unwa': 'melband_roformer_inst_v1.ckpt', |
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'MelBand Roformer Kim | Inst V2 by Unwa': 'melband_roformer_inst_v2.ckpt', |
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'MelBand Roformer Kim | InstVoc Duality V1 by Unwa': 'melband_roformer_instvoc_duality_v1.ckpt', |
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'MelBand Roformer Kim | InstVoc Duality V2 by Unwa': 'melband_roformer_instvox_duality_v2.ckpt', |
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} |
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MDX23C_MODELS = [ |
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'MDX23C_D1581.ckpt', |
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'MDX23C-8KFFT-InstVoc_HQ.ckpt', |
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'MDX23C-8KFFT-InstVoc_HQ_2.ckpt', |
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] |
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MDXNET_MODELS = [ |
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'UVR-MDX-NET-Inst_full_292.onnx', |
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'UVR-MDX-NET_Inst_187_beta.onnx', |
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'UVR-MDX-NET_Inst_82_beta.onnx', |
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'UVR-MDX-NET_Inst_90_beta.onnx', |
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'UVR-MDX-NET_Main_340.onnx', |
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'UVR-MDX-NET_Main_390.onnx', |
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'UVR-MDX-NET_Main_406.onnx', |
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'UVR-MDX-NET_Main_427.onnx', |
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'UVR-MDX-NET_Main_438.onnx', |
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'UVR-MDX-NET-Inst_HQ_1.onnx', |
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'UVR-MDX-NET-Inst_HQ_2.onnx', |
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'UVR-MDX-NET-Inst_HQ_3.onnx', |
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'UVR-MDX-NET-Inst_HQ_4.onnx', |
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'UVR_MDXNET_Main.onnx', |
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'UVR-MDX-NET-Inst_Main.onnx', |
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'UVR_MDXNET_1_9703.onnx', |
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'UVR_MDXNET_2_9682.onnx', |
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'UVR_MDXNET_3_9662.onnx', |
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'UVR-MDX-NET-Inst_1.onnx', |
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'UVR-MDX-NET-Inst_2.onnx', |
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'UVR-MDX-NET-Inst_3.onnx', |
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'UVR_MDXNET_KARA.onnx', |
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'UVR_MDXNET_KARA_2.onnx', |
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'UVR_MDXNET_9482.onnx', |
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'UVR-MDX-NET-Voc_FT.onnx', |
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'Kim_Vocal_1.onnx', |
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'Kim_Vocal_2.onnx', |
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'Kim_Inst.onnx', |
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'Reverb_HQ_By_FoxJoy.onnx', |
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'UVR-MDX-NET_Crowd_HQ_1.onnx', |
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'kuielab_a_vocals.onnx', |
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'kuielab_a_other.onnx', |
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'kuielab_a_bass.onnx', |
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'kuielab_a_drums.onnx', |
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'kuielab_b_vocals.onnx', |
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'kuielab_b_other.onnx', |
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'kuielab_b_bass.onnx', |
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'kuielab_b_drums.onnx', |
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] |
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VR_ARCH_MODELS = [ |
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'1_HP-UVR.pth', |
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'2_HP-UVR.pth', |
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'3_HP-Vocal-UVR.pth', |
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'4_HP-Vocal-UVR.pth', |
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'5_HP-Karaoke-UVR.pth', |
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'6_HP-Karaoke-UVR.pth', |
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'7_HP2-UVR.pth', |
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'8_HP2-UVR.pth', |
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'9_HP2-UVR.pth', |
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'10_SP-UVR-2B-32000-1.pth', |
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'11_SP-UVR-2B-32000-2.pth', |
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'12_SP-UVR-3B-44100.pth', |
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'13_SP-UVR-4B-44100-1.pth', |
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'14_SP-UVR-4B-44100-2.pth', |
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'15_SP-UVR-MID-44100-1.pth', |
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'16_SP-UVR-MID-44100-2.pth', |
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'17_HP-Wind_Inst-UVR.pth', |
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'UVR-DeEcho-DeReverb.pth', |
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'UVR-De-Echo-Normal.pth', |
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'UVR-De-Echo-Aggressive.pth', |
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'UVR-DeNoise.pth', |
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'UVR-DeNoise-Lite.pth', |
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'UVR-BVE-4B_SN-44100-1.pth', |
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'MGM_HIGHEND_v4.pth', |
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'MGM_LOWEND_A_v4.pth', |
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'MGM_LOWEND_B_v4.pth', |
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'MGM_MAIN_v4.pth', |
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] |
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DEMUCS_MODELS = [ |
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'htdemucs_ft.yaml', |
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'htdemucs_6s.yaml', |
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'htdemucs.yaml', |
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'hdemucs_mmi.yaml', |
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] |
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def print_message(input_file, model_name): |
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"""Prints information about the audio separation process.""" |
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base_name = os.path.splitext(os.path.basename(input_file))[0] |
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print("\n") |
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print("🎵 Audio-Separator 🎵") |
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print("Input audio:", base_name) |
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print("Separation Model:", model_name) |
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print("Audio Separation Process...") |
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def prepare_output_dir(input_file, output_dir): |
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"""Create a directory for the output files and clean it if it already exists.""" |
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base_name = os.path.splitext(os.path.basename(input_file))[0] |
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out_dir = os.path.join(output_dir, base_name) |
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try: |
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if os.path.exists(out_dir): |
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shutil.rmtree(out_dir) |
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os.makedirs(out_dir) |
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except Exception as e: |
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raise RuntimeError(f"Failed to prepare output directory {out_dir}: {e}") |
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return out_dir |
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|
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def roformer_separator(audio, model_key, seg_size, override_seg_size, overlap, pitch_shift, model_dir, out_dir, out_format, norm_thresh, amp_thresh, batch_size, progress=gr.Progress(track_tqdm=True)): |
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"""Separate audio using Roformer model.""" |
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base_name = os.path.splitext(os.path.basename(audio))[0] |
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print_message(audio, model_key) |
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model = ROFORMER_MODELS[model_key] |
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try: |
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out_dir = prepare_output_dir(audio, out_dir) |
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separator = Separator( |
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log_level=logging.WARNING, |
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model_file_dir=model_dir, |
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output_dir=out_dir, |
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output_format=out_format, |
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normalization_threshold=norm_thresh, |
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amplification_threshold=amp_thresh, |
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use_autocast=use_autocast, |
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mdxc_params={ |
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"segment_size": seg_size, |
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"override_model_segment_size": override_seg_size, |
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"batch_size": batch_size, |
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"overlap": overlap, |
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"pitch_shift": pitch_shift, |
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} |
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) |
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progress(0.2, desc="Model loaded...") |
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separator.load_model(model_filename=model) |
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progress(0.7, desc="Audio separated...") |
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separation = separator.separate(audio, f"{base_name}_(Stem1)", f"{base_name}_(Stem2)") |
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print(f"Separation complete!\nResults: {', '.join(separation)}") |
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stems = [os.path.join(out_dir, file_name) for file_name in separation] |
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return stems[1], stems[0] |
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except Exception as e: |
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raise RuntimeError(f"Roformer separation failed: {e}") from e |
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|
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def mdx23c_separator(audio, model, seg_size, override_seg_size, overlap, pitch_shift, model_dir, out_dir, out_format, norm_thresh, amp_thresh, batch_size, progress=gr.Progress(track_tqdm=True)): |
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"""Separate audio using MDX23C model.""" |
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base_name = os.path.splitext(os.path.basename(audio))[0] |
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print_message(audio, model) |
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try: |
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out_dir = prepare_output_dir(audio, out_dir) |
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separator = Separator( |
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log_level=logging.WARNING, |
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model_file_dir=model_dir, |
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output_dir=out_dir, |
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output_format=out_format, |
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normalization_threshold=norm_thresh, |
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amplification_threshold=amp_thresh, |
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use_autocast=use_autocast, |
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mdxc_params={ |
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"segment_size": seg_size, |
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"override_model_segment_size": override_seg_size, |
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"batch_size": batch_size, |
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"overlap": overlap, |
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"pitch_shift": pitch_shift, |
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} |
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) |
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progress(0.2, desc="Model loaded...") |
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separator.load_model(model_filename=model) |
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progress(0.7, desc="Audio separated...") |
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separation = separator.separate(audio, f"{base_name}_(Stem1)", f"{base_name}_(Stem2)") |
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print(f"Separation complete!\nResults: {', '.join(separation)}") |
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stems = [os.path.join(out_dir, file_name) for file_name in separation] |
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return stems[1], stems[0] |
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except Exception as e: |
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raise RuntimeError(f"MDX23C separation failed: {e}") from e |
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|
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def mdx_separator(audio, model, hop_length, seg_size, overlap, denoise, model_dir, out_dir, out_format, norm_thresh, amp_thresh, batch_size, progress=gr.Progress(track_tqdm=True)): |
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"""Separate audio using MDX-NET model.""" |
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base_name = os.path.splitext(os.path.basename(audio))[0] |
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print_message(audio, model) |
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try: |
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out_dir = prepare_output_dir(audio, out_dir) |
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separator = Separator( |
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log_level=logging.WARNING, |
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model_file_dir=model_dir, |
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output_dir=out_dir, |
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output_format=out_format, |
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normalization_threshold=norm_thresh, |
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amplification_threshold=amp_thresh, |
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use_autocast=use_autocast, |
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mdx_params={ |
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"hop_length": hop_length, |
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"segment_size": seg_size, |
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"overlap": overlap, |
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"batch_size": batch_size, |
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"enable_denoise": denoise, |
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} |
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) |
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progress(0.2, desc="Model loaded...") |
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separator.load_model(model_filename=model) |
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progress(0.7, desc="Audio separated...") |
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separation = separator.separate(audio, f"{base_name}_(Stem1)", f"{base_name}_(Stem2)") |
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print(f"Separation complete!\nResults: {', '.join(separation)}") |
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stems = [os.path.join(out_dir, file_name) for file_name in separation] |
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return stems[0], stems[1] |
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except Exception as e: |
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raise RuntimeError(f"MDX-NET separation failed: {e}") from e |
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def vr_separator(audio, model, window_size, aggression, tta, post_process, post_process_threshold, high_end_process, model_dir, out_dir, out_format, norm_thresh, amp_thresh, batch_size, progress=gr.Progress(track_tqdm=True)): |
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"""Separate audio using VR ARCH model.""" |
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base_name = os.path.splitext(os.path.basename(audio))[0] |
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print_message(audio, model) |
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try: |
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out_dir = prepare_output_dir(audio, out_dir) |
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separator = Separator( |
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log_level=logging.WARNING, |
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model_file_dir=model_dir, |
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output_dir=out_dir, |
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output_format=out_format, |
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normalization_threshold=norm_thresh, |
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amplification_threshold=amp_thresh, |
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use_autocast=use_autocast, |
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vr_params={ |
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"batch_size": batch_size, |
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"window_size": window_size, |
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"aggression": aggression, |
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"enable_tta": tta, |
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"enable_post_process": post_process, |
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"post_process_threshold": post_process_threshold, |
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"high_end_process": high_end_process, |
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} |
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) |
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|
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progress(0.2, desc="Model loaded...") |
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separator.load_model(model_filename=model) |
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|
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progress(0.7, desc="Audio separated...") |
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separation = separator.separate(audio, f"{base_name}_(Stem1)", f"{base_name}_(Stem2)") |
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print(f"Separation complete!\nResults: {', '.join(separation)}") |
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|
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stems = [os.path.join(out_dir, file_name) for file_name in separation] |
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return stems[0], stems[1] |
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except Exception as e: |
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raise RuntimeError(f"VR ARCH separation failed: {e}") from e |
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|
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def demucs_separator(audio, model, seg_size, shifts, overlap, segments_enabled, model_dir, out_dir, out_format, norm_thresh, amp_thresh, progress=gr.Progress(track_tqdm=True)): |
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"""Separate audio using Demucs model.""" |
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print_message(audio, model) |
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try: |
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out_dir = prepare_output_dir(audio, out_dir) |
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separator = Separator( |
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log_level=logging.WARNING, |
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model_file_dir=model_dir, |
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output_dir=out_dir, |
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output_format=out_format, |
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normalization_threshold=norm_thresh, |
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amplification_threshold=amp_thresh, |
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use_autocast=use_autocast, |
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demucs_params={ |
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"segment_size": seg_size, |
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"shifts": shifts, |
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"overlap": overlap, |
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"segments_enabled": segments_enabled, |
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} |
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) |
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|
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progress(0.2, desc="Model loaded...") |
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separator.load_model(model_filename=model) |
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|
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progress(0.7, desc="Audio separated...") |
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separation = separator.separate(audio) |
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print(f"Separation complete!\nResults: {', '.join(separation)}") |
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|
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stems = [os.path.join(out_dir, file_name) for file_name in separation] |
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|
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if model == "htdemucs_6s.yaml": |
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return stems[0], stems[1], stems[2], stems[3], stems[4], stems[5] |
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else: |
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return stems[0], stems[1], stems[2], stems[3], None, None |
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except Exception as e: |
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raise RuntimeError(f"Demucs separation failed: {e}") from e |
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|
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def update_stems(model): |
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if model == "htdemucs_6s.yaml": |
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return gr.update(visible=True) |
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else: |
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return gr.update(visible=False) |
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|
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with gr.Blocks( |
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title="🎵 Audio-Separator 🎵", |
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css="footer{display:none !important}", |
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theme=gr.themes.Default( |
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spacing_size="sm", |
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radius_size="lg", |
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) |
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) as app: |
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gr.HTML("<h1> 🎵 Audio-Separator 🎵 </h1>") |
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with gr.Accordion("General settings", open=False): |
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with gr.Group(): |
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model_file_dir = gr.Textbox(value="/tmp/audio-separator-models/", label="Directory to cache model files", info="The directory where model files are stored.", placeholder="/tmp/audio-separator-models/") |
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with gr.Row(): |
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output_dir = gr.Textbox(value="output", label="File output directory", info="The directory where output files will be saved.", placeholder="output") |
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output_format = gr.Dropdown(value="wav", choices=["wav", "flac", "mp3"], label="Output Format", info="The format of the output audio file.") |
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with gr.Row(): |
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norm_threshold = gr.Slider(minimum=0.1, maximum=1, step=0.1, value=0.9, label="Normalization threshold", info="The threshold for audio normalization.") |
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amp_threshold = gr.Slider(minimum=0.1, maximum=1, step=0.1, value=0.6, label="Amplification threshold", info="The threshold for audio amplification.") |
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with gr.Row(): |
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batch_size = gr.Slider(minimum=1, maximum=16, step=1, value=1, label="Batch Size", info="Larger consumes more RAM but may process slightly faster.") |
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|
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with gr.Tab("Roformer"): |
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with gr.Group(): |
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with gr.Row(): |
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roformer_model = gr.Dropdown(label="Select the Model", choices=list(ROFORMER_MODELS.keys())) |
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with gr.Row(): |
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roformer_seg_size = gr.Slider(minimum=32, maximum=4000, step=32, value=256, label="Segment Size", info="Larger consumes more resources, but may give better results.") |
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roformer_override_seg_size = gr.Checkbox(value=False, label="Override segment size", info="Override model default segment size instead of using the model default value.") |
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roformer_overlap = gr.Slider(minimum=2, maximum=10, step=1, value=8, label="Overlap", info="Amount of overlap between prediction windows. Lower is better but slower.") |
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roformer_pitch_shift = gr.Slider(minimum=-12, maximum=12, step=1, value=0, label="Pitch shift", info="Shift audio pitch by a number of semitones while processing. may improve output for deep/high vocals.") |
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with gr.Row(): |
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roformer_audio = gr.Audio(label="Input Audio", type="filepath") |
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with gr.Row(): |
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roformer_button = gr.Button("Separate!", variant="primary") |
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with gr.Row(): |
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roformer_stem1 = gr.Audio(label="Stem 1", type="filepath", interactive=False) |
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roformer_stem2 = gr.Audio(label="Stem 2", type="filepath", interactive=False) |
|
|
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with gr.Tab("MDX23C"): |
|
with gr.Group(): |
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with gr.Row(): |
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mdx23c_model = gr.Dropdown(label="Select the Model", choices=MDX23C_MODELS) |
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with gr.Row(): |
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mdx23c_seg_size = gr.Slider(minimum=32, maximum=4000, step=32, value=256, label="Segment Size", info="Larger consumes more resources, but may give better results.") |
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mdx23c_override_seg_size = gr.Checkbox(value=False, label="Override segment size", info="Override model default segment size instead of using the model default value.") |
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mdx23c_overlap = gr.Slider(minimum=2, maximum=50, step=1, value=8, label="Overlap", info="Amount of overlap between prediction windows. Higher is better but slower.") |
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mdx23c_pitch_shift = gr.Slider(minimum=-12, maximum=12, step=1, value=0, label="Pitch shift", info="Shift audio pitch by a number of semitones while processing. may improve output for deep/high vocals.") |
|
with gr.Row(): |
|
mdx23c_audio = gr.Audio(label="Input Audio", type="filepath") |
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with gr.Row(): |
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mdx23c_button = gr.Button("Separate!", variant="primary") |
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with gr.Row(): |
|
mdx23c_stem1 = gr.Audio(label="Stem 1", type="filepath", interactive=False) |
|
mdx23c_stem2 = gr.Audio(label="Stem 2", type="filepath", interactive=False) |
|
|
|
with gr.Tab("MDX-NET"): |
|
with gr.Group(): |
|
with gr.Row(): |
|
mdx_model = gr.Dropdown(label="Select the Model", choices=MDXNET_MODELS) |
|
with gr.Row(): |
|
mdx_hop_length = gr.Slider(minimum=32, maximum=2048, step=32, value=1024, label="Hop Length", info="Usually called stride in neural networks; only change if you know what you're doing.") |
|
mdx_seg_size = gr.Slider(minimum=32, maximum=4000, step=32, value=256, label="Segment Size", info="Larger consumes more resources, but may give better results.") |
|
mdx_overlap = gr.Slider(minimum=0.001, maximum=0.999, step=0.001, value=0.25, label="Overlap", info="Amount of overlap between prediction windows. Higher is better but slower.") |
|
mdx_denoise = gr.Checkbox(value=False, label="Denoise", info="Enable denoising after separation.") |
|
with gr.Row(): |
|
mdx_audio = gr.Audio(label="Input Audio", type="filepath") |
|
with gr.Row(): |
|
mdx_button = gr.Button("Separate!", variant="primary") |
|
with gr.Row(): |
|
mdx_stem1 = gr.Audio(label="Stem 1", type="filepath", interactive=False) |
|
mdx_stem2 = gr.Audio(label="Stem 2", type="filepath", interactive=False) |
|
|
|
with gr.Tab("VR ARCH"): |
|
with gr.Group(): |
|
with gr.Row(): |
|
vr_model = gr.Dropdown(label="Select the Model", choices=VR_ARCH_MODELS) |
|
with gr.Row(): |
|
vr_window_size = gr.Slider(minimum=320, maximum=1024, step=32, value=512, label="Window Size", info="Balance quality and speed. 1024 = fast but lower, 320 = slower but better quality.") |
|
vr_aggression = gr.Slider(minimum=1, maximum=50, step=1, value=5, label="Agression", info="Intensity of primary stem extraction.") |
|
vr_tta = gr.Checkbox(value=False, label="TTA", info="Enable Test-Time-Augmentation; slow but improves quality.") |
|
vr_post_process = gr.Checkbox(value=False, label="Post Process", info="Identify leftover artifacts within vocal output; may improve separation for some songs.") |
|
vr_post_process_threshold = gr.Slider(minimum=0.1, maximum=0.3, step=0.1, value=0.2, label="Post Process Threshold", info="Threshold for post-processing.") |
|
vr_high_end_process = gr.Checkbox(value=False, label="High End Process", info="Mirror the missing frequency range of the output.") |
|
with gr.Row(): |
|
vr_audio = gr.Audio(label="Input Audio", type="filepath") |
|
with gr.Row(): |
|
vr_button = gr.Button("Separate!", variant="primary") |
|
with gr.Row(): |
|
vr_stem1 = gr.Audio(label="Stem 1", type="filepath", interactive=False) |
|
vr_stem2 = gr.Audio(label="Stem 2", type="filepath", interactive=False) |
|
|
|
with gr.Tab("Demucs"): |
|
with gr.Group(): |
|
with gr.Row(): |
|
demucs_model = gr.Dropdown(label="Select the Model", choices=DEMUCS_MODELS) |
|
with gr.Row(): |
|
demucs_seg_size = gr.Slider(minimum=1, maximum=100, step=1, value=40, label="Segment Size", info="Size of segments into which the audio is split. Higher = slower but better quality.") |
|
demucs_shifts = gr.Slider(minimum=0, maximum=20, step=1, value=2, label="Shifts", info="Number of predictions with random shifts, higher = slower but better quality.") |
|
demucs_overlap = gr.Slider(minimum=0.001, maximum=0.999, step=0.001, value=0.25, label="Overlap", info="Overlap between prediction windows. Higher = slower but better quality.") |
|
demucs_segments_enabled = gr.Checkbox(value=True, label="Segment-wise processing", info="Enable segment-wise processing.") |
|
with gr.Row(): |
|
demucs_audio = gr.Audio(label="Input Audio", type="filepath") |
|
with gr.Row(): |
|
demucs_button = gr.Button("Separate!", variant="primary") |
|
with gr.Row(): |
|
demucs_stem1 = gr.Audio(label="Stem 1", type="filepath", interactive=False) |
|
demucs_stem2 = gr.Audio(label="Stem 2", type="filepath", interactive=False) |
|
with gr.Row(): |
|
demucs_stem3 = gr.Audio(label="Stem 3", type="filepath", interactive=False) |
|
demucs_stem4 = gr.Audio(label="Stem 4", type="filepath", interactive=False) |
|
with gr.Row(visible=False) as stem6: |
|
demucs_stem5 = gr.Audio(label="Stem 5", type="filepath", interactive=False) |
|
demucs_stem6 = gr.Audio(label="Stem 6", type="filepath", interactive=False) |
|
|
|
demucs_model.change(update_stems, inputs=[demucs_model], outputs=stem6) |
|
|
|
roformer_button.click( |
|
roformer_separator, |
|
inputs=[ |
|
roformer_audio, |
|
roformer_model, |
|
roformer_seg_size, |
|
roformer_override_seg_size, |
|
roformer_overlap, |
|
roformer_pitch_shift, |
|
model_file_dir, |
|
output_dir, |
|
output_format, |
|
norm_threshold, |
|
amp_threshold, |
|
batch_size, |
|
], |
|
outputs=[roformer_stem1, roformer_stem2], |
|
) |
|
mdx23c_button.click( |
|
mdx23c_separator, |
|
inputs=[ |
|
mdx23c_audio, |
|
mdx23c_model, |
|
mdx23c_seg_size, |
|
mdx23c_override_seg_size, |
|
mdx23c_overlap, |
|
mdx23c_pitch_shift, |
|
model_file_dir, |
|
output_dir, |
|
output_format, |
|
norm_threshold, |
|
amp_threshold, |
|
batch_size, |
|
], |
|
outputs=[mdx23c_stem1, mdx23c_stem2], |
|
) |
|
mdx_button.click( |
|
mdx_separator, |
|
inputs=[ |
|
mdx_audio, |
|
mdx_model, |
|
mdx_hop_length, |
|
mdx_seg_size, |
|
mdx_overlap, |
|
mdx_denoise, |
|
model_file_dir, |
|
output_dir, |
|
output_format, |
|
norm_threshold, |
|
amp_threshold, |
|
batch_size, |
|
], |
|
outputs=[mdx_stem1, mdx_stem2], |
|
) |
|
vr_button.click( |
|
vr_separator, |
|
inputs=[ |
|
vr_audio, |
|
vr_model, |
|
vr_window_size, |
|
vr_aggression, |
|
vr_tta, |
|
vr_post_process, |
|
vr_post_process_threshold, |
|
vr_high_end_process, |
|
model_file_dir, |
|
output_dir, |
|
output_format, |
|
norm_threshold, |
|
amp_threshold, |
|
batch_size, |
|
], |
|
outputs=[vr_stem1, vr_stem2], |
|
) |
|
demucs_button.click( |
|
demucs_separator, |
|
inputs=[ |
|
demucs_audio, |
|
demucs_model, |
|
demucs_seg_size, |
|
demucs_shifts, |
|
demucs_overlap, |
|
demucs_segments_enabled, |
|
model_file_dir, |
|
output_dir, |
|
output_format, |
|
norm_threshold, |
|
amp_threshold, |
|
], |
|
outputs=[demucs_stem1, demucs_stem2, demucs_stem3, demucs_stem4, demucs_stem5, demucs_stem6], |
|
) |
|
|
|
def main(): |
|
app.launch(share=True) |
|
|
|
if __name__ == "__main__": |
|
main() |
|
|
