import os import tempfile import gradio as gr from audio_separator.separator import Separator # Model lists ROFORMER_MODELS = { 'BS-Roformer-Viperx-1297.ckpt': 'model_bs_roformer_ep_317_sdr_12.9755.ckpt', 'BS-Roformer-Viperx-1296.ckpt': 'model_bs_roformer_ep_368_sdr_12.9628.ckpt', 'BS-Roformer-Viperx-1053.ckpt': 'model_bs_roformer_ep_937_sdr_10.5309.ckpt', 'BS-Roformer-De-Reverb.ckpt': 'deverb_bs_roformer_8_384dim_10depth.ckpt', 'Mel-Roformer-Viperx-1143.ckpt': 'model_mel_band_roformer_ep_3005_sdr_11.4360.ckpt', 'Mel-Roformer-Crowd-Aufr33-Viperx.ckpt': 'mel_band_roformer_crowd_aufr33_viperx_sdr_8.7144.ckpt', 'Mel-Roformer-Karaoke-Aufr33-Viperx.ckpt': 'mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt', 'Mel-Roformer-Denoise-Aufr33': 'denoise_mel_band_roformer_aufr33_sdr_27.9959.ckpt', 'Mel-Roformer-Denoise-Aufr33-Aggr': 'denoise_mel_band_roformer_aufr33_aggr_sdr_27.9768.ckpt', } MDX23C_MODELS = [ 'MDX23C_D1581.ckpt', 'MDX23C-8KFFT-InstVoc_HQ.ckpt', 'MDX23C-8KFFT-InstVoc_HQ_2.ckpt', ] MDXNET_MODELS = [ 'UVR-MDX-NET-Inst_full_292.onnx', 'UVR-MDX-NET_Inst_187_beta.onnx', 'UVR-MDX-NET_Inst_82_beta.onnx', 'UVR-MDX-NET_Inst_90_beta.onnx', 'UVR-MDX-NET_Main_340.onnx', 'UVR-MDX-NET_Main_390.onnx', 'UVR-MDX-NET_Main_406.onnx', 'UVR-MDX-NET_Main_427.onnx', 'UVR-MDX-NET_Main_438.onnx', 'UVR-MDX-NET-Inst_HQ_1.onnx', 'UVR-MDX-NET-Inst_HQ_2.onnx', 'UVR-MDX-NET-Inst_HQ_3.onnx', 'UVR-MDX-NET-Inst_HQ_4.onnx', 'UVR_MDXNET_Main.onnx', 'UVR-MDX-NET-Inst_Main.onnx', 'UVR_MDXNET_1_9703.onnx', 'UVR_MDXNET_2_9682.onnx', 'UVR_MDXNET_3_9662.onnx', 'UVR-MDX-NET-Inst_1.onnx', 'UVR-MDX-NET-Inst_2.onnx', 'UVR-MDX-NET-Inst_3.onnx', 'UVR_MDXNET_KARA.onnx', 'UVR_MDXNET_KARA_2.onnx', 'UVR_MDXNET_9482.onnx', 'UVR-MDX-NET-Voc_FT.onnx', 'Kim_Vocal_1.onnx', 'Kim_Vocal_2.onnx', 'Kim_Inst.onnx', 'Reverb_HQ_By_FoxJoy.onnx', 'UVR-MDX-NET_Crowd_HQ_1.onnx', 'kuielab_a_vocals.onnx', 'kuielab_a_other.onnx', 'kuielab_a_bass.onnx', 'kuielab_a_drums.onnx', 'kuielab_b_vocals.onnx', 'kuielab_b_other.onnx', 'kuielab_b_bass.onnx', 'kuielab_b_drums.onnx', ] VR_ARCH_MODELS = [ '1_HP-UVR.pth', '2_HP-UVR.pth', '3_HP-Vocal-UVR.pth', '4_HP-Vocal-UVR.pth', '5_HP-Karaoke-UVR.pth', '6_HP-Karaoke-UVR.pth', '7_HP2-UVR.pth', '8_HP2-UVR.pth', '9_HP2-UVR.pth', '10_SP-UVR-2B-32000-1.pth', '11_SP-UVR-2B-32000-2.pth', '12_SP-UVR-3B-44100.pth', '13_SP-UVR-4B-44100-1.pth', '14_SP-UVR-4B-44100-2.pth', '15_SP-UVR-MID-44100-1.pth', '16_SP-UVR-MID-44100-2.pth', '17_HP-Wind_Inst-UVR.pth', 'UVR-DeEcho-DeReverb.pth', 'UVR-De-Echo-Normal.pth', 'UVR-De-Echo-Aggressive.pth', 'UVR-DeNoise.pth', 'UVR-DeNoise-Lite.pth', 'UVR-BVE-4B_SN-44100-1.pth', 'MGM_HIGHEND_v4.pth', 'MGM_LOWEND_A_v4.pth', 'MGM_LOWEND_B_v4.pth', 'MGM_MAIN_v4.pth', ] DEMUCS_MODELS = [ 'htdemucs_ft.yaml', 'htdemucs_6s.yaml', 'htdemucs.yaml', 'hdemucs_mmi.yaml', ] def rename_stems(input_file, output_dir, stems, output_format): """Rename stems to the format of the input file name with __(StemX) suffix.""" base_name = os.path.splitext(os.path.basename(input_file))[0] renamed_stems = [] for i, stem in enumerate(stems): new_name = f"{base_name}_(Stem{i+1}).{output_format}" new_path = os.path.join(output_dir, new_name) os.rename(stem, new_path) renamed_stems.append(new_path) return renamed_stems def roformer_separator(audio, model, seg_size, overlap, model_dir, out_dir, out_format, norm_thresh, amp_thresh): """Separate audio using Roformer model.""" with tempfile.TemporaryDirectory() as tmp_dir: separator = Separator( model_file_dir=model_dir, output_dir=tmp_dir, output_format=out_format, normalization_threshold=norm_thresh, amplification_threshold=amp_thresh, mdxc_params={ "batch_size": 1, "segment_size": seg_size, "overlap": overlap, } ) separator.load_model(model_filename=model) separation = separator.separate(audio) stems = rename_stems(audio, out_dir, separation, out_format) return stems[0], stems[1] def mdx23c_separator(audio, model, seg_size, overlap, model_dir, out_dir, out_format, norm_thresh, amp_thresh): """Separate audio using MDX23C model.""" with tempfile.TemporaryDirectory() as tmp_dir: separator = Separator( model_file_dir=model_dir, output_dir=tmp_dir, output_format=out_format, normalization_threshold=norm_thresh, amplification_threshold=amp_thresh, mdxc_params={ "batch_size": 1, "segment_size": seg_size, "overlap": overlap, } ) separator.load_model(model_filename=model) separation = separator.separate(audio) stems = rename_stems(audio, out_dir, separation, out_format) return stems[0], stems[1] def mdx_separator(audio, model, hop_length, seg_size, overlap, denoise, model_dir, out_dir, out_format, norm_thresh, amp_thresh): """Separate audio using MDX-NET model.""" with tempfile.TemporaryDirectory() as tmp_dir: separator = Separator( model_file_dir=model_dir, output_dir=tmp_dir, output_format=out_format, normalization_threshold=norm_thresh, amplification_threshold=amp_thresh, mdx_params={ "batch_size": 1, "hop_length": hop_length, "segment_size": seg_size, "overlap": overlap, "enable_denoise": denoise, } ) separator.load_model(model_filename=model) separation = separator.separate(audio) stems = rename_stems(audio, out_dir, separation, out_format) return stems[0], stems[1] 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): """Separate audio using VR ARCH model.""" with tempfile.TemporaryDirectory() as tmp_dir: separator = Separator( model_file_dir=model_dir, output_dir=tmp_dir, output_format=out_format, normalization_threshold=norm_thresh, amplification_threshold=amp_thresh, vr_params={ "batch_size": 1, "window_size": window_size, "aggression": aggression, "enable_tta": tta, "enable_post_process": post_process, "post_process_threshold": post_process_threshold, "high_end_process": high_end_process, } ) separator.load_model(model_filename=model) separation = separator.separate(audio) stems = rename_stems(audio, out_dir, separation, out_format) return stems[0], stems[1] def demucs_separator(audio, model, seg_size, shifts, overlap, segments_enabled, model_dir, out_dir, out_format, norm_thresh, amp_thresh): """Separate audio using Demucs model.""" with tempfile.TemporaryDirectory() as tmp_dir: separator = Separator( model_file_dir=model_dir, output_dir=tmp_dir, output_format=out_format, normalization_threshold=norm_thresh, amplification_threshold=amp_thresh, demucs_params={ "segment_size": seg_size, "shifts": shifts, "overlap": overlap, "segments_enabled": segments_enabled, } ) separator.load_model(model_filename=model) separation = separator.separate(audio) stems = rename_stems(audio, out_dir, separation, out_format) return stems[0], stems[1], stems[2], stems[3] with gr.Blocks(title="🎵 Audio Separator (by Politrees) 🎵", css="footer{display:none !important}") as app: with gr.Accordion("General settings", open=False): model_file_dir = gr.Textbox(value="/tmp/audio-separator-models/", label="Directory for storing model files", placeholder="/tmp/audio-separator-models/", interactive=False) with gr.Row(): output_dir = gr.Textbox(value="", label="File output directory", placeholder="/content/output", interactive=False) output_format = gr.Dropdown(value="wav", choices=["wav", "flac", "mp3"], label="Output Format") with gr.Row(): norm_threshold = gr.Slider(value=0.9, step=0.1, minimum=0, maximum=1, label="Normalization", info="max peak amplitude to normalize input and output audio.") amp_threshold = gr.Slider(value=0.6, step=0.1, minimum=0, maximum=1, label="Amplification", info="min peak amplitude to amplify input and output audio.") with gr.Tab("Roformer"): with gr.Row(): roformer_model = gr.Dropdown(label="Select the Model", choices=list(ROFORMER_MODELS.keys())) with gr.Row(): 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.") roformer_overlap = gr.Slider(minimum=2, maximum=4, step=1, value=4, label="Overlap", info="Amount of overlap between prediction windows.") with gr.Row(): roformer_audio = gr.Audio(label="Input Audio", type="numpy") with gr.Row(): roformer_button = gr.Button("Separate!", variant="primary") with gr.Row(): roformer_stem1 = gr.Audio(label="Stem 1", type="filepath", interactive=False) roformer_stem2 = gr.Audio(label="Stem 2", type="filepath", interactive=False) with gr.Tab("MDX23C"): with gr.Row(): mdx23c_model = gr.Dropdown(label="Select the Model", choices=MDX23C_MODELS) with gr.Row(): 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.") mdx23c_overlap = gr.Slider(minimum=2, maximum=50, step=1, value=8, label="Overlap", info="Amount of overlap between prediction windows.") with gr.Row(): mdx23c_audio = gr.Audio(label="Input Audio", type="numpy") with gr.Row(): mdx23c_button = gr.Button("Separate!", variant="primary") 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.Row(): mdx_model = gr.Dropdown(label="Select the Model", choices=MDXNET_MODELS) with gr.Row(): mdx_hop_length = gr.Slider(minimum=0.001, maximum=0.999, step=0.001, value=0.25, label="Hop Length") 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") mdx_denoise = gr.Checkbox(value=True, label="Denoise", info="Enable denoising during separation.") with gr.Row(): mdx_audio = gr.Audio(label="Input Audio", type="numpy") 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.Row(): vr_model = gr.Dropdown(label="Select the Model", choices=VR_ARCH_MODELS) with gr.Row(): vr_window_size = gr.Dropdown(minimum=320, maximum=1024, step=32, value=512, label="Window Size") 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=True, label="TTA", info="Enable Test-Time-Augmentation; slow but improves quality.") vr_post_process = gr.Checkbox(value=True, label="Post Process", info="Enable post-processing.") 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="numpy") 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.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=50, label="Segment Size") 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") demucs_segments_enabled = gr.Checkbox(value=True, label="Segment-wise processing") with gr.Row(): demucs_audio = gr.Audio(label="Input Audio", type="numpy") 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) roformer_button.click( roformer_separator, inputs=[ roformer_audio, roformer_model, roformer_seg_size, roformer_overlap, model_file_dir, output_dir, output_format, norm_threshold, amp_threshold, ], outputs=[roformer_stem1, roformer_stem2], ) mdx23c_button.click( mdx23c_separator, inputs=[ mdx23c_audio, mdx23c_model, mdx23c_seg_size, mdx23c_overlap, model_file_dir, output_dir, output_format, norm_threshold, amp_threshold, ], 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, ], 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, ], 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], ) app.launch(share=True)