Create app.py

app.py

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+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)