import os import torch import logging import yt_dlp import spaces import gradio as gr from audio_separator.separator import Separator device = "cuda" if torch.cuda.is_available() else "cpu" use_autocast = device == "cuda" #=========================# # Roformer Models # #=========================# roformer_models = { 'BS-Roformer-Viperx-1297': 'model_bs_roformer_ep_317_sdr_12.9755.ckpt', 'BS-Roformer-Viperx-1296': 'model_bs_roformer_ep_368_sdr_12.9628.ckpt', 'BS-Roformer-Viperx-1053': 'model_bs_roformer_ep_937_sdr_10.5309.ckpt', 'Mel-Roformer-Viperx-1143': 'model_mel_band_roformer_ep_3005_sdr_11.4360.ckpt', 'BS-Roformer-De-Reverb': 'deverb_bs_roformer_8_384dim_10depth.ckpt', 'Mel-Roformer-Crowd-Aufr33-Viperx': 'mel_band_roformer_crowd_aufr33_viperx_sdr_8.7144.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', 'Mel-Roformer-Karaoke-Aufr33-Viperx': 'mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt', 'MelBand Roformer Kim | Inst V1 by Unwa' : 'melband_roformer_inst_v1.ckpt', 'MelBand Roformer Kim | Inst V2 by Unwa' : 'melband_roformer_inst_v2.ckpt', 'MelBand Roformer Kim | InstVoc Duality V1 by Unwa' : 'melband_roformer_instvoc_duality_v1.ckpt', 'MelBand Roformer Kim | InstVoc Duality V2 by Unwa' : 'melband_roformer_instvox_duality_v2.ckpt', } #=========================# # MDX23C Models # #=========================# mdx23c_models = [ 'MDX23C_D1581.ckpt', 'MDX23C-8KFFT-InstVoc_HQ.ckpt', 'MDX23C-8KFFT-InstVoc_HQ_2.ckpt', ] #=========================# # MDXN-NET Models # #=========================# 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-MDX-NET-Inst_HQ_5.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 # #========================# vrarch_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-De-Echo-Aggressive.pth', 'UVR-De-Echo-Normal.pth', 'UVR-DeEcho-DeReverb.pth', 'UVR-DeNoise-Lite.pth', 'UVR-DeNoise.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 # #=======================# demucs_models = [ 'htdemucs_ft.yaml', 'htdemucs_6s.yaml', 'htdemucs.yaml', 'hdemucs_mmi.yaml', ] output_format = [ 'wav', 'flac', 'mp3', 'ogg', 'opus', 'm4a', 'aiff', 'ac3' ] found_files = [] logs = [] out_dir = "./outputs" models_dir = "./models" extensions = (".wav", ".flac", ".mp3", ".ogg", ".opus", ".m4a", ".aiff", ".ac3") def download_audio(url, output_dir="ytdl"): os.makedirs(output_dir, exist_ok=True) ydl_opts = { 'format': 'bestaudio/best', 'postprocessors': [{ 'key': 'FFmpegExtractAudio', 'preferredcodec': 'wav', 'preferredquality': '32', }], 'outtmpl': os.path.join(output_dir, '%(title)s.%(ext)s'), 'postprocessor_args': [ '-acodec', 'pcm_f32le' ], } try: with yt_dlp.YoutubeDL(ydl_opts) as ydl: info = ydl.extract_info(url, download=False) video_title = info['title'] ydl.download([url]) file_path = os.path.join(output_dir, f"{video_title}.wav") if os.path.exists(file_path): return os.path.abspath(file_path) else: raise Exception("Something went wrong") except Exception as e: raise Exception(f"Error extracting audio with yt-dlp: {str(e)}") @spaces.GPU(duration=60) def roformer_separator(audio, model_key, out_format, segment_size, override_seg_size, overlap, batch_size, norm_thresh, amp_thresh, progress=gr.Progress(track_tqdm=True)): base_name = os.path.splitext(os.path.basename(audio))[0] roformer_model = roformer_models[model_key] try: separator = Separator( log_level=logging.WARNING, model_file_dir=models_dir, output_dir=out_dir, output_format=out_format, use_autocast=use_autocast, normalization_threshold=norm_thresh, amplification_threshold=amp_thresh, mdxc_params={ "segment_size": segment_size, "override_model_segment_size": override_seg_size, "batch_size": batch_size, "overlap": overlap, } ) progress(0.2, desc="Loading model...") separator.load_model(model_filename=roformer_model) progress(0.7, desc="Separating audio...") separation = separator.separate(audio, f"{base_name}_(Stem1)", f"{base_name}_(Stem2)") stems = [os.path.join(out_dir, file_name) for file_name in separation] return stems[1], stems[0] except Exception as e: raise RuntimeError(f"Roformer separation failed: {e}") from e @spaces.GPU(duration=60) def mdxc_separator(audio, model, out_format, segment_size, override_seg_size, overlap, batch_size, norm_thresh, amp_thresh, progress=gr.Progress(track_tqdm=True)): base_name = os.path.splitext(os.path.basename(audio))[0] try: separator = Separator( log_level=logging.WARNING, model_file_dir=models_dir, output_dir=out_dir, output_format=out_format, use_autocast=use_autocast, normalization_threshold=norm_thresh, amplification_threshold=amp_thresh, mdxc_params={ "segment_size": segment_size, "override_model_segment_size": override_seg_size, "batch_size": batch_size, "overlap": overlap, } ) progress(0.2, desc="Loading model...") separator.load_model(model_filename=model) progress(0.7, desc="Separating audio...") separation = separator.separate(audio, f"{base_name}_(Stem1)", f"{base_name}_(Stem2)") stems = [os.path.join(out_dir, file_name) for file_name in separation] return stems[1], stems[0] except Exception as e: raise RuntimeError(f"MDX23C separation failed: {e}") from e @spaces.GPU(duration=60) def mdxnet_separator(audio, model, out_format, hop_length, segment_size, denoise, overlap, batch_size, norm_thresh, amp_thresh, progress=gr.Progress(track_tqdm=True)): base_name = os.path.splitext(os.path.basename(audio))[0] try: separator = Separator( log_level=logging.WARNING, model_file_dir=models_dir, output_dir=out_dir, output_format=out_format, use_autocast=use_autocast, normalization_threshold=norm_thresh, amplification_threshold=amp_thresh, mdx_params={ "hop_length": hop_length, "segment_size": segment_size, "overlap": overlap, "batch_size": batch_size, "enable_denoise": denoise, } ) progress(0.2, desc="Loading model...") separator.load_model(model_filename=model) progress(0.7, desc="Separating audio...") separation = separator.separate(audio, f"{base_name}_(Stem1)", f"{base_name}_(Stem2)") stems = [os.path.join(out_dir, file_name) for file_name in separation] return stems[0], stems[1] except Exception as e: raise RuntimeError(f"MDX-NET separation failed: {e}") from e @spaces.GPU(duration=60) def vrarch_separator(audio, model, out_format, window_size, aggression, tta, post_process, post_process_threshold, high_end_process, batch_size, norm_thresh, amp_thresh, progress=gr.Progress(track_tqdm=True)): base_name = os.path.splitext(os.path.basename(audio))[0] try: separator = Separator( log_level=logging.WARNING, model_file_dir=models_dir, output_dir=out_dir, output_format=out_format, use_autocast=use_autocast, normalization_threshold=norm_thresh, amplification_threshold=amp_thresh, vr_params={ "batch_size": batch_size, "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, } ) progress(0.2, desc="Loading model...") separator.load_model(model_filename=model) progress(0.7, desc="Separating audio...") separation = separator.separate(audio, f"{base_name}_(Stem1)", f"{base_name}_(Stem2)") stems = [os.path.join(out_dir, file_name) for file_name in separation] return stems[0], stems[1] except Exception as e: raise RuntimeError(f"VR ARCH separation failed: {e}") from e @spaces.GPU(duration=60) def demucs_separator(audio, model, out_format, shifts, segment_size, segments_enabled, overlap, batch_size, norm_thresh, amp_thresh, progress=gr.Progress(track_tqdm=True)): base_name = os.path.splitext(os.path.basename(audio))[0] try: separator = Separator( log_level=logging.WARNING, model_file_dir=models_dir, output_dir=out_dir, output_format=out_format, use_autocast=use_autocast, normalization_threshold=norm_thresh, amplification_threshold=amp_thresh, demucs_params={ "batch_size": batch_size, "segment_size": segment_size, "shifts": shifts, "overlap": overlap, "segments_enabled": segments_enabled, } ) progress(0.2, desc="Loading model...") separator.load_model(model_filename=model) progress(0.7, desc="Separating audio...") separation = separator.separate(audio) stems = [os.path.join(out_dir, file_name) for file_name in separation] if model == "htdemucs_6s.yaml": return stems[0], stems[1], stems[2], stems[3], stems[4], stems[5] else: return stems[0], stems[1], stems[2], stems[3], None, None except Exception as e: raise RuntimeError(f"Demucs separation failed: {e}") from e def update_stems(model): if model == "htdemucs_6s.yaml": return gr.update(visible=True) else: return gr.update(visible=False) @spaces.GPU(duration=60) def roformer_batch(path_input, path_output, model_key, out_format, segment_size, override_seg_size, overlap, batch_size, norm_thresh, amp_thresh): found_files.clear() logs.clear() roformer_model = roformer_models[model_key] for audio_files in os.listdir(path_input): if audio_files.endswith(extensions): found_files.append(audio_files) total_files = len(found_files) if total_files == 0: logs.append("No valid audio files.") yield "\n".join(logs) else: logs.append(f"{total_files} audio files found") found_files.sort() for audio_files in found_files: file_path = os.path.join(path_input, audio_files) base_name = os.path.splitext(os.path.basename(file_path))[0] try: separator = Separator( log_level=logging.WARNING, model_file_dir=models_dir, output_dir=path_output, output_format=out_format, use_autocast=use_autocast, normalization_threshold=norm_thresh, amplification_threshold=amp_thresh, mdxc_params={ "segment_size": segment_size, "override_model_segment_size": override_seg_size, "batch_size": batch_size, "overlap": overlap, } ) logs.append("Loading model...") yield "\n".join(logs) separator.load_model(model_filename=roformer_model) logs.append(f"Separating file: {audio_files}") yield "\n".join(logs) separator.separate(file_path, f"{base_name}_(Stem1)", f"{base_name}_(Stem2)") logs.append(f"File: {audio_files} separated!") yield "\n".join(logs) except Exception as e: raise RuntimeError(f"Roformer batch separation failed: {e}") from e @spaces.GPU(duration=60) def mdx23c_batch(path_input, path_output, model, out_format, segment_size, override_seg_size, overlap, batch_size, norm_thresh, amp_thresh): found_files.clear() logs.clear() for audio_files in os.listdir(path_input): if audio_files.endswith(extensions): found_files.append(audio_files) total_files = len(found_files) if total_files == 0: logs.append("No valid audio files.") yield "\n".join(logs) else: logs.append(f"{total_files} audio files found") found_files.sort() for audio_files in found_files: file_path = os.path.join(path_input, audio_files) base_name = os.path.splitext(os.path.basename(file_path))[0] try: separator = Separator( log_level=logging.WARNING, model_file_dir=models_dir, output_dir=path_output, output_format=out_format, use_autocast=use_autocast, normalization_threshold=norm_thresh, amplification_threshold=amp_thresh, mdxc_params={ "segment_size": segment_size, "override_model_segment_size": override_seg_size, "batch_size": batch_size, "overlap": overlap, } ) logs.append("Loading model...") yield "\n".join(logs) separator.load_model(model_filename=model) logs.append(f"Separating file: {audio_files}") yield "\n".join(logs) separator.separate(file_path, f"{base_name}_(Stem1)", f"{base_name}_(Stem2)") logs.append(f"File: {audio_files} separated!") yield "\n".join(logs) except Exception as e: raise RuntimeError(f"Roformer batch separation failed: {e}") from e @spaces.GPU(duration=60) def mdxnet_batch(path_input, path_output, model, out_format, hop_length, segment_size, denoise, overlap, batch_size, norm_thresh, amp_thresh): found_files.clear() logs.clear() for audio_files in os.listdir(path_input): if audio_files.endswith(extensions): found_files.append(audio_files) total_files = len(found_files) if total_files == 0: logs.append("No valid audio files.") yield "\n".join(logs) else: logs.append(f"{total_files} audio files found") found_files.sort() for audio_files in found_files: file_path = os.path.join(path_input, audio_files) base_name = os.path.splitext(os.path.basename(file_path))[0] try: separator = Separator( log_level=logging.WARNING, model_file_dir=models_dir, output_dir=path_output, output_format=out_format, use_autocast=use_autocast, normalization_threshold=norm_thresh, amplification_threshold=amp_thresh, mdx_params={ "hop_length": hop_length, "segment_size": segment_size, "overlap": overlap, "batch_size": batch_size, "enable_denoise": denoise, } ) logs.append("Loading model...") yield "\n".join(logs) separator.load_model(model_filename=model) logs.append(f"Separating file: {audio_files}") yield "\n".join(logs) separator.separate(file_path, f"{base_name}_(Stem1)", f"{base_name}_(Stem2)") logs.append(f"File: {audio_files} separated!") yield "\n".join(logs) except Exception as e: raise RuntimeError(f"Roformer batch separation failed: {e}") from e @spaces.GPU(duration=60) def vrarch_batch(path_input, path_output, model, out_format, window_size, aggression, tta, post_process, post_process_threshold, high_end_process, batch_size, norm_thresh, amp_thresh): found_files.clear() logs.clear() for audio_files in os.listdir(path_input): if audio_files.endswith(extensions): found_files.append(audio_files) total_files = len(found_files) if total_files == 0: logs.append("No valid audio files.") yield "\n".join(logs) else: logs.append(f"{total_files} audio files found") found_files.sort() for audio_files in found_files: file_path = os.path.join(path_input, audio_files) base_name = os.path.splitext(os.path.basename(file_path))[0] try: separator = Separator( log_level=logging.WARNING, model_file_dir=models_dir, output_dir=path_output, output_format=out_format, use_autocast=use_autocast, normalization_threshold=norm_thresh, amplification_threshold=amp_thresh, vr_params={ "batch_size": batch_size, "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, } ) logs.append("Loading model...") yield "\n".join(logs) separator.load_model(model_filename=model) logs.append(f"Separating file: {audio_files}") yield "\n".join(logs) separator.separate(file_path, f"{base_name}_(Stem1)", f"{base_name}_(Stem2)") logs.append(f"File: {audio_files} separated!") yield "\n".join(logs) except Exception as e: raise RuntimeError(f"Roformer batch separation failed: {e}") from e @spaces.GPU(duration=60) def demucs_batch(path_input, path_output, model, out_format, shifts, segment_size, segments_enabled, overlap, batch_size, norm_thresh, amp_thresh): found_files.clear() logs.clear() for audio_files in os.listdir(path_input): if audio_files.endswith(extensions): found_files.append(audio_files) total_files = len(found_files) if total_files == 0: logs.append("No valid audio files.") yield "\n".join(logs) else: logs.append(f"{total_files} audio files found") found_files.sort() for audio_files in found_files: file_path = os.path.join(path_input, audio_files) try: separator = Separator( log_level=logging.WARNING, model_file_dir=models_dir, output_dir=path_output, output_format=out_format, use_autocast=use_autocast, normalization_threshold=norm_thresh, amplification_threshold=amp_thresh, demucs_params={ "batch_size": batch_size, "segment_size": segment_size, "shifts": shifts, "overlap": overlap, "segments_enabled": segments_enabled, } ) logs.append("Loading model...") yield "\n".join(logs) separator.load_model(model_filename=model) logs.append(f"Separating file: {audio_files}") yield "\n".join(logs) separator.separate(file_path) logs.append(f"File: {audio_files} separated!") yield "\n".join(logs) except Exception as e: raise RuntimeError(f"Roformer batch separation failed: {e}") from e