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| # import os | |
| # import gradio as gr | |
| # from scipy.io.wavfile import write | |
| # import subprocess | |
| # import torch | |
| # from audio_separator import Separator # Ensure this is correctly implemented | |
| # def inference(audio): | |
| # os.makedirs("out", exist_ok=True) | |
| # audio_path = 'test.wav' | |
| # write(audio_path, audio[0], audio[1]) | |
| # device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| # if device=='cuda': | |
| # use_cuda=True | |
| # print(f"Using device: {device}") | |
| # else: | |
| # use_cuda=False | |
| # print(f"Using device: {device}") | |
| # try: | |
| # # Using subprocess.run for better control | |
| # command = f"python3 -m demucs.separate -n htdemucs_6s -d {device} {audio_path} -o out" | |
| # process = subprocess.run(command, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) | |
| # print("Demucs script output:", process.stdout.decode()) | |
| # except subprocess.CalledProcessError as e: | |
| # print("Error in Demucs script:", e.stderr.decode()) | |
| # return None | |
| # try: | |
| # # Separating the stems using your custom separator | |
| # separator = Separator("./out/htdemucs_6s/test/vocals.wav", model_name='UVR_MDXNET_KARA_2', use_cuda=use_cuda, output_format='mp3') | |
| # primary_stem_path, secondary_stem_path = separator.separate() | |
| # except Exception as e: | |
| # print("Error in custom separation:", str(e)) | |
| # return None | |
| # # Collecting all file paths | |
| # files = [f"./out/htdemucs_6s/test/{stem}.wav" for stem in ["vocals", "bass", "drums", "other", "piano", "guitar"]] | |
| # files.extend([secondary_stem_path,primary_stem_path ]) | |
| # # Check if files exist | |
| # existing_files = [file for file in files if os.path.isfile(file)] | |
| # if not existing_files: | |
| # print("No files were created.") | |
| # return None | |
| # return existing_files | |
| # # Gradio Interface | |
| # title = "Source Separation Demo" | |
| # description = "Music Source Separation in the Waveform Domain. To use it, simply upload your audio." | |
| # gr.Interface( | |
| # inference, | |
| # gr.components.Audio(type="numpy", label="Input"), | |
| # [gr.components.Audio(type="filepath", label=stem) for stem in ["Full Vocals","Bass", "Drums", "Other", "Piano", "Guitar", "Lead Vocals", "Backing Vocals" ]], | |
| # title=title, | |
| # description=description, | |
| # ).launch() | |
| import os | |
| import gradio as gr | |
| from scipy.io.wavfile import write | |
| import subprocess | |
| import torch | |
| # Assuming audio_separator is available in your environment | |
| from audio_separator import Separator | |
| def inference(audio, vocals, bass, drums, other, piano, guitar, lead_vocals, backing_vocals): | |
| os.makedirs("out", exist_ok=True) | |
| audio_path = 'test.wav' | |
| write(audio_path, audio[0], audio[1]) | |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| print(f"Using device: {device}") | |
| try: | |
| command = f"python3 -m demucs.separate -n htdemucs_6s -d {device} {audio_path} -o out" | |
| process = subprocess.run(command, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) | |
| print("Demucs script output:", process.stdout.decode()) | |
| except subprocess.CalledProcessError as e: | |
| print("Error in Demucs script:", e.stderr.decode()) | |
| return [gr.Audio(visible=False)] * 8 + ["Failed to process audio."] | |
| try: | |
| separator = Separator("./out/htdemucs_6s/test/vocals.wav", model_name='UVR_MDXNET_KARA_2', use_cuda=device=='cuda', output_format='wav') | |
| primary_stem_path, secondary_stem_path = separator.separate() | |
| except Exception as e: | |
| print("Error in custom separation:", str(e)) | |
| return [gr.Audio(visible=False)] * 8 + ["Failed to process audio."] | |
| stem_paths = { | |
| "vocals": "./out/htdemucs_6s/test/vocals.wav" if vocals else None, | |
| "bass": "./out/htdemucs_6s/test/bass.wav" if bass else None, | |
| "drums": "./out/htdemucs_6s/test/drums.wav" if drums else None, | |
| "other": "./out/htdemucs_6s/test/other.wav" if other else None, | |
| "piano": "./out/htdemucs_6s/test/piano.wav" if piano else None, | |
| "guitar": "./out/htdemucs_6s/test/guitar.wav" if guitar else None, | |
| "lead_vocals": primary_stem_path if lead_vocals else None, | |
| "backing_vocals": secondary_stem_path if backing_vocals else None | |
| } | |
| return tuple([gr.Audio(stem_paths[stem], visible=bool(stem_paths[stem])) for stem in stem_paths]) + ("Done! Successfully processed.",) | |
| # Define checkboxes for each stem | |
| checkbox_labels = ["Full Vocals", "Bass", "Drums", "Other", "Piano", "Guitar", "Lead Vocals", "Backing Vocals"] | |
| checkboxes = [gr.components.Checkbox(label=label) for label in checkbox_labels] | |
| # Gradio Interface | |
| title = "Source Separation Demo" | |
| description = "Music Source Separation in the Waveform Domain. Upload your audio to begin." | |
| iface = gr.Interface( | |
| inference, | |
| [gr.components.Audio(type="numpy", label="Input")] + checkboxes, | |
| [gr.Audio(label=label, visible=False) for label in checkbox_labels] + [gr.Label()], | |
| title=title, | |
| description=description, | |
| ) | |
| iface.launch() | |