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| """ | |
| Copyright (c) Meta Platforms, Inc. and affiliates. | |
| All rights reserved. | |
| This source code is licensed under the license found in the | |
| LICENSE file in the root directory of this source tree. | |
| """ | |
| from tempfile import NamedTemporaryFile | |
| import torch | |
| import gradio as gr | |
| from scipy.io.wavfile import write | |
| from audiocraft.models import MusicGen | |
| import os | |
| from audiocraft.data.audio import audio_write | |
| MODEL = None | |
| def split_process(audio, chosen_out_track): | |
| os.makedirs("out", exist_ok=True) | |
| write('test.wav', audio[0], audio[1]) | |
| os.system("python3 -m demucs.separate -n mdx_extra_q -d cpu test.wav -o out") | |
| #return "./out/mdx_extra_q/test/vocals.wav","./out/mdx_extra_q/test/bass.wav","./out/mdx_extra_q/test/drums.wav","./out/mdx_extra_q/test/other.wav" | |
| if chosen_out_track == "vocals": | |
| return "./out/mdx_extra_q/test/vocals.wav" | |
| elif chosen_out_track == "bass": | |
| return "./out/mdx_extra_q/test/bass.wav" | |
| elif chosen_out_track == "drums": | |
| return "./out/mdx_extra_q/test/drums.wav" | |
| elif chosen_out_track == "other": | |
| return "./out/mdx_extra_q/test/other.wav" | |
| elif chosen_out_track == "all-in": | |
| return "test.wav" | |
| def load_model(version): | |
| print("Loading model", version) | |
| return MusicGen.get_pretrained(version) | |
| def predict(music_prompt, melody, duration, cfg_coef): | |
| text = music_prompt | |
| global MODEL | |
| topk = int(250) | |
| if MODEL is None or MODEL.name != "melody": | |
| MODEL = load_model("melody") | |
| if duration > MODEL.lm.cfg.dataset.segment_duration: | |
| raise gr.Error("MusicGen currently supports durations of up to 30 seconds!") | |
| MODEL.set_generation_params( | |
| use_sampling=True, | |
| top_k=250, | |
| top_p=0, | |
| temperature=1.0, | |
| cfg_coef=cfg_coef, | |
| duration=duration, | |
| ) | |
| if melody: | |
| sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t().unsqueeze(0) | |
| print(melody.shape) | |
| if melody.dim() == 2: | |
| melody = melody[None] | |
| melody = melody[..., :int(sr * MODEL.lm.cfg.dataset.segment_duration)] | |
| output = MODEL.generate_with_chroma( | |
| descriptions=[text], | |
| melody_wavs=melody, | |
| melody_sample_rate=sr, | |
| progress=False | |
| ) | |
| else: | |
| output = MODEL.generate(descriptions=[text], progress=False) | |
| output = output.detach().cpu().float()[0] | |
| with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file: | |
| audio_write(file.name, output, MODEL.sample_rate, strategy="loudness", add_suffix=False) | |
| #waveform_video = gr.make_waveform(file.name) | |
| return file.name | |
| css=""" | |
| #col-container {max-width: 910px; margin-left: auto; margin-right: auto;} | |
| a {text-decoration-line: underline; font-weight: 600;} | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.Markdown( | |
| """ | |
| # Split Audio Tracks to MusicGen | |
| Upload an audio file, split audio tracks with Demucs, choose a track as conditional sound for MusicGen, get a remix ! <br/> | |
| *** Careful, MusicGen model loaded here can only handle up to 30 second audio, please use the audio component gradio feature to edit your audio before conditioning *** | |
| <br/> | |
| <br/> | |
| [](https://huggingface.co/spaces/fffiloni/SplitTrack2MusicGen?duplicate=true) for longer audio, more control and no queue.</p> | |
| """ | |
| ) | |
| with gr.Column(): | |
| uploaded_sound = gr.Audio(type="numpy", label="Input", source="upload") | |
| with gr.Row(): | |
| chosen_track = gr.Radio(["vocals", "bass", "drums", "other", "all-in"], label="Track", info="Which track from your audio do you want to mashup ?", value="vocals") | |
| load_sound_btn = gr.Button('Load your chosen track') | |
| #split_vocals = gr.Audio(type="filepath", label="Vocals") | |
| #split_bass = gr.Audio(type="filepath", label="Bass") | |
| #split_drums = gr.Audio(type="filepath", label="Drums") | |
| #split_others = gr.Audio(type="filepath", label="Other") | |
| with gr.Row(): | |
| music_prompt = gr.Textbox(label="Musical Prompt", info="Describe what kind of music you wish for", interactive=True, placeholder="lofi slow bpm electro chill with organic samples") | |
| melody = gr.Audio(source="upload", type="numpy", label="Track Condition (from previous step)", interactive=False) | |
| with gr.Row(): | |
| #model = gr.Radio(["melody", "medium", "small", "large"], label="MusicGen Model", value="melody", interactive=True) | |
| duration = gr.Slider(minimum=1, maximum=30, value=10, step=1, label="Generated Music Duration", interactive=True) | |
| cfg_coef = gr.Slider(label="Classifier Free Guidance", minimum=1.0, maximum=10.0, step=0.1, value=3.0, interactive=True) | |
| with gr.Row(): | |
| submit = gr.Button("Submit") | |
| #with gr.Row(): | |
| # topk = gr.Number(label="Top-k", value=250, interactive=True) | |
| # topp = gr.Number(label="Top-p", value=0, interactive=True) | |
| # temperature = gr.Number(label="Temperature", value=1.0, interactive=True) | |
| # cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True) | |
| output = gr.Audio(label="Generated Music") | |
| gr.Examples( | |
| fn=predict, | |
| examples=[ | |
| [ | |
| "An 80s driving pop song with heavy drums and synth pads in the background", | |
| None, | |
| 10, | |
| 3.0 | |
| ], | |
| [ | |
| "A cheerful country song with acoustic guitars", | |
| None, | |
| 10, | |
| 3.0 | |
| ], | |
| [ | |
| "90s rock song with electric guitar and heavy drums", | |
| None, | |
| 10, | |
| 3.0 | |
| ], | |
| [ | |
| "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130", | |
| None, | |
| 10, | |
| 3.0 | |
| ], | |
| [ | |
| "lofi slow bpm electro chill with organic samples", | |
| None, | |
| 10, | |
| 3.0 | |
| ], | |
| ], | |
| inputs=[music_prompt, melody, duration, cfg_coef], | |
| outputs=[output] | |
| ) | |
| load_sound_btn.click(split_process, inputs=[uploaded_sound, chosen_track], outputs=[melody]) | |
| submit.click(predict, inputs=[music_prompt, melody, duration, cfg_coef], outputs=[output]) | |
| demo.queue(max_size=32).launch() | |