app.py

"""
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 tempfile
import os
from audiocraft.data.audio import audio_write


MODEL = None

import yt_dlp as youtube_dl
from moviepy.editor import VideoFileClip

YT_LENGTH_LIMIT_S = 480  # limit to 1 hour YouTube files

def download_yt_audio(yt_url, filename):
    info_loader = youtube_dl.YoutubeDL()
    
    try:
        info = info_loader.extract_info(yt_url, download=False)
    except youtube_dl.utils.DownloadError as err:
        raise gr.Error(str(err))
    
    file_length = info["duration_string"]
    file_h_m_s = file_length.split(":")
    file_h_m_s = [int(sub_length) for sub_length in file_h_m_s]
    
    if len(file_h_m_s) == 1:
        file_h_m_s.insert(0, 0)
    if len(file_h_m_s) == 2:
        file_h_m_s.insert(0, 0)
    file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2]
    
    if file_length_s > YT_LENGTH_LIMIT_S:
        yt_length_limit_hms = time.strftime("%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S))
        file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length_s))
        raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.")
    
    ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"}
    
    with youtube_dl.YoutubeDL(ydl_opts) as ydl:
        try:
            ydl.download([yt_url])
        except youtube_dl.utils.ExtractorError as err:
            raise gr.Error(str(err))


def convert_to_mp3(input_path, output_path):
    try:
        video_clip = VideoFileClip(input_path)
        audio_clip = video_clip.audio
        print("Converting to MP3...")
        audio_clip.write_audiofile(output_path)
    except Exception as e:
        print("Error:", e)

def load_youtube_audio(yt_link):
    
    with tempfile.TemporaryDirectory() as tmpdirname:
        filepath = os.path.join(tmpdirname, "video.mp4")
        download_yt_audio(yt_link, filepath)

        mp3_output_path = "video_sound.mp3"
        convert_to_mp3(filepath, mp3_output_path)
        print("Conversion complete. MP3 saved at:", mp3_output_path)
      
    return mp3_output_path

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 -j 4 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/>
            [![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm.svg)](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():
                youtube_link = gr.Textbox(show_label=False, placeholder="TEMPORARILY DISABLED • you can also paste YT link and load it", interactive=False)
                yt_load_btn = gr.Button("Load YT song", interactive=False)
                
            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]
        )
    yt_load_btn.click(fn=load_youtube_audio, inputs=[youtube_link], outputs=[uploaded_sound], queue=False, api_name=False)
    load_sound_btn.click(split_process, inputs=[uploaded_sound, chosen_track], outputs=[melody], api_name="splt_trck")
    submit.click(predict, inputs=[music_prompt, melody, duration, cfg_coef], outputs=[output])
    

demo.queue(max_size=32).launch()