app.py

import argparse
from concurrent.futures import ProcessPoolExecutor
import time
import subprocess as sp
from pathlib import Path
import typing as tp
import warnings
from tempfile import NamedTemporaryFile
import gradio as gr
from audiocraft.data.audio import audio_write
from audiocraft.models import MusicGen

MODEL = None
INTERRUPTING = False

# We have to wrap subprocess call to clean a bit the log when using gr.make_waveform
_old_call = sp.call


def _call_nostderr(*args, **kwargs):
    # Avoid ffmpeg vomiting on the logs.
    kwargs['stderr'] = sp.DEVNULL
    kwargs['stdout'] = sp.DEVNULL
    _old_call(*args, **kwargs)


sp.call = _call_nostderr
# Preallocating the pool of processes.
pool = ProcessPoolExecutor(4)
pool.__enter__()

def interrupt():
    global INTERRUPTING
    INTERRUPTING = True

class FileCleaner:
    def __init__(self, file_lifetime: float = 3600):
        self.file_lifetime = file_lifetime
        self.files = []

    def add(self, path: tp.Union[str, Path]):
        self._cleanup()
        self.files.append((time.time(), Path(path)))

    def _cleanup(self):
        now = time.time()
        for time_added, path in list(self.files):
            if now - time_added > self.file_lifetime:
                if path.exists():
                    path.unlink()
                self.files.pop(0)
            else:
                break


file_cleaner = FileCleaner()

def make_waveform(*args, **kwargs):
    # Further remove some warnings.
    waveform_start = time.time()
    with warnings.catch_warnings():
        warnings.simplefilter('ignore')
        out = gr.make_waveform(*args, **kwargs)
        print("Make a video took", time.time() - waveform_start)
        return out

def load_model(version='facebook/musicgen-medium'):
    global MODEL
    print("Loading model", version)
    if MODEL is None or MODEL.name != version:
        MODEL = MusicGen.get_pretrained(version)

def _do_predictions(texts, duration, progress=False, **gen_kwargs):
    MODEL.set_generation_params(duration=duration, **gen_kwargs)
    generate_start = time.time()
    outputs = MODEL.generate(texts, progress=progress)
    outputs = outputs.detach().cpu().float()
    pending_videos = []
    out_wavs = []
    for output in outputs:
        with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
            audio_write(
                file.name, output, MODEL.sample_rate, strategy="loudness",
                loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
            pending_videos.append(pool.submit(make_waveform, file.name))
            out_wavs.append(file.name)
            file_cleaner.add(file.name)
    out_videos = [pending_video.result() for pending_video in pending_videos]
    for video in out_videos:
        file_cleaner.add(video)
    print("generation took", time.time() - generate_start)
    print("Tempfiles currently stored: ", len(file_cleaner.files))
    return out_videos, out_wavs

def predict_full(model, text, duration, bpm, topk, topp, temperature, cfg_coef, progress=gr.Progress()):

    text = "lofi " + text + " bpm: " + str(bpm)

    global INTERRUPTING
    INTERRUPTING = False
    if temperature < 0:
        raise gr.Error("Temperature must be >= 0.")
    if topk < 0:
        raise gr.Error("Topk must be non-negative.")
    if topp < 0:
        raise gr.Error("Topp must be non-negative.")

    topk = int(topk)
    load_model(model)

    def _progress(generated, to_generate):
        progress((min(generated, to_generate), to_generate))
        if INTERRUPTING:
            raise gr.Error("Interrupted.")
    MODEL.set_custom_progress_callback(_progress)

    videos, wavs = _do_predictions(
        [text], duration, progress=True,
        top_k=topk, top_p=topp, temperature=temperature, cfg_coef=cfg_coef)
    
    return videos[0], wavs[0], None, None

def ui(launch_kwargs):
  with gr.Blocks() as interface:
        gr.Markdown(
            """
            # Lofi University
            Generate lofi tracks to help study.
            """
        )
        with gr.Row():
            with gr.Column():
                with gr.Row():
                    text = gr.Text(label="Describe your lofi", interactive=True)
                with gr.Row():
                    submit = gr.Button("Submit")
                    _ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
                with gr.Row():
                    model = gr.Radio(["facebook/musicgen-medium", "facebook/musicgen-small",
                                        "facebook/musicgen-large"],
                                        label="Model", value="facebook/musicgen-medium", interactive=True)
                with gr.Row():
                    bpm = gr.Slider(minimum=50, maximum=150, value=80, label="BPM", interactive=True)
                with gr.Row():
                    duration = gr.Slider(minimum=1, maximum=120, value=10, label="Duration", interactive=True)
                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)
            with gr.Column():
                output = gr.Video(label="Generated Music")
                audio_output = gr.Audio(label="Generated Music (wav)", type='filepath')

        submit.click(predict_full, inputs=[model, text, duration, bpm, topk, topp, temperature, cfg_coef], outputs=[output, audio_output])

        gr.Examples(
            fn=predict_full,
            examples=[
                [
                    "Dreamy synth layers with light beats",
                    "facebook/musicgen-medium",
                ],
                [
                    "Mellow piano chords are accompanied by a subtle, relaxed drum loop",
                    "facebook/musicgen-medium",
                ],
            ],
            inputs=[text, model],
            outputs=[output]
        )
        interface.queue().launch(**launch_kwargs)

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--server_port',
        type=int,
        default=0,
        help='Port to run the server listener on',
    )
    parser.add_argument(
        '--inbrowser', action='store_true', help='Open in browser'
    )
    parser.add_argument(
        '--share', action='store_true', help='Share the gradio UI'
    )

    args = parser.parse_args()

    launch_kwargs = {}

    if args.server_port:
        launch_kwargs['server_port'] = args.server_port
    if args.inbrowser:
        launch_kwargs['inbrowser'] = args.inbrowser
    if args.share:
        launch_kwargs['share'] = args.share

    ui(launch_kwargs)