app.py

import argparse
from concurrent.futures import ProcessPoolExecutor
import logging
import os
from pathlib import Path
import subprocess as sp
import sys
from tempfile import NamedTemporaryFile
import time
import typing as tp
import warnings

import torch
import gradio as gr
from audiocraft.data.audio_utils import convert_audio
from audiocraft.data.audio import audio_write
from audiocraft.models import MusicGen

MODEL = None  # Last used model
INTERRUPTING = False
pool = ProcessPoolExecutor(4)
pool.__enter__()


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 load_model(version='facebook/musicgen-small'):
    global MODEL
    print("Loading model", version)
    if MODEL is None or MODEL.name != version:
        del MODEL
        torch.cuda.empty_cache()
        MODEL = None
        MODEL = MusicGen.get_pretrained(version)


def _do_predictions(texts, duration):
    MODEL.set_generation_params(duration=duration)
    outputs = MODEL.generate(texts)
    outputs = outputs.detach().cpu().float()
    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)
            out_wavs.append(file.name)
            file_cleaner.add(file.name)
    return out_wavs


def predict(text, duration):
    load_model('facebook/musicgen-small')
    wav_files = _do_predictions([text], duration)
    return wav_files[0]  # Return the first file in the list


def ui(launch_kwargs):
    with gr.Blocks() as demo:
        gr.Markdown(
            """
            # MelodyLM: Generate Music from Text.
            Team Members:
            1. Sunil Gopal C V
            2. Sudhan S
            3. Shreyas Gutti Srinivas
            4. Sushanth R
            """
        )
        with gr.Row():
            text = gr.Text(label="Input Text", interactive=True)
            duration = gr.Slider(minimum=1, maximum=120, value=10, label="Duration", interactive=True)
            submit = gr.Button("Submit")
        with gr.Row():
            audio_output = gr.Audio(label="Generated Music", type='filepath')
        submit.click(predict, inputs=[text, duration], outputs=[audio_output])

        gr.Markdown("""
        ## Guide:
        ### Input:
        1. Text Structure: Provide a short, descriptive phrase or sentence that encapsulates the mood, genre, and style of the music you want. For example, "Pop dance track with catchy melodies, tropical percussion, and upbeat rhythms, perfect for the beach."
        2. Keywords and Phrases: Mention specific musical elements you want to include, such as instruments (e.g., guitar, piano), styles (e.g., jazz, classical), or other characteristics (e.g., slow, fast, mellow, energetic).
        3. Examples: 
        - Example 1: "Relaxing ambient track with soft piano, gentle synths, and a serene atmosphere."
        - Example 2: "Energetic rock song with electric guitar riffs, powerful drums, and a lively tempo."

        ### Output:
        You can play, pause, and download the generated music track based on your input description.
        """)

        demo.queue(max_size=8 * 4).launch(**launch_kwargs)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--listen',
        type=str,
        default='0.0.0.0' if 'SPACE_ID' in os.environ else '127.0.0.1',
        help='IP to listen on for connections to Gradio',
    )
    parser.add_argument(
        '--username', type=str, default='', help='Username for authentication'
    )
    parser.add_argument(
        '--password', type=str, default='', help='Password for authentication'
    )
    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 = {}
    launch_kwargs['server_name'] = args.listen

    if args.username and args.password:
        launch_kwargs['auth'] = (args.username, args.password)
    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

    logging.basicConfig(level=logging.INFO, stream=sys.stderr)

    # Show the interface
    ui(launch_kwargs)