app.py

import os
import gradio as gr
from scipy.io.wavfile import write
import subprocess 
import argparse
from concurrent.futures import ProcessPoolExecutor
import time
import typing as tp
import warnings
from pathlib import Path
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
IS_BATCHED = "facebook/MusicGen" in os.environ.get('SPACE_ID', '')
MAX_BATCH_SIZE = 6
BATCHED_DURATION = 15
INTERRUPTING = False

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):
    be = time.time()
    with warnings.catch_warnings():
        warnings.simplefilter('ignore')
        out = gr.make_waveform(*args, **kwargs)
        print("Make a video took", time.time() - be)
        return out


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


def _do_predictions(texts, melodies, duration, progress=False, **gen_kwargs):
    MODEL.set_generation_params(duration=duration, **gen_kwargs)
    print("new batch", len(texts), texts, [None if m is None else (m[0], m[1].shape) for m in melodies])
    be = time.time()
    processed_melodies = []
    target_sr = 32000
    target_ac = 1
    for melody in melodies:
        if melody is None:
            processed_melodies.append(None)
        else:
            sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t()
            if melody.dim() == 1:
                melody = melody[None]
            melody = melody[..., :int(sr * duration)]
            melody = convert_audio(melody, sr, target_sr, target_ac)
            processed_melodies.append(melody)

    if any(m is not None for m in processed_melodies):
        outputs = MODEL.generate_with_chroma(
            descriptions=texts,
            melody_wavs=processed_melodies,
            melody_sample_rate=target_sr,
            progress=progress,
        )
    else:
        outputs = MODEL.generate(texts, progress=progress)

    outputs = outputs.detach().cpu().float()
    out_files = []
    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_files.append(pool.submit(make_waveform, file.name))
            file_cleaner.add(file.name)
    res = [out_file.result() for out_file in out_files]
    for file in res:
        file_cleaner.add(file)
    print("batch finished", len(texts), time.time() - be)
    print("Tempfiles currently stored: ", len(file_cleaner.files))
    return res


def predict_batched(texts, melodies):
    max_text_length = 512
    texts = [text[:max_text_length] for text in texts]
    load_model('melody')
    res = _do_predictions(texts, melodies, BATCHED_DURATION)
    return [res]


def predict_full(model, text, melody, duration, topk, topp, temperature, cfg_coef, progress=gr.Progress()):
    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((generated, to_generate))
        if INTERRUPTING:
            raise gr.Error("Interrupted.")
    MODEL.set_custom_progress_callback(_progress)

    outs = _do_predictions(
        [text], [melody], duration, progress=True,
        top_k=topk, top_p=topp, temperature=temperature, cfg_coef=cfg_coef)
    return outs[0]


def toggle_audio_src(choice):
    if choice == "mic":
        return gr.update(source="microphone", value=None, label="Microphone")
    else:
        return gr.update(source="upload", value=None, label="File")


def ui_full(launch_kwargs):
    with gr.Blocks() as interface:
        gr.Markdown(
            """
            # MusicGen and Demucs Combination
            This is a combined demo of MusicGen and Demucs.
            MusicGen is a model for music generation based on text prompts,
            and Demucs is a model for music source separation.
            """
        )
        with gr.Row():
            with gr.Column():
                with gr.Row():
                    text = gr.Text(label="Input Text", interactive=True)
                    with gr.Column():
                        radio = gr.Radio(["file", "mic"], value="file",
                                         label="Condition on a Melody (optional) File or Mic")
                        melody = gr.Audio(source="upload", type="numpy", label="Melody File",
                                          interactive=True, elem_id="melody-input")
                with gr.Row():
                    submit = gr.Button("Generate Music")
                with gr.Row():
                    audio_output = gr.Audio(type="numpy", label="Generated Music")
                    vocals_output = gr.Audio(type="filepath", label="Vocals")
                    bass_output = gr.Audio(type="filepath", label="Bass")
                    drums_output = gr.Audio(type="filepath", label="Drums")
                    other_output = gr.Audio(type="filepath", label="Other")
        submit.click(predict_full,
                     inputs=[text, melody, 10, 250, 0, 1.0, 3.0],
                     outputs=[audio_output, vocals_output, bass_output, drums_output, other_output])
        radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
        gr.Examples(
            fn=predict_full,
            examples=[
                [
                    "An 80s driving pop song with heavy drums and synth pads in the background",
                    "./assets/bach.mp3",
                ],
                [
                    "A cheerful country song with acoustic guitars",
                    "./assets/bolero_ravel.mp3",
                ],
                [
                    "90s rock song with electric guitar and heavy drums",
                    None,
                ],
                [
                    "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions",
                    "./assets/bach.mp3",
                ],
                [
                    "lofi slow bpm electro chill with organic samples",
                    None,
                ],
            ],
            inputs=[text, melody],
            outputs=[audio_output, vocals_output, bass_output, drums_output, other_output]
        )

    gr.Interface(
        fn=inference,
        inputs=gr.inputs.Audio(type="numpy", label="Input Audio"),
        outputs=[
            gr.outputs.Audio(type="filepath", label="Vocals"),
            gr.outputs.Audio(type="filepath", label="Bass"),
            gr.outputs.Audio(type="filepath", label="Drums"),
            gr.outputs.Audio(type="filepath", label="Other"),
        ],
        title="MusicGen and Demucs Combination",
        description="A combined demo of MusicGen and Demucs",
        article="",
    ).launch(enable_queue=True)

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

    ui_full(launch_kwargs)