app_batched.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.
"""

import argparse
from concurrent.futures import ProcessPoolExecutor
import subprocess as sp
from tempfile import NamedTemporaryFile
import time
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

_old_call = sp.call


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


sp.call = _call_nostderr
pool = ProcessPoolExecutor(3)
pool.__enter__()


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():
    print("Loading model")
    return MusicGen.get_pretrained("melody")


def predict(texts, melodies):
    global MODEL
    if MODEL is None:
        MODEL = load_model()

    duration = 12
    max_text_length = 512
    texts = [text[:max_text_length] for text in texts]
    MODEL.set_generation_params(duration=duration)

    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)

    outputs = MODEL.generate_with_chroma(
        descriptions=texts,
        melody_wavs=processed_melodies,
        melody_sample_rate=target_sr,
        progress=False
    )

    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))
    res = [[out_file.result() for out_file in out_files]]
    print("batch finished", len(texts), time.time() - be)
    return res


def ui(**kwargs):
    with gr.Blocks() as demo:
        gr.Markdown(
            """
            # MusicGen

            This is the demo for [MusicGen](https://github.com/facebookresearch/audiocraft), a simple and controllable model for music generation
            presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284).
            <br/>
            <a href="https://huggingface.co/spaces/musicgen/MusicGen?duplicate=true" style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank">
            <img style="margin-bottom: 0em;display: inline;margin-top: -.25em;" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
            for longer sequences, more control and no queue.</p>
            """
        )
        with gr.Row():
            with gr.Column():
                with gr.Row():
                    text = gr.Text(label="Describe your music", lines=2, interactive=True)
                    melody = gr.Audio(source="upload", type="numpy", label="Condition on a melody (optional)", interactive=True)
                with gr.Row():
                    submit = gr.Button("Generate")
            with gr.Column():
                output = gr.Video(label="Generated Music")
        submit.click(predict, inputs=[text, melody], outputs=[output], batch=True, max_batch_size=8)
        gr.Examples(
            fn=predict,
            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 bpm: 130",
                    "./assets/bach.mp3",
                ],
                [
                    "lofi slow bpm electro chill with organic samples",
                    None,
                ],
            ],
            inputs=[text, melody],
            outputs=[output]
        )
        gr.Markdown("""
        ### More details

        The model will generate 12 seconds of audio based on the description you provided.
        You can optionaly provide a reference audio from which a broad melody will be extracted.
        The model will then try to follow both the description and melody provided.
        All samples are generated with the `melody` model.

        You can also use your own GPU or a Google Colab by following the instructions on our repo.

        See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
        for more details.
        """)

        # Show the interface
        launch_kwargs = {}
        username = kwargs.get('username')
        password = kwargs.get('password')
        server_port = kwargs.get('server_port', 0)
        inbrowser = kwargs.get('inbrowser', False)
        share = kwargs.get('share', False)
        server_name = kwargs.get('listen')

        launch_kwargs['server_name'] = server_name

        if username and password:
            launch_kwargs['auth'] = (username, password)
        if server_port > 0:
            launch_kwargs['server_port'] = server_port
        if inbrowser:
            launch_kwargs['inbrowser'] = inbrowser
        if share:
            launch_kwargs['share'] = share
        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',
        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()

    ui(
        username=args.username,
        password=args.password,
        inbrowser=args.inbrowser,
        server_port=args.server_port,
        share=args.share,
        listen=args.listen
    )