audiocraft/demos/musicgen_style_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.

# Updated to account for UI changes from https://github.com/rkfg/audiocraft/blob/long/app.py
# also released under the MIT license.

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

from einops import rearrange
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, MultiBandDiffusion


MODEL = None  # Last used model
SPACE_ID = os.environ.get('SPACE_ID', '')
INTERRUPTING = False
MBD = None
# 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.
    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='facebook/musicgen-style'):
    global MODEL
    print("Loading model", version)
    if MODEL is None or MODEL.name != version:
        # Clear PyTorch CUDA cache and delete model
        del MODEL
        torch.cuda.empty_cache()
        MODEL = None  # in case loading would crash
        MODEL = MusicGen.get_pretrained(version)


def load_diffusion():
    global MBD
    if MBD is None:
        print("loading MBD")
        MBD = MultiBandDiffusion.get_mbd_musicgen()


def _do_predictions(texts, melodies, duration, top_k, top_p, temperature, cfg_coef, cfg_coef_beta, eval_q, excerpt_length, progress=False, gradio_progress=None):
    MODEL.set_generation_params(duration=duration, top_k=top_k, top_p=top_p, temperature=temperature, cfg_coef=cfg_coef, cfg_coef_beta=cfg_coef_beta)
    MODEL.set_style_conditioner_params(eval_q=eval_q, excerpt_length=excerpt_length)
    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)

    try:
        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,
                return_tokens=USE_DIFFUSION
            )
        else:
            outputs = MODEL.generate(texts, progress=progress, return_tokens=USE_DIFFUSION)
    except RuntimeError as e:
        raise gr.Error("Error while generating " + e.args[0])
    if USE_DIFFUSION:
        if gradio_progress is not None:
            gradio_progress(1, desc='Running MultiBandDiffusion...')
        tokens = outputs[1]
        outputs_diffusion = MBD.tokens_to_wav(tokens)
        outputs = torch.cat([outputs[0], outputs_diffusion], dim=0)
    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("batch finished", len(texts), time.time() - be)
    print("Tempfiles currently stored: ", len(file_cleaner.files))
    return out_videos, out_wavs


def predict_full(model, model_path, decoder, text, melody, duration, topk, topp, temperature, cfg_coef, double_cfg, cfg_coef_beta, eval_q, excerpt_length, progress=gr.Progress()):
    global INTERRUPTING
    global USE_DIFFUSION
    INTERRUPTING = False
    progress(0, desc="Loading model...")
    model_path = model_path.strip()
    if model_path:
        if not Path(model_path).exists():
            raise gr.Error(f"Model path {model_path} doesn't exist.")
        if not Path(model_path).is_dir():
            raise gr.Error(f"Model path {model_path} must be a folder containing "
                           "state_dict.bin and compression_state_dict_.bin.")
        model = model_path
    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.")
    if eval_q < 1 or eval_q > 6:
        raise gr.Error("eval_q must be an integer between 1 and 6 included.")
    if excerpt_length > 4.5:
        raise gr.Error("excerpt_length must be <= 4.5 seconds")

    topk = int(topk)
    eval_q = int(eval_q)
    if decoder == "MultiBand_Diffusion":
        USE_DIFFUSION = True
        progress(0, desc="Loading diffusion model...")
        load_diffusion()
    else:
        USE_DIFFUSION = False
    load_model(model)

    if double_cfg != "Yes":
        cfg_coef_beta = None
    max_generated = 0

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

    videos, wavs = _do_predictions(
        [text], [melody], duration, progress=True,
        top_k=topk, top_p=topp, temperature=temperature, cfg_coef=cfg_coef,
        cfg_coef_beta=cfg_coef_beta, eval_q=eval_q, excerpt_length=excerpt_length,
        gradio_progress=progress)
    if USE_DIFFUSION:
        return videos[0], wavs[0], videos[1], wavs[1]
    return videos[0], wavs[0], None, None


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 toggle_diffusion(choice):
    if choice == "MultiBand_Diffusion":
        return [gr.update(visible=True)] * 2
    else:
        return [gr.update(visible=False)] * 2


def ui_full(launch_kwargs):
    with gr.Blocks() as interface:
        gr.Markdown(
            """

            # MusicGen-Style

            This is your private demo for [MusicGen-Style](https://github.com/facebookresearch/audiocraft),

            a simple and controllable model for music generation

            presented at: ["Audio Conditioning for Music Generation via Discrete Bottleneck Features"](https://arxiv.org/abs/2407.12563)

            """
        )
        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(sources=["upload"], type="numpy", label="File",
                                          interactive=True, elem_id="melody-input")
                with gr.Row():
                    submit = gr.Button("Submit")
                    # Adapted from https://github.com/rkfg/audiocraft/blob/long/app.py, MIT license.
                    _ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
                with gr.Row():
                    model = gr.Radio(["facebook/musicgen-style"],
                                     label="Model", value="facebook/musicgen-style", interactive=True)
                    model_path = gr.Text(label="Model Path (custom models)")
                with gr.Row():
                    decoder = gr.Radio(["Default", "MultiBand_Diffusion"],
                                       label="Decoder", value="Default", interactive=True)
                with gr.Row():
                    duration = gr.Slider(minimum=1, maximum=120, value=10, label="Duration", interactive=True)
                    eval_q = gr.Slider(minimum=1, maximum=6, value=3, step=1, label="Number of RVQ in the style conditioner", 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="CFG alpha", value=3.0, interactive=True)
                    double_cfg = gr.Radio(["Yes", "No"], 
                                          label="Use Double Classifier Free Guidance (if No, CFG beta is useless). Only use it if you have input text and a melody file.", value="Yes", interactive=True)
                    cfg_coef_beta = gr.Number(label="CFG beta (double CFG)", value=5.0, interactive=True)
                    excerpt_length = gr.Number(label="length used of the conditioning (has to be <= 4.5 seconds)", value=3.0, interactive=True)
            with gr.Column():
                output = gr.Video(label="Generated Music")
                audio_output = gr.Audio(label="Generated Music (wav)", type='filepath')
                diffusion_output = gr.Video(label="MultiBand Diffusion Decoder")
                audio_diffusion = gr.Audio(label="MultiBand Diffusion Decoder (wav)", type='filepath')
        submit.click(toggle_diffusion, decoder, [diffusion_output, audio_diffusion], queue=False,
                     show_progress=False).then(predict_full, inputs=[model, model_path, decoder, text, melody, duration, topk, topp,
                                                                     temperature, cfg_coef, double_cfg, cfg_coef_beta, eval_q, excerpt_length],
                                               outputs=[output, audio_output, diffusion_output, audio_diffusion])
        radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)

        gr.Examples(
            fn=predict_full,
            examples=[
                [
                    "80s New Wave with synthesizer",
                    "./assets/electronic.mp3",
                    "facebook/musicgen-style",
                    "Default"
                ],
            ],
            inputs=[text, melody, model, decoder],
            outputs=[output]
        )
        gr.Markdown(
            """

            ### More details



            The model can generate a short music extract based on 3 different input setups:

                1) A textual description. In that case we recommend to use simple (not double!) classifier free guidance with the CFG coef = 3.



                2) A audio excerpt that it use for style conditioning. The audio shouldn't be longer that 4.5 seconds. If so, 

                    a random subsequence will be subsample with the length being chosen by the user. We recommend this length to be between 1.5 and 4.5 seconds. 

                    We recommend simple CFG with the coef = 3.



                3) Both a textual description and an audio input. In that case the user should use double CFG with alpha=3 and beta=4. Then, if the model 

                    adheres too much to the text description, the user should lower beta. If the model adheres too much to the style, the user can augment beta. 

            The model can generate up to 30 seconds of audio in one pass.



            The model was trained with description from a stock music catalog, descriptions that will work best

            should include some level of details on the instruments present, along with some intended use case

            (e.g. adding "perfect for a commercial" can somehow help).



            We also present two way of decoding the audio tokens

            1. Use the default GAN based compression model. It can suffer from artifacts especially

                for crashes, snares etc.

            2. Use [MultiBand Diffusion](https://arxiv.org/abs/2308.02560). Should improve the audio quality,

                at an extra computational cost. When this is selected, we provide both the GAN based decoded

                audio, and the one obtained with MBD.



            See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft/blob/main/docs/MUSICGEN_STYLE.md)

            for more details.

            """
        )

        interface.queue().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_full(launch_kwargs)