app.py

import argparse
from concurrent.futures import ProcessPoolExecutor
import os
from pathlib import Path
import subprocess as sp
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, MultiBandDiffusion


MODEL = None  # Last used model
IS_BATCHED = "facebook/MusicGen" in os.environ.get('SPACE_ID', '')
print(IS_BATCHED)
MAX_BATCH_SIZE = 12
BATCHED_DURATION = 15
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-melody'):
    global MODEL
    print("Loading model", version)
    if MODEL is None or MODEL.name != version:
        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, 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,
            return_tokens=USE_DIFFUSION
        )
    else:
        outputs = MODEL.generate(texts, progress=progress, return_tokens=USE_DIFFUSION)
    if USE_DIFFUSION:
        outputs_diffusion = MBD.tokens_to_wav(outputs[1])
        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_batched(texts, melodies):
    max_text_length = 512
    texts = [text[:max_text_length] for text in texts]
    load_model('facebook/musicgen-melody')
    res = _do_predictions(texts, melodies, BATCHED_DURATION)
    return res


def predict_full(model, decoder, text, melody, duration, topk, topp, temperature, cfg_coef, progress=gr.Progress()):
    global INTERRUPTING
    global USE_DIFFUSION
    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)
    if decoder == "MultiBand_Diffusion":
        USE_DIFFUSION = True
        load_diffusion()
    else:
        USE_DIFFUSION = False
    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], [melody], duration, progress=True,
        top_k=topk, top_p=topp, temperature=temperature, cfg_coef=cfg_coef)
    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:
        
        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="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-melody", "facebook/musicgen-medium", "facebook/musicgen-small",
                                      "facebook/musicgen-large"],
                                     label="Model", value="facebook/musicgen-melody", interactive=True)
                # with gr.Row():
                #     decoder = gr.Radio(["Default", "MultiBand_Diffusion"],
                #                        label="Decoder", value="Default", interactive=True)
                # decoder = "Default"
                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')
                # diffusion_output = gr.Video(label="MultiBand Diffusion Decoder")
                # audio_diffusion = gr.Audio(label="MultiBand Diffusion Decoder (wav)", type='filepath')

        melody = gr.Audio(source= None, type="numpy", label="File",
                                    interactive=False, visible= False, elem_id="melody-input")
        decoder = gr.Radio(["Default", "MultiBand_Diffusion"],
                                       label="Decoder", value="Default", interactive=True, visible= False)
        # duration = gr.Slider(minimum=1, maximum=120, value=10, label="Duration", interactive=True, visible= False)
        topk = gr.Number(label="Top-k", value=250, interactive=True, visible= False)
        topp = gr.Number(label="Top-p", value=0, interactive=True, visible= False)
        temperature = gr.Number(label="Temperature", value=1.0, interactive=True, visible= False)
        cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True, visible= False)
        diffusion_output = gr.Video(label="MultiBand Diffusion Decoder" , visible=False)
        audio_diffusion = gr.Audio(label="MultiBand Diffusion Decoder (wav)", type='filepath', visible= False)
        
        print("melody", melody)
        print("decoder", decoder)
        print("topk", topk)
        print("topp", topp)
        print("cfg_coef", cfg_coef)
        print("diffusion_output" , diffusion_output)
        print("audio_diffusion" , audio_diffusion)
        
        submit.click(toggle_diffusion, decoder, [diffusion_output, audio_diffusion], queue=False,
                     show_progress=False).then(predict_full, inputs=[model, decoder, text, melody, duration, topk, topp,
                                                                     temperature, cfg_coef],
                                               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=[
        #         [
        #             "An 80s driving pop song with heavy drums and synth pads in the background",
        #             "./assets/bach.mp3",
        #             "facebook/musicgen-melody",
        #             "Default"
        #         ],
        #         [
        #             "A cheerful country song with acoustic guitars",
        #             "./assets/bolero_ravel.mp3",
        #             "facebook/musicgen-melody",
        #             "Default"
        #         ],
        #         [
        #             "90s rock song with electric guitar and heavy drums",
        #             None,
        #             "facebook/musicgen-medium",
        #             "Default"
        #         ],
        #         [
        #             "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions",
        #             "./assets/bach.mp3",
        #             "facebook/musicgen-melody",
        #             "Default"
        #         ],
        #         [
        #             "lofi slow bpm electro chill with organic samples",
        #             None,
        #             "facebook/musicgen-medium",
        #             "Default"
        #         ],
        #         [
        #             "Punk rock with loud drum and power guitar",
        #             None,
        #             "facebook/musicgen-medium",
        #             "MultiBand_Diffusion"
        #         ],
        #     ],
        #     inputs=[text, melody, model, decoder],
        #     outputs=[output]
        # )
        gr.Markdown(
            """
            """
        )

        interface.queue().launch(**launch_kwargs)

def ui_batched(launch_kwargs):
    with gr.Blocks() as demo:
        gr.Markdown(
            """
            This project generate Music from prompt.
            """
        )
        with gr.Row():
            with gr.Column():
                with gr.Row():
                    text = gr.Text(label="Describe your music", lines=2, 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="File",
                                          interactive=True, elem_id="melody-input")
                with gr.Row():
                    submit = gr.Button("Generate")
            with gr.Column():
                output = gr.Video(label="Generated Music")
                audio_output = gr.Audio(label="Generated Music (wav)", type='filepath')
        submit.click(predict_batched, inputs=[text, melody],
                     outputs=[output, audio_output], batch=True, max_batch_size=MAX_BATCH_SIZE)
        radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
        gr.Examples(
            fn=predict_batched,
            # 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,
            #     ],
            # ],
            examples=[

            ],
            inputs=[text, melody],
            outputs=[output]
        )
        gr.Markdown("""
                
        """)

        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

    # Show the interface
    if IS_BATCHED:
        global USE_DIFFUSION
        USE_DIFFUSION = False
        ui_batched(launch_kwargs)
    else:
        ui_full(launch_kwargs)