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
from concurrent.futures import Future
import torch
import gradio as gr
import pydub
from scipy.io.wavfile import write

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
# We have to wrap subprocess call to clean a bit the log when using gr.make_waveform
_old_call = sp.call
files = [
    "./out/mdx_extra_q/test/vocals.wav",
    "./out/mdx_extra_q/test/bass.wav",
    "./out/mdx_extra_q/test/drums.wav",
    "./out/mdx_extra_q/test/other.wav"
]

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
# Preallocating the pool of processes.
pool = ProcessPoolExecutor(3)
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='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(file.name)  # Store the filename as a string
            file_cleaner.add(file.name)
    res = [out_file for out_file in out_files]
    for file in res:
        if isinstance(file, Future):  # Check if it's a Future object
            file = file.result()  # Extract the filename from the Future object
        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 inference(outs[0])

def inference(audio):
    print (audio)
    os.makedirs("out", exist_ok=True)
    write('test.wav', audio[0], audio[1])

    command = "python3 -m demucs.separate -n mdx_extra_q -d cpu test.wav -o out"
    process = sp.run(command, shell=True, stdout=sp.PIPE, stderr=sp.PIPE)
    print("Demucs script output:", process.stdout.decode())
  
    os.makedirs("out", exist_ok=True)
    write('test.wav', audio[0], audio[1])
    result = os.system("python3 -m demucs.separate -n mdx_extra_q -d cpu test.wav -o out")
    print(f"Demucs script result: {result}")

    # Check if files exist before returning
    files = ["./out/mdx_extra_q/test/vocals.wav",
             "./out/mdx_extra_q/test/bass.wav",
             "./out/mdx_extra_q/test/drums.wav",
             "./out/mdx_extra_q/test/other.wav"]
    for file in files:
        if not os.path.isfile(file):
            print(f"File not found: {file}")
        else:
            print(f"File exists: {file}")

    return files


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):
    interface = gr.Interface(
        fn=predict_full,
        inputs=[
            gr.Radio(["melody", "medium", "small", "large"], label="Model", default="melody"),
            gr.Text(label="Input Text"),
            gr.Audio(source="upload", type="numpy", label="File", interactive=True, elem_id="melody-input"),
            gr.Slider(minimum=1, maximum=120, default=10, label="Duration", step=1),
            gr.Number(label="Top-k", default=250),
            gr.Number(label="Top-p", default=0),
            gr.Number(label="Temperature", default=1.0),
            gr.Number(label="Classifier Free Guidance", default=3.0),
        ],
        outputs=[
            gr.Audio(label=f"Generated Music {i+1}") for i in range(len(files))
        ],
        title="MusicGen",
        description="This is your private demo for MusicGen, a simple and controllable model for music generation.",
        allow_flagging="never",
        layout="vertical",
        **launch_kwargs
    )

    interface.launch()


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
    ui_full(launch_kwargs)


def ui_batched(launch_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/facebook/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)
                    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.Audio(file, label=f"Generated Music {i+1}") for i, file in enumerate(files)]
        submit.click(predict_batched, inputs=[text, melody],
                     outputs=[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,
                ],
            ],
            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.
        """)

        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:
        ui_batched(launch_kwargs)
    else:
        ui_full(launch_kwargs)