demos/musicgen_app.py

import spaces
import argparse
import logging
import os
from pathlib import Path
import subprocess as sp
import sys
import time
import typing as tp
from tempfile import NamedTemporaryFile

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.encodec import InterleaveStereoCompressionModel
from audiocraft.models import MusicGen, MultiBandDiffusion
import multiprocessing as mp

# --- Utility Functions and Classes ---

class FileCleaner:  # Unchanged
    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):  # Unchanged
    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

# --- Worker Process ---
@spaces.GPU(required=True)
def model_worker(model_name: str, task_queue: mp.Queue, result_queue: mp.Queue):
    """
    Persistent worker process that loads the model and handles prediction tasks.
    """
    try:
        device = 'cuda' if torch.cuda.is_available() else 'cpu'
        model = MusicGen.get_pretrained(model_name, device=device)
        mbd = MultiBandDiffusion.get_mbd_musicgen(device=device)  # Load MBD here too

        while True:
            task = task_queue.get()
            if task is None:  # Sentinel value to exit
                break

            task_id, text, melody, duration, use_diffusion, gen_params = task

            try:
                model.set_generation_params(duration=duration, **gen_params)
                target_sr = model.sample_rate
                target_ac = 1
                processed_melody = None
                if melody:
                    sr, melody_data = melody
                    melody_tensor = torch.from_numpy(melody_data).to(device).float().t()
                    if melody_tensor.ndim == 1:
                        melody_tensor = melody_tensor.unsqueeze(0)
                    melody_tensor = melody_tensor[..., :int(sr * duration)]
                    processed_melody = convert_audio(melody_tensor, sr, target_sr, target_ac)

                if processed_melody is not None:
                    output, tokens = model.generate_with_chroma(
                        descriptions=[text],
                        melody_wavs=[processed_melody],
                        melody_sample_rate=target_sr,
                        progress=True,
                        return_tokens=True
                    )
                else:
                    output, tokens = model.generate([text], progress=True, return_tokens=True)

                output = output.detach().cpu()

                if use_diffusion:
                    if isinstance(model.compression_model, InterleaveStereoCompressionModel):
                        left, right = model.compression_model.get_left_right_codes(tokens)
                        tokens = torch.cat([left, right])
                    outputs_diffusion = mbd.tokens_to_wav(tokens)
                    if isinstance(model.compression_model, InterleaveStereoCompressionModel):
                        assert outputs_diffusion.shape[1] == 1  # output is mono
                        outputs_diffusion = rearrange(outputs_diffusion, '(s b) c t -> b (s c) t', s=2)
                    outputs_diffusion = outputs_diffusion.detach().cpu()
                    result_queue.put((task_id, (output, outputs_diffusion))) # Send BOTH results.
                else:
                    result_queue.put((task_id, (output, None)))  # Send back the result

            except Exception as e:
                result_queue.put((task_id, e))  # Send back the exception

    except Exception as e:
        result_queue.put((-1,e)) #Fatal error on loading.

# --- Gradio Interface Functions ---

class Predictor:
    def __init__(self, model_name: str):
        self.task_queue = mp.Queue()
        self.result_queue = mp.Queue()
        self.process = mp.Process(target=model_worker, args=(model_name, self.task_queue, self.result_queue))
        self.process.start()
        self.current_task_id = 0
        self._check_initialization()


    def _check_initialization(self):
        """Check if the worker process initialized successfully."""
        # Give it some time to either load or report failure.
        time.sleep(2)
        try:
            task_id, result = self.result_queue.get(timeout=3) # Get result from model_worker

            if isinstance(result, Exception):
                if task_id == -1:
                    raise RuntimeError("Model loading failed in worker process.") from result
        except:
            pass # Expected if model loads fast enough

    def predict(self, text, melody, duration, use_diffusion, **gen_params):
        """
        Submits a prediction task to the worker process.
        """
        self.current_task_id += 1
        task = (self.current_task_id, text, melody, duration, use_diffusion, gen_params)
        self.task_queue.put(task)
        return self.current_task_id

    def get_result(self, task_id):
        """
        Retrieves the result of a prediction task.  Blocks until the result is available.
        """
        while True: # Loop to get the correct task
            result_task_id, result = self.result_queue.get()
            if result_task_id == task_id:
                if isinstance(result, Exception):
                    raise result  # Re-raise the exception in the main process
                return result # (wav, diffusion_wav) or (wav, None)

    def shutdown(self):
        """
        Shuts down the worker process.
        """
        self.task_queue.put(None)  # Send sentinel value to stop the worker
        self.process.join()  # Wait for the process to terminate


# Global predictor instance
_predictor = None

def get_predictor(model_name:str = 'facebook/musicgen-melody'):
    global _predictor
    if _predictor is None:
        _predictor = Predictor(model_name)
    return _predictor

def predict_full(model, model_path, use_mbd, text, melody, duration, topk, topp, temperature, cfg_coef):

    predictor = get_predictor(model)
    task_id = predictor.predict(
        text=text,
        melody=melody,
        duration=duration,
        use_diffusion=use_mbd,
        top_k=topk,
        top_p=topp,
        temperature=temperature,
        cfg_coef=cfg_coef,
    )

    wav, diffusion_wav = predictor.get_result(task_id)

    # Save and return audio files
    wav_paths = []
    video_paths = []

    # Save standard output
    with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
        audio_write(
            file.name, wav[0], 32000, strategy="loudness", #hardcoded sample rate
            loudness_headroom_db=16, loudness_compressor=True, add_suffix=False
        )
        wav_paths.append(file.name)
        video_paths.append(make_waveform(file.name)) # Make and clean up video
        file_cleaner.add(file.name)

    # Save MBD output if used
    if diffusion_wav is not None:
        with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
            audio_write(
                file.name, diffusion_wav[0], 32000, strategy="loudness", #hardcoded sample rate
                loudness_headroom_db=16, loudness_compressor=True, add_suffix=False
            )
            wav_paths.append(file.name)
            video_paths.append(make_waveform(file.name)) # Make and clean up video
            file_cleaner.add(file.name)

    if use_mbd:
         return video_paths[0], wav_paths[0], video_paths[1], wav_paths[1]
    return video_paths[0], wav_paths[0], None, None


def toggle_audio_src(choice):
    if choice == "mic":
        return gr.update(sources="microphone", value=None, label="Microphone")
    else:
        return gr.update(sources="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
# --- Gradio UI ---

def ui_full(launch_kwargs):
    with gr.Blocks() as interface:
        gr.Markdown(
            """
            # MusicGen
            This is your private 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)
            """
        )
        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")
                    # _ = gr.Button("Interrupt").click(fn=interrupt, queue=False)  # Interrupt is now handled implicitly
                with gr.Row():
                    model = gr.Radio(["facebook/musicgen-melody", "facebook/musicgen-medium", "facebook/musicgen-small",
                                      "facebook/musicgen-large", "facebook/musicgen-melody-large",
                                      "facebook/musicgen-stereo-small", "facebook/musicgen-stereo-medium",
                                      "facebook/musicgen-stereo-melody", "facebook/musicgen-stereo-large",
                                      "facebook/musicgen-stereo-melody-large"],
                                     label="Model", value="facebook/musicgen-melody", interactive=True)
                    model_path = gr.Text(label="Model Path (custom models)", interactive=False, visible=False)  # Keep, but hide
                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)
                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", visible=False)
                audio_diffusion = gr.Audio(label="MultiBand Diffusion Decoder (wav)", type='filepath', visible=False)

        submit.click(
            toggle_diffusion, decoder, [diffusion_output, audio_diffusion], queue=False
        ).then(
            predict_full,
            inputs=[model, model_path, 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(
            """
            ### More details

            The model will generate a short music extract based on the description you provided.
            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).

            Using one of the `melody` model (e.g. `musicgen-melody-*`), you can optionally 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.
            For best results, the melody should be 30 seconds long (I know, the samples we provide are not...)

            It is now possible to extend the generation by feeding back the end of the previous chunk of audio.
            This can take a long time, and the model might lose consistency. The model might also
            decide at arbitrary positions that the song ends.

            **WARNING:** Choosing long durations will take a long time to generate (2min might take ~10min).
            An overlap of 12 seconds is kept with the previously generated chunk, and 18 "new" seconds
            are generated each time.

            We present 10 model variations:
            1. facebook/musicgen-melody -- a music generation model capable of generating music condition
                on text and melody inputs.  **Note**, you can also use text only.
            2. facebook/musicgen-small -- a 300M transformer decoder conditioned on text only.
            3. facebook/musicgen-medium -- a 1.5B transformer decoder conditioned on text only.
            4. facebook/musicgen-large -- a 3.3B transformer decoder conditioned on text only.
            5. facebook/musicgen-melody-large -- a 3.3B transformer decoder conditioned on text and melody.
            6. facebook/musicgen-stereo-small -- a 300M transformer decoder conditioned on text only, fine tuned for stereo output.
            7. facebook/musicgen-stereo-medium -- a 1.5B transformer decoder conditioned on text only, fine tuned for stereo output.
            8. facebook/musicgen-stereo-melody -- a 1.5B transformer decoder conditioned on text and melody, fine tuned for stereo output.
            9. facebook/musicgen-stereo-large -- a 3.3B transformer decoder conditioned on text only, fine tuned for stereo output.
           10. facebook/musicgen-stereo-melody-large -- a 3.3B transformer decoder conditioned on text and melody, fine tuned for stereo output.

            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.md)
            for more details.
            """
        )

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

# --- Main Entry Point ---
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)
    # Added predictor shutdown
    try:
        ui_full(launch_kwargs)
    finally:
        if _predictor is not None:
            _predictor.shutdown()