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.
"""

from tempfile import NamedTemporaryFile
import argparse
import torch
import gradio as gr
import os
import time
import warnings
from audiocraft.models import MusicGen
from audiocraft.data.audio import audio_write
from audiocraft.data.audio_utils import apply_fade, apply_tafade
from audiocraft.utils.extend import generate_music_segments, add_settings_to_image, INTERRUPTING
import numpy as np
import random

MODEL = None
MODELS = None
IS_SHARED_SPACE = "Surn/UnlimitedMusicGen" in os.environ.get('SPACE_ID', '')
INTERRUPTED = False
UNLOAD_MODEL = False
MOVE_TO_CPU = False

def interrupt_callback():
    return INTERRUPTED

def interrupt():
    global INTERRUPTING
    INTERRUPTING = True


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):
    global MODEL, MODELS, UNLOAD_MODEL
    print("Loading model", version)
    if MODELS is None:
        return MusicGen.get_pretrained(version)
    else:
        t1 = time.monotonic()
        if MODEL is not None:
            MODEL.to('cpu') # move to cache
            print("Previous model moved to CPU in %.2fs" % (time.monotonic() - t1))
            t1 = time.monotonic()
        if MODELS.get(version) is None:
            print("Loading model %s from disk" % version)
            result = MusicGen.get_pretrained(version)
            MODELS[version] = result
            print("Model loaded in %.2fs" % (time.monotonic() - t1))
            return result
        result = MODELS[version].to('cuda')
        print("Cached model loaded in %.2fs" % (time.monotonic() - t1))
        return result


def predict(model, text, melody, duration, dimension, topk, topp, temperature, cfg_coef, background, title, include_settings, settings_font, settings_font_color, seed, overlap=1):
    global MODEL, INTERRUPTED, INTERRUPTING
    output_segments = None

    INTERRUPTED = False
    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.")

    if MODEL is None or MODEL.name != model:
        MODEL = load_model(model)
    else:
        if MOVE_TO_CPU:
            MODEL.to('cuda')
    
    # prevent hacking
    duration = min(duration, 720)
    overlap =  min(overlap, 15)
    # 

    output = None
    segment_duration = duration
    initial_duration = duration
    output_segments = []
    while duration > 0:
        if not output_segments: # first pass of long or short song
            if segment_duration > MODEL.lm.cfg.dataset.segment_duration:
                segment_duration = MODEL.lm.cfg.dataset.segment_duration
            else:
                segment_duration = duration
        else: # next pass of long song
            if duration + overlap < MODEL.lm.cfg.dataset.segment_duration:
                segment_duration = duration + overlap
            else:
                segment_duration = MODEL.lm.cfg.dataset.segment_duration
        # implement seed
        if seed < 0:
            seed = random.randint(0, 0xffff_ffff_ffff)
        torch.manual_seed(seed)


        print(f'Segment duration: {segment_duration}, duration: {duration}, overlap: {overlap}')
        MODEL.set_generation_params(
            use_sampling=True,
            top_k=topk,
            top_p=topp,
            temperature=temperature,
            cfg_coef=cfg_coef,
            duration=segment_duration,
            two_step_cfg=False,
            rep_penalty=0.5
        )

        if melody:
            # todo return excess duration, load next model and continue in loop structure building up output_segments
            if duration > MODEL.lm.cfg.dataset.segment_duration:
                output_segments, duration = generate_music_segments(text, melody, seed, MODEL, duration, overlap, MODEL.lm.cfg.dataset.segment_duration)
            else:
                # pure original code
                sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t().unsqueeze(0)
                print(melody.shape)
                if melody.dim() == 2:
                    melody = melody[None]
                melody = melody[..., :int(sr * MODEL.lm.cfg.dataset.segment_duration)]
                output = MODEL.generate_with_chroma(
                    descriptions=[text],
                    melody_wavs=melody,
                    melody_sample_rate=sr,
                    progress=True
                )
            # All output_segments are populated, so we can break the loop or set duration to 0
            break
        else:
            #output = MODEL.generate(descriptions=[text], progress=False)
            if not output_segments:
                next_segment = MODEL.generate(descriptions=[text], progress=True)
                duration -= segment_duration
            else:
                last_chunk = output_segments[-1][:, :, -overlap*MODEL.sample_rate:]
                next_segment = MODEL.generate_continuation(last_chunk, MODEL.sample_rate, descriptions=[text], progress=False)
                duration -= segment_duration - overlap
            output_segments.append(next_segment)

        if INTERRUPTING:
            INTERRUPTED = True
            INTERRUPTING = False
            print("Function execution interrupted!")
            raise gr.Error("Interrupted.")

    if output_segments:
        try:
            # Combine the output segments into one long audio file or stack tracks
            #output_segments = [segment.detach().cpu().float()[0] for segment in output_segments]
            #output = torch.cat(output_segments, dim=dimension)
            
            output = output_segments[0]
            for i in range(1, len(output_segments)):
                overlap_samples = overlap * MODEL.sample_rate
                #stack tracks and fade out/in
                overlapping_output_fadeout = output[:, :, -overlap_samples:]
                overlapping_output_fadeout = apply_fade(overlapping_output_fadeout,sample_rate=MODEL.sample_rate,duration=overlap,out=True,start=True, curve_end=0.0, current_device=MODEL.device)
                #overlapping_output_fadeout = apply_tafade(overlapping_output_fadeout,sample_rate=MODEL.sample_rate,duration=overlap,out=True,start=True,shape="exponential")

                overlapping_output_fadein = output_segments[i][:, :, :overlap_samples]
                overlapping_output_fadein = apply_fade(overlapping_output_fadein,sample_rate=MODEL.sample_rate,duration=overlap,out=False,start=False, curve_start=0.0, current_device=MODEL.device)
                #overlapping_output_fadein = apply_tafade(overlapping_output_fadein,sample_rate=MODEL.sample_rate,duration=overlap,out=False,start=False, shape="linear")

                overlapping_output = torch.cat([overlapping_output_fadeout[:, :, :-(overlap_samples // 2)], overlapping_output_fadein],dim=2)
                print(f" overlap size Fade:{overlapping_output.size()}\n output: {output.size()}\n segment: {output_segments[i].size()}")
                ##overlapping_output = torch.cat([output[:, :, -overlap_samples:], output_segments[i][:, :, :overlap_samples]], dim=1) #stack tracks
                ##print(f" overlap size stack:{overlapping_output.size()}\n output: {output.size()}\n segment: {output_segments[i].size()}")
                #overlapping_output = torch.cat([output[:, :, -overlap_samples:], output_segments[i][:, :, :overlap_samples]], dim=2) #stack tracks
                #print(f" overlap size cat:{overlapping_output.size()}\n output: {output.size()}\n segment: {output_segments[i].size()}")               
                output = torch.cat([output[:, :, :-overlap_samples], overlapping_output, output_segments[i][:, :, overlap_samples:]], dim=dimension)
            output = output.detach().cpu().float()[0]
        except Exception as e:
            print(f"Error combining segments: {e}. Using the first segment only.")
            output = output_segments[0].detach().cpu().float()[0]
    else:
        output = output.detach().cpu().float()[0]

    with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
        if include_settings:
            video_description = f"{text}\n Duration: {str(initial_duration)} Dimension: {dimension}\n Top-k:{topk} Top-p:{topp}\n Randomness:{temperature}\n cfg:{cfg_coef} overlap: {overlap}\n Seed: {seed}\n Model: {model}\n Melody File:#todo"
            background = add_settings_to_image(title, video_description, background_path=background, font=settings_font, font_color=settings_font_color)
        audio_write(
            file.name, output, MODEL.sample_rate, strategy="loudness",
            loudness_headroom_db=18, loudness_compressor=True, add_suffix=False, channels=2)
        waveform_video = make_waveform(file.name,bg_image=background, bar_count=45)
    if MOVE_TO_CPU:
        MODEL.to('cpu')
    if UNLOAD_MODEL:
        MODEL = None
    torch.cuda.empty_cache()
    torch.cuda.ipc_collect()
    return waveform_video, seed

def ui(**kwargs):
    css="""
    #col-container {max-width: 910px; margin-left: auto; margin-right: auto;}
    a {text-decoration-line: underline; font-weight: 600;}
    """
    with gr.Blocks(title="UnlimitedMusicGen", css=css) as demo:
        gr.Markdown(
            """            
            # UnlimitedMusicGen
            This is your private demo for [UnlimitedMusicGen](https://github.com/Oncorporation/audiocraft), a simple and controllable model for music generation
            presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284)
            
            Disclaimer: This won't run on CPU only. Clone this App and run on GPU instance!
                        
            Todo: Working on improved Melody Conditioned Music Generation transitions and consistency.
            """
        )
        if IS_SHARED_SPACE and not torch.cuda.is_available():
            gr.Markdown("""
                ⚠ This Space doesn't work in this shared UI ⚠

                <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>
                to use it privately, or use the <a href="https://huggingface.co/spaces/facebook/MusicGen">public demo</a>
                """)
        with gr.Row():
            with gr.Column():
                with gr.Row():
                    text = gr.Text(label="Input Text", interactive=True, value="4/4 100bpm 320kbps 48khz, Industrial/Electronic Soundtrack, Dark, Intense, Sci-Fi")
                    melody = gr.Audio(source="upload", type="numpy", label="Melody Condition (optional)", interactive=True)
                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():
                    background= gr.Image(value="./assets/background.png", source="upload", label="Background", shape=(768,512), type="filepath", interactive=True)
                    include_settings = gr.Checkbox(label="Add Settings to background", value=True, interactive=True)
                with gr.Row():
                    title = gr.Textbox(label="Title", value="UnlimitedMusicGen", interactive=True)
                    settings_font = gr.Text(label="Settings Font", value="./assets/arial.ttf", interactive=True)
                    settings_font_color = gr.ColorPicker(label="Settings Font Color", value="#c87f05", interactive=True)
                with gr.Row():
                    model = gr.Radio(["melody", "medium", "small", "large"], label="Model", value="melody", interactive=True)
                with gr.Row():
                    duration = gr.Slider(minimum=1, maximum=720, value=10, label="Duration", interactive=True)
                    overlap = gr.Slider(minimum=1, maximum=15, value=5, step=1, label="Overlap", interactive=True)
                    dimension = gr.Slider(minimum=-2, maximum=2, value=2, step=1, label="Dimension", info="determines which direction to add new segements of audio. (1 = stack tracks, 2 = lengthen, -2..0 = ?)", interactive=True)
                with gr.Row():
                    topk = gr.Number(label="Top-k", value=250, precision=0, interactive=True)
                    topp = gr.Number(label="Top-p", value=0, precision=0, interactive=True)
                    temperature = gr.Number(label="Randomness Temperature", value=0.75, precision=None, interactive=True)
                    cfg_coef = gr.Number(label="Classifier Free Guidance", value=5.5, precision=None, interactive=True)
                with gr.Row():
                    seed = gr.Number(label="Seed", value=-1, precision=0, interactive=True)
                    gr.Button('\U0001f3b2\ufe0f').style(full_width=False).click(fn=lambda: -1, outputs=[seed], queue=False)
                    reuse_seed = gr.Button('\u267b\ufe0f').style(full_width=False)
            with gr.Column() as c:
                output = gr.Video(label="Generated Music")
                seed_used = gr.Number(label='Seed used', value=-1, interactive=False)

        reuse_seed.click(fn=lambda x: x, inputs=[seed_used], outputs=[seed], queue=False)
        submit.click(predict, inputs=[model, text, melody, duration, dimension, topk, topp, temperature, cfg_coef, background, title, include_settings, settings_font, settings_font_color, seed, overlap], outputs=[output, seed_used])
        gr.Examples(
            fn=predict,
            examples=[
                [
                    "4/4 120bpm 320kbps 48khz, An 80s driving pop song with heavy drums and synth pads in the background",
                    "./assets/bach.mp3",
                    "melody"
                ],
                [
                    "4/4 120bpm 320kbps 48khz, A cheerful country song with acoustic guitars",
                    "./assets/bolero_ravel.mp3",
                    "melody"
                ],
                [
                    "4/4 120bpm 320kbps 48khz, 90s rock song with electric guitar and heavy drums",
                    None,
                    "medium"
                ],
                [
                    "4/4 120bpm 320kbps 48khz, a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions",
                    "./assets/bach.mp3",
                    "melody"
                ],
                [
                    "4/4 320kbps 48khz, lofi slow bpm electro chill with organic samples",
                    None,
                    "medium",
                ],
            ],
            inputs=[text, melody, model],
            outputs=[output]
        )

        # Show the interface
        launch_kwargs = {}
        share = kwargs.get('share', False)
        if share:
            launch_kwargs['share'] = share



        demo.queue(max_size=15).launch(**launch_kwargs )

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    
    parser.add_argument(
        '--share', action='store_true', help='Share the gradio UI'
    )
    parser.add_argument(
        '--unload_model', action='store_true', help='Unload the model after every generation to save GPU memory'
    )

    parser.add_argument(
        '--unload_to_cpu', action='store_true', help='Move the model to main RAM after every generation to save GPU memory but reload faster than after full unload (see above)'
    )

    parser.add_argument(
        '--cache', action='store_true', help='Cache models in RAM to quickly switch between them'
    )

    args = parser.parse_args()
    UNLOAD_MODEL = args.unload_model
    MOVE_TO_CPU = args.unload_to_cpu
    if args.cache:
        MODELS = {}

    ui(
        unload_to_cpu = MOVE_TO_CPU,
        share=args.share
        
    )