""" 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.utils.extend import generate_music_segments, add_settings_to_image import numpy as np import random MODEL = None MODELS = None IS_SHARED_SPACE = "musicgen/MusicGen" in os.environ.get('SPACE_ID', '') INTERRUPTED = False UNLOAD_MODEL = False MOVE_TO_CPU = False 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 output_segments = None topk = int(topk) if MODEL is None or MODEL.name != model: MODEL = load_model(model) else: if MOVE_TO_CPU: MODEL.to('cuda') 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, ) 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, MODEL, seed, 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=True) duration -= segment_duration - overlap output_segments.append(next_segment) 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 output = torch.cat([output[:, :, :-overlap_samples], 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}" 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=16, loudness_compressor=True, add_suffix=False) waveform_video = make_waveform(file.name,bg_image=background, bar_count=40) 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) """ ) if IS_SHARED_SPACE: gr.Markdown(""" ⚠ This Space doesn't work in this shared UI ⚠ Duplicate Space to use it privately, or use the public demo """) 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="arial.ttf", interactive=True) settings_font_color = gr.ColorPicker(label="Settings Font Color", value="#ffffff", 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=1000, value=10, label="Duration", interactive=True) overlap = gr.Slider(minimum=1, maximum=29, 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, interactive=True) topp = gr.Number(label="Top-p", value=0, interactive=True) temperature = gr.Number(label="Randomness Temperature", value=1.0, precision=2, interactive=True) cfg_coef = gr.Number(label="Classifier Free Guidance", value=5.0, precision=2, 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=[ [ "An 80s driving pop song with heavy drums and synth pads in the background", "./assets/bach.mp3", "melody" ], [ "A cheerful country song with acoustic guitars", "./assets/bolero_ravel.mp3", "melody" ], [ "90s rock song with electric guitar and heavy drums", None, "medium" ], [ "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions", "./assets/bach.mp3", "melody" ], [ "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 )