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Sleeping
| """ | |
| 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 | |
| #from pathlib import Path | |
| #from typing import List, Union | |
| import librosa | |
| MODEL = None | |
| MODELS = None | |
| IS_SHARED_SPACE = "Surn/UnlimitedMusicGen" in os.environ.get('SPACE_ID', '') | |
| INTERRUPTED = False | |
| UNLOAD_MODEL = False | |
| MOVE_TO_CPU = False | |
| MAX_PROMPT_INDEX = 0 | |
| def interrupt_callback(): | |
| return INTERRUPTED | |
| def interrupt(): | |
| global INTERRUPTING | |
| INTERRUPTING = True | |
| 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 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 get_filename(file): | |
| # extract filename from file object | |
| filename = None | |
| if file is not None: | |
| filename = file.name | |
| return filename | |
| def get_filename_from_filepath(filepath): | |
| file_name = os.path.basename(filepath) | |
| file_base, file_extension = os.path.splitext(file_name) | |
| return file_base, file_extension | |
| def get_melody(melody_filepath): | |
| audio_data= list(librosa.load(melody_filepath, sr=None)) | |
| audio_data[0], audio_data[1] = audio_data[1], audio_data[0] | |
| melody = tuple(audio_data) | |
| return melody | |
| def load_melody_filepath(melody_filepath, title): | |
| # get melody filename | |
| #$Union[str, os.PathLike] | |
| symbols = ['_', '.', '-'] | |
| if (melody_filepath is None) or (melody_filepath == ""): | |
| return title, gr.update(maximum=0, value=0) , gr.update(value="melody", interactive=True) | |
| if (title is None) or ("MusicGen" in title) or (title == ""): | |
| melody_name, melody_extension = get_filename_from_filepath(melody_filepath) | |
| # fix melody name for symbols | |
| for symbol in symbols: | |
| melody_name = melody_name.replace(symbol, ' ').title() | |
| else: | |
| melody_name = title | |
| print(f"Melody name: {melody_name}, Melody Filepath: {melody_filepath}\n") | |
| # get melody length in number of segments and modify the UI | |
| melody = get_melody(melody_filepath) | |
| sr, melody_data = melody[0], melody[1] | |
| segment_samples = sr * 30 | |
| total_melodys = max(min((len(melody_data) // segment_samples), 25), 0) | |
| print(f"Melody length: {len(melody_data)}, Melody segments: {total_melodys}\n") | |
| MAX_PROMPT_INDEX = total_melodys | |
| return gr.Textbox.update(value=melody_name), gr.update(maximum=MAX_PROMPT_INDEX, value=0), gr.update(value="melody", interactive=False) | |
| def predict(model, text, melody_filepath, duration, dimension, topk, topp, temperature, cfg_coef, background, title, settings_font, settings_font_color, seed, overlap=1, prompt_index = 0, include_title = True, include_settings = True, harmony_only = False): | |
| global MODEL, INTERRUPTED, INTERRUPTING, MOVE_TO_CPU | |
| output_segments = None | |
| melody_name = "Not Used" | |
| melody = None | |
| if melody_filepath: | |
| melody_name, melody_extension = get_filename_from_filepath(melody_filepath) | |
| melody = get_melody(melody_filepath) | |
| 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, prompt_index, harmony_only=False) | |
| 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="linear") | |
| 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: | |
| 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 Condition:{melody_name}\n Sample Segment: {prompt_index}" | |
| if include_settings or include_title: | |
| background = add_settings_to_image(title if include_title else "", video_description if include_settings else "", 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, file.name, seed | |
| def ui(**kwargs): | |
| css=""" | |
| #col-container {max-width: 910px; margin-left: auto; margin-right: auto;} | |
| a {text-decoration-line: underline; font-weight: 600;} | |
| #btn-generate {background-image:linear-gradient(to right bottom, rgb(157, 255, 157), rgb(229, 255, 235));} | |
| #btn-generate:hover {background-image:linear-gradient(to right bottom, rgb(229, 255, 229), rgb(255, 255, 255));} | |
| #btn-generate:active {background-image:linear-gradient(to right bottom, rgb(229, 255, 235), rgb(157, 255, 157));} | |
| """ | |
| 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 transitions between 30 second segments, improve Interrupt. | |
| """ | |
| ) | |
| 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="Describe your music", interactive=True, value="4/4 100bpm 320kbps 48khz, Industrial/Electronic Soundtrack, Dark, Intense, Sci-Fi") | |
| with gr.Column(): | |
| duration = gr.Slider(minimum=1, maximum=720, value=10, label="Duration", interactive=True) | |
| model = gr.Radio(["melody", "medium", "small", "large"], label="AI Model", value="melody", interactive=True) | |
| with gr.Row(): | |
| submit = gr.Button("Generate", elem_id="btn-generate") | |
| # Adapted from https://github.com/rkfg/audiocraft/blob/long/app.py, MIT license. | |
| _ = gr.Button("Interrupt", elem_id="btn-interrupt").click(fn=interrupt, queue=False) | |
| with gr.Row(): | |
| with gr.Column(): | |
| radio = gr.Radio(["file", "mic"], value="file", label="Condition on a melody (optional) File or Mic") | |
| melody_filepath = gr.Audio(source="upload", type="filepath", label="Melody Condition (optional)", interactive=True, elem_id="melody-input") | |
| with gr.Column(): | |
| harmony_only = gr.Radio(label="Use Harmony Only",choices=["No", "Yes"], value="No", interactive=True, info="Remove Drums?") | |
| prompt_index = gr.Slider(label="Melody Condition Sample Segment", minimum=-1, maximum=MAX_PROMPT_INDEX, step=1, value=0, interactive=True, info="Which 30 second segment to condition with, - 1 condition each segment independantly") | |
| with gr.Accordion("Video", open=False): | |
| with gr.Row(): | |
| background= gr.Image(value="./assets/background.png", source="upload", label="Background", shape=(768,512), type="filepath", interactive=True) | |
| with gr.Column(): | |
| include_title = gr.Checkbox(label="Add Title", value=True, 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.Accordion("Expert", open=False): | |
| with gr.Row(): | |
| overlap = gr.Slider(minimum=1, maximum=15, value=2, step=1, label="Verse 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=280, precision=0, interactive=True) | |
| topp = gr.Number(label="Top-p", value=1450, 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=8.5, 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") | |
| wave_file = gr.File(label=".wav file", elem_id="output_wavefile", interactive=True) | |
| seed_used = gr.Number(label='Seed used', value=-1, interactive=False) | |
| radio.change(toggle_audio_src, radio, [melody_filepath], queue=False, show_progress=False) | |
| melody_filepath.change(load_melody_filepath, inputs=[melody_filepath, title], outputs=[title, prompt_index , model], api_name="melody_filepath_change", queue=False) | |
| reuse_seed.click(fn=lambda x: x, inputs=[seed_used], outputs=[seed], queue=False, api_name="reuse_seed") | |
| submit.click(predict, inputs=[model, text,melody_filepath, duration, dimension, topk, topp, temperature, cfg_coef, background, title, settings_font, settings_font_color, seed, overlap, prompt_index, include_title, include_settings, harmony_only], outputs=[output, wave_file, seed_used], api_name="submit") | |
| 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", | |
| "80s Pop Synth" | |
| ], | |
| [ | |
| "4/4 120bpm 320kbps 48khz, A cheerful country song with acoustic guitars", | |
| "./assets/bolero_ravel.mp3", | |
| "melody", | |
| "Country Guitar" | |
| ], | |
| [ | |
| "4/4 120bpm 320kbps 48khz, 90s rock song with electric guitar and heavy drums", | |
| None, | |
| "medium", | |
| "90s Rock Guitar" | |
| ], | |
| [ | |
| "4/4 120bpm 320kbps 48khz, a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions", | |
| "./assets/bach.mp3", | |
| "melody", | |
| "EDM my Bach" | |
| ], | |
| [ | |
| "4/4 320kbps 48khz, lofi slow bpm electro chill with organic samples", | |
| None, | |
| "medium", | |
| "LoFi Chill" | |
| ], | |
| ], | |
| inputs=[text, melody_filepath, model, title], | |
| outputs=[output] | |
| ) | |
| # Show the interface | |
| launch_kwargs = {} | |
| share = kwargs.get('share', False) | |
| server_port = kwargs.get('server_port', 0) | |
| server_name = kwargs.get('listen') | |
| launch_kwargs['server_name'] = server_name | |
| if server_port > 0: | |
| launch_kwargs['server_port'] = server_port | |
| if share: | |
| launch_kwargs['share'] = share | |
| launch_kwargs['favicon_path']= "./assets/favicon.ico" | |
| demo.queue(max_size=12).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' | |
| ) | |
| 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() | |
| 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 | |
| launch_kwargs['favicon_path']= "./assets/favicon.ico" | |
| 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 | |
| ) | |