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| """ | |
| 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. | |
| """ | |
| import random | |
| from tempfile import NamedTemporaryFile | |
| import argparse | |
| import time | |
| import torch | |
| import gradio as gr | |
| import os | |
| import numpy as np | |
| from audiocraft.models import MusicGen | |
| from audiocraft.data.audio import audio_write | |
| from audiocraft.data.audio_utils import convert_audio | |
| import subprocess, random, string | |
| MODEL = None | |
| IS_SHARED_SPACE = "musicgen/MusicGen" in os.environ.get('SPACE_ID', '') | |
| INTERRUPTED = False | |
| UNLOAD_MODEL = False | |
| def interrupt(): | |
| global INTERRUPTED | |
| INTERRUPTED = True | |
| print('Interrupted!') | |
| def generate_random_string(length): | |
| characters = string.ascii_letters + string.digits | |
| return ''.join(random.choice(characters) for _ in range(length)) | |
| def resize_video(input_path, output_path, target_width, target_height): | |
| ffmpeg_cmd = [ | |
| 'ffmpeg', | |
| '-y', | |
| '-i', input_path, | |
| '-vf', f'scale={target_width}:{target_height}', | |
| '-c:a', 'copy', | |
| output_path | |
| ] | |
| subprocess.run(ffmpeg_cmd) | |
| def load_model(version): | |
| print("Loading model", version) | |
| return MusicGen.get_pretrained(version) | |
| def predict(model, text, melody, sample, duration, topk, topp, temperature, cfg_coef, seed, overlap=5, recondition=True, background="./assets/background.png", progress=gr.Progress()): | |
| global MODEL | |
| global INTERRUPTED | |
| INTERRUPTED = False | |
| topk = int(topk) | |
| if MODEL is None or MODEL.name != model: | |
| MODEL = load_model(model) | |
| if duration > MODEL.lm.cfg.dataset.segment_duration and melody is not None: | |
| raise gr.Error("Generating music longer than 30 seconds with melody conditioning is not yet supported!") | |
| output = None | |
| first_chunk = None | |
| total_samples = duration * 50 + 3 | |
| segment_duration = duration | |
| if seed < 0: | |
| seed = random.randint(0, 0xffff_ffff_ffff) | |
| torch.manual_seed(seed) | |
| predict.last_progress_update = time.monotonic() | |
| while duration > 0: | |
| if INTERRUPTED: | |
| break | |
| if output is None: # 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 | |
| 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, | |
| ) | |
| def updateProgress(step: int, total: int): | |
| now = time.monotonic() | |
| if now - predict.last_progress_update > 1: | |
| progress((total_samples - duration * 50 - 3 + step, total_samples)) | |
| predict.last_progress_update = now | |
| if sample: | |
| def normalize_audio(audio_data): | |
| audio_data = audio_data.astype(np.float32) | |
| max_value = np.max(np.abs(audio_data)) | |
| audio_data = audio_data / max_value | |
| return audio_data | |
| globalSR, sampleM = sample[0], sample[1] | |
| sampleM = normalize_audio(sampleM) | |
| sampleM = torch.from_numpy(sampleM).t() | |
| if sampleM.dim() > 1: | |
| sampleM = convert_audio(sampleM, globalSR, 32000, 1) | |
| sampleM = sampleM.to(MODEL.device).float().unsqueeze(0) | |
| if sampleM.dim() == 2: | |
| sampleM = sampleM[None] | |
| sample_length = sampleM.shape[sampleM.dim() - 1] / 32000 | |
| if output is None: | |
| next_segment = sampleM | |
| duration -= sample_length | |
| else: | |
| if first_chunk is None and MODEL.name == "melody" and recondition: | |
| first_chunk = output[:, :, | |
| :MODEL.lm.cfg.dataset.segment_duration*MODEL.sample_rate] | |
| last_chunk = output[:, :, -overlap*32000:] | |
| next_segment = MODEL.generate_continuation(last_chunk, | |
| 32000, descriptions=[text], progress=updateProgress, | |
| melody_wavs=(first_chunk), resample=False) | |
| duration -= segment_duration - overlap | |
| elif melody: | |
| 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)] | |
| next_segment = MODEL.generate_with_chroma( | |
| descriptions=[text], | |
| melody_wavs=melody, | |
| melody_sample_rate=sr, | |
| progress=updateProgress | |
| ) | |
| duration -= segment_duration | |
| else: | |
| if output is None: | |
| next_segment = MODEL.generate(descriptions=[text], | |
| progress=updateProgress) | |
| duration -= segment_duration | |
| else: | |
| if first_chunk is None and MODEL.name == "melody" and recondition: | |
| first_chunk = output[:, :, | |
| :MODEL.lm.cfg.dataset.segment_duration*MODEL.sample_rate] | |
| last_chunk = output[:, :, -overlap*MODEL.sample_rate:] | |
| next_segment = MODEL.generate_continuation(last_chunk, | |
| MODEL.sample_rate, descriptions=[text], | |
| progress=updateProgress, melody_wavs=(first_chunk), resample=False) | |
| duration -= segment_duration - overlap | |
| if output is None: | |
| output = next_segment | |
| else: | |
| output = torch.cat([output[:, :, :-overlap*MODEL.sample_rate], next_segment], 2) | |
| output = output.detach().cpu().float()[0] | |
| 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) | |
| waveform_video = gr.make_waveform(file.name, bg_image=background, bg_color="#21b0fe" , bars_color=('#fe218b', '#fed700'), fg_alpha=1.0, bar_count=75) | |
| if background is None or len(background) == 0: | |
| random_string = generate_random_string(12) | |
| random_string = f"{random_string}.mp4" | |
| resize_video(waveform_video, random_string, 900, 300) | |
| waveform_video = random_string | |
| global UNLOAD_MODEL | |
| if UNLOAD_MODEL: | |
| MODEL = None | |
| torch.cuda.empty_cache() | |
| return waveform_video, seed | |
| def ui(**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://arxiv.org/abs/2306.05284) | |
| """ | |
| ) | |
| if IS_SHARED_SPACE: | |
| 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) | |
| melody = gr.Audio(source="upload", type="numpy", label="Melody Condition (optional)", interactive=True) | |
| sample = gr.Audio(source="upload", type="numpy", label="Music Sample (optional)", interactive=True) | |
| with gr.Row(): | |
| submit = gr.Button("Generate", variant="primary") | |
| gr.Button("Interrupt").click(fn=interrupt, queue=False) | |
| with gr.Row(): | |
| background = gr.Image(source="upload", label="Background", type="filepath", 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=300, value=10, step=1, label="Duration", interactive=True) | |
| with gr.Row(): | |
| overlap = gr.Slider(minimum=1, maximum=29, value=5, step=1, label="Overlap", interactive=True) | |
| recondition = gr.Checkbox(False, label='Condition next chunks with the first chunk') | |
| 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.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, sample, duration, topk, topp, temperature, cfg_coef, seed, overlap, recondition, background], outputs=[output, seed_used]) | |
| def update_recondition(name: str): | |
| enabled = name == 'melody' | |
| return recondition.update(interactive=enabled, value=None if enabled else False) | |
| model.change(fn=update_recondition, inputs=[model], outputs=[recondition]) | |
| 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] | |
| ) | |
| gr.Markdown( | |
| """ | |
| ### More details | |
| The model will generate a short music extract based on the description you provided. | |
| You can generate up to 30 seconds of audio. | |
| We present 4 model variations: | |
| 1. Melody -- a music generation model capable of generating music condition on text and melody inputs. **Note**, you can also use text only. | |
| 2. Small -- a 300M transformer decoder conditioned on text only. | |
| 3. Medium -- a 1.5B transformer decoder conditioned on text only. | |
| 4. Large -- a 3.3B transformer decoder conditioned on text only (might OOM for the longest sequences.) | |
| When using `melody`, ou 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. | |
| 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. | |
| """ | |
| ) | |
| # Show the interface | |
| launch_kwargs = {} | |
| username = kwargs.get('username') | |
| password = kwargs.get('password') | |
| server_port = kwargs.get('server_port', 0) | |
| inbrowser = kwargs.get('inbrowser', False) | |
| share = kwargs.get('share', False) | |
| server_name = kwargs.get('listen') | |
| launch_kwargs['server_name'] = server_name | |
| if username and password: | |
| launch_kwargs['auth'] = (username, password) | |
| if server_port > 0: | |
| launch_kwargs['server_port'] = server_port | |
| if inbrowser: | |
| launch_kwargs['inbrowser'] = inbrowser | |
| if share: | |
| launch_kwargs['share'] = share | |
| interface.queue().launch(**launch_kwargs) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument( | |
| '--listen', | |
| type=str, | |
| default='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' | |
| ) | |
| args = parser.parse_args() | |
| UNLOAD_MODEL = args.unload_model | |
| ui( | |
| username=args.username, | |
| password=args.password, | |
| inbrowser=args.inbrowser, | |
| server_port=args.server_port, | |
| share=args.share, | |
| listen=args.listen | |
| ) |