""" 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 from audiocraft.models import MusicGen from audiocraft.data.audio import audio_write from audiocraft.utils.extend import generate_music_segments import numpy as np MODEL = None IS_SHARED_SPACE = "musicgen/MusicGen" in os.environ.get('SPACE_ID', '') def load_model(version): print("Loading model", version) return MusicGen.get_pretrained(version) def predict(model, text, melody, duration, dimension, topk, topp, temperature, cfg_coef, background): global MODEL output_segments = None topk = int(topk) if MODEL is None or MODEL.name != model: MODEL = load_model(model) if duration > MODEL.lm.cfg.dataset.segment_duration: segment_duration = MODEL.lm.cfg.dataset.segment_duration else: segment_duration = duration MODEL.set_generation_params( use_sampling=True, top_k=topk, top_p=topp, temperature=temperature, cfg_coef=cfg_coef, duration=segment_duration, ) if melody: if duration > MODEL.lm.cfg.dataset.segment_duration: output_segments = generate_music_segments(text, melody, MODEL, duration, 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 ) else: output = MODEL.generate(descriptions=[text], progress=False) if output_segments: try: # Combine the output segments into one long audio file output_segments = [segment.detach().cpu().float()[0] for segment in output_segments] output = torch.cat(output_segments, dim=dimension) 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: 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, bar_count=40) return waveform_video 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://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) melody = gr.Audio(source="upload", type="numpy", label="Melody Condition (optional)", interactive=True) with gr.Row(): submit = gr.Button("Submit") with gr.Row(): background= gr.Image(value="./assets/background.png", source="upload", label="Background", shape=(768,512), 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=1000, value=10, label="Duration", interactive=True) dimension = gr.Slider(minimum=-2, maximum=1, value=1, step=1, label="Dimension", info="determines which direction to add new segements of audio. (0 = stack tracks, 1 = lengthen, -1 = ?)", 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") submit.click(predict, inputs=[model, text, melody, duration, dimension, topk, topp, temperature, cfg_coef, background], outputs=[output]) 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, max_threads=1) 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=7859, 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() ui( username=args.username, password=args.password, inbrowser=args.inbrowser, server_port=args.server_port, share=args.share, listen=args.listen )