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Running
on
T4
| """ | |
| 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 torch | |
| import gradio as gr | |
| from audiocraft.data.audio_utils import convert_audio | |
| from audiocraft.data.audio import audio_write | |
| from hf_loading import get_pretrained | |
| MODEL = None | |
| def load_model(): | |
| print("Loading model") | |
| return get_pretrained("melody") | |
| def predict(texts, melodies): | |
| global MODEL | |
| if MODEL is None: | |
| MODEL = load_model() | |
| duration = 12 | |
| MODEL.set_generation_params(duration=duration) | |
| print(texts, melodies) | |
| processed_melodies = [] | |
| target_sr = 32000 | |
| target_ac = 1 | |
| for melody in melodies: | |
| if melody is None: | |
| processed_melodies.append(None) | |
| else: | |
| sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t() | |
| if melody.dim() == 1: | |
| melody = melody[None] | |
| melody = melody[..., :int(sr * duration)] | |
| melody = convert_audio(melody, sr, target_sr, target_ac) | |
| processed_melodies.append(melody) | |
| outputs = MODEL.generate_with_chroma( | |
| descriptions=texts, | |
| melody_wavs=processed_melodies, | |
| melody_sample_rate=target_sr, | |
| progress=False | |
| ) | |
| outputs = outputs.detach().cpu().float() | |
| out_files = [] | |
| for output in outputs: | |
| with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file: | |
| audio_write(file.name, output, MODEL.sample_rate, strategy="loudness", add_suffix=False) | |
| out_files.append([file.name]) | |
| return out_files | |
| with gr.Blocks() as demo: | |
| gr.Markdown( | |
| """ | |
| # MusicGen | |
| This is the demo for MusicGen, a simple and controllable model for music generation | |
| presented at: "Simple and Controllable Music Generation". | |
| Enter the description of the music you want and an optional audio used for melody conditioning. | |
| This will generate a 12s extract with the `melody` model. For generating longer sequences | |
| (up to 30 seconds), use the Colab demo or your own GPU. | |
| See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft) | |
| for more details. | |
| """ | |
| ) | |
| 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.Column(): | |
| output = gr.Audio(label="Generated Music", type="filepath", format="wav") | |
| submit.click(predict, inputs=[text, melody], outputs=[output], batch=True, max_batch_size=12) | |
| gr.Examples( | |
| fn=predict, | |
| examples=[ | |
| [ | |
| "An 80s driving pop song with heavy drums and synth pads in the background", | |
| "./assets/bach.mp3", | |
| ], | |
| [ | |
| "90s rock song with electric guitar and heavy drums", | |
| None, | |
| ], | |
| [ | |
| "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions", | |
| "./assets/bach.mp3", | |
| ] | |
| ], | |
| inputs=[text, melody], | |
| outputs=[output] | |
| ) | |
| demo.launch() | |