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""" |
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Copyright (c) Meta Platforms, Inc. and affiliates. |
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All rights reserved. |
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This source code is licensed under the license found in the |
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LICENSE file in the root directory of this source tree. |
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""" |
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from tempfile import NamedTemporaryFile |
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import argparse |
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import torch |
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import gradio as gr |
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from audiocraft.models import MusicGen |
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from audiocraft.data.audio import audio_write |
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MODEL = None |
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def load_model(version): |
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print("Loading model", version) |
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return MusicGen.get_pretrained(version) |
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def predict(model, text, melody, duration, topk, topp, temperature, cfg_coef): |
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global MODEL |
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topk = int(topk) |
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if MODEL is None or MODEL.name != model: |
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MODEL = load_model(model) |
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if duration > MODEL.lm.cfg.dataset.segment_duration: |
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raise gr.Error("MusicGen currently supports durations of up to 30 seconds!") |
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MODEL.set_generation_params( |
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use_sampling=True, |
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top_k=topk, |
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top_p=topp, |
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temperature=temperature, |
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cfg_coef=cfg_coef, |
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duration=duration, |
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) |
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if melody: |
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sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t().unsqueeze(0) |
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print(melody.shape) |
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if melody.dim() == 2: |
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melody = melody[None] |
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melody = melody[..., :int(sr * MODEL.lm.cfg.dataset.segment_duration)] |
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output = MODEL.generate_with_chroma( |
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descriptions=[text], |
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melody_wavs=melody, |
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melody_sample_rate=sr, |
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progress=False |
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) |
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else: |
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output = MODEL.generate(descriptions=[text], progress=False) |
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output = output.detach().cpu().float()[0] |
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with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file: |
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audio_write( |
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file.name, output, MODEL.sample_rate, strategy="loudness", |
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loudness_headroom_db=16, loudness_compressor=True, add_suffix=False) |
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waveform_video = gr.make_waveform(file.name) |
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return waveform_video |
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def ui(**kwargs): |
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with gr.Blocks() as interface: |
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gr.Markdown( |
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""" |
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# MusicGen |
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This is the demo for [MusicGen](https://github.com/facebookresearch/audiocraft), a simple and controllable model for music generation |
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presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284). |
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<br/> |
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<a href="https://huggingface.co/spaces/musicgen/MusicGen?duplicate=true" style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank"> |
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<img style="margin-bottom: 0em;display: inline;margin-top: -.25em;" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> |
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for longer sequences, more control and no queue.</p> |
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""" |
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) |
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with gr.Row(): |
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with gr.Column(): |
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with gr.Row(): |
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text = gr.Text(label="Input Text", interactive=True) |
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melody = gr.Audio(source="upload", type="numpy", label="Melody Condition (optional)", interactive=True) |
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with gr.Row(): |
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submit = gr.Button("Submit") |
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with gr.Row(): |
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model = gr.Radio(["melody", "medium", "small", "large"], label="Model", value="melody", interactive=True) |
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with gr.Row(): |
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duration = gr.Slider(minimum=1, maximum=30, value=10, label="Duration", interactive=True) |
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with gr.Row(): |
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topk = gr.Number(label="Top-k", value=250, interactive=True) |
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topp = gr.Number(label="Top-p", value=0, interactive=True) |
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temperature = gr.Number(label="Temperature", value=1.0, interactive=True) |
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cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True) |
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with gr.Column(): |
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output = gr.Video(label="Generated Music") |
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submit.click(predict, inputs=[model, text, melody, duration, topk, topp, temperature, cfg_coef], outputs=[output]) |
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gr.Examples( |
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fn=predict, |
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examples=[ |
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[ |
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"An 80s driving pop song with heavy drums and synth pads in the background", |
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"./assets/bach.mp3", |
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"melody" |
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], |
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[ |
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"A cheerful country song with acoustic guitars", |
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"./assets/bolero_ravel.mp3", |
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"melody" |
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], |
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[ |
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"90s rock song with electric guitar and heavy drums", |
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None, |
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"medium" |
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], |
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[ |
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"a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions", |
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"./assets/bach.mp3", |
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"melody" |
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], |
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[ |
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"lofi slow bpm electro chill with organic samples", |
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None, |
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"medium", |
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], |
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], |
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inputs=[text, melody, model], |
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outputs=[output] |
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) |
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gr.Markdown( |
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""" |
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### More details |
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The model will generate a short music extract based on the description you provided. |
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You can generate up to 30 seconds of audio. |
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We present 4 model variations: |
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1. Melody -- a music generation model capable of generating music condition on text and melody inputs. **Note**, you can also use text only. |
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2. Small -- a 300M transformer decoder conditioned on text only. |
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3. Medium -- a 1.5B transformer decoder conditioned on text only. |
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4. Large -- a 3.3B transformer decoder conditioned on text only (might OOM for the longest sequences.) |
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When using `melody`, ou can optionaly provide a reference audio from |
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which a broad melody will be extracted. The model will then try to follow both the description and melody provided. |
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You can also use your own GPU or a Google Colab by following the instructions on our repo. |
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See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft) |
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for more details. |
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""" |
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) |
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launch_kwargs = {} |
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username = kwargs.get('username') |
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password = kwargs.get('password') |
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server_port = kwargs.get('server_port', 0) |
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inbrowser = kwargs.get('inbrowser', False) |
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share = kwargs.get('share', False) |
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server_name = kwargs.get('listen') |
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launch_kwargs['server_name'] = server_name |
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if username and password: |
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launch_kwargs['auth'] = (username, password) |
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if server_port > 0: |
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launch_kwargs['server_port'] = server_port |
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if inbrowser: |
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launch_kwargs['inbrowser'] = inbrowser |
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if share: |
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launch_kwargs['share'] = share |
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interface.queue().launch(**launch_kwargs, max_threads=1) |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument( |
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'--listen', |
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type=str, |
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default='127.0.0.1', |
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help='IP to listen on for connections to Gradio', |
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) |
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parser.add_argument( |
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'--username', type=str, default='', help='Username for authentication' |
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) |
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parser.add_argument( |
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'--password', type=str, default='', help='Password for authentication' |
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) |
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parser.add_argument( |
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'--server_port', |
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type=int, |
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default=0, |
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help='Port to run the server listener on', |
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) |
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parser.add_argument( |
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'--inbrowser', action='store_true', help='Open in browser' |
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) |
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parser.add_argument( |
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'--share', action='store_true', help='Share the gradio UI' |
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) |
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args = parser.parse_args() |
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ui( |
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username=args.username, |
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password=args.password, |
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inbrowser=args.inbrowser, |
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server_port=args.server_port, |
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share=args.share, |
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listen=args.listen |
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) |
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