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musicgen_app.py

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+import argparse
+from concurrent.futures import ProcessPoolExecutor
+import logging
+import os
+from pathlib import Path
+import subprocess as sp
+import sys
+from tempfile import NamedTemporaryFile
+import time
+import typing as tp
+import warnings
+
+import torch
+import gradio as gr
+from audiocraft.data.audio_utils import convert_audio
+from audiocraft.data.audio import audio_write
+from audiocraft.models import MusicGen
+
+MODEL = None  # Last used model
+INTERRUPTING = False
+pool = ProcessPoolExecutor(4)
+pool.__enter__()
+
+
+class FileCleaner:
+    def __init__(self, file_lifetime: float = 3600):
+        self.file_lifetime = file_lifetime
+        self.files = []
+
+    def add(self, path: tp.Union[str, Path]):
+        self._cleanup()
+        self.files.append((time.time(), Path(path)))
+
+    def _cleanup(self):
+        now = time.time()
+        for time_added, path in list(self.files):
+            if now - time_added > self.file_lifetime:
+                if path.exists():
+                    path.unlink()
+                self.files.pop(0)
+            else:
+                break
+
+
+file_cleaner = FileCleaner()
+
+
+def load_model(version='facebook/musicgen-small'):
+    global MODEL
+    print("Loading model", version)
+    if MODEL is None or MODEL.name != version:
+        del MODEL
+        torch.cuda.empty_cache()
+        MODEL = None
+        MODEL = MusicGen.get_pretrained(version)
+
+
+def _do_predictions(texts, duration):
+    MODEL.set_generation_params(duration=duration)
+    outputs = MODEL.generate(texts)
+    outputs = outputs.detach().cpu().float()
+    out_wavs = []
+    for output in outputs:
+        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)
+            out_wavs.append(file.name)
+            file_cleaner.add(file.name)
+    return out_wavs
+
+
+def predict(text, duration):
+    load_model('facebook/musicgen-small')
+    wav_files = _do_predictions([text], duration)
+    return wav_files[0]  # Return the first file in the list
+
+
+def ui(launch_kwargs):
+    with gr.Blocks() as demo:
+        gr.Markdown(
+            """
+            # MusicGen
+            This demo uses the MusicGen model to generate music based on a text prompt.
+            """
+        )
+        with gr.Row():
+            text = gr.Text(label="Input Text", interactive=True)
+            duration = gr.Slider(minimum=1, maximum=120, value=10, label="Duration", interactive=True)
+            submit = gr.Button("Submit")
+        with gr.Row():
+            audio_output = gr.Audio(label="Generated Music", type='filepath')
+        submit.click(predict, inputs=[text, duration], outputs=[audio_output])
+
+        gr.Markdown("""
+        ### More details
+
+        This model generates audio based on a textual description. You can specify the duration of the generated audio.
+        """)
+
+        demo.queue(max_size=8 * 4).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'
+    )
+
+    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
+
+    logging.basicConfig(level=logging.INFO, stream=sys.stderr)
+
+    # Show the interface
+    ui(launch_kwargs)

requirements.txt

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+# please make sure you have already a pytorch install that is cuda enabled!
+av==11.0.0
+einops
+flashy>=0.0.1
+hydra-core>=1.1
+hydra_colorlog
+julius
+num2words
+numpy<2.0.0
+sentencepiece
+spacy>=3.6.1
+torch==2.1.0
+torchaudio>=2.0.0,<2.1.2
+huggingface_hub
+tqdm
+transformers>=4.31.0  # need Encodec there.
+xformers<0.0.23
+demucs
+librosa
+soundfile
+gradio
+torchmetrics
+encodec
+protobuf
+torchvision==0.16.0
+torchtext==0.16.0
+pesq
+pystoi