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added music gen and audio gen
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lollms/functions/generate_music.py
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73
lollms/functions/generate_music.py
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# Lollms function call definition file
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# File Name: music_generator.py
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# Author: ParisNeo
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# Description: This function generates music based on a given prompt and duration, saving the output to a unique file in the discussion folder.
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# Import necessary libraries
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import torchaudio
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from audiocraft.models import musicgen
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import torch
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from pathlib import Path
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from lollms.utilities import PackageManager
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from ascii_colors import trace_exception
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from functools import partial
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# Check for required packages and install if necessary
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if not PackageManager.check_package_installed("audiocraft"):
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PackageManager.install_package("audiocraft")
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# Function to generate music
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def generate_music(processor, client, generation_prompt: str, duration: int, model_name: str = "facebook/musicgen-melody", device: str="cuda:0") -> str:
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"""
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Generates music based on the given prompt and duration, saving it to a unique file in the discussion folder.
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Parameters:
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- processor: The processor object used for managing the generation process.
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- client: The client object containing discussion information.
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- generation_prompt: The prompt for music generation.
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- duration: The duration of the music in seconds.
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- model_name: The name of the pretrained music generation model.
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- device: The device to run the model on (e.g., 'cpu' or 'cuda').
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Returns:
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- The path of the saved music file.
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"""
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try:
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# Load the pretrained music generation model
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music_model = musicgen.MusicGen.get_pretrained(model_name, device=device)
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# Set generation parameters
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music_model.set_generation_params(duration=duration)
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# Generate music
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res = music_model.generate([generation_prompt])
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# Create output folder if it doesn't exist
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output_folder = client.discussion.discussion_folder / "generated_music"
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output_folder.mkdir(parents=True, exist_ok=True)
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# Generate a unique file name
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output_file = output_folder / f"music_generation_{len(list(output_folder.glob('*.wav')))}.wav"
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# Save the generated music to the specified file
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torchaudio.save(output_file, res.reshape(1, -1).cpu(), 32000)
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# Return the path of the saved file
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return str(output_file)
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except Exception as e:
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return trace_exception(e)
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# Metadata function for the music generation function
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def generate_music_function(processor, client):
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return {
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"function_name": "generate_music", # The function name in string
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"function": partial(generate_music, processor=processor, client=client), # The function to be called with preset parameters
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"function_description": "Generates music based on a prompt and duration, saving it to a unique file in the discussion folder.", # Description of the function
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"function_parameters": [ # Parameters needed for the function
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{"name": "generation_prompt", "type": "str"},
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{"name": "duration", "type": "int"},
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{"name": "model_name", "type": "str"},
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{"name": "device", "type": "str"}
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]
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}
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@ -21,11 +21,13 @@ import threading
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import pipmaster as pm
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import pipmaster as pm
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if not pm.is_installed("torch"):
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if not pm.is_installed("torch"):
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pm.install_or_update("torch torchvision torchaudio", "https://download.pytorch.org/whl/cu121")
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ASCIIColors.yellow("Diffusers: Torch not found. Installing it")
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pm.install_multiple(["torch","torchvision","torchaudio"], "https://download.pytorch.org/whl/cu121", force_reinstall=True)
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import torch
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import torch
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if not torch.cuda.is_available():
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if not torch.cuda.is_available():
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pm.install_or_update("torch torchvision torchaudio", "https://download.pytorch.org/whl/cu121")
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ASCIIColors.yellow("Diffusers: Torch not using cuda. Reinstalling it")
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pm.install_multiple(["torch","torchvision","torchaudio"], "https://download.pytorch.org/whl/cu121", force_reinstall=True)
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