Spaces:
Running
on
Zero
Running
on
Zero
File size: 1,776 Bytes
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import time
import spaces
import torch
from audiocraft.data.audio import audio_write
from audiocraft.models import AudioGen, MusicGen
# Load the pretrained models and move them to GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
print("Using device:", device)
sound_model = AudioGen.get_pretrained('facebook/audiogen-medium')
music_model = MusicGen.get_pretrained('facebook/musicgen-small')
# Set generation parameters (for example, audio duration of 5 seconds)
sound_model.set_generation_params(duration=5)
music_model.set_generation_params(duration=5)
@spaces.GPU(duration=20)
def generate_sound(prompt: str):
"""
Generate sound using Audiocraft based on the given prompt.
Args:
- prompt (str): The description of the sound/music to generate.
Returns:
- str: The path to the saved audio file.
"""
descriptions = [prompt]
timestamp = str(time.time()).replace(".", "")
wav = sound_model.generate(descriptions) # Generate audio
output_path = f'{prompt}_{timestamp}'
audio_write(output_path, wav[0].cpu(), sound_model.sample_rate, strategy="loudness")
return f"{output_path}.wav"
@spaces.GPU(duration=15)
def generate_music(prompt: str):
"""
Generate music using Audiocraft based on the given prompt.
Args:
- prompt (str): The description of the music to generate.
Returns:
- str: The path to the saved audio file.
"""
descriptions = [prompt]
timestamp = str(time.time()).replace(".", "")
wav = music_model.generate(descriptions) # Generate music
output_path = f'{prompt}_{timestamp}'
audio_write(output_path, wav[0].cpu(), music_model.sample_rate, strategy="loudness")
return f"{output_path}.wav"
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