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""" | |
import streamlit as st | |
from transformers import pipeline | |
pipe = pipeline ('sentiment-analysis') | |
text = st.text_area("some text") | |
if text: | |
out = pipe(text) | |
st.json(out) | |
""" | |
import torch | |
import torchaudio | |
from einops import rearrange | |
from stable_audio_tools import get_pretrained_model | |
from stable_audio_tools.inference.generation import generate_diffusion_cond | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
# Download model | |
model, model_config = get_pretrained_model("stabilityai/stable-audio-open-1.0") | |
sample_rate = model_config["sample_rate"] | |
sample_size = model_config["sample_size"] | |
model = model.to(device) | |
# Set up text and timing conditioning | |
conditioning = [{ | |
"prompt": "128 BPM tech house drum loop", | |
}] | |
# Generate stereo audio | |
output = generate_diffusion_cond( | |
model, | |
conditioning=conditioning, | |
sample_size=sample_size, | |
device=device | |
) | |
# Rearrange audio batch to a single sequence | |
output = rearrange(output, "b d n -> d (b n)") | |
# Peak normalize, clip, convert to int16, and save to file | |
output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu() | |
torchaudio.save("output.wav", output, sample_rate) | |