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import streamlit as st
import torch
from diffusers import AudioLDMPipeline
from transformers import AutoProcessor, ClapModel
# make Space compatible with CPU duplicates
if torch.cuda.is_available():
device = "cuda"
torch_dtype = torch.float16
else:
device = "cpu"
torch_dtype = torch.float32
# load the diffusers pipeline
repo_id = "cvssp/audioldm-m-full"
pipe = AudioLDMPipeline.from_pretrained(repo_id, torch_dtype=torch_dtype).to(device)
pipe.unet = torch.compile(pipe.unet)
# CLAP model (only required for automatic scoring)
clap_model = ClapModel.from_pretrained("sanchit-gandhi/clap-htsat-unfused-m-full").to(device)
processor = AutoProcessor.from_pretrained("sanchit-gandhi/clap-htsat-unfused-m-full")
generator = torch.Generator(device)
# Streamlit app setup
st.set_page_config(
page_title="Text to Music",
page_icon="🎵",
)
text_input = st.text_input("Input text", "A hammer is hitting a wooden surface")
negative_prompt = st.text_input("Negative prompt", "low quality, average quality")
st.markdown("### Configuration")
seed = st.number_input("Seed", value=45)
duration = st.slider("Duration (seconds)", 2.5, 10.0, 5.0, 2.5)
guidance_scale = st.slider("Guidance scale", 0.0, 4.0, 2.5, 0.5)
n_candidates = st.slider("Number waveforms to generate", 1, 3, 3, 1)
def score_waveforms(text, waveforms):
inputs = processor(text=text, audios=list(waveforms), return_tensors="pt", padding=True)
inputs = {key: inputs[key].to(device) for key in inputs}
with torch.no_grad():
logits_per_text = clap_model(**inputs).logits_per_text # this is the audio-text similarity score
probs = logits_per_text.softmax(dim=-1) # we can take the softmax to get the label probabilities
most_probable = torch.argmax(probs) # and now select the most likely audio waveform
waveform = waveforms[most_probable]
return waveform
if st.button("Submit"):
if text_input is None:
st.error("Please provide a text input.")
else:
waveforms = pipe(
text_input,
audio_length_in_s=duration,
guidance_scale=guidance_scale,
num_inference_steps=100,
negative_prompt=negative_prompt,
num_waveforms_per_prompt=n_candidates if n_candidates else 1,
generator=generator.manual_seed(int(seed)),
)["audios"]
if waveforms.shape[0] > 1:
waveform = score_waveforms(text_input, waveforms)
else:
waveform = waveforms[0]
# Spécifiez le taux d'échantillonnage (sample_rate) et le format audio
st.audio(waveform, format="audio/wav", sample_rate=16000)
browser.gatherUsageStats = False
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