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| import streamlit as st | |
| import torch | |
| from diffusers import AudioLDMPipeline | |
| from transformers import AutoProcessor, ClapModel | |
| st.set_option('browser.gatherUsageStats', False) | |
| # 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 | |