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import gradio as gr
import torch
from diffusers import AudioLDMPipeline
from transformers import AutoProcessor, ClapModel
device = "cpu"
torch_dtype = torch.float32
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 = 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)
def text2audio(text, negative_prompt, duration, guidance_scale, random_seed, n_candidates):
if text is None:
raise gr.Error("请提供文本输入")
waveforms = pipe(
text,
audio_length_in_s=duration,
guidance_scale=guidance_scale,
negative_prompt=negative_prompt,
num_waveforms_per_prompt=n_candidates if n_candidates else 1,
generator=generator.manual_seed(int(random_seed)),
)["audios"]
if waveforms.shape[0] > 1:
waveform = score_waveforms(text, waveforms)
else:
waveform = waveforms[0]
return gr.make_waveform((16000, waveform))
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
probs = logits_per_text.softmax(dim=-1)
most_probable = torch.argmax(probs)
waveform = waveforms[most_probable]
return waveform
iface = gr.Blocks()
with iface:
with gr.Group():
with gr.Box():
textbox = gr.Textbox(
max_lines=1,
label="要求",
info="要求",
elem_id="prompt-in",
)
negative_textbox = gr.Textbox(
max_lines=1,
label="更详细的要求",
info="更详细的要求",
elem_id="prompt-in",
)
with gr.Accordion("展开更多选项", open=False):
seed = gr.Number(
value=45,
label="种子",
info="不同种子有不同结果,相同种子有相同结果",
)
duration = gr.Slider(2.5, 10, value=5, step=2.5, label="持续时间(秒)")
guidance_scale = gr.Slider(
0,
4,
value=2.5,
step=0.5,
label="质量",
info="大有更好的质量和与文本的相关性；小有更好的多样性",
)
n_candidates = gr.Slider(
1,
3,
value=3,
step=1,
label="候选数量",
info="这个数字控制候选数量",
)
outputs = gr.Video(label="Output", elem_id="output-video")
btn = gr.Button("Submit").style(full_width=True)
btn.click(
text2audio,
inputs=[textbox, negative_textbox, duration, guidance_scale, seed, n_candidates],
outputs=[outputs],
)
iface.queue(max_size=10).launch(debug=True)
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