Create app.py

app.py

@@ -0,0 +1,105 @@
+import gradio as gr
+import torch
+from diffusers import AudioLDMPipeline
+
+from transformers import AutoProcessor, ClapModel
+
+
+device = "mps"
+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)