initial commit

app.py

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+import sys
+print(sys.path)
+sys.path.append('/home/user/audio_ai/diffusers_harp/venv/src')
+
+from pyharp import ModelCard, build_endpoint, save_and_return_filepath
+
+from audiotools import AudioSignal
+import scipy
+import torch
+import gradio as gr
+from diffusers import AudioLDM2Pipeline
+import subprocess as sp
+
+
+#harp_deps = [
+#"descript-audiotools"]
+#
+#try:
+#    from pyharp import ModelCard, build_endpoint, save_and_return_filepath
+#except ImportError:
+#    print("Installing harp dependencies...")
+#    sp.check_call(["pip", "install", *harp_deps])
+#    sp.check_call(["pip", "install", "-e git+https://github.com/audacitorch/pyharp.git#egg=pyharp"])
+#    sp.check_call(["pip", "install", "pydantic<2.0.0"])
+#    from pyharp import ModelCard, build_endpoint, save_and_return_filepath
+
+# Create a Model Card
+card = ModelCard(
+    name='Diffusers AudioLDM2 Style Transfer',
+    description='AudioLDM2 style transfer, operates on region selected in track.',
+    author='Team Audio',
+    tags=['AudioLDM', 'Diffusers', 'Style Transfer']
+)
+
+# Load the model
+repo_id = "cvssp/audioldm2"
+pipe = AudioLDM2Pipeline.from_pretrained(repo_id, torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+
+
+
+def process_fn(input_audio_path, prompt, negative_prompt, seed, num_inference_steps, audio_length_in_s, num_waveforms_per_prompt):
+    """
+    This function defines the audio processing steps
+
+    Args:
+        input_audio_path (str): the audio filepath to be processed.
+
+        <YOUR_KWARGS>: additional keyword arguments necessary for processing.
+            NOTE: These should correspond to and match order of UI elements defined below.
+
+    Returns:
+        output_audio_path (str): the filepath of the processed audio.
+    """
+
+    sig = AudioSignal(input_audio_path)
+    outfile = "./output.wav"
+
+    #prompt = "The sound of a hammer hitting a wooden surface."
+    #negative_prompt = "Low quality."
+
+    # set the seed for generator
+    generator = torch.Generator("cuda").manual_seed(int(seed))
+
+    audio = pipe(
+        prompt,
+        negative_prompt=negative_prompt,
+        num_inference_steps=num_inference_steps,
+        audio_length_in_s=audio_length_in_s,
+        num_waveforms_per_prompt=num_waveforms_per_prompt,
+        generator=generator,
+    ).audios
+
+    scipy.io.wavfile.write(outfile, rate=16000, data=audio[0])
+    return outfile
+
+
+# Build the endpoint
+with gr.Blocks() as webapp:
+    # Define your Gradio interface
+    inputs = [
+        gr.Audio(
+            label="Audio Input", 
+            type="filepath"
+        ), 
+        gr.Text(
+            label="Prompt", 
+            interactive=True
+        ),
+        gr.Text(
+            label="Negative Prompt", 
+            interactive=True
+        ),
+        gr.Slider(
+            label="seed",
+            minimum="0",
+            maximum="65535",
+            value="0",
+            step="1"
+        ),
+        gr.Slider(
+            minimum=1, maximum=500, 
+            step=1, value=1, 
+            label="Inference Steps"
+        ),
+        gr.Slider(
+            minimum=2.5, maximum=10.0, 
+            step=2.5, value=2.5, 
+            label="Duration"
+        ),
+        gr.Slider(
+            minimum=1, maximum=10, 
+            step=1, value=1, 
+            label="Waveforms Per Prompt"
+        ),
+    ]
+    
+    # make an output audio widget
+    output = gr.Audio(label="Audio Output", type="filepath")
+
+    # Build the endpoint
+    ctrls_data, ctrls_button, process_button, cancel_button = build_endpoint(inputs, output, process_fn, card)
+
+#webapp.queue()
+webapp.launch(share=True)

requirements.txt

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+-e git+https://github.com/audacitorch/pyharp.git#egg=pyharp
+descript-audiotools
+diffusers
+torch
+scipy
+transformers
+accelerate
+datetime
+numpy
+gradio