initial commit

README.md

@@ -1,8 +1,9 @@
 ---
-title: Ldm Styletransfer
+title: AudioLDM Style Transfer HARP plugin
 emoji: 🏢
 colorFrom: pink
 colorTo: blue
+python_version: 3.8
 sdk: gradio
 sdk_version: 4.15.0
 app_file: app.py

app.py

@@ -0,0 +1,114 @@
+from pyharp import ModelCard, build_endpoint, save_and_return_filepath
+from audiotools import AudioSignal
+from audioldm import build_model, style_transfer
+import gradio as gr
+import soundfile as sf
+
+audioldm = build_model(model_name="audioldm-m-full")
+
+def save_wave(waveform, savepath, name="outwav"):
+    if type(name) is not list:
+        name = [name] * waveform.shape[0]
+
+    for i in range(waveform.shape[0]):
+        path = os.path.join(
+            savepath,
+            "%s_%s.wav"
+            % (
+                os.path.basename(name[i])
+                if (not ".wav" in name[i])
+                else os.path.basename(name[i]).split(".")[0],
+                i,
+            ),
+        )
+        print("Save audio to %s" % path)
+        sf.write(path, waveform[i, 0], samplerate=16000)
+
+def process_fn(input_audio_path, prompt, seed, guidance_scale, num_inference_steps, num_candidates, audio_length_in_s, transfer_strength):
+    waveform = style_transfer(
+        audioldm,
+        prompt,
+        audio_file,
+        transfer_strength,
+        int(seed),
+        duration = audio_length_in_s,
+        guidance_scale = guidance_scale,
+        ddim_steps = int(num_inference_steps),
+        batchsize = int(num_candidates),
+        config=None,
+    )
+    waveform = waveform[:,None,:]
+    sf.write("./output.wav", waveform[0, 0], samplerate=16000)
+    
+    #save_wave(waveform, "./", name="output.wav")  #broken, always appends _0.wav
+    return "./output.wav"
+
+
+card = ModelCard(
+    name='AudioLDM Variations',
+    description='AudioLDM Variation Generator, operates on region selected in track.',
+    author='Team Audio',
+    tags=['AudioLDM', 'Variations', 'audio-to-audio']
+)
+
+with gr.Blocks() as webapp:
+    # Define your Gradio interface
+    inputs = [
+        gr.Audio(
+            label="Audio Input", 
+            type="filepath"
+        ), 
+        gr.Slider(
+            label="seed",
+            minimum="0",
+            maximum="65535",
+            value="43534",
+            step="1"
+        ),
+        gr.Slider(
+            minimum=0, maximum=10, 
+            step=0.1, value=2.5, 
+            label="Guidance Scale"
+        ),
+        gr.Slider(
+            minimum=1, maximum=500, 
+            step=1, value=200, 
+            label="Inference Steps"
+        ),
+        gr.Slider(
+            minimum=1, maximum=10, 
+            step=1, value=1, 
+            label="Candidates"
+        ),
+        gr.Slider(
+            minimum=2.5, maximum=10.0, 
+            step=2.5, value=5, 
+            label="Duration"
+        ),
+    ]
+
+    output = gr.Audio(label="Audio Output", type="filepath")
+
+    ctrls_data, ctrls_button, process_button, cancel_button = build_endpoint(inputs, output, process_fn, card)
+
+# queue the webapp: https://www.gradio.app/guides/setting-up-a-demo-for-maximum-performance
+#webapp.queue()
+webapp.launch(share=True)
+
+
+
+for audio_file in input_files:
+    waveform = style_transfer(
+        audioldm,
+        PROMPT,
+        audio_file,
+        TRANSFER_STRENGTH,
+        SEED,
+        duration = DURATION,
+        guidance_scale = GUIDANCE_SCALE,
+        ddim_steps = STEPS,
+        batchsize = N_VARIATIONS,
+        config=None,
+    )
+    waveform = waveform[:,None,:]
+    save_wave(waveform, OUTPUT_DIRECTORY, name=f"{os.path.basename(audio_file)}_STYLE_{PROMPT}")

requirements.txt

@@ -0,0 +1,4 @@
+-e git+https://github.com/audacitorch/pyharp.git#egg=pyharp
+descript-audiotools
+transformers==4.29.0
+audioldm