Create app.py

app.py

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+import gradio as gr
+from playdiffusion import PlayDiffusion, RVCInput
+import os
+import wget
+
+# --- Model Downloading ---
+print("--- Checking and Downloading Model Assets ---")
+MODEL_FILES = {
+    "kmeans_10k.npy": "https://huggingface.co/PlayHT/PlayDiffusion/resolve/main/kmeans_10k.npy",
+    "last_250k_fixed.pkl": "https://huggingface.co/PlayHT/PlayDiffusion/resolve/main/last_250k_fixed.pkl",
+    "tokenizer-multi_bpe16384_merged_extended_58M.json": "https://huggingface.co/PlayHT/PlayDiffusion/resolve/main/tokenizer-multi_bpe16384_merged_extended_58M.json",
+    "v090_g_01105000": "https://huggingface.co/PlayHT/PlayDiffusion/resolve/main/v090_g_01105000",
+    "voice_encoder_1992000.pt": "https://huggingface.co/PlayHT/PlayDiffusion/resolve/main/voice_encoder_1992000.pt",
+    "xlsr2_1b_v2_custom.pt": "https://huggingface.co/PlayHT/PlayDiffusion/resolve/main/xlsr2_1b_v2_custom.pt"
+}
+
+for filename, url in MODEL_FILES.items():
+    if not os.path.exists(filename):
+        print(f"Downloading {filename}...")
+        wget.download(url, filename)
+    else:
+        print(f"{filename} already exists. Skipping download.")
+
+# --- Gradio App ---
+print("Initializing PlayDiffusion... This will load the models into memory.")
+inpainter = PlayDiffusion()
+print("PlayDiffusion initialized successfully.")
+
+def speech_rvc(rvc_source_speech, rvc_target_voice):
+    if rvc_source_speech is None or rvc_target_voice is None:
+        raise gr.Error("Please provide both a source speech audio and a target voice audio.")
+    print("Starting voice conversion...")
+    converted_audio = inpainter.rvc(RVCInput(source_speech=rvc_source_speech, target_voice=rvc_target_voice))
+    print("Voice conversion finished.")
+    return converted_audio
+
+with gr.Blocks(theme=gr.themes.Soft(), title="PlayDiffusion Voice Conversion") as demo:
+    gr.Markdown("# 🗣️ PlayDiffusion Voice Conversion")
+    gr.Markdown("Upload a **Source Speech** audio and a **Target Voice** audio to convert the speech.")
+
+    with gr.Row():
+        rvc_source_speech = gr.Audio(label="Source Speech", sources=["upload", "microphone"], type="filepath")
+        rvc_target_voice = gr.Audio(label="Target Voice", sources=["upload", "microphone"], type="filepath")
+
+    rvc_submit = gr.Button("🚀 Run Voice Conversion", variant="primary")
+    gr.Markdown("### Converted Speech Output")
+    rvc_output = gr.Audio(label="Result", interactive=False)
+
+    rvc_submit.click(fn=speech_rvc, inputs=[rvc_source_speech, rvc_target_voice], outputs=[rvc_output])
+
+demo.launch()