Create app.py

app.py

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+import gradio as gr
+import torch
+import torchaudio
+import os
+import numpy as np
+from liquid_audio import LFM2AudioModel, LFM2AudioProcessor
+
+# --- CONFIGURATION ---
+MODEL_ID = "LiquidAI/LFM2.5-Audio-1.5B"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+print(f"⏳ Loading model: {MODEL_ID} on {device}...")
+try:
+    processor = LFM2AudioProcessor.from_pretrained(MODEL_ID)
+    model = LFM2AudioModel.from_pretrained(MODEL_ID).to(device)
+    print("✅ Model loaded successfully!")
+except Exception as e:
+    print(f"❌ Error loading model: {e}")
+    model = None
+
+def process_audio(input_audio_path):
+    if model is None:
+        return None, "Error: Model not loaded."
+        
+    try:
+        # 1. Load and process the audio file
+        # Gradio passes audio as a filepath string
+        waveform, sample_rate = torchaudio.load(input_audio_path)
+        
+        # Resample if necessary (Model typically expects 16kHz)
+        if sample_rate != 16000:
+            resampler = torchaudio.transforms.Resample(sample_rate, 16000)
+            waveform = resampler(waveform)
+            sample_rate = 16000
+
+        # 2. Prepare inputs for the model
+        inputs = processor(
+            audio=waveform, 
+            sampling_rate=sample_rate, 
+            return_tensors="pt"
+        ).to(device)
+
+        # 3. Generate response (Interleaved Audio-to-Audio)
+        # The generate parameters might need tuning based on specific version
+        with torch.no_grad():
+            generated_ids = model.generate(
+                **inputs,
+                max_new_tokens=256, # Adjust length as needed
+                do_sample=True,
+                temperature=0.7
+            )
+
+        # 4. Decode the output to audio
+        # The processor handles converting tokens back to waveform
+        output_waveform = processor.batch_decode(generated_ids)[0]
+        
+        # Save to a temporary file to return to Gradio
+        output_path = "output_response.wav"
+        torchaudio.save(output_path, torch.tensor(output_waveform), 16000)
+        
+        return output_path
+
+    except Exception as e:
+        return None, f"Error during inference: {str(e)}"
+
+# --- GRADIO INTERFACE ---
+with gr.Blocks(title="Liquid LFM2.5 Audio") as demo:
+    gr.Markdown("# 💧 LiquidAI LFM2.5 Audio (Speech-to-Speech)")
+    
+    with gr.Row():
+        input_audio = gr.Audio(sources=["microphone", "upload"], type="filepath", label="Input Speech")
+        output_audio = gr.Audio(type="filepath", label="Response", autoplay=True)
+    
+    submit_btn = gr.Button("Generate Response", variant="primary")
+    
+    submit_btn.click(
+        fn=process_audio,
+        inputs=[input_audio],
+        outputs=[output_audio]
+    )
+
+if __name__ == "__main__":
+    demo.launch()