Create app.py

app.py

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+import torch
+import torchaudio
+import gradio as gr
+from liquid_audio import LFM2AudioModel, LFM2AudioProcessor, ChatState, LFMModality
+
+HF_REPO = "LiquidAI/LFM2-Audio-1.5B"
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load processor and model
+processor = LFM2AudioProcessor.from_pretrained(HF_REPO)
+model = LFM2AudioModel.from_pretrained(HF_REPO).to(device).eval()
+
+# Persistent chat state
+chat = ChatState(processor)
+
+def reset_chat():
+    global chat
+    chat = ChatState(processor)
+    return [], "Chat reset successfully."
+
+def generate_response(audio_input, text_input, history):
+    global chat
+
+    # Initialize system prompt if first turn
+    if not history:
+        chat.new_turn("system")
+        chat.add_text("You are a helpful multimodal AI assistant that can reply with both text and audio.")
+        chat.end_turn()
+
+    # New user turn
+    chat.new_turn("user")
+    if text_input:
+        chat.add_text(text_input)
+    if audio_input:
+        wav, sr = torchaudio.load(audio_input)
+        chat.add_audio(wav, sr)
+    chat.end_turn()
+
+    # Assistant generation
+    chat.new_turn("assistant")
+
+    text_out, audio_out, modality_out = [], [], []
+
+    for t in model.generate_interleaved(
+        **chat, max_new_tokens=512, audio_temperature=1.0, audio_top_k=4
+    ):
+        if t.numel() == 1:
+            text_out.append(t)
+            modality_out.append(LFMModality.TEXT)
+        else:
+            audio_out.append(t)
+            modality_out.append(LFMModality.AUDIO_OUT)
+
+    decoded_text, audio_path = "", None
+
+    # Decode text output
+    if text_out:
+        tokens = torch.stack(text_out, 1)
+        decoded_text = processor.text.decode(tokens)
+
+    # Decode audio output
+    if audio_out:
+        mimi_codes = torch.stack(audio_out[:-1], 1).unsqueeze(0)
+        with torch.no_grad():
+            waveform = processor.mimi.decode(mimi_codes)[0]
+        audio_path = "assistant_reply.wav"
+        torchaudio.save(audio_path, waveform.cpu(), 24000)
+
+    # Add to chat history
+    history.append((text_input or "[Audio Input]", decoded_text or "[Audio Output]"))
+    chat.append(
+        text=torch.stack(text_out, 1) if text_out else None,
+        audio_out=torch.stack(audio_out, 1) if audio_out else None,
+        modality_flag=torch.tensor(modality_out),
+    )
+    chat.end_turn()
+
+    return history, decoded_text, audio_path
+
+
+# === Gradio UI ===
+with gr.Blocks(title="🎧 LFM2-Audio-1.5B Chat") as demo:
+    gr.Markdown("## 🎧 LFM2-Audio-1.5B — Multimodal AI Chatbot")
+    gr.Markdown("Chat using **text or voice** — get replies in **text and audio** form.")
+
+    with gr.Row():
+        text_inp = gr.Textbox(label="💬 Type your message", placeholder="Say something...")
+        audio_inp = gr.Audio(source="microphone", type="filepath", label="🎙 Record / Upload Audio")
+
+    with gr.Row():
+        send_btn = gr.Button("Generate Response", variant="primary")
+        reset_btn = gr.Button("🔁 Reset Chat")
+
+    chatbox = gr.Chatbot(label="Conversation History", height=400)
+    text_out = gr.Textbox(label="📝 Text Response")
+    audio_out = gr.Audio(label="🔊 Audio Response", type="filepath")
+
+    send_btn.click(
+        generate_response,
+        inputs=[audio_inp, text_inp, chatbox],
+        outputs=[chatbox, text_out, audio_out],
+    )
+
+    reset_btn.click(reset_chat, outputs=[chatbox, text_out])
+
+demo.queue().launch()