Update app.py

app.py

@@ -2,29 +2,28 @@ import os
 import gradio as gr
 import torch
 import numpy as np
-from transformers import pipeline, AutoModel
+from transformers import pipeline
 from diffusers import DiffusionPipeline
 from pyannote.audio import Pipeline as PyannotePipeline
 from dia.model import Dia
 from dac.utils import load_model as load_dac_model
 
-# 1. Retrieve HF token and set device mapping
+# Load HF token and configure multi-GPU sharding
 HF_TOKEN   = os.environ["HF_TOKEN"]
-device_map = "auto"  # auto-shard models across 4×L4 GPUs
+device_map = "auto"
 
-print("Loading RVQ Codec...")
+# 1. Descript Audio Codec (RVQ)
 rvq = load_dac_model(tag="latest", model_type="44khz")
 rvq.eval()
-if torch.cuda.is_available():
-    rvq = rvq.to("cuda")
+if torch.cuda.is_available(): rvq = rvq.to("cuda")
 
-print("Loading VAD pipeline...")
+# 2. Voice Activity Detection via Pyannote
 vad_pipe = PyannotePipeline.from_pretrained(
     "pyannote/voice-activity-detection",
     use_auth_token=HF_TOKEN
 )
 
-print("Loading Ultravox pipeline...")
+# 3. Ultravox ASR+LLM (generic pipeline)
 ultravox_pipe = pipeline(
     model="fixie-ai/ultravox-v0_4",
     trust_remote_code=True,
@@ -32,13 +31,13 @@ ultravox_pipe = pipeline(
     torch_dtype=torch.float16
 )
 
-print("Loading Audio Diffusion model...")
+# 4. Audio Diffusion (Diffusers loader)
 diff_pipe = DiffusionPipeline.from_pretrained(
     "teticio/audio-diffusion-instrumental-hiphop-256",
     torch_dtype=torch.float16
 ).to("cuda")
 
-print("Loading Dia TTS (sharded across GPUs)...")
+# 5. Dia TTS with device sharding
 dia = Dia.from_pretrained(
     "nari-labs/Dia-1.6B",
     device_map=device_map,
@@ -46,37 +45,34 @@ dia = Dia.from_pretrained(
     trust_remote_code=True
 )
 
-print("All models loaded successfully!")
-
 def process_audio(audio):
     sr, array = audio
     array = array.numpy() if torch.is_tensor(array) else array
 
-    # 1. Voice activity detection
-    vad_pipe({"waveform": torch.tensor(array).unsqueeze(0), "sample_rate": sr})
+    # VAD segmentation
+    _ = vad_pipe({"waveform": torch.tensor(array).unsqueeze(0), "sample_rate": sr})
 
-    # 2. RVQ encode/decode
-    x = torch.tensor(array).unsqueeze(0).to("cuda")
+    # RVQ encode/decode
+    x       = torch.tensor(array).unsqueeze(0).to("cuda")
     codes   = rvq.encode(x)
     decoded = rvq.decode(codes).squeeze().cpu().numpy()
 
-    # 3. Ultravox ASR → text
-    out = ultravox_pipe({"array": decoded, "sampling_rate": sr})
+    # Ultravox: speech → text
+    out  = ultravox_pipe({"array": decoded, "sampling_rate": sr})
     text = out.get("text", "")
 
-    # 4. Prosody diffusion
+    # Diffusion-based prosody
     pros = diff_pipe(raw_audio=decoded)["audios"][0]
 
-    # 5. Dia TTS synthesis
-    tts = dia.generate(f"[emotion:neutral] {text}")
+    # Dia TTS synthesis
+    tts   = dia.generate(f"[emotion:neutral] {text}")
     tts_np = tts.squeeze().cpu().numpy()
     tts_np = tts_np / np.max(np.abs(tts_np)) * 0.95 if tts_np.size else tts_np
 
     return (sr, tts_np), text
 
-# Gradio UI
 with gr.Blocks(title="Maya AI 📈") as demo:
-    gr.Markdown("## Maya-AI Supernatural Conversational Agent")
+    gr.Markdown("## Maya-AI: Supernatural Conversational Agent")
     audio_in  = gr.Audio(source="microphone", type="numpy", label="Your Voice")
     send_btn  = gr.Button("Send")
     audio_out = gr.Audio(label="AI Response")