Update app.py

app.py

@@ -9,25 +9,21 @@ from dia.model import Dia
 from dac.utils import load_model as load_dac_model
 from accelerate import init_empty_weights, load_checkpoint_and_dispatch
 
-# Retrieve HF token from Secrets
 HF_TOKEN = os.environ["HF_TOKEN"]
-
-# Automatic multi-GPU sharding across 4× L4 GPUs
 device_map = "auto"
 
-# 1. Descript Audio Codec (RVQ)
+# 1. RVQ Codec
 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")
 
-# 2. Voice Activity Detection via Pyannote
+# 2. VAD
 vad_pipe = PyannotePipeline.from_pretrained(
     "pyannote/voice-activity-detection",
     use_auth_token=HF_TOKEN
 )
 
-# 3. Ultravox ASR+LLM
+# 3. Ultravox
 ultravox_pipe = pipeline(
     model="fixie-ai/ultravox-v0_4",
     trust_remote_code=True,
@@ -35,17 +31,15 @@ ultravox_pipe = pipeline(
     torch_dtype=torch.float16
 )
 
-# 4. Audio Diffusion (direct load via Diffusers)
+# 4. Audio Diffusion
 diff_pipe = DiffusionPipeline.from_pretrained(
-    "teticio/audio-diffusion-instrumental-hiphop-256",
-    torch_dtype=torch.float16
+    "teticio/audio-diffusion-instrumental-hiphop-256"
 ).to("cuda")
 
-# 5. Dia TTS (multi-GPU dispatch)
+# 5. Dia TTS
 with init_empty_weights():
     dia = Dia.from_pretrained(
         "nari-labs/Dia-1.6B",
-        torch_dtype=torch.float16,
         trust_remote_code=True
     )
 dia = load_checkpoint_and_dispatch(
@@ -55,40 +49,35 @@ dia = load_checkpoint_and_dispatch(
     dtype=torch.float16
 )
 
-# 6. Inference Function
+# Inference
 def process_audio(audio):
     sr, array = audio
     array = array.numpy() if torch.is_tensor(array) else array
 
-    # VAD
     _ = vad_pipe(array, sampling_rate=sr)
-
-    # RVQ encode/decode
-    tensor = torch.tensor(array).unsqueeze(0).to("cuda")
-    codes = rvq.encode(tensor)
+    x = torch.tensor(array).unsqueeze(0).to("cuda")
+    codes = rvq.encode(x)
     decoded = rvq.decode(codes).squeeze().cpu().numpy()
 
-    # Ultravox inference
     ultra_out = ultravox_pipe({"array": decoded, "sampling_rate": sr})
     text = ultra_out.get("text", "")
 
-    # Diffusion enhancement
     pros = diff_pipe(raw_audio=decoded)["audios"][0]
 
-    # Dia TTS
-    tts = dia.generate(f"[emotion:neutral] {text}").squeeze().cpu().numpy()
-    tts = tts / np.max(np.abs(tts)) * 0.95
+    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
 
-    return (sr, tts), text
+    return (sr, tts_np), text
 
-# 7. Gradio UI
+# UI
 with gr.Blocks(title="Maya AI 📈") as demo:
     gr.Markdown("## Maya-AI: Supernatural Conversational Agent")
-    audio_input = gr.Audio(source="microphone", type="numpy", label="Your Voice")
-    send_button = gr.Button("Send")
-    audio_output = gr.Audio(label="AI’s Response")
-    text_output = gr.Textbox(label="Generated Text")
-    send_button.click(process_audio, inputs=audio_input, outputs=[audio_output, text_output])
+    audio_in = gr.Audio(source="microphone", type="numpy")
+    send_btn = gr.Button("Send")
+    audio_out = gr.Audio()
+    text_out = gr.Textbox()
+    send_btn.click(process_audio, inputs=audio_in, outputs=[audio_out, text_out])
 
 if __name__ == "__main__":
     demo.launch()