app.py

import os
import gradio as gr
import torch
import numpy as np
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
from accelerate import init_empty_weights, load_checkpoint_and_dispatch

HF_TOKEN = os.environ["HF_TOKEN"]
device_map = "auto"

# 1. RVQ Codec
rvq = load_dac_model(tag="latest", model_type="44khz")
rvq.eval()
if torch.cuda.is_available(): rvq = rvq.to("cuda")

# 2. VAD
vad_pipe = PyannotePipeline.from_pretrained(
    "pyannote/voice-activity-detection",
    use_auth_token=HF_TOKEN
)

# 3. Ultravox
ultravox_pipe = pipeline(
    model="fixie-ai/ultravox-v0_4",
    trust_remote_code=True,
    device_map=device_map,
    torch_dtype=torch.float16
)

# 4. Audio Diffusion
diff_pipe = DiffusionPipeline.from_pretrained(
    "teticio/audio-diffusion-instrumental-hiphop-256"
).to("cuda")

# 5. Dia TTS
with init_empty_weights():
    dia = Dia.from_pretrained(
        "nari-labs/Dia-1.6B",
        trust_remote_code=True
    )
dia = load_checkpoint_and_dispatch(
    dia,
    "nari-labs/Dia-1.6B",
    device_map=device_map,
    dtype=torch.float16
)

# Inference
def process_audio(audio):
    sr, array = audio
    array = array.numpy() if torch.is_tensor(array) else array

    _ = vad_pipe(array, sampling_rate=sr)
    x = torch.tensor(array).unsqueeze(0).to("cuda")
    codes = rvq.encode(x)
    decoded = rvq.decode(codes).squeeze().cpu().numpy()

    ultra_out = ultravox_pipe({"array": decoded, "sampling_rate": sr})
    text = ultra_out.get("text", "")

    pros = diff_pipe(raw_audio=decoded)["audios"][0]

    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_np), text

# UI
with gr.Blocks(title="Maya AI 📈") as demo:
    gr.Markdown("## Maya-AI: Supernatural Conversational Agent")
    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()