app.py

import os
import json
import asyncio
import torch
from fastapi import FastAPI, WebSocket, WebSocketDisconnect
from dotenv import load_dotenv
from snac import SNAC
from transformers import AutoModelForCausalLM, AutoTokenizer
from huggingface_hub import login, snapshot_download

# — ENV & HF‑AUTH —
load_dotenv()
HF_TOKEN = os.getenv("HF_TOKEN")
if HF_TOKEN:
    login(token=HF_TOKEN)

# — Device —
device = "cuda" if torch.cuda.is_available() else "cpu"

# — SNAC laden —
print("Loading SNAC model...")
snac = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").to(device)

# — Orpheus‑Modell vorbereiten —
model_name = "SebastianBodza/Kartoffel_Orpheus-3B_german_natural-v0.1"

# Nur Konfig+Weights (ermöglicht schlankeren Container)
snapshot_download(
    repo_id=model_name,
    allow_patterns=["config.json", "*.safetensors", "model.safetensors.index.json"],
    ignore_patterns=[
        "optimizer.pt", "pytorch_model.bin", "training_args.bin",
        "scheduler.pt", "tokenizer.json", "tokenizer_config.json",
        "special_tokens_map.json", "vocab.json", "merges.txt", "tokenizer.*"
    ]
)

print("Loading Orpheus model...")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
).to(device)
model.config.pad_token_id = model.config.eos_token_id

tokenizer = AutoTokenizer.from_pretrained(model_name)


# — Hilfsfunktionen —

def process_prompt(text: str, voice: str):
    prompt = f"{voice}: {text}"
    inputs = tokenizer(prompt, return_tensors="pt").to(device)
    # füge Start-/End-Tokens hinzu
    start = torch.tensor([[128259]], device=device)
    end   = torch.tensor([[128009, 128260]], device=device)
    input_ids = torch.cat([start, inputs.input_ids, end], dim=1)
    return input_ids

def parse_output(generated_ids: torch.LongTensor):
    token_to_find   = 128257
    token_to_remove = 128258

    idxs = (generated_ids == token_to_find).nonzero(as_tuple=True)[1]
    if idxs.numel() > 0:
        cropped = generated_ids[:, idxs[-1].item() + 1 :]
    else:
        cropped = generated_ids

    row = cropped[0][cropped[0] != token_to_remove]
    return row.tolist()

def redistribute_codes(code_list: list[int], snac_model: SNAC):
    layer1, layer2, layer3 = [], [], []
    for i in range((len(code_list) + 1) // 7):
        base = code_list[7*i : 7*i+7]
        layer1.append(base[0])
        layer2.append(base[1] -   4096)
        layer3.append(base[2] - 2*4096)
        layer3.append(base[3] - 3*4096)
        layer2.append(base[4] - 4*4096)
        layer3.append(base[5] - 5*4096)
        layer3.append(base[6] - 6*4096)

    dev = next(snac_model.parameters()).device
    codes = [
        torch.tensor(layer1, device=dev).unsqueeze(0),
        torch.tensor(layer2, device=dev).unsqueeze(0),
        torch.tensor(layer3, device=dev).unsqueeze(0),
    ]
    audio = snac_model.decode(codes)
    return audio.detach().squeeze().cpu().numpy()


# — FastAPI App —  
app = FastAPI()

@app.get("/")
async def hello():
    return {"message": "Hello, Orpheus TTS is up and running!"}

@app.websocket("/ws/tts")
async def tts_ws(ws: WebSocket):
    await ws.accept()
    try:
        # **Nur EIN Request pro Connection**
        raw = await ws.receive_text()
        data = json.loads(raw)
        text  = data.get("text", "")
        voice = data.get("voice", "Jakob")

        # 1) Text → input_ids
        input_ids = process_prompt(text, voice)

        # 2) Generation
        gen_ids = model.generate(
            input_ids=input_ids,
            max_new_tokens=2000,    # hier kannst du hochsetzen
            do_sample=True,
            temperature=0.7,
            top_p=0.95,
            repetition_penalty=1.1,
            eos_token_id=model.config.eos_token_id,
        )

        # 3) Token → Audio
        codes    = parse_output(gen_ids)
        audio_np = redistribute_codes(codes, snac)

        # 4) PCM16-Bytes in ~0.1s‑Chunks streamen
        pcm16 = (audio_np * 32767).astype("int16").tobytes()
        chunk_size = 2400 * 2  # 2400 Samples @24kHz = 0.1s * 2 Byte
        for i in range(0, len(pcm16), chunk_size):
            await ws.send_bytes(pcm16[i : i+chunk_size])
            await asyncio.sleep(0.1)

        # Sauber schließen, Client erhält ConnectionClosedOK
        await ws.close()

    except WebSocketDisconnect:
        print("Client disconnected")
    except Exception as e:
        # Log und saubere Fehler‑Closure
        print("Error in /ws/tts:", e)
        await ws.close(code=1011)