app.py

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

# — HF‑Token & Login —
HF_TOKEN = os.getenv("HF_TOKEN")
if HF_TOKEN:
    login(HF_TOKEN)

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

# — Modell‑Parameter —
MODEL_NAME         = "SebastianBodza/Kartoffel_Orpheus-3B_german_natural-v0.1"
START_MARKER       = 128259        # <|startoftranscript|>
RESTART_MARKER     = 128257        # <|startoftranscript_again|>
EOS_TOKEN          = 128258        # <|endoftranscript|>
AUDIO_TOKEN_OFFSET = 128266        # Offset zum Zurückrechnen
BLOCK_TOKENS       = 7             # SNAC erwartet 7 Audio‑Tokens pro Block
CHUNK_TOKENS       = 50            # Anzahl neuer Tokens pro Generate‑Runde

# — FastAPI instanziieren —
app = FastAPI()

# — Damit GET / nicht 404 wirft —
@app.get("/")
async def read_root():
    return {"message": "Orpheus TTS Server ist live 🎙️"}

# — Modelle bei Startup laden —
@app.on_event("startup")
async def load_models():
    global tokenizer, model, snac
    # SNAC laden
    snac = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").to(device)
    # Tokenizer
    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
    # TTS‑LM
    model = AutoModelForCausalLM.from_pretrained(
        MODEL_NAME,
        device_map="auto",
        torch_dtype=torch.bfloat16 if device=="cuda" else None,
        low_cpu_mem_usage=True
    )
    model.config.pad_token_id = EOS_TOKEN

# — Eingabe aufbereiten —
def prepare_inputs(text: str, voice: str):
    prompt     = f"{voice}: {text}"
    input_ids  = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
    start      = torch.tensor([[START_MARKER]], device=device)
    end        = torch.tensor([[128009, EOS_TOKEN]], device=device)
    ids        = torch.cat([start, input_ids, end], dim=1)
    attn_mask  = torch.ones_like(ids)
    return ids, attn_mask

# — Aus 7 Audio‑Tokens ein PCM‑Block erzeugen —
def decode_block(block: list[int]) -> bytes:
    l1, l2, l3 = [], [], []
    b = block
    l1.append(b[0])
    l2.append(b[1] -   4096)
    l3.append(b[2] - 2*4096)
    l3.append(b[3] - 3*4096)
    l2.append(b[4] - 4*4096)
    l3.append(b[5] - 5*4096)
    l3.append(b[6] - 6*4096)
    codes = [
        torch.tensor(l1, device=device).unsqueeze(0),
        torch.tensor(l2, device=device).unsqueeze(0),
        torch.tensor(l3, device=device).unsqueeze(0),
    ]
    audio = snac.decode(codes).squeeze().cpu().numpy()
    pcm16 = (audio * 32767).astype("int16").tobytes()
    return pcm16

# — Generator: kleine Chunks token‑weise erzeugen und block‑weise dekodieren —
async def generate_and_stream(ws: WebSocket, ids, attn_mask):
    buffer: list[int] = []
    past_kvs = None

    while True:
        # wir rufen model.generate in Häppchen auf
        outputs = model.generate(
            input_ids      = ids      if past_kvs is None else None,
            attention_mask = attn_mask if past_kvs is None else None,
            past_key_values= past_kvs,
            use_cache      = True,
            max_new_tokens = CHUNK_TOKENS,
            do_sample      = True,
            temperature    = 0.7,
            top_p          = 0.95,
            repetition_penalty = 1.1,
            eos_token_id   = EOS_TOKEN,
            pad_token_id   = EOS_TOKEN,
            return_dict_in_generate = True,
            output_scores           = False,
        )

        # update past_kvs
        past_kvs = outputs.past_key_values

        # erhalte nur die gerade neu generierten Token
        seq       = outputs.sequences[0]
        new_tokens = seq[-CHUNK_TOKENS:].tolist() if past_kvs is not None else seq[ids.shape[-1]:].tolist()

        for tok in new_tokens:
            # Neustart bei erneutem START‑Marker
            if tok == RESTART_MARKER:
                buffer = []
                continue
            # Ende
            if tok == EOS_TOKEN:
                return
            # Audio‑Code berechnen
            buffer.append(tok - AUDIO_TOKEN_OFFSET)
            # sobald 7 Audio‑Tokens, dekodieren und streamen
            if len(buffer) >= BLOCK_TOKENS:
                block = buffer[:BLOCK_TOKENS]
                buffer = buffer[BLOCK_TOKENS:]
                pcm   = decode_block(block)
                await ws.send_bytes(pcm)

# — WebSocket‑Endpoint für TTS Streaming —
@app.websocket("/ws/tts")
async def tts_ws(ws: WebSocket):
    await ws.accept()
    try:
        data = await ws.receive_text()
        req  = json.loads(data)
        text  = req.get("text", "")
        voice = req.get("voice", "Jakob")

        ids, attn_mask = prepare_inputs(text, voice)
        await generate_and_stream(ws, ids, attn_mask)

        await ws.close()
    except WebSocketDisconnect:
        pass
    except Exception as e:
        print("Error in /ws/tts:", e)
        await ws.close(code=1011)

if __name__ == "__main__":
    import uvicorn
    uvicorn.run("app:app", host="0.0.0.0", port=7860)