api.py

# app.py

import asyncio
import base64
import io
import json
import time
from typing import AsyncGenerator

import numpy as np
import soundfile as sf
import torch
from fastapi import FastAPI, HTTPException
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
from omnivoice import OmniVoice

# =========================================================
# App
# =========================================================

app = FastAPI(title="OmniVoice OpenAI-Compatible TTS")

# =========================================================
# Constants
# =========================================================

SAMPLE_RATE = 24000
NUM_CHANNELS = 1
BYTES_PER_SAMPLE = 2

FRAME_MS = 20

CHUNK_SIZE = int(
    SAMPLE_RATE * (FRAME_MS / 1000) * BYTES_PER_SAMPLE * NUM_CHANNELS
)

# =========================================================
# Fixed Voice Config
# =========================================================

FIXED_REF_AUDIO = "ref_audio/women_ref_1.mp3"

FIXED_REF_TEXT = (
    "شوفي يا حلوة هالكريم الجديد للبشرة، يخلي وجهك مثل القمر!"
)

FIXED_INSTRUCT = "female, young adult, high pitch"

# =========================================================
# Load Model
# =========================================================

model = OmniVoice.from_pretrained(
    "/home/riftuser/OmniVoice/exp_v1/omnivoice_finetune/checkpoint-5000",
    device_map="cuda:0",
    dtype=torch.float16,
)

# Prevent concurrent GPU inference crashes
generation_lock = asyncio.Lock()

# =========================================================
# Request Schema
# =========================================================

class SpeechRequest(BaseModel):

    model: str = "omnivoice"

    input: str

    speed: float = 1.1

    response_format: str = "pcm"

    # audio | sse
    stream_format: str = "audio"


# =========================================================
# Audio Helpers
# =========================================================

def float32_to_pcm16(audio: np.ndarray) -> bytes:

    audio = np.clip(audio, -1, 1)

    pcm16 = (audio * 32767).astype(np.int16)

    return pcm16.tobytes()


# =========================================================
# Generate Audio
# =========================================================

async def generate_audio(req: SpeechRequest) -> np.ndarray:

    async with generation_lock:

        def _generate():

            with torch.inference_mode():
                
                print("*" * 50)
                print("user text : " , req.input)
                print("*" * 50)

                audio = model.generate(
                    text=req.input,
                    ref_audio=FIXED_REF_AUDIO,
                    ref_text=FIXED_REF_TEXT,
                    instruct=FIXED_INSTRUCT,
                    speed=req.speed,
                    num_step = 30,
                    guidance_scale=2.0,
                    t_shift=0.1,
                    position_temperature=3,
                    layer_penalty_factor=5.0,
                )

            return audio[0]

        return await asyncio.to_thread(_generate)


# =========================================================
# Raw Audio Stream
# =========================================================

async def audio_stream_generator(
    req: SpeechRequest,
) -> AsyncGenerator[bytes, None]:

    audio = await generate_audio(req)

    if req.response_format == "pcm":

        pcm_bytes = float32_to_pcm16(audio)

        for i in range(0, len(pcm_bytes), CHUNK_SIZE):

            yield pcm_bytes[i:i + CHUNK_SIZE]

            await asyncio.sleep(0)

    elif req.response_format == "wav":

        buffer = io.BytesIO()

        sf.write(
            buffer,
            audio,
            SAMPLE_RATE,
            format="WAV",
        )

        buffer.seek(0)

        while True:

            chunk = buffer.read(4096)

            if not chunk:
                break

            yield chunk

            await asyncio.sleep(0)

    else:

        raise HTTPException(
            status_code=400,
            detail=f"Unsupported response_format: {req.response_format}"
        )


# =========================================================
# SSE Stream
# =========================================================

async def sse_stream_generator(
    req: SpeechRequest,
) -> AsyncGenerator[str, None]:

    start_time = time.time()

    audio = await generate_audio(req)

    generation_time = time.time() - start_time

    pcm_bytes = float32_to_pcm16(audio)

    for i in range(0, len(pcm_bytes), CHUNK_SIZE):

        chunk = pcm_bytes[i:i + CHUNK_SIZE]

        b64_chunk = base64.b64encode(chunk).decode("utf-8")

        event = {
            "type": "speech.audio.delta",
            "delta": b64_chunk,
        }

        yield f"data: {json.dumps(event)}\n\n"

        await asyncio.sleep(0)

    audio_duration = len(audio) / SAMPLE_RATE

    usage = {
        "input_tokens": len(req.input.split()),
        "output_tokens": int(audio_duration * 50),
    }

    done_event = {
        "type": "speech.audio.done",
        "usage": usage,
        "metrics": {
            "generation_time_sec": generation_time,
            "audio_duration_sec": audio_duration,
            "rtf": round(generation_time / audio_duration, 4),
        }
    }

    yield f"data: {json.dumps(done_event)}\n\n"

    yield "data: [DONE]\n\n"


# =========================================================
# OpenAI-Compatible Endpoint
# =========================================================

@app.post("/v1/audio/speech")
async def create_speech(req: SpeechRequest):

    if req.stream_format == "sse":

        return StreamingResponse(
            sse_stream_generator(req),
            media_type="text/event-stream",
            headers={
                "Cache-Control": "no-cache",
                "Connection": "keep-alive",
            },
        )

    media_type = (
        "audio/pcm"
        if req.response_format == "pcm"
        else "audio/wav"
    )

    return StreamingResponse(
        audio_stream_generator(req),
        media_type=media_type,
    )


# =========================================================
# Health
# =========================================================

@app.get("/health")
async def health():

    return {
        "status": "ok",
        "sample_rate": SAMPLE_RATE,
        "voice": {
            "ref_audio": FIXED_REF_AUDIO,
            "instruct": FIXED_INSTRUCT,
        }
    }