app.py

from fastapi import FastAPI, Response
from fastapi.responses import FileResponse
from kokoro import KPipeline
import soundfile as sf
import os
import numpy as np
import torch 
from huggingface_hub import InferenceClient

def llm_chat_response(text):
    HF_TOKEN = os.getenv("HF_TOKEN")
    client = InferenceClient(api_key=HF_TOKEN)
    messages = [
	{
		"role": "user",
		"content": [
			{
				"type": "text",
				"text": text + str('describe in one line only')
			} #,
			# {
			# 	"type": "image_url",
			# 	"image_url": {
			# 		"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
			# 	}
			# }
            ]
	}
    ]

    response_from_llama = client.chat.completions.create(
    model="meta-llama/Llama-3.2-11B-Vision-Instruct", 
	messages=messages, 
	max_tokens=500)

    return response_from_llama.choices[0].message['content']

app = FastAPI()

# Initialize pipeline once at startup
pipeline = KPipeline(lang_code='a')

@app.post("/generate")
async def generate_audio(text: str, voice: str = "af_heart", speed: float = 1.0):
    
    text_reply = llm_chat_response(text)
    
    # Generate audio
    generator = pipeline(
        text_reply, 
        voice=voice,
        speed=speed,
        split_pattern=r'\n+'
    )
    
    # # Save first segment only for demo
    # for i, (gs, ps, audio) in enumerate(generator):
    #     sf.write(f"output_{i}.wav", audio, 24000)
    #     return FileResponse(
    #         f"output_{i}.wav",
    #         media_type="audio/wav",
    #         filename="output.wav"
    #     )
    
    # return Response("No audio generated", status_code=400)


    # Process only the first segment for demo
    for i, (gs, ps, audio) in enumerate(generator):

        # Convert PyTorch tensor to NumPy array
        audio_numpy = audio.cpu().numpy()
        # Convert to 16-bit PCM
        
        # Ensure the audio is in the range [-1, 1]
        audio_numpy = np.clip(audio_numpy, -1, 1)
        # Convert to 16-bit signed integers
        pcm_data = (audio_numpy * 32767).astype(np.int16)
        
        # Convert to bytes (automatically uses row-major order)
        raw_audio = pcm_data.tobytes()
        
        # Return PCM data with minimal necessary headers
        return Response(
            content=raw_audio,
            media_type="application/octet-stream",
            headers={
                "Content-Disposition": f'attachment; filename="output.pcm"',
                "X-Sample-Rate": "24000",
                "X-Bits-Per-Sample": "16",
                "X-Endianness": "little"
            }
        )
    
    return Response("No audio generated", status_code=400)