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# 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(
# provider="hf-inference",
# api_key=HF_TOKEN,)
# response_from_llama = client.chat.completions.create(
# model="meta-llama/Llama-3.2-11B-Vision-Instruct",
# messages=[
# {
# "role": "user",
# "content": [
# {
# "type": "text",
# "text": "Describe this image in one sentence."
# }#,
# # {
# # "type": "image_url",
# # "image_url": {
# # "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
# # }
# # }
# ]
# }
# ],
# 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)
from fastapi import FastAPI, Response
from fastapi.responses import FileResponse
from kokoro import KPipeline
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(
provider="novita", # Use the provider that supports conversational image-text tasks.
api_key=HF_TOKEN,
)
# Build the message payload; here we append a prompt suffix when no image is involved.
messages = [{
"role": "user",
"content": [
{
"type": "text",
"text": text + " describe in one line only"
}
]
}]
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 using the pipeline
generator = pipeline(
text_reply,
voice=voice,
speed=speed,
split_pattern=r'\n+'
)
# Process only the first segment for demonstration
for i, (gs, ps, audio) in enumerate(generator):
# Convert PyTorch tensor to NumPy array and prepare 16-bit PCM data
audio_numpy = audio.cpu().numpy()
audio_numpy = np.clip(audio_numpy, -1, 1)
pcm_data = (audio_numpy * 32767).astype(np.int16)
raw_audio = pcm_data.tobytes()
return Response(
content=raw_audio,
media_type="application/octet-stream",
headers={
"Content-Disposition": 'attachment; filename="output.pcm"',
"X-Sample-Rate": "24000",
"X-Bits-Per-Sample": "16",
"X-Endianness": "little"
}
)
return Response("No audio generated", status_code=400)
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