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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List, Tuple
from huggingface_hub import InferenceClient
import edge_tts
import tempfile
import asyncio
import os
from fastapi.responses import FileResponse
from groq import Groq
app = FastAPI()
# Initialize the client for Hugging Face Inference API
# client = InferenceClient("unsloth/gemma-2b-it-bnb-4bit")
client = Groq(
api_key='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'
)
async def text_to_speech(text, voice, rate, pitch):
voice_short_name = voice.split(" - ")[0]
rate_str = f"{rate:+d}%"
pitch_str = f"{pitch:+d}Hz"
communicate = edge_tts.Communicate(text, voice_short_name, rate=rate_str, pitch=pitch_str)
submaker = edge_tts.SubMaker()
with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
tmp_path = tmp_file.name
async for chunk in communicate.stream():
if chunk["type"] == "audio":
tmp_file.write(chunk["data"])
elif chunk["type"] == "WordBoundary":
submaker.create_sub((chunk["offset"], chunk["duration"]), chunk["text"])
# with open('test.vtt', "w", encoding="utf-8") as file:
with tempfile.NamedTemporaryFile(delete=False, suffix=".txt", mode='w+', encoding='utf-8') as tmp_file:
tmp_vtt_path = tmp_file.name
tmp_file.write(submaker.generate_subs())
return tmp_path, tmp_vtt_path, None
def tts_interface(text, voice, rate, pitch):
audio, vtt, warning = asyncio.run(text_to_speech(text, voice, rate, pitch))
return audio, vtt, warning
@app.get("/")
def greet_json():
return {"Hello": "World!"}
# Define a model for the incoming request
class ChatRequest(BaseModel):
message: str
history: List[Tuple[str, str]] = []
system_message: str
max_tokens: int = 512
temperature: float = 0.7
top_p: float = 0.95
@app.get("/file/")
def file(path: str):
return FileResponse(path, media_type="audio/mpeg", filename="audio.mp3")
@app.get("/file-vtt/")
def fileVtt(path: str):
return FileResponse(path)
# Define a route to handle POST requests
@app.post("/chat")
def chat(request: ChatRequest):
messages = [{"role": "system", "content": request.system_message}]
for val in request.history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": request.message})
try:
response = client.chat.completions.create(
model="llama-3.1-8b-instant",
messages=messages,
max_tokens=request.max_tokens,
stream=False,
stop=None,
temperature=request.temperature,
top_p=request.top_p,
)
data = tts_interface((response.choices[0].message.content.replace('**', '')).replace('**', ''), 'en-GB-MaisieNeural - en-GB (Female)', 0, 0)
if os.path.exists(data[0]):
return {
"text": response.choices[0].message.content.replace('**', ''),
"audio" : data[0],
"vtt" : data[1]
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e)) |