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from fastapi import APIRouter, File, UploadFile, HTTPException
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
from ai4b import BhashiniClient
from fast_langdetect import detect
import io
import base64
router = APIRouter()
ULCA_USER_ID = os.getenv("ULCA_USER_ID")
ULCA_API_KEY = os.geteenv("ULCA_API_KEY")
client = BhashiniClient(user_id=USER_ID, api_key=ULCA_API_KEY)
class TTSRequest(BaseModel):
text: str
gender: str = "female"
SUPPORTED_LANGUAGES = {'pa', 'mr', 'bn', 'en', 'as', 'or', 'ta', 'te', 'kn', 'gu', 'hi', 'ml'}
def detect_language(text):
text = text.replace("\n", " ")
try:
result = detect(text, low_memory=False)
detected_lang = result['lang']
if detected_lang in SUPPORTED_LANGUAGES:
return detected_lang
except:
pass
if any('\u0980' <= char <= '\u09FF' for char in text):
return 'brx'
elif any('\uABC0' <= char <= '\uABFF' for char in text):
return 'mni'
return 'en'
@router.post("/tts")
async def text_to_speech(request: TTSRequest):
try:
detected_language = detect_language(request.text)
tts_result = client.tts(
request.text,
source_language=detected_language,
gender=request.gender
)
audio_base64 = tts_result['pipelineResponse'][0]['audio'][0]['audioContent']
audio_data = base64.b64decode(audio_base64)
return StreamingResponse(io.BytesIO(audio_data), media_type="audio/wav")
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@router.post("/asr")
async def speech_to_text(file: UploadFile = File(...), source_language: str = "ml"):
try:
audio_content = await file.read()
asr_result = client.asr(audio_content, source_language=source_language)
return {"transcription": asr_result}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e)) |