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import json
import whisper
import requests
large_multi = whisper.load_model("large-v3")
# def send_results_to_api(data, result_url):
# headers = {"Content-Type":"application/json"}
# response = requests.post(result_url, json= data, headers=headers)
# if response.status_code == 200:
# return response.json()
# else:
# return {"error": f"Failed to send results to API: {response.status_code}"}
def process_audio(params):
solutions=[]
params = json.loads(params)
audio_files = params.get("urls",[])
if not params.get("normalfileID",[]):
file_ids = [None]*len(audio_files)
else:
file_ids = params.get("normalfileID",[])
# api = params.get("api", "")
# job_id = params.get("job_id", "")
for audio, file_id in zip(audio_files,file_ids):
result_multi = large_multi.transcribe(audio)
text = result_multi['text']
answer_dict = {}
answer_dict.update({'url':audio, 'answer':text, "qcUser": None, "normalfileID": file_id})
solutions.append(answer_dict)
# result_url = f"{api}/{job_id}"
# send_results_to_api(solutions, result_url)
return json.dumps({"solutions":solutions})
import gradio as gr
inputt = gr.Textbox(label = "Parameter in json format Eg. {'audio_files':['file1.mp3','file2.wav'], 'api':'https://api.example.com', 'job_id':'1001'}")
outputt = gr.JSON()
application = gr.Interface(fn=process_audio, inputs = inputt, outputs= outputt, title="Multilingual (Hindi/English) Audio transcription with API Intergration")
application.launch() |