Update main.py

main.py

@@ -1,26 +1,5 @@
 #uvicorn app:app --host 0.0.0.0 --port 8000 --reload
 
-# from fastapi import FastAPI
-# from transformers import pipeline
-
-# pipe = pipeline("automatic-speech-recognition", model="Pranjal12345/whisper-small-ne-pranjal")
-
-# audio_path = "/home/pranjal/Downloads/chinese_audio.mp3"
-
-# with open("/home/pranjal/Downloads/chinese_audio.mp3", "rb") as audio_file:
-#     audio_data = audio_file.read()
-
-# app = FastAPI()
-
-# @app.get("/")
-# def hello():
-#     output = pipe(input)
-#     return {"Output": output}
-
-
-
-
-
 
 from fastapi import FastAPI
 from transformers import WhisperProcessor, WhisperForConditionalGeneration
@@ -28,26 +7,19 @@ import librosa
 
 app = FastAPI()
 
-# Load model and processor
 processor = WhisperProcessor.from_pretrained("openai/whisper-small")
 model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
 model.config.forced_decoder_ids = None
 
-# Path to your audio file
 audio_file_path = "output.mp3"
 
-# Read the audio file
 audio_data, _ = librosa.load(audio_file_path, sr=16000)
 
 @app.get("/")
 def transcribe_audio():
-        # Process the audio data using the Whisper processor
         input_features = processor(audio_data.tolist(), return_tensors="pt").input_features
         
-        # Generate transcription
         predicted_ids = model.generate(input_features)
         transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
         
-        return {"transcription": transcription[0]}
-
-
+        return {"transcription": transcription[0]}