# #uvicorn app:app --host 0.0.0.0 --port 8000 --reload
# # from fastapi import FastAPI
# # from transformers import WhisperProcessor, WhisperForConditionalGeneration
# # import librosa
# # import uvicorn
# # app = FastAPI()
# # processor = WhisperProcessor.from_pretrained("openai/whisper-small")
# # model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
# # model.config.forced_decoder_ids = None
# # audio_file_path = "output.mp3"
# # audio_data, _ = librosa.load(audio_file_path, sr=16000)
# # @app.get("/")
# # def transcribe_audio():
# # input_features = processor(audio_data.tolist(), return_tensors="pt").input_features
# # predicted_ids = model.generate(input_features)
# # transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
# # return {"transcription": transcription[0]}
# # if __name__ == "__main__":
# # import uvicorn
# # uvicorn.run(app, host="0.0.0.0", port=8000)
# # if __name__=='__main__':
# # uvicorn.run('main:app', reload=True)
# #uvicorn app:app --host 0.0.0.0 --port 8000 --reload
# #curl -X GET "http://localhost:8000/?text=I%20like%20Apples"
# #http://localhost:8000/?text=I%20like%20Apples
# # from fastapi import FastAPI
# # from transformers import WhisperProcessor, WhisperForConditionalGeneration
# # import librosa
# # import uvicorn
# # 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 = "/home/pranjal/Downloads/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]}
# # if __name__ == "__main__":
# # import uvicorn
# # uvicorn.run(app, host="0.0.0.0", port=8000)
# # if __name__=='__app__':
# # uvicorn.run('main:app', reload=True)
# from fastapi import FastAPI, UploadFile, File
# from transformers import WhisperProcessor, WhisperForConditionalGeneration
# import librosa
# from fastapi.responses import HTMLResponse
# import uvicorn
# import io
# 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
# @app.get("/")
# def read_root():
# html_form = """
#
#
# ASR Transcription
#
#
#
# """
# return HTMLResponse(content=html_form, status_code=200)
# @app.post("/transcribe")
# async def transcribe_audio(audio_file: UploadFile):
# try:
# # Read the uploaded audio file
# audio_data = await audio_file.read()
# # Process the audio data using the Whisper processor
# audio_data, _ = librosa.load(io.BytesIO(audio_data), sr=16000)
# 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]}
# except Exception as e:
# return {"error": str(e)}
# if __name__ == "__app__":
# uvicorn.run(app, host="0.0.0.0", port=8000, reload=True)
#uvicorn app:app --host 0.0.0.0 --port 8000 --reload
from fastapi import FastAPI, UploadFile, File
from transformers import WhisperProcessor, WhisperForConditionalGeneration
import librosa
from fastapi.responses import HTMLResponse
import uvicorn
import io
app = FastAPI()
# # Load model and processor
# processor = WhisperProcessor.from_pretrained("openai/whisper-medium")
# model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-medium")
# model.config.forced_decoder_ids = None
import whisper
model = whisper.load_model("small")
@app.get("/")
def read_root():
html_form = """
ASR Transcription
"""
return HTMLResponse(content=html_form, status_code=200)
@app.post("/transcribe")
async def transcribe_audio(audio_file: UploadFile):
try:
# Read the uploaded audio file
audio_data = await audio_file.read()
# Process the audio data using the Whisper processor
# audio_data, _ = librosa.load(io.BytesIO(audio_data), sr=16000)
# 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)
result = model.transcribe("/home/pranjal/Downloads/rt.mp3")
return {"transcription": result['text']}
except Exception as e:
return {"error": str(e)}
# if __name__ == "__app__":
# uvicorn.run(app, host="0.0.0.0", port=8000, reload=True)