# # #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 # # # # 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) #uvicorn app:app --host 0.0.0.0 --port 8000 --reload from fastapi import FastAPI, UploadFile, File from transformers import WhisperProcessor, WhisperForConditionalGeneration from fastapi.responses import HTMLResponse import librosa import io import re html_tag_remover = re.compile(r'<[^>]+>') def remove_tags(text): return html_tag_remover.sub('', text) app = FastAPI() processor = WhisperProcessor.from_pretrained("openai/whisper-medium") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-medium") model.config.forced_decoder_ids = None chunk_duration = 30 overlap_duration = 5 @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): audio_data = await audio_file.read() audio_data, _ = librosa.load(io.BytesIO(audio_data), sr=16000) transcription = [] start = 0 while start < len(audio_data): end = start + chunk_duration * 16000 audio_chunk = audio_data[start:end] input_features = processor(audio_chunk.tolist(), return_tensors="pt").input_features predicted_ids = model.generate(input_features, max_length=1000) chunk_transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) transcription.extend(chunk_transcription) start = end - overlap_duration * 16000 final_transcription = " ".join(transcription) final_transcription = remove_tags(final_transcription) return final_transcription