├── audo_sample.m4a
├── README.md
└── streamlit_whisper.py


/audo_sample.m4a:
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https://raw.githubusercontent.com/EnkrateiaLucca/openai_whisper/HEAD/audo_sample.m4a


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/README.md:
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1 | # Whisper App with Streamlit
2 | 
3 | Python script for my article and Youtube video on building 
4 | a streamlit app to use whisper for speech-to-text transcription
5 | 
6 | - *Youtube Video*: https://www.youtube.com/watch?v=cNLXzXyuzUs&t=375s
7 | - *Article*: https://medium.com/p/9cd1ab6a24b0
8 | 


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/streamlit_whisper.py:
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 1 | # Install dependencies
 2 | 
 3 | # Linux
 4 | # sudo apt update && sudo apt install ffmpeg
 5 | 
 6 | # MacOS
 7 | # brew install ffmpeg
 8 | 
 9 | # Windows
10 | # chco install ffmpeg
11 | 
12 | # Installing pytorch
13 | # conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
14 | 
15 | # Installing Whisper
16 | # pip install git+https://github.com/openai/whisper.git -q
17 | 
18 | # pip install streamlit
19 | import streamlit as st
20 | import whisper
21 | 
22 | st.title("Whisper App")
23 | 
24 | # upload audio file with streamlit
25 | audio_file = st.file_uploader("Upload Audio", type=["wav", "mp3", "m4a"])
26 | 
27 | model = whisper.load_model("base")
28 | st.text("Whisper Model Loaded")
29 | 
30 | 
31 | if st.sidebar.button("Transcribe Audio"):
32 |     if audio_file is not None:
33 |         st.sidebar.success("Transcribing Audio")
34 |         transcription = model.transcribe(audio_file.name)
35 |         st.sidebar.success("Transcription Complete")
36 |         st.markdown(transcription["text"])
37 |     else:
38 |         st.sidebar.error("Please upload an audio file")
39 | 
40 | 
41 | st.sidebar.header("Play Original Audio File")
42 | st.sidebar.audio(audio_file)
43 | 
44 | 
45 | 
46 | 
47 | 
48 | 
49 | 
50 | 


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