File size: 2,026 Bytes
84b0762 fc6bc0e 84b0762 fc6bc0e 84b0762 fc6bc0e 84b0762 fc6bc0e 435f259 fc6bc0e 84b0762 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | import streamlit as st
import ffmpeg
import os
from transformers import pipeline
import tempfile
# Define transcription function
def extract_audio(video_path, output_audio_path):
"""Extract audio from a video file."""
ffmpeg.input(video_path).output(output_audio_path, format="mp3", ac=1, ar="16000").run(overwrite_output=True)
return output_audio_path
def transcribe_audio(audio_path):
"""Transcribe audio using OpenAI Whisper."""
asr_pipeline = pipeline("automatic-speech-recognition", model="openai/whisper-base")
transcription = asr_pipeline(audio_path, return_timestamps=True)
return transcription["text"]
# Streamlit UI
st.title("Video-to-Text Transcription App")
st.write("Upload a video file to transcribe its audio content into text.")
# File upload
uploaded_file = st.file_uploader("Upload your video file (e.g., .mp4, .mov, etc.)", type=["mp4", "mov", "avi", "mkv"])
if uploaded_file is not None:
with st.spinner("Processing video..."):
# Save uploaded file to a temporary directory
with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_video:
temp_video.write(uploaded_file.read())
video_path = temp_video.name
# Extract audio
audio_path = os.path.join(tempfile.gettempdir(), "extracted_audio.mp3")
extract_audio(video_path, audio_path)
# Transcribe audio
transcription = transcribe_audio(audio_path)
# Display transcription
st.subheader("Transcription")
st.text_area("Transcribed Text", transcription, height=300)
# Save transcription to file
output_file = "transcription.txt"
with open(output_file, "w") as f:
f.write(transcription)
# Download transcription
with open(output_file, "rb") as file:
st.download_button(
label="Download Transcription",
data=file,
file_name="transcription.txt",
mime="text/plain"
)
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