Video Transcription & Sentiment Analysis

Project Overview

1. Extract audio from a video file.
2. Segment audio into 5-seconds chunks.
3. Enhance audio quantity and save as new chunks.
4. Perform speech-to-text transcription using Whisper.
5. Conduct sentiment analysis on the transcribed text using TextBlob.
6. Save results in CSV.
7. Visualize:
   • Histogram - voice activity per 5-second window
   • Sentiment Classification Chart - Statistical Positive / Neutral / Negative Ratio

📺 Explanation Video:

⚙️ Installation

pip install -r requirements.txt
sudo apt install ffmpeg  # Linux
brew install ffmpeg  # macOS
winget install ffmpeg  # Windows

requirements.txt contents:

pandas
numpy
opencv-python
matplotlib
tqdm
ipython
speechrecognition
pydub
textblob
nltk
seaborn
vosk
wave

Execution Instructions

Ensure you have download the video.

python main.ipynb

Processing Details

Step 1: Load Video & Get Metadata

🔹 Model: OpenCV 🔹 Input: Video files (.mp4 format) 🔹 Output: Metadata

MetaData
# of frames	10691.0
Frame Height	10691.0
Frame Width	10691.0
Frame per second	29.964424428092247

Step 2: Extracting Audio

🔹 Model: ffmpeg
🔹 Input: Video files (.mp4 format)
🔹 Output: Audio files (.wav format)

1️⃣ Invoke ffmpeg to extract audio from video.

2️⃣ "-ac", "1": Convert audio to single-channel

3️⃣ "-ar", "16000": Set audio sample rate to 16 kHz (16,000 Hz).

4️⃣ "-acodec", "pcm_s16le": Specify the audio encoding format as pcm_s16le, i.e. linear PCM encoding, 16-bit small-end format (uncompressed audio format, commonly used for high-quality audio)

Step 3: Segmenting Audio

🔹 Model: AudioSegment 🔹 Input: Audio files (.wav format)
🔹 Output: chunk_i.wav

1️⃣ Split into 5-second segments for efficient processing.

Step 4: Enhance Segmenting Audio

🔹 Model: AudioSegment 🔹 Input: chunk_i.wav
🔹 Output: chunk_i_enhanced.wav

1️⃣ Enhance the volume audio + 10: Increase audio by 10 decibels (dB) to increase the volume and make the audio louder

2️⃣ Human voice is usually between 300 Hz and 3 kHz.

high_pass_filter(louder_audio, cutoff=300): Frequencies in the audio that are lower than 300 Hz are filtered out, removing low-frequency noises, such as wind, bass, etc.

low_pass_filter(filtered_audio, cutoff=3000): Frequencies in the audio above 3000 Hz will be filtered out.

3️⃣ Normalise to balance the volume normalized_audio = normalize(filtered_audio): adjust the overall volume of the audio so that the maximum volume of the audio reaches a certain standard value (usually 0 dB) to help balance the volume of the audio from being too high or too low

4️⃣ Save the processed audio

Step 5: Transcribe each chunk

🔹 Model: Whisper (base model)
🔹 Input: chunk_i_enhanced.wav
🔹 Output: CSV file with timestamps and transcriptions

Timestamp	Transcription
0s	Okay, so the
5s
10s	you're going to complete and use the key auto...
15s	upgrade vehicle, and keep your needs off the ...
20s	Okay, so when you see that some driver indica...

The output file is named: whisper_video_1731617801_transcripts.csv

Step 6: Sentiment Analysis

🔹 Model: TextBlob (A lexicon-based sentiment analysis tool)
🔹 Input: chunk_i_enhanced.wav
🔹 Output: CSV file with timestamps, transcriptions, Sentiment

Timestamp	Transcription	Sentiment
0		Neutral
5	Okay, so the	Positive
10	you're going to complete and use the key auto...	Positive
15	upgrade vehicle, and keep your needs off the ...	Positive
20	Okay, so when you see that some driver indica...	Positive

The output file is named: video_1731617801_sentiments.csv

Step 7: Visualization & Analysis

1️⃣ Voice Activity Histogram

• Histogram - voice activity per 5-second window

2️⃣ Sentiment Distribution Plot

• Sentiment Classification Chart - Statistical Positive / Neutral / Negative Ratio