Faster Whisper SRT Converter

Faster Whisper SRT Converter

Purpose

Convert audio and video files to SRT subtitle files using the faster-whisper speech recognition engine. Supports multiple Whisper model sizes, real-time progress display, and automatic audio extraction from video files.

When to Use

• User wants to convert audio files (MP3, WAV, M4A, FLAC, OGG, AAC, WMA) to SRT subtitles
• User wants to extract subtitles from video files (MP4, MKV, AVI, MOV, WEBM, FLV)
• User mentions "transcribe", "subtitles", "SRT", "字幕", "逐字稿"

Prerequisites

• Python 3.10+
• faster-whisper and tqdm packages (pip install -r requirements.txt)
• FFmpeg (only required for video file input)

Usage

Step 1: Run the Script

Execute the conversion script with the target audio or video file:

python faster_whisper_srt.py <input_file> [--model MODEL] [--max-chars MAX_CHARS]

Step 2: Choose a Model (Optional)

Available models, from fastest to most accurate:

Step 3: Check Output

The output SRT file will be saved in the same directory as the input file, named: <original_name>_<model>.srt

Examples

# Basic usage (default: medium model, 40 chars per line)
python faster_whisper_srt.py interview.mp3

# Use a fast model for testing
python faster_whisper_srt.py interview.mp3 --model tiny

# Use the best model for final output
python faster_whisper_srt.py interview.mp3 --model large-v3-turbo

# Shorter subtitle lines
python faster_whisper_srt.py interview.mp3 --max-chars 25

# Video file input (requires FFmpeg)
python faster_whisper_srt.py presentation.mp4 --model medium

Notes

• First-time use of a model will trigger an automatic download. Subsequent runs use the cached model.
• The script defaults to Chinese (zh) language detection. Modify the language parameter in the script for other languages.
• Video processing requires FFmpeg to be installed and available in PATH.
• Audio-only files (MP3, WAV, etc.) do NOT require FFmpeg.