4x faster than the fastest Whisper implementation AND more accurate.

• Batch transcribe multiple files in a directory and optionally all sub-directories.
• Optional timestamps with configurable segment intervals
• Works on GPU (CUDA) and CPU, Windows or Linux
• Supported file types: AAC, AMR, ASF, AVI, FLAC, M4A, MKV, MP3, MP4, WAV, WEBM, WMA
• Link to article on Medium

Installation

1. Windows (installer)

Download and run Elegant_Transcriber_Setup.exe (right-click and run as administrator)

2. Windows (from source)

Download the latest release, unzip and extract, then navigate to the directory containing main.py and run:

python -m venv .

.\Scripts\activate

python install.py

python main.py

3. Linux (from source)

Download the latest release, unzip and extract, then navigate to the directory containing main.py and run:

python3 -m venv .

source bin/activate

python install.py

python main.py

Benchmarks (GPU)

• ~2.5 hour audio file here: sam_altman_lex_podcast_367.flac

Library	Model	Batch	Chunk	VRAM Usage	Time	Real Time	Quality Ranking
Elegant Transcriber (NeMo)	Parakeet TDT 0.6B v2	1	90s	~3.3 GB	14.9s	580x	#8
Transformers	Whisper Large v3	32	Default	~12.4 GB	52.2s	166x	#32
WhisperS2T Reborn (Ctranslate2)	Whisper Large v3	32	Default	~13.4 GB	66.9s	129x	#32
Faster-Whisper (Ctranslate2)	Whisper Large v3	32	Default	~12.5 GB	75.9s	114x	#32
WhisperX (Ctranslate2)	Whisper Large v3	32	Default	~12.8 GB	71.8s	120x	#32
Transformers	Granite 4.0 1B Speech	12	30s	~6.3 GB	97.7s	88x	#1
Elegant Transcriber (NeMo)	Canary-Qwen-2.5b	1	40s	~11.1 GB	639.8ss	13.5x	#2

All models were run in bfloat16.
All VRAM measurements include model weights and inference overhead and subtract background usage.
All parameters were chosen to achieve a maximum throughput of ~90% CUDA core usage on an RTX 4090.

Benchmarks (CPU)

• ~13 minute private audio file.
• CPU tests use a shorter audio sample to keep runtimes manageable.

Library	Model	Batch	Chunk	RAM Usage	Time	Real Time	Quality Ranking
Elegant Transcriber	Parakeet TDT 0.6B v2	1	90s	~5.6 GB	29.0s	26.8x	#8
Faster-Whisper (Ctranslate2)	Whisper Large v3	1	Default	~6.5 GB	211.8s	3.67x	#32
WhisperS2T Reborn (Ctranslate2)	Whisper Large v3	1	Default	~6.6 GB	257.9s	3.02x	#32
Transformers	Whisper Large v3	1	Default	~6.6 GB	311.1s	2.50x	#32
Elegant Transcriber (NeMo)	Canary-Qwen-2.5b	1	40s	~11.1 GB	370.1ss	2.1x	#2
WhisperX (Ctranslate2)	Whisper Large v3	1	Default	~7.3 GB	396.4s	1.96x	#32

All models were loaded in float32 for CPU compatibility.
20 threads were used on an Intel 13900k resulting in ~90% CPU usage.
I couldn't get Granite Speech to run...

Special Thanks

• Nvidia for the Parkeet models, which are hands down the best balance of accuracy and compute time for most people IMHO.
• IBM for Granite Speech Models, which, as of March, 2026, rank #1 on the ASR leaderboard in terms of accuracy. I'll include them in a later release.
• OpenAI for the older Whisper models setting the gold standard for so many years.