Whisper Local Setup: Fast Speech-to-Text on Your Own Hardware

OpenAI's Whisper is the best open-source speech recognition model available. Running it locally means infinite transcriptions, complete privacy, and no API costs. The large-v3 model transcribes a one-hour recording in under 3 minutes on an RTX 4090.

Whisper Model Sizes

ModelVRAMSpeed (RTX 4090)Word Error Rate
tiny1 GB~32x realtime~12%
base1 GB~16x realtime~9%
small2 GB~6x realtime~6%
large-v36 GB~1.5x realtime~2.7% ★
turbo6 GB~8x realtime~3.5%

faster-whisper: 4x Speed Improvement

pip install faster-whisper

python3 -c "
from faster_whisper import WhisperModel

# Load large-v3 on GPU (6GB VRAM)
model = WhisperModel('large-v3', device='cuda', compute_type='float16')

# Transcribe
segments, info = model.transcribe('meeting.mp3', beam_size=5)
print(f'Language: {info.language}, probability: {info.language_probability:.2f}')
for segment in segments:
    print(f'[{segment.start:.1f}s - {segment.end:.1f}s] {segment.text}')
"

Real-Time Transcription

pip install whisper-live

# Start real-time transcription server
python -m whisper_live.server --port 9090 --model large-v3

# Client sends microphone audio, server streams back text

GPU vs CPU Performance

On CPU only, large-v3 runs at ~0.1x realtime (very slow). An 8GB GPU (RTX 3070 or RTX 4060) runs large-v3 at ~1.5x realtime, making it viable for transcribing pre-recorded audio. For real-time transcription, you want a faster GPU. See recommended hardware at LLMHardware.io's Whisper hardware guide.

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