mkurman Overview
OpenAI Whisper is a general-purpose speech recognition model supporting multilingual transcription, translation to English, and speaker-agnostic ASR. Models range from tiny (39M params) to large (1.55B params). Robust to noise, accents, and technical vocabulary.
Installation
uv pip install openai-whisper
ffmpeg # required for audio loading
Basic Transcription
import whisper
model = whisper.load_model("base")
result = model.transcribe("audio.mp3")
print(result["text"])
Multilingual and Translation
# Transcribe in original language
result = model.transcribe("french_audio.mp3", language="fr")
# Translate to English
result = model.transcribe("german_audio.mp3", task="translate")
print(result["text"]) # English output
Word-Level Timestamps
result = model.transcribe("lecture.mp3", word_timestamps=True)
for segment in result["segments"]:
for word in segment["words"]:
print(f"{word['word']}: {word['start']:.2f}-{word['end']:.2f}")
Model Size Selection
# tiny (fast, less accurate) → base → small → medium → large (slow, most accurate)
sizes = ["tiny", "base", "small", "medium", "large"]
for s in sizes:
m = whisper.load_model(s)
# ~1GB VRAM for base, ~10GB for large
result = m.transcribe("podcast.mp3")