Don’t want to install WhisperX? Drop the file here.
The first 20 minutes of any recording are transcribed free: a recording up to 20 minutes transcribes in full, a longer one is previewed, and a pack from $6 (500 minutes) transcribes the rest and opens every export, and credits never expire.
Install WhisperX for word-accurate timestamps and speaker diarization, and fix the classic failures: pyannote auth token errors, CUDA/cuDNN mismatches, alignment model downloads.
pip install whisperx in a fresh venv, Python 3.9–3.11. GPU needs a torch build matching your CUDA version FIRST, then whisperx.
whisperx audio.mp3 --model large-v2 --align_model WAV2VEC2_ASR_LARGE_LV60K_960H
--diarize needs a Hugging Face token with pyannote terms accepted: --hf_token hf_…
You must accept the user conditions for BOTH pyannote/segmentation-3.0 and pyannote/speaker-diarization-3.1 on Hugging Face while logged in, then pass a token that has read scope.
faster-whisper wheels are built against specific cuDNN versions. Match torch + CUDA first (pip install torch --index-url https://download.pytorch.org/whl/cu121), then reinstall whisperx.
wav2vec2 alignment models exist for ~10 languages; for others WhisperX falls back to segment timestamps. Check the supported list before promising word-level output.
Use --compute_type int8 (halves VRAM, minor accuracy cost) or --batch_size 4.
The 70× realtime figure is GPU batch mode. CPU runs are roughly realtime; if you don't have an NVIDIA GPU, a hosted run is usually faster end-to-end.
More on WhisperX: the full WhisperX page · WhisperX alternatives · all transcription tools
Skip the setup and send the file or the link: see MP3 to text for long recordings, transcribing a podcast episode and the SRT generator.
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