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whisperx_transcription [2026/10/10 17:18] – external edit (Unknown date) 127.0.0.1whisperx_transcription [2026/10/10 17:20] (current) – [Local Speech-to-Text with WhisperX] millerjs
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 ====== Local Speech-to-Text with WhisperX ====== ====== Local Speech-to-Text with WhisperX ======
-I had a batch of meeting recordings (2-3 speakers, 45-60 minutes each) that I wanted to transcribe with speaker identification — you know, "who said what and when." I didn't want to upload any of it to a cloud service, so I went looking for something that would run entirely locally on my desktop. I found [[https://github.com/m-bain/whisperX|WhisperX]], which combines OpenAI's Whisper for the actual speech-to-text, [[https://github.com/pyannote/pyannote-audio|pyannote.audio]] for figuring out who's speaking, and forced alignment for word-level timestamps. It runs on CPU, doesn't need a GPU, and outputs the usual subtitle formats with speaker labels like ''[SPEAKER_00]'' and ''[SPEAKER_01]''. +This is running on a SFF Optiplex 7070 with an i5-9500 (6 cores / 6 threads, AVX2, 32 GB RAM, Debian 13). No GPU, no cloud, just the CPU. A 50-minute recording takes about 48 minutes end-to-end with the large-v3 model in int8 mode. Not fast, but the machine is usually idle so I just kick it off and walk away. At some point I'd like to put a low profile GPU in here to help with accelerating these kinds of tasks, but this works fine for now.
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-This is running on my i5-9500 desktop (6 cores / 6 threads, AVX2, 32 GB RAM, Debian 13). No GPU, no cloud, just the CPU. A 50-minute recording takes about 48 minutes end-to-end with the large-v3 model in int8 mode. Not fast, but the machine is usually idle so I just kick it off and walk away.+
  
 ===== System Prerequisites ===== ===== System Prerequisites =====
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