Great question! Let’s break this down based on how Google Cloud Speech-to-Text works and what actually drives transcription accuracy:

• First, what matters most for the Speech-to-Text API:
  The model is optimized to recognize speech patterns, so its performance hinges on three key factors:

  • Sampling rate: Your -ar 44100 works perfectly, though Google actually recommends 16000Hz for most speech use cases—since speech only uses frequencies up to ~8000Hz, 16k sampling captures all necessary phonetic details without unnecessary file size bloat.
  • Audio clarity: Low background noise, clear enunciation, and minimal compression artifacts are far more impactful than bitrate alone.
  • Bitrate’s role: Bitrate controls how much data is used to encode audio per second. For speech specifically, the threshold for "good enough" is much lower than for music.

• 64k vs. 128k for speech transcription:

  • For clean, pure speech: A 64k bitrate (using modern codecs like AAC) is more than sufficient to preserve all the speech features the model needs. Cranking it up to 128k won’t add meaningful information that improves accuracy—you’ll just end up with a larger file without tangible benefits.
  • For complex audio (speech + music, heavy background noise): Higher bitrates might preserve more subtle details that help the model distinguish speech from background sounds, but the improvement is usually marginal. A better fix here would be using noise-reduction filters (like ffmpeg’s arnndn or afftdn) instead of just increasing bitrate.
  • If you’re using older codecs like MP3: 64k might introduce slight compression artifacts that muddy speech. In this case, switching to 128k could reduce artifacts and boost accuracy a little—but switching to AAC at 64k would likely yield better results than MP3 at 128k.

• Practical next steps:

  1. Consider switching to a 16000Hz sampling rate if possible—it’s Google’s sweet spot for speech, cutting file size without losing accuracy.
  2. For most cases, stick with 64k AAC. If you’re unsure, run a quick test: transcode the same source audio to both 64k and 128k, then compare the transcript results. Chances are you won’t see a meaningful difference.
  3. If your source audio is low-quality, prioritize noise reduction over bitrate adjustments—this will have a far bigger impact on transcription accuracy.

内容的提问来源于stack exchange，提问作者Jash Shah