How to Get Accurate Audio Transcripts
Speech-to-text accuracy isn't just a property of the model you're using — it's heavily shaped by the audio you feed it. The same transcription engine can produce a near-perfect transcript from a clean recording and a noticeably rougher one from a noisy or poorly recorded file, without anything about the underlying model changing at all.
What actually degrades accuracy
A few specific conditions reliably cause more transcription errors, regardless of which engine is doing the work:
- Background noise — hiss, hum, or ambient sound competing with the voice makes it harder for the model to isolate speech from everything else in the signal.
- Overlapping speech — two people talking at once is genuinely difficult for any transcription system, since it has to separate simultaneous voices rather than just recognize one continuous stream.
- Low recording volume or clipping — audio that's too quiet or distorted from being too loud both reduce the clarity a model has to work with.
- Heavy accents or unclear speech — models trained on typical speech patterns can struggle more with mumbled, fast, or heavily accented delivery.
Why cleaning up audio first improves the transcript
Since background noise is one of the most common and most fixable causes of transcription errors, running a noisy recording through a cleanup step before transcribing it directly addresses the biggest lever you actually control. This matters most for recordings made outside a controlled environment — phone recordings, field interviews, voice memos picked up in a noisy room — where the source audio itself, not the transcription engine, is the limiting factor on accuracy.
Timestamps and export formats
A transcript with per-segment timestamps is useful for more than just reading text back — it's what makes an SRT caption export possible, since captions need to know exactly when each line of text should appear and disappear on screen. If your goal is captions for a video, make sure you're exporting to SRT rather than plain text; if you just need searchable text to pull quotes from, plain text is simpler to work with.
Setting realistic expectations on speed
Transcription is meaningfully slower than most other audio processing tasks, because CPU-based transcription models process a file sequentially rather than in the near-instant way a format conversion or a simple effect can run. A longer file taking several minutes to transcribe is expected behavior for this kind of tool, not a sign something's wrong — budget for that wait time rather than expecting the same near-instant turnaround as trimming or converting a file.
A practical workflow
If your source recording has noticeable background noise, run it through cleanup before transcribing rather than after — you want the transcription model working from the cleanest version of the audio available. Our Speech to Text tool auto-detects language and exports as plain text or SRT captions. For noisy speech recordings, running the file through the Voice Cleaner first is a straightforward way to improve transcription accuracy before you start.