I am using the SpeechRecognition library in Python to transcribe .wav files, but I keep hitting a RequestError or a timeout whenever the file is longer than a minute. I’m currently using the Google Web Speech API, but the process just hangs and eventually fails. Is there a way to increase the timeout limit, or should I be splitting my audio into smaller chunks before sending it to the API? I'm also curious if switching to a production-grade service like Google Cloud Speech-to-Text or OpenAI Whisper would handle larger files more reliably.
3 answers
The timeout issue occurs because most free or web-based STT APIs have a strict limit on the duration of the audio they can process in a single request (often 60 seconds). To fix this, you should use a library like pydub to split your .wav file into smaller segments. A popular method is to use split_on_silence, which cuts the audio during natural pauses. You then loop through these chunks and transcribe them individually. This not only prevents timeouts but also improves accuracy by providing the model with clearer, shorter context.
That chunking strategy is a lifesaver for long recordings! However, if the audio is a continuous stream of speech without many pauses, split_on_silence might struggle. In those cases, would it be better to just split the file at fixed 30-second intervals? Also, does the SpeechRecognition library have a built-in parameter to increase the connection timeout specifically for slower internet connections?
If you have a decent GPU, I highly recommend using OpenAI's Whisper locally. Since it runs on your own machine, you don't have to worry about API timeouts or internet speed. It handles long files much more gracefully by using its own internal windowing system.
I agree with Maria. Whisper is a game-changer for this specific issue. Because it processes the audio locally (or via a dedicated server), you bypass the payload limits that cause the Google Web Speech API to hang. I recently used the whisper-large-v3 model for a project involving hour-long interviews, and the reliability was night and day compared to cloud-based requests. Plus, the timestamping feature makes it much easier to sync the text back to the original audio!
Robert, you're right; fixed-interval splitting is a safer fallback for fast-talkers. In the recognize_google() method, you can't manually extend the API's internal timeout easily. However, for professional software development, I recommend moving to the Google Cloud Speech-to-Text client library. It offers an asynchronous or long_running_recognize method that can handle files up to 8 hours long by uploading them to an S3 or GCS bucket first. This eliminates the timeout issue entirely.