whisper

To transcribe multilingual audio into written transcripts, use pre-trained Whisper models ranging from tiny to large to process podcasts, meetings, and interviews. This requires Python with the openai-whisper, transformers, and torch packages installed.

Yes, you can detect spoken language and translate audio to English automatically. Whisper provides language identification alongside its speech recognition capabilities, detecting the spoken language in audio content and optionally translating the transcription output into English when needed.

Yes, you need Python and torch to run Whisper for speech recognition. The implementation requires a Python environment with the openai-whisper, transformers, and torch packages installed to execute the pre-trained models for audio processing and language identification.

To balance transcription speed and accuracy, Whisper offers model sizes from tiny to large. Smaller models prioritize faster audio processing, while larger models deliver higher accuracy for multilingual speech recognition, allowing you to choose based on your specific transcription requirements.

Limitations of using Whisper for audio processing include the dependency on heavy packages like torch and transformers, and the trade-off between model size and processing speed. Selecting larger models for high accuracy in multilingual speech recognition demands significantly more computational resources.