whisper

Audio transcription in Python uses the openai-whisper package to load an ASR model and process audio files directly into searchable text. It requires Python 3.8-3.11 and FFmpeg installed to handle audio decoding.

Multilingual speech recognition can run offline using Whisper by loading local ASR models. It supports 99 languages and various accents, allowing audio processing without an internet connection.

Whisper speech recognition runs on both CPU and GPU environments. Users select from model sizes ranging from tiny to large to balance transcription latency, accuracy, and available hardware constraints.

Generating meeting notes from audio is best achieved using Whisper's ASR models to convert speech recordings into accurate, searchable text. It offers flexible model sizes to balance processing speed and transcription accuracy.

Translating non-English audio to English text uses Whisper's multilingual ASR capabilities to transcribe speech across 99 languages. It provides translation outputs suitable for downstream multilingual content processing pipelines.

Audio transcription requires Python 3.8-3.11, the openai-whisper package, and FFmpeg. Optional dependencies include transformers and torch frameworks, which are useful for running larger ASR models on a GPU.