Open source speech recognition models offer customization, are possibly free, can be used for both online and offline deployment, but require more work on data aggregation. They can handle languages with small amounts of available data and provide an opportunity to define security and privacy levels. In contrast, APIs for speech recognition come ready to use, are secure, fast, and easy to integrate, but rarely free, don't offer customization options, and may struggle with rare languages. Popular open source tools include Project DeepSpeech, CMUSphinx, Kaldi, Wav2Letter++, and Alizé, each with unique features and architectures. APIs such as Google Speech-to-Text, Symbl Conversation API, AWS Transcribe, Microsoft Azure Speech to Text, Rev.ai, Deepgram, and Speechmatics are also available for speech recognition tasks.