Top 10 open source library for speech to text

Using a speech to text tool is a very convenient way to convert spoken language into editable text. Not only does the speech to text tool greatly increase productivity, but it is also extremely easy to use, as all you need to do is press the record button to start recording and converting it to text.
Many speech to text tools also support multiple languages, making it easy to convert spoken language to text in different languages. Using speech to text tools can help you save a lot of time, especially when you need to enter a lot of text, because you can enter text in spoken language instead of typing it manually.

Here are some of the better voice to text tools

1. Kaldi,https://github.com/kaldi-asr/kaldi, Speaker diarization, Language identification, Neural network support, Easy adaptation to new languages, Support for many languages
2. DeepSpeech,https://github.com/mozilla/DeepSpeech, Pre-trained models, On-device inference, Support for multiple languages, Easy integration with other applications
3. PocketSphinx,https://github.com/cmusphinx/pocketsphinx, Small footprint, Supports multiple languages, Works offline, Good accuracy in noisy environments
4. CMU Sphinx,https://github.com/cmusphinx/sphinxbase, Easy to use, Works well in noisy environments, Supports many languages
5. Mozilla TTS,https://github.com/mozilla/TTS, High-quality Speech synthesis technology allows users to generate artificial speech. And customize it with different voices and accents while also supporting multiple languages.
6. Rasa,https://rasa.com/docs/rasa/, NLU and dialogue management, Support for multiple languages, Can be integrated with chatbots and voice assistants
7. TensorFlow,https://www.tensorflow.org/, Flexible and powerful, Large community, Supports for many AI applications, including speech recognition
8. PyTorch,https://pytorch.org/, Easy to use, Good for research and experimentation, Supports for speech recognition through the touch audio library
9. OpenNMT,https://opennmt.net/, Machine translation and speech recognition, Support for multiple languages, Good performance on large datasets
10. Hugging Face,https://huggingface.co/, Pre-trained models for speech recognition, Natural language processing, and other AI applications, Large community, Easy to use API

Conclusion

The above information is based on my knowledge cutoff and some research, and may not be completely up-to-date or comprehensive. Please refer to each library’s official documentation for the latest and most accurate information.