<s>[INST] What open source speech to text libraries would you suggest? [/INST] There are several open source speech-to-text libraries available, each with its own strengths and weaknesses. Here are a few popular options:

1. CMU Sphinx - This is a well-known open source speech recognition system that uses Hidden Markov Models (HMMs) to recognize speech. It supports multiple languages, and it can be run both offline and online.

2. Kaldi - This is another popular open source speech recognition toolkit that uses Finite-State Transducers (FSTs) to model speech. It is designed to be scalable and can handle large datasets.

3. Mozilla DeepSpeech - This is an open source speech recognition engine developed by Mozilla that uses deep learning techniques to transcribe speech. It supports multiple languages and has achieved state-of-the-art accuracy on some benchmarks.

4. Wav2Letter++ - This is a fast and efficient open source speech recognition toolkit developed by Facebook AI Research. It uses a combination of convolutional and recurrent neural networks to transcribe speech.

5. Julius - This is an open source large vocabulary continuous speech recognition (LVCSR) engine developed by the Interactive Speech Technology Consortium (ISTC). It supports several languages, and it can be used for both offline and online speech recognition.

These are just a few examples of the many open source speech-to-text libraries available. You should evaluate each option to determine which one is best suited for your particular use case.</s>\ <s>[INST] Which one has the most amount of different voices? [/INST] The Mozilla DeepSpeech library has the most amount of different voices, as it uses a deep learning-based approach which allows it to recognize multiple languages. Additionally, it has achieved state-of-the-art accuracy on some benchmarks, so it may be the most suitable option for your use case.</s>\ <s>[INST] How much does it cost and how does this compare to the other options you gave me? [/INST] 