What open source speech to text libraries would you suggest?
Wav2Vec2 is a popular model for speech to text by Meta (formerly Facebook) that is available on open source platforms such as Huggingface and Github.

In Python, you can start using this model with the transformers model from Huggingface:
```
 from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
 from datasets import load_dataset
 import torch
 
 # load model and tokenizer
 processor = Wav2Vec2Processor.from_pretrained(facebook/wav2vec2-base-960h)
 model = Wav2Vec2ForCTC.from_pretrained(facebook/wav2vec2-base-960h)
     
 # load dummy dataset and read soundfiles
 ds = load_dataset(patrickvonplaten/librispeech_asr_dummy, clean, split=validation)
 
 # tokenize
 input_values = processor(ds[0][audio][array], return_tensors=pt, padding=longest).input_values  # Batch size 1
 
 # retrieve logits
 logits = model(input_values).logits
 
 # take argmax and decode
 predicted_ids = torch.argmax(logits, dim=-1)
 transcription = processor.batch_decode(predicted_ids)
```

However, if you want a lighter weight model or want to use a different platform, you may have to find other solutions.