<|im_start|>system
You are a helpful AI assistant.<|im_end|>
<|im_start|>user
What open source speech to text libraries would you suggest?<|im_end|>
<|im_start|>assistant
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.<|im_end|>
<|im_start|>user
Which one has the most amount of different voices?<|im_end|>
<|im_start|>assistant
The number of available voices in a speech-to-text library typically depends on the language models and acoustic models used in the system. However, the availability of voices may also be impacted by the licensing and data restrictions of the specific toolkit.

Among the speech-to-text libraries mentioned, CMU Sphinx and Kaldi are designed to support multiple languages, and they may have more pre-trained language models and acoustic models available for different voices.

However, it's important to note that the availability of different voices may vary depending on the specific application and the quality of the audio being processed. In general, it's recommended to evaluate the accuracy and suitability of each toolkit for your specific use case, rather than solely considering the number of available voices.<|im_end|>