Now, if you’re like me (and let’s be real here, who isn’t?), you might be wondering what exactly this means.

It allows us to reduce the size of our models without sacrificing accuracy or performance. And now, thanks to Apple Silicon (the fancy new chips in their latest Macs), we can run these models on our favorite devices with ease!

But wait there’s more! Whisper is a state-of-the-art speech recognition model that uses transformer architecture and has achieved impressive results on various benchmarks. And now, thanks to ggml, we can use it for inference (i.e., making predictions) with just a few lines of code!

So how do you get started? Well, first you’ll need to download the pre-trained model from ggml’s website and convert it into a format that Whisper can understand. This involves using their quantization tool (which is also open-source) to reduce the size of the model without losing any accuracy or performance.

Once you have your converted model, you can load it into Python using the ggml library and start making predictions! Here’s an example script that demonstrates how to do this:

# Import necessary libraries
import os # Importing the os library to access files and directories
from ggml import Model, load # Importing the ggml library for loading and using the model
from whisper.asr import Whisper # Importing the whisper library for speech recognition

# Load pre-trained model from disk (assuming it's in the same directory as your script)
model_path = 'whisper.bin' # Setting the path for the pre-trained model file
quant_params_path = 'whisper.ptq' # Setting the path for the quantized parameters file

# Load quantized parameters and convert them to a ggml format
quant_params = load(quant_params_path, map_location='cpu') # Loading the quantized parameters file and specifying the location to be the CPU
ggml_model = Model.new('whisper', [0], ['input'], ['output']) # Creating a new ggml model with the name 'whisper' and specifying the input and output layers
for param in ggml_model.parameters(): # Looping through each parameter in the ggml model
    param[:] = quant_params[param.name].float() / 255.0 # Scaling the parameters to match the input range of -128 to +127 by dividing them by 255.0
ggml_model.save('whisper') # Saving the ggml model with the name 'whisper'

And that’s it! You can now use this model for inference using Whisper, which is a powerful and flexible tool for speech recognition. And thanks to ggml’s quantization and compression techniques, you can run these models on your favorite devices with ease even if they don’t have the latest and greatest hardware!

It might sound like a mouthful, but trust us once you get started, you won’t be able to stop! And who knows? Maybe one day we’ll all be speaking in code instead of words…