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How much computing power would one need to get this working completely local running a half decent llm fine tuned to sound like santa with all tts, stt and the pipecat inbetween?

Floats Are Weird 2 years ago

That's called the stability of the algorithm. The first algorithm is not stable whereas the second with the logarithm is.

Another example: compare the two algorithms, that are actually pure-mathematically equal:

  def fc(x): 
      return sqrt(x+1) - sqrt(x)
  def fd(x): 
      return 1/(sqrt(x+1) + sqrt(x))

For large values of x (1e16 for example) plot the results and see the difference.
  xs = np.linspace(10\*14,10\*16,10000) 
  plt.figure(figsize=(8, 6), dpi=120) 
  plt.plot(xs,[fc(x) for x in xs]) 
  plt.plot(xs,[fd(x) for x in xs])

I had worked on such a tool by extending pyqtgraph's flowcharts. Mainly executing the nodes when their input changes and showing a glowing box around the one running at the moment. Flowcharts supports multiple terminals already and I had added some custom nodes. Node execution must be delegated to a background thread so the gui is not blocked while the node executes. Doubleclicking a node shows a window for setting up the node. A custom class of nodes shows upon doubleclicking a simple python editor, upon execution the terminals get automatically the values of the variables carrying the same name as the terminals. It has been a lot of fun. I had used petl and pyspark for lazy loading so one could build large etl workflows.

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