Neil Stoker nmstoker

## treemodel.py
from PySide6.QtCore import Qt, QAbstractItemModel, QModelIndex, QVariant

class Node:
    def __init__(self, name, parent=None, private_data=None):
        self.name = name
        self.parentNode = parent
        self.childNodes = []
        self.privateData = private_data

        if parent:

## demo.py
import requests

say_text = input('What should I say?')
url = f'http://<REPLACE_WITH_RPI_NETWORK_NAME_IP>:5002/api/tts?text={say_text}' # You will be doing a GET with this format of URL: http://0.0.0.0:5002/api/tts?text=Welcome
r = requests.get(url)
with open('response.wav', 'wb') as f:
  f.write(r.content)
print('saved wav for {}'.format(url))

## PersonNameList1.txt
Aaron,de,ˈʔaːʀɔn
Aaron,en,ˈɛəˈɹən
Aaron,en,ˈɛɹ.ən
Abbe,sv,²abɛ
Abby,en,ˈæb.i
Abdul,en,ɑbˈdʊl
Abel,en,ˈeɪ.bl̩
Abel,pt,a.ˈbɛw
Abel,pt,a.ˈbɛɫ
Abel,sh,ǎːbel

## Steps.md

      
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                nmstoker
                / Steps.md
            
            
              Last active
              June 10, 2019 11:16
            
              
                Steps to try out DeepSpeech with pre-release 0.5.0a11 model
              
          
    Downloaded the DeepSpeech GPU wheel for my OS + intended version of Python (3.7)

https://github.com/mozilla/DeepSpeech/releases/download/v0.5.0-alpha.11/deepspeech_gpu-0.5.0a11-cp37-cp37m-manylinux1_x86_64.whl
Downloaded the model (as per link from https://discourse.mozilla.org/t/deep-speech-0-5-0-pre-release-model-checkpoint/41236) and expanded files

https://drive.google.com/file/d/1vNokDsmUdwP9rk1c68JUOS0SCO5lHv3m/view?usp=sharing
Conda environment for DeepSpeech GPU

conda create -n deepspeech_051a11 python=3.7

  
## convert.py
from rasa_nlu.converters import load_data

# This re-uses the Rasa NLU converters code to turn a JSON Rasa NLU training
# file into MD format and save it

# Assumes you have Rasa NLU installed :-)

# If you want other options, look at the NLU code to work out how to handle them

# USE AT YOUR OWN RISK
	from PySide6.QtCore import Qt, QAbstractItemModel, QModelIndex, QVariant

	class Node:
	def __init__(self, name, parent=None, private_data=None):
	self.name = name
	self.parentNode = parent
	self.childNodes = []
	self.privateData = private_data

	if parent:
	import requests

	say_text = input('What should I say?')
	url = f'http://<REPLACE_WITH_RPI_NETWORK_NAME_IP>:5002/api/tts?text={say_text}' # You will be doing a GET with this format of URL: http://0.0.0.0:5002/api/tts?text=Welcome
	r = requests.get(url)
	with open('response.wav', 'wb') as f:
	f.write(r.content)
	print('saved wav for {}'.format(url))
	Aaron,de,ˈʔaːʀɔn
	Aaron,en,ˈɛəˈɹən
	Aaron,en,ˈɛɹ.ən
	Abbe,sv,²abɛ
	Abby,en,ˈæb.i
	Abdul,en,ɑbˈdʊl
	Abel,en,ˈeɪ.bl̩
	Abel,pt,a.ˈbɛw
	Abel,pt,a.ˈbɛɫ
	Abel,sh,ǎːbel
	from rasa_nlu.converters import load_data

	# This re-uses the Rasa NLU converters code to turn a JSON Rasa NLU training
	# file into MD format and save it

	# Assumes you have Rasa NLU installed :-)

	# If you want other options, look at the NLU code to work out how to handle them

	# USE AT YOUR OWN RISK