File size: 4,254 Bytes
1e820d2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 |
import numpy as np
import whisper
from torch import nn
from torch import Tensor
class PyAI:
def __init__(self, useGPU: bool):
self.GPU = useGPU
class Algorithms:
def KNN(x, y, returnValues = 0):
distances = []
for axisX, axisY in zip(x, y):
distance = axisX - axisY
absDistance = np.absolute(distance)
distances.append(absDistance)
sortedDistances = []
checkDistance = min(distances, key = lambda x:np.absolute(x-i))
sortedDistances.append(checkDistance)
distances.remove(checkDistance)
if returnValues == 0:
return sortedDistances[0]
else:
return sortedDistances[0:returnValues-1]
def RNN(w, u, b, x):
yt = 0
ht = 1 / 1 (w * x + u * yt ** -1 + b) ** -1
yt = 1 / 1 (w * ht + b) ** -1
return yt
def ReLU(self, x: list, *y: list, **u: list):
X, Y, U = [Tensor(x2) for x2 in x], [Tensor(y2) for y2 in y], [Tensor(u2) for u2 in u]
if self.GPU:
relu = nn.ReLU().to("cuda")
else:
relu = nn.ReLU().to("cpu")
newX, newY, newU = [relu(x) for x in X], [relu(y) for y in Y], [relu(u) for u in U]
if newU is not None:
return newX, newY, newU
elif newY is not None:
return newX, newY
else:
return newX
class Audio:
def __init__(self, audio: str):
self.model = whisper.load_model("base")
self.audio = audio
def generateTextFromAudio(self) -> str:
aud = whisper.load_audio(self.audio)
aud = whisper.pad_or_trim(aud)
self.mel = whisper.log_mel_spectrogram(aud).to(self.model.device)
self.model.detect_language(self.mel)
options = whisper.DecodingOptions()
result = whisper.decode(self.model, self.mel, options)
return result.text
def translateText(self, text: str, dataSet: str) -> str:
with open(dataSet, "r") as d:
data = d.read()
translation = text.translate(data)
return translation
def getLang(self):
i, lang = self.model.detect_language(self.mel)
return max(lang, key=lang.get)
class NLP:
def __init__(self, text: str):
self.text = text
self.sentences = text.split(".")
self.words = text.split(" ")
self._past = ["was", "had", "did"]
self._present = ["is", "has"]
self._future = ["will", "shall"]
def setTokensTo(self, letters: bool, *words: bool, **sentences: bool):
self.tokens = []
if letters:
tokens = iter(self.text)
for t in tokens:
self.tokens.append(t)
elif words:
for t in self.words:
self.tokens.append(t)
elif sentences:
for t in self.sentences:
self.tokens.append(t)
else:
self.tokens.append("ERROR")
def getTense(self):
self.past = False
self.present = False
self.future = False
if self.sentences in self._past:
self.past = True
elif self.sentences in self._present:
self.present = True
elif self.sentences in self._future:
self.future = True
else:
return "ERROR - Tense :: Not Enough Data"
return self.past, self.present, self.future
def getWords(self):
return self.words
def getSentences(self):
return self.sentences
def getTokens(self):
return self.tokens |