Marco Lee marcoleewow

I hereby claim:

To claim this, I am signing this object:

	import os
	import numpy as np

	import coremltools
	from coremltools.models.neural_network import flexible_shape_utils

	import torch
	from torch import nn

	import onnx

	import editdistance


	def cer(y_true: str, y_pred: str) -> float:
	assert isinstance(y_true, str) and isinstance(y_pred, str)
	n_err = editdistance.eval(y_true.rstrip(), y_pred.rstrip())
	return n_err / len(y_true)


	def wer(transcript: str, reference: str) -> float:

	import torch
	from torch import nn
	import torch.nn.functional as F
	import os
	import onnx
	from onnx_coreml import convert
	import pytest
	from coremltools.proto import NeuralNetwork_pb2 #type: ignore

	"""

	import numpy as np
	import tensorflow as tf

	def CTCLossExactPathTest():

	BIG_NEG_NUMBER = -100
	logit = np.array([[BIG_NEG_NUMBER, 0., BIG_NEG_NUMBER, BIG_NEG_NUMBER],
	[0., BIG_NEG_NUMBER, BIG_NEG_NUMBER, BIG_NEG_NUMBER],
	[BIG_NEG_NUMBER, BIG_NEG_NUMBER, 0., BIG_NEG_NUMBER]])
	"""

	import numpy as np
	from keras import backend as K
	from keras.layers import *

	from keras.preprocessing.image import ImageDataGenerator
	from keras.models import Sequential, Model
	from keras.optimizers import SGD, Adam

	np.random.seed(1337) # for reproducibility