def run_tests(self):
from kaldi.cudamatrix import cuda_available
if cuda_available():
from kaldi.cudamatrix import CuDevice
CuDevice.instantiate().set_debug_stride_mode(True)
CuDevice.instantiate().select_gpu_id("yes")
super(test_cuda, self).run_tests()
CuDevice.instantiate().print_profile()
else:
print("CUDA not available. Running tests on CPU.")
super(test_cuda, self).run_tests()
def testCuVectorGetItem(self):
v = CuVector()
with self.assertRaises(IndexError):
v[0]
v = CuVector.new([3, 5, 7, 11, 13])
self.assertAlmostEqual(3.0, v[0])
self.assertAlmostEqual(7.0, v[2])
self.assertAlmostEqual(13.0, v[4])
with self.assertRaises(IndexError):
v[5]
if __name__ == '__main__':
if cuda_available():
from kaldi.cudamatrix import CuDevice
for i in range(2):
CuDevice.instantiate().set_debug_stride_mode(True)
if i == 0:
CuDevice.instantiate().select_gpu_id("no")
else:
CuDevice.instantiate().select_gpu_id("yes")
unittest.main()
CuDevice.instantiate().print_profile()
else:
unittest.main()
#!/usr/bin/env python
from __future__ import print_function
from kaldi.asr import NnetLatticeFasterBatchRecognizer
from kaldi.decoder import LatticeFasterDecoderOptions
from kaldi.nnet3 import NnetBatchComputerOptions
from kaldi.util.table import SequentialMatrixReader
from kaldi.cudamatrix import cuda_available
if cuda_available():
from kaldi.cudamatrix import CuDevice
CuDevice.instantiate().select_gpu_id('yes')
CuDevice.instantiate().allow_multithreading()
# Construct recognizer
decoder_opts = LatticeFasterDecoderOptions()
decoder_opts.beam = 13
decoder_opts.max_active = 7000
compute_opts = NnetBatchComputerOptions()
compute_opts.acoustic_scale = 1.0
compute_opts.frame_subsampling_factor = 3
compute_opts.frames_per_chunk = 150
asr = NnetLatticeFasterBatchRecognizer.from_files(
"final.mdl", "HCLG.fst", "words.txt",
decoder_opts=decoder_opts, compute_opts=compute_opts, num_threads=4)
# Define feature pipelines as Kaldi rspecifiers
feats_rspec = "ark:compute-mfcc-feats --config=mfcc.conf scp:wav.scp ark:- |"
ivectors_rspec = (
"ark:compute-mfcc-feats --config=mfcc.conf scp:wav.scp ark:-"
matrices[matrix].resize(0, 0)
gflops = num_floats_processed / (tim.elapsed() * 1.0e9)
print(
"For CuMatrix::Reize float, for size multiple {}, speed was {} gigaflops"
.format(size_multiple, gflops))
def testCudaMatrixResize():
sizes = [1, 2, 4, 8, 16, 32]
for s in sizes:
aux(s)
if __name__ == '__main__':
if cuda_available():
from kaldi.cudamatrix import CuDevice
for loop in range(2):
CuDevice.Instantiate().SetDebugStrideMode(True)
if loop == 0:
CuDevice.Instantiate().SelectGpuId("no")
else:
CuDevice.Instantiate().SelectGpuId("yes")
testCudaMatrixResize()
CuDevice.Instantiate().PrintProfile()
else:
testCudaMatrixResize()