ফেব্রুয়ারী ২২, ২০১৯ —
Posted by Jonathan Shen
Lingvo is the international language Esperanto word for “language”. This naming alludes to the roots of the Lingvo framework — it was developed as a general deep learning framework using TensorFlow with a focus on sequence models for language-related tasks such as machine translation, speech recognition, and speech synthesis.
Internally, the framework gained traction and t…
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Figure 1: An overview of the Lingvo framework, outlining how models are instantiated, trained, and exported for evaluation and serving. |
def Task(cls):
p = model.AsrModel.Params()
p.name = 'librispeech'
# Initialize encoder params.
ep = p.encoder
# Data consists 240 dimensional frames (80 x 3 frames), which we
# re-interpret as individual 80 dimensional frames. See also,
# LibrispeechCommonAsrInputParams.
ep.input_shape = [None, None, 80, 1]
ep.lstm_cell_size = 1024
ep.num_lstm_layers = 4
ep.conv_filter_shapes = [(3, 3, 1, 32), (3, 3, 32, 32)]
ep.conv_filter_strides = [(2, 2), (2, 2)]
ep.cnn_tpl.params_init = py_utils.WeightInit.Gaussian(0.001)
# Disable conv LSTM layers.
ep.num_conv_lstm_layers = 0
# Initialize decoder params.
dp = p.decoder
dp.rnn_cell_dim = 1024
dp.rnn_layers = 2
dp.source_dim = 2048
# Use functional while based unrolling.
dp.use_while_loop_based_unrolling = False
tp = p.train
tp.learning_rate = 2.5e-4
tp.lr_schedule = lr_schedule.ContinuousLearningRateSchedule.Params().Set(
start_step=50000, half_life_steps=100000, min=0.01)
# Setting p.eval.samples_per_summary to a large value ensures that dev,
# devother, test, testother are evaluated completely (since num_samples for
# each of these sets is less than 5000), while train summaries will be
# computed on 5000 examples.
p.eval.samples_per_summary = 5000
p.eval.decoder_samples_per_summary = 0
# Use variational weight noise to prevent overfitting.
p.vn.global_vn = True
p.train.vn_std = 0.075
p.train.vn_start_step = 20000
return p
Figure 2: An example of a task configuration in Lingvo. Hyperparameters for each experiment is configured in its own class separate from the code that builds the network and checked into version control. Source. |
ফেব্রুয়ারী ২২, ২০১৯
—
Posted by Jonathan Shen
Lingvo is the international language Esperanto word for “language”. This naming alludes to the roots of the Lingvo framework — it was developed as a general deep learning framework using TensorFlow with a focus on sequence models for language-related tasks such as machine translation, speech recognition, and speech synthesis.
Internally, the framework gained traction and t…