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Added QuartzNet config
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Signed-off-by: Vitaly Lavrukhin <vlavrukhin@nvidia.com>
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@vsl9
vsl9 committed Oct 30, 2019
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256 changes: 256 additions & 0 deletions example_configs/speech2text/quartznet15x5_LibriSpeech.py
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# pylint: skip-file
# QuartzNet paper: https://arxiv.org/abs/1910.10261
import tensorflow as tf
from open_seq2seq.models import Speech2Text
from open_seq2seq.encoders import TDNNEncoder
from open_seq2seq.decoders import FullyConnectedCTCDecoder
from open_seq2seq.data.speech2text.speech2text import Speech2TextDataLayer
from open_seq2seq.losses import CTCLoss
from open_seq2seq.optimizers.lr_policies import cosine_decay
from open_seq2seq.optimizers.novograd import NovoGrad

residual_dense = False # Enable or disable Dense Residual

base_model = Speech2Text

base_params = {
"random_seed": 0,
"use_horovod": True,
"num_epochs": 400,

"num_gpus": 8,
"batch_size_per_gpu": 32,
"iter_size": 1,

"save_summaries_steps": 100,
"print_loss_steps": 10,
"print_samples_steps": 2200,
"eval_steps": 2200,
"save_checkpoint_steps": 1100,
"logdir": "jasper_log_folder",
"num_checkpoints": 2,

"optimizer": NovoGrad,
"optimizer_params": {
"beta1": 0.95,
"beta2": 0.5,
"epsilon": 1e-08,
"weight_decay": 0.001,
"grad_averaging": False,
},
"lr_policy": cosine_decay,
"lr_policy_params": {
"learning_rate": 0.01,
"min_lr": 0.0,
"warmup_steps": 1000
},

"dtype": tf.float32,
# "loss_scaling": "Backoff",

"summaries": ['learning_rate', 'variables', 'gradients', 'larc_summaries',
'variable_norm', 'gradient_norm', 'global_gradient_norm'],

"encoder": TDNNEncoder,
"encoder_params": {
"convnet_layers": [
{
"type": "sep_conv1d", "repeat": 1,
"kernel_size": [33], "stride": [2],
"num_channels": 256, "padding": "SAME",
"dilation":[1]
},
{
"type": "sep_conv1d", "repeat": 5,
"kernel_size": [33], "stride": [1],
"num_channels": 256, "padding": "SAME",
"dilation":[1],
"residual": True, "residual_dense": residual_dense
},
{
"type": "sep_conv1d", "repeat": 5,
"kernel_size": [33], "stride": [1],
"num_channels": 256, "padding": "SAME",
"dilation":[1],
"residual": True, "residual_dense": residual_dense
},
{
"type": "sep_conv1d", "repeat": 5,
"kernel_size": [33], "stride": [1],
"num_channels": 256, "padding": "SAME",
"dilation":[1],
"residual": True, "residual_dense": residual_dense
},
{
"type": "sep_conv1d", "repeat": 5,
"kernel_size": [39], "stride": [1],
"num_channels": 256, "padding": "SAME",
"dilation":[1],
"residual": True, "residual_dense": residual_dense
},
{
"type": "sep_conv1d", "repeat": 5,
"kernel_size": [39], "stride": [1],
"num_channels": 256, "padding": "SAME",
"dilation":[1],
"residual": True, "residual_dense": residual_dense
},
{
"type": "sep_conv1d", "repeat": 5,
"kernel_size": [39], "stride": [1],
"num_channels": 256, "padding": "SAME",
"dilation":[1],
"residual": True, "residual_dense": residual_dense
},
{
"type": "sep_conv1d", "repeat": 5,
"kernel_size": [51], "stride": [1],
"num_channels": 512, "padding": "SAME",
"dilation":[1],
"residual": True, "residual_dense": residual_dense
},
{
"type": "sep_conv1d", "repeat": 5,
"kernel_size": [51], "stride": [1],
"num_channels": 512, "padding": "SAME",
"dilation":[1],
"residual": True, "residual_dense": residual_dense
},
{
"type": "sep_conv1d", "repeat": 5,
"kernel_size": [51], "stride": [1],
"num_channels": 512, "padding": "SAME",
"dilation":[1],
"residual": True, "residual_dense": residual_dense
},
{
"type": "sep_conv1d", "repeat": 5,
"kernel_size": [63], "stride": [1],
"num_channels": 512, "padding": "SAME",
"dilation":[1],
"residual": True, "residual_dense": residual_dense
},
{
"type": "sep_conv1d", "repeat": 5,
"kernel_size": [63], "stride": [1],
"num_channels": 512, "padding": "SAME",
"dilation":[1],
"residual": True, "residual_dense": residual_dense
},
{
"type": "sep_conv1d", "repeat": 5,
"kernel_size": [63], "stride": [1],
"num_channels": 512, "padding": "SAME",
"dilation":[1],
"residual": True, "residual_dense": residual_dense
},
{
"type": "sep_conv1d", "repeat": 5,
"kernel_size": [75], "stride": [1],
"num_channels": 512, "padding": "SAME",
"dilation":[1],
"residual": True, "residual_dense": residual_dense
},
{
"type": "sep_conv1d", "repeat": 5,
"kernel_size": [75], "stride": [1],
"num_channels": 512, "padding": "SAME",
"dilation":[1],
"residual": True, "residual_dense": residual_dense
},
{
"type": "sep_conv1d", "repeat": 5,
"kernel_size": [75], "stride": [1],
"num_channels": 512, "padding": "SAME",
"dilation":[1],
"residual": True, "residual_dense": residual_dense
},
{
"type": "sep_conv1d", "repeat": 1,
"kernel_size": [87], "stride": [1],
"num_channels": 512, "padding": "SAME",
"dilation":[2],
"residual": True, "residual_dense": residual_dense
},
{
"type": "conv1d", "repeat": 1,
"kernel_size": [1], "stride": [1],
"num_channels": 1024, "padding": "SAME",
"dilation":[1]
}
],

"dropout_keep_prob": 1.0,

"initializer": tf.contrib.layers.xavier_initializer,
"initializer_params": {
'uniform': False,
},
"normalization": "batch_norm",
"activation_fn": tf.nn.relu,
"data_format": "channels_last",
"use_conv_mask": True,
},

"decoder": FullyConnectedCTCDecoder,
"decoder_params": {
"initializer": tf.contrib.layers.xavier_initializer,
"use_language_model": False,
"infer_logits_to_pickle": False,
},
"loss": CTCLoss,
"loss_params": {},

"data_layer": Speech2TextDataLayer,
"data_layer_params": {
"num_audio_features": 64,
"input_type": "logfbank",
"vocab_file": "open_seq2seq/test_utils/toy_speech_data/vocab.txt",
"norm_per_feature": True,
"window": "hanning",
"precompute_mel_basis": True,
"sample_freq": 16000,
"pad_to": 16,
"dither": 1e-5,
"backend": "librosa",
},
}

train_params = {
"data_layer": Speech2TextDataLayer,
"data_layer_params": {
"augmentation": {
'n_freq_mask': 2,
'n_time_mask': 2,
'width_freq_mask': 6,
'width_time_mask': 6,
},
"dataset_files": [
"/data/librispeech/librivox-train-clean-100.csv",
"/data/librispeech/librivox-train-clean-360.csv",
"/data/librispeech/librivox-train-other-500.csv"
],
"max_duration": 16.7,
"shuffle": True,
},
}

eval_params = {
"data_layer": Speech2TextDataLayer,
"data_layer_params": {
"dataset_files": [
"/data/librispeech/librivox-dev-clean.csv",
],
"shuffle": False,
},
}

infer_params = {
"data_layer": Speech2TextDataLayer,
"data_layer_params": {
"dataset_files": [
"/data/librispeech/librivox-test-clean.csv",
],
"shuffle": False,
},
}

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