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Signed-off-by: Vitaly Lavrukhin <[email protected]>
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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 | ||
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residual_dense = False # Enable or disable Dense Residual | ||
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base_model = Speech2Text | ||
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base_params = { | ||
"random_seed": 0, | ||
"use_horovod": True, | ||
"num_epochs": 400, | ||
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"num_gpus": 8, | ||
"batch_size_per_gpu": 32, | ||
"iter_size": 1, | ||
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"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, | ||
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"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 | ||
}, | ||
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"dtype": tf.float32, | ||
# "loss_scaling": "Backoff", | ||
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"summaries": ['learning_rate', 'variables', 'gradients', 'larc_summaries', | ||
'variable_norm', 'gradient_norm', 'global_gradient_norm'], | ||
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"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] | ||
} | ||
], | ||
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"dropout_keep_prob": 1.0, | ||
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"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, | ||
}, | ||
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"decoder": FullyConnectedCTCDecoder, | ||
"decoder_params": { | ||
"initializer": tf.contrib.layers.xavier_initializer, | ||
"use_language_model": False, | ||
"infer_logits_to_pickle": False, | ||
}, | ||
"loss": CTCLoss, | ||
"loss_params": {}, | ||
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"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", | ||
}, | ||
} | ||
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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, | ||
}, | ||
} | ||
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eval_params = { | ||
"data_layer": Speech2TextDataLayer, | ||
"data_layer_params": { | ||
"dataset_files": [ | ||
"/data/librispeech/librivox-dev-clean.csv", | ||
], | ||
"shuffle": False, | ||
}, | ||
} | ||
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infer_params = { | ||
"data_layer": Speech2TextDataLayer, | ||
"data_layer_params": { | ||
"dataset_files": [ | ||
"/data/librispeech/librivox-test-clean.csv", | ||
], | ||
"shuffle": False, | ||
}, | ||
} |