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@ -11,27 +11,19 @@
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# limitations under the License.
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"""Deepspeech2 ASR Model"""
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"""Deepspeech2 ASR Online Model"""
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from typing import Optional
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from typing import Optional
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import paddle
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import paddle
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import paddle.nn.functional as F
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import paddle.nn.functional as F
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from paddle import nn
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from paddle import nn
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from paddle.fluid.layers import fc
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from paddle.nn import GRU
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from paddle.nn import LayerList
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from paddle.nn import LayerNorm
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from paddle.nn import Linear
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from paddle.nn import LSTM
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from yacs.config import CfgNode
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from yacs.config import CfgNode
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from deepspeech.models.ds2_online.conv import ConvStack
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from deepspeech.models.ds2_online.conv import Conv2dSubsampling4Online
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from deepspeech.models.ds2_online.rnn import RNNStack
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from deepspeech.modules.ctc import CTCDecoder
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from deepspeech.modules.ctc import CTCDecoder
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from deepspeech.utils import layer_tools
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from deepspeech.utils import layer_tools
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from deepspeech.utils.checkpoint import Checkpoint
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from deepspeech.utils.checkpoint import Checkpoint
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from deepspeech.utils.log import Log
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from deepspeech.utils.log import Log
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logger = Log(__name__).getlog()
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logger = Log(__name__).getlog()
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__all__ = ['DeepSpeech2ModelOnline', 'DeepSpeech2InferModeOnline']
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__all__ = ['DeepSpeech2ModelOnline', 'DeepSpeech2InferModeOnline']
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@ -55,46 +47,48 @@ class CRNNEncoder(nn.Layer):
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self.num_rnn_layers = num_rnn_layers
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self.num_rnn_layers = num_rnn_layers
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self.num_fc_layers = num_fc_layers
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self.num_fc_layers = num_fc_layers
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self.fc_layers_size_list = fc_layers_size_list
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self.fc_layers_size_list = fc_layers_size_list
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self.conv = ConvStack(feat_size, num_conv_layers)
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self.conv = Conv2dSubsampling4Online(feat_size, 32, dropout_rate=0.0)
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i_size = self.conv.output_height # H after conv stack
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i_size = self.conv.output_dim
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self.rnn = LayerList()
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self.rnn = nn.LayerList()
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self.layernorm_list = LayerList()
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self.layernorm_list = nn.LayerList()
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self.fc_layers_list = LayerList()
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self.fc_layers_list = nn.LayerList()
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rnn_direction = 'forward'
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rnn_direction = 'forward'
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layernorm_size = rnn_size
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layernorm_size = rnn_size
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if use_gru == True:
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if use_gru == True:
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self.rnn.append(
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self.rnn.append(
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GRU(input_size=i_size,
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nn.GRU(
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input_size=i_size,
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hidden_size=rnn_size,
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hidden_size=rnn_size,
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num_layers=1,
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num_layers=1,
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direction=rnn_direction))
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direction=rnn_direction))
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self.layernorm_list.append(LayerNorm(layernorm_size))
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self.layernorm_list.append(nn.LayerNorm(layernorm_size))
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for i in range(1, num_rnn_layers):
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for i in range(1, num_rnn_layers):
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self.rnn.append(
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self.rnn.append(
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GRU(input_size=layernorm_size,
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nn.GRU(
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input_size=layernorm_size,
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hidden_size=rnn_size,
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hidden_size=rnn_size,
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num_layers=1,
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num_layers=1,
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direction=rnn_direction))
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direction=rnn_direction))
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self.layernorm_list.append(LayerNorm(layernorm_size))
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self.layernorm_list.append(nn.LayerNorm(layernorm_size))
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else:
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else:
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self.rnn.append(
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self.rnn.append(
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LSTM(
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nn.LSTM(
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input_size=i_size,
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input_size=i_size,
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hidden_size=rnn_size,
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hidden_size=rnn_size,
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num_layers=1,
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num_layers=1,
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direction=rnn_direction))
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direction=rnn_direction))
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self.layernorm_list.append(LayerNorm(layernorm_size))
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self.layernorm_list.append(nn.LayerNorm(layernorm_size))
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for i in range(1, num_rnn_layers):
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for i in range(1, num_rnn_layers):
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self.rnn.append(
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self.rnn.append(
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LSTM(
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nn.LSTM(
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input_size=layernorm_size,
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input_size=layernorm_size,
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hidden_size=rnn_size,
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hidden_size=rnn_size,
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num_layers=1,
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num_layers=1,
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direction=rnn_direction))
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direction=rnn_direction))
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self.layernorm_list.append(LayerNorm(layernorm_size))
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self.layernorm_list.append(nn.LayerNorm(layernorm_size))
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fc_input_size = layernorm_size
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fc_input_size = layernorm_size
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for i in range(self.num_fc_layers):
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for i in range(self.num_fc_layers):
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self.fc_layers_list.append(
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self.fc_layers_list.append(
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@ -117,20 +111,16 @@ class CRNNEncoder(nn.Layer):
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x (Tensor): encoder outputs, [B, T, D]
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x (Tensor): encoder outputs, [B, T, D]
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x_lens (Tensor): encoder length, [B]
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x_lens (Tensor): encoder length, [B]
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"""
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"""
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# [B, T, D] -> [B, D, T]
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# [B, T, D]
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audio = audio.transpose([0, 2, 1])
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x = audio
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# [B, D, T] -> [B, C=1, D, T]
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x = audio.unsqueeze(1)
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x_lens = audio_len
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x_lens = audio_len
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# convolution group
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# convolution group
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x, x_lens = self.conv(x, x_lens)
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x, x_lens = self.conv(x, x_lens)
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# convert data from convolution feature map to sequence of vectors
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# convert data from convolution feature map to sequence of vectors
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#B, C, D, T = paddle.shape(x) # not work under jit
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#B, C, D, T = paddle.shape(x) # not work under jit
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x = x.transpose([0, 3, 1, 2]) #[B, T, C, D]
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#x = x.transpose([0, 3, 1, 2]) #[B, T, C, D]
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#x = x.reshape([B, T, C * D]) #[B, T, C*D] # not work under jit
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#x = x.reshape([B, T, C * D]) #[B, T, C*D] # not work under jit
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x = x.reshape([0, 0, -1]) #[B, T, C*D]
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#x = x.reshape([0, 0, -1]) #[B, T, C*D]
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# remove padding part
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# remove padding part
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x, output_state = self.rnn[0](x, None, x_lens)
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x, output_state = self.rnn[0](x, None, x_lens)
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