fix paddling len bug

pull/786/head
huangyuxin 3 years ago
parent 317ffea5e5
commit 2451a177b0

@ -455,10 +455,12 @@ class DeepSpeech2ExportTester(DeepSpeech2Tester):
x_batch = audio.numpy() x_batch = audio.numpy()
batch_size, Tmax, x_dim = x_batch.shape batch_size, Tmax, x_dim = x_batch.shape
x_len_batch = audio_len.numpy().astype(np.int64) x_len_batch = audio_len.numpy().astype(np.int64)
if (Tmax - chunk_size) % chunk_stride != 0:
padding_len_batch = chunk_stride - ( padding_len_batch = chunk_stride - (
Tmax - chunk_size Tmax - chunk_size
) % chunk_stride # The length of padding for the batch ) % chunk_stride # The length of padding for the batch
else:
padding_len_batch = 0
x_list = np.split(x_batch, batch_size, axis=0) x_list = np.split(x_batch, batch_size, axis=0)
x_len_list = np.split(x_len_batch, batch_size, axis=0) x_len_list = np.split(x_len_batch, batch_size, axis=0)

@ -100,12 +100,12 @@ class CRNNEncoder(nn.Layer):
"""Compute Encoder outputs """Compute Encoder outputs
Args: Args:
x (Tensor): [B, feature_size, D] x (Tensor): [B, T, D]
x_lens (Tensor): [B] x_lens (Tensor): [B]
init_state_h_box(Tensor): init_states h for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size] init_state_h_box(Tensor): init_states h for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
init_state_c_box(Tensor): init_states c for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size] init_state_c_box(Tensor): init_states c for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
Return: Return:
x (Tensor): encoder outputs, [B, size, D] x (Tensor): encoder outputs, [B, T, D]
x_lens (Tensor): encoder length, [B] x_lens (Tensor): encoder length, [B]
final_state_h_box(Tensor): final_states h for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size] final_state_h_box(Tensor): final_states h for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
final_state_c_box(Tensor): final_states c for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size] final_state_c_box(Tensor): final_states c for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]

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