手把手教你语音识别（三）
手把手教你语音识别3来了
·
朋友们，手把手语音识别第三部分来了，这部分开始讲解网络搭建部分，同样是手把手教你哦，不要错过。
1、读入数据
这部分，就不展开讲了，之前的文章讲过：数据处理，这次直接从读入数据的格式到怼入网络开始讲，不过，由于网络很大，层数很多，所以要一层层的去讲解，本次咱们讲解的是encode里面的embeding层，也就是词嵌入层，这里可以认为embedding就是一个lookup，也就是一个速查表，用id->embedding做一个对应，这样的话，根据词的id，能通过vocab.txt查找到这个id对应的字或词，比如a、b、c、d、你、我等，也可以找到这个词的embedding，从而计算他们的关系，比如距离、语义关系等等。
import soundfile
audio, audio_sample_rate = soundfile.read("C:\Users\Desktop\asr16.wav", dtype="int16",always_2d=True)
import numpy as np
audio = audio.mean(axis=1, dtype=np.int16)
def pcm16to32(audio):
assert (audio.dtype == np.int16)
audio = audio.astype("float32")
bits = np.iinfo(np.int16).bits
audio = audio / (2**(bits - 1))
return audio
def pcm32to16(audio):
assert (audio.dtype == np.float32)
bits = np.iinfo(np.int16).bits
audio = audio * (2**(bits - 1))
audio = np.round(audio).astype("int16")
return audio
audio = pcm16to32(audio)
audio=pcm32to16(audio)
with open("transform.pkl", "rb") as tf:
preprocessing = pickle.load(tf)
audio = preprocessing(audio,**{"train":False}
audio = paddle.to_tensor(audio, dtype='float32').unsqueeze(axis=0)
2、进入模型
前面的数据读取已经完成，并且进行了transform，也就是预处理，经过这些操作以后，特征已经经过预处理，就比较好用了，具体经过那些处理，前面的文章讲过了，忘记的可以自行翻阅，下面就是要进入网络层，进入网络层之前，可以看到，现在的audio的shape为(1,363,80)，前面的文章也讲过，这个363很重要，需要记住这个数，从现在开始就可以忘记了，因为后面经过很多次reshape后，这个363就没了。
（1）查看网络
在搭建网络之前，得先看看网络啥样，咱们先展示下网络层把，这个之前加载网络讲过，这里展示下网络的summary。
U2Model(
(encoder): ConformerEncoder(
(embed): Conv2dSubsampling4(
(pos_enc): RelPositionalEncoding(
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
)
(conv): Sequential(
(0): Conv2D(1, 512, kernel_size=[3, 3], stride=[2, 2], data_format=NCHW)
(1): ReLU()
(2): Conv2D(512, 512, kernel_size=[3, 3], stride=[2, 2], data_format=NCHW)
(3): ReLU()
)
(out): Sequential(
(0): Linear(in_features=9728, out_features=512, dtype=float32)
)
)
(after_norm): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(encoders): LayerList(
(0): ConformerEncoderLayer(
(self_attn): RelPositionMultiHeadedAttention(
(linear_q): Linear(in_features=512, out_features=512, dtype=float32)
(linear_k): Linear(in_features=512, out_features=512, dtype=float32)
(linear_v): Linear(in_features=512, out_features=512, dtype=float32)
(linear_out): Linear(in_features=512, out_features=512, dtype=float32)
(dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train)
(linear_pos): Linear(in_features=512, out_features=512, dtype=float32)
)
(feed_forward): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): Swish()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(feed_forward_macaron): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): Swish()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(conv_module): ConvolutionModule(
(pointwise_conv1): Conv1D(512, 1024, kernel_size=[1], data_format=NCL)
(depthwise_conv): Conv1D(512, 512, kernel_size=[15], padding=7, groups=512, data_format=NCL)
(norm): LayerNorm(normalized_shape=[512], epsilon=1e-05)
(pointwise_conv2): Conv1D(512, 512, kernel_size=[1], data_format=NCL)
(activation): Swish()
)
(norm_ff): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_mha): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_ff_macaron): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_conv): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_final): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(concat_linear): Linear(in_features=1024, out_features=512, dtype=float32)
)
(1): ConformerEncoderLayer(
(self_attn): RelPositionMultiHeadedAttention(
(linear_q): Linear(in_features=512, out_features=512, dtype=float32)
(linear_k): Linear(in_features=512, out_features=512, dtype=float32)
(linear_v): Linear(in_features=512, out_features=512, dtype=float32)
(linear_out): Linear(in_features=512, out_features=512, dtype=float32)
(dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train)
(linear_pos): Linear(in_features=512, out_features=512, dtype=float32)
)
(feed_forward): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): Swish()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(feed_forward_macaron): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): Swish()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(conv_module): ConvolutionModule(
(pointwise_conv1): Conv1D(512, 1024, kernel_size=[1], data_format=NCL)
(depthwise_conv): Conv1D(512, 512, kernel_size=[15], padding=7, groups=512, data_format=NCL)
(norm): LayerNorm(normalized_shape=[512], epsilon=1e-05)
(pointwise_conv2): Conv1D(512, 512, kernel_size=[1], data_format=NCL)
(activation): Swish()
)
(norm_ff): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_mha): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_ff_macaron): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_conv): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_final): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(concat_linear): Linear(in_features=1024, out_features=512, dtype=float32)
)
(2): ConformerEncoderLayer(
(self_attn): RelPositionMultiHeadedAttention(
(linear_q): Linear(in_features=512, out_features=512, dtype=float32)
(linear_k): Linear(in_features=512, out_features=512, dtype=float32)
(linear_v): Linear(in_features=512, out_features=512, dtype=float32)
(linear_out): Linear(in_features=512, out_features=512, dtype=float32)
(dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train)
(linear_pos): Linear(in_features=512, out_features=512, dtype=float32)
)
(feed_forward): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): Swish()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(feed_forward_macaron): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): Swish()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(conv_module): ConvolutionModule(
(pointwise_conv1): Conv1D(512, 1024, kernel_size=[1], data_format=NCL)
(depthwise_conv): Conv1D(512, 512, kernel_size=[15], padding=7, groups=512, data_format=NCL)
(norm): LayerNorm(normalized_shape=[512], epsilon=1e-05)
(pointwise_conv2): Conv1D(512, 512, kernel_size=[1], data_format=NCL)
(activation): Swish()
)
(norm_ff): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_mha): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_ff_macaron): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_conv): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_final): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(concat_linear): Linear(in_features=1024, out_features=512, dtype=float32)
)
(3): ConformerEncoderLayer(
(self_attn): RelPositionMultiHeadedAttention(
(linear_q): Linear(in_features=512, out_features=512, dtype=float32)
(linear_k): Linear(in_features=512, out_features=512, dtype=float32)
(linear_v): Linear(in_features=512, out_features=512, dtype=float32)
(linear_out): Linear(in_features=512, out_features=512, dtype=float32)
(dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train)
(linear_pos): Linear(in_features=512, out_features=512, dtype=float32)
)
(feed_forward): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): Swish()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(feed_forward_macaron): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): Swish()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(conv_module): ConvolutionModule(
(pointwise_conv1): Conv1D(512, 1024, kernel_size=[1], data_format=NCL)
(depthwise_conv): Conv1D(512, 512, kernel_size=[15], padding=7, groups=512, data_format=NCL)
(norm): LayerNorm(normalized_shape=[512], epsilon=1e-05)
(pointwise_conv2): Conv1D(512, 512, kernel_size=[1], data_format=NCL)
(activation): Swish()
)
(norm_ff): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_mha): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_ff_macaron): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_conv): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_final): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(concat_linear): Linear(in_features=1024, out_features=512, dtype=float32)
)
(4): ConformerEncoderLayer(
(self_attn): RelPositionMultiHeadedAttention(
(linear_q): Linear(in_features=512, out_features=512, dtype=float32)
(linear_k): Linear(in_features=512, out_features=512, dtype=float32)
(linear_v): Linear(in_features=512, out_features=512, dtype=float32)
(linear_out): Linear(in_features=512, out_features=512, dtype=float32)
(dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train)
(linear_pos): Linear(in_features=512, out_features=512, dtype=float32)
)
(feed_forward): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): Swish()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(feed_forward_macaron): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): Swish()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(conv_module): ConvolutionModule(
(pointwise_conv1): Conv1D(512, 1024, kernel_size=[1], data_format=NCL)
(depthwise_conv): Conv1D(512, 512, kernel_size=[15], padding=7, groups=512, data_format=NCL)
(norm): LayerNorm(normalized_shape=[512], epsilon=1e-05)
(pointwise_conv2): Conv1D(512, 512, kernel_size=[1], data_format=NCL)
(activation): Swish()
)
(norm_ff): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_mha): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_ff_macaron): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_conv): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_final): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(concat_linear): Linear(in_features=1024, out_features=512, dtype=float32)
)
(5): ConformerEncoderLayer(
(self_attn): RelPositionMultiHeadedAttention(
(linear_q): Linear(in_features=512, out_features=512, dtype=float32)
(linear_k): Linear(in_features=512, out_features=512, dtype=float32)
(linear_v): Linear(in_features=512, out_features=512, dtype=float32)
(linear_out): Linear(in_features=512, out_features=512, dtype=float32)
(dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train)
(linear_pos): Linear(in_features=512, out_features=512, dtype=float32)
)
(feed_forward): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): Swish()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(feed_forward_macaron): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): Swish()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(conv_module): ConvolutionModule(
(pointwise_conv1): Conv1D(512, 1024, kernel_size=[1], data_format=NCL)
(depthwise_conv): Conv1D(512, 512, kernel_size=[15], padding=7, groups=512, data_format=NCL)
(norm): LayerNorm(normalized_shape=[512], epsilon=1e-05)
(pointwise_conv2): Conv1D(512, 512, kernel_size=[1], data_format=NCL)
(activation): Swish()
)
(norm_ff): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_mha): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_ff_macaron): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_conv): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_final): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(concat_linear): Linear(in_features=1024, out_features=512, dtype=float32)
)
(6): ConformerEncoderLayer(
(self_attn): RelPositionMultiHeadedAttention(
(linear_q): Linear(in_features=512, out_features=512, dtype=float32)
(linear_k): Linear(in_features=512, out_features=512, dtype=float32)
(linear_v): Linear(in_features=512, out_features=512, dtype=float32)
(linear_out): Linear(in_features=512, out_features=512, dtype=float32)
(dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train)
(linear_pos): Linear(in_features=512, out_features=512, dtype=float32)
)
(feed_forward): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): Swish()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(feed_forward_macaron): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): Swish()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(conv_module): ConvolutionModule(
(pointwise_conv1): Conv1D(512, 1024, kernel_size=[1], data_format=NCL)
(depthwise_conv): Conv1D(512, 512, kernel_size=[15], padding=7, groups=512, data_format=NCL)
(norm): LayerNorm(normalized_shape=[512], epsilon=1e-05)
(pointwise_conv2): Conv1D(512, 512, kernel_size=[1], data_format=NCL)
(activation): Swish()
)
(norm_ff): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_mha): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_ff_macaron): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_conv): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_final): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(concat_linear): Linear(in_features=1024, out_features=512, dtype=float32)
)
(7): ConformerEncoderLayer(
(self_attn): RelPositionMultiHeadedAttention(
(linear_q): Linear(in_features=512, out_features=512, dtype=float32)
(linear_k): Linear(in_features=512, out_features=512, dtype=float32)
(linear_v): Linear(in_features=512, out_features=512, dtype=float32)
(linear_out): Linear(in_features=512, out_features=512, dtype=float32)
(dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train)
(linear_pos): Linear(in_features=512, out_features=512, dtype=float32)
)
(feed_forward): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): Swish()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(feed_forward_macaron): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): Swish()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(conv_module): ConvolutionModule(
(pointwise_conv1): Conv1D(512, 1024, kernel_size=[1], data_format=NCL)
(depthwise_conv): Conv1D(512, 512, kernel_size=[15], padding=7, groups=512, data_format=NCL)
(norm): LayerNorm(normalized_shape=[512], epsilon=1e-05)
(pointwise_conv2): Conv1D(512, 512, kernel_size=[1], data_format=NCL)
(activation): Swish()
)
(norm_ff): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_mha): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_ff_macaron): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_conv): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_final): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(concat_linear): Linear(in_features=1024, out_features=512, dtype=float32)
)
(8): ConformerEncoderLayer(
(self_attn): RelPositionMultiHeadedAttention(
(linear_q): Linear(in_features=512, out_features=512, dtype=float32)
(linear_k): Linear(in_features=512, out_features=512, dtype=float32)
(linear_v): Linear(in_features=512, out_features=512, dtype=float32)
(linear_out): Linear(in_features=512, out_features=512, dtype=float32)
(dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train)
(linear_pos): Linear(in_features=512, out_features=512, dtype=float32)
)
(feed_forward): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): Swish()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(feed_forward_macaron): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): Swish()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(conv_module): ConvolutionModule(
(pointwise_conv1): Conv1D(512, 1024, kernel_size=[1], data_format=NCL)
(depthwise_conv): Conv1D(512, 512, kernel_size=[15], padding=7, groups=512, data_format=NCL)
(norm): LayerNorm(normalized_shape=[512], epsilon=1e-05)
(pointwise_conv2): Conv1D(512, 512, kernel_size=[1], data_format=NCL)
(activation): Swish()
)
(norm_ff): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_mha): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_ff_macaron): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_conv): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_final): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(concat_linear): Linear(in_features=1024, out_features=512, dtype=float32)
)
(9): ConformerEncoderLayer(
(self_attn): RelPositionMultiHeadedAttention(
(linear_q): Linear(in_features=512, out_features=512, dtype=float32)
(linear_k): Linear(in_features=512, out_features=512, dtype=float32)
(linear_v): Linear(in_features=512, out_features=512, dtype=float32)
(linear_out): Linear(in_features=512, out_features=512, dtype=float32)
(dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train)
(linear_pos): Linear(in_features=512, out_features=512, dtype=float32)
)
(feed_forward): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): Swish()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(feed_forward_macaron): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): Swish()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(conv_module): ConvolutionModule(
(pointwise_conv1): Conv1D(512, 1024, kernel_size=[1], data_format=NCL)
(depthwise_conv): Conv1D(512, 512, kernel_size=[15], padding=7, groups=512, data_format=NCL)
(norm): LayerNorm(normalized_shape=[512], epsilon=1e-05)
(pointwise_conv2): Conv1D(512, 512, kernel_size=[1], data_format=NCL)
(activation): Swish()
)
(norm_ff): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_mha): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_ff_macaron): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_conv): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_final): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(concat_linear): Linear(in_features=1024, out_features=512, dtype=float32)
)
(10): ConformerEncoderLayer(
(self_attn): RelPositionMultiHeadedAttention(
(linear_q): Linear(in_features=512, out_features=512, dtype=float32)
(linear_k): Linear(in_features=512, out_features=512, dtype=float32)
(linear_v): Linear(in_features=512, out_features=512, dtype=float32)
(linear_out): Linear(in_features=512, out_features=512, dtype=float32)
(dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train)
(linear_pos): Linear(in_features=512, out_features=512, dtype=float32)
)
(feed_forward): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): Swish()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(feed_forward_macaron): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): Swish()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(conv_module): ConvolutionModule(
(pointwise_conv1): Conv1D(512, 1024, kernel_size=[1], data_format=NCL)
(depthwise_conv): Conv1D(512, 512, kernel_size=[15], padding=7, groups=512, data_format=NCL)
(norm): LayerNorm(normalized_shape=[512], epsilon=1e-05)
(pointwise_conv2): Conv1D(512, 512, kernel_size=[1], data_format=NCL)
(activation): Swish()
)
(norm_ff): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_mha): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_ff_macaron): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_conv): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_final): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(concat_linear): Linear(in_features=1024, out_features=512, dtype=float32)
)
(11): ConformerEncoderLayer(
(self_attn): RelPositionMultiHeadedAttention(
(linear_q): Linear(in_features=512, out_features=512, dtype=float32)
(linear_k): Linear(in_features=512, out_features=512, dtype=float32)
(linear_v): Linear(in_features=512, out_features=512, dtype=float32)
(linear_out): Linear(in_features=512, out_features=512, dtype=float32)
(dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train)
(linear_pos): Linear(in_features=512, out_features=512, dtype=float32)
)
(feed_forward): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): Swish()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(feed_forward_macaron): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): Swish()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(conv_module): ConvolutionModule(
(pointwise_conv1): Conv1D(512, 1024, kernel_size=[1], data_format=NCL)
(depthwise_conv): Conv1D(512, 512, kernel_size=[15], padding=7, groups=512, data_format=NCL)
(norm): LayerNorm(normalized_shape=[512], epsilon=1e-05)
(pointwise_conv2): Conv1D(512, 512, kernel_size=[1], data_format=NCL)
(activation): Swish()
)
(norm_ff): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_mha): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_ff_macaron): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_conv): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm_final): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(concat_linear): Linear(in_features=1024, out_features=512, dtype=float32)
)
)
)
(decoder): TransformerDecoder(
(embed): Sequential(
(0): Embedding(5537, 512, sparse=False)
(1): PositionalEncoding(
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
)
)
(after_norm): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(output_layer): Linear(in_features=512, out_features=5537, dtype=float32)
(decoders): LayerList(
(0): DecoderLayer(
(self_attn): MultiHeadedAttention(
(linear_q): Linear(in_features=512, out_features=512, dtype=float32)
(linear_k): Linear(in_features=512, out_features=512, dtype=float32)
(linear_v): Linear(in_features=512, out_features=512, dtype=float32)
(linear_out): Linear(in_features=512, out_features=512, dtype=float32)
(dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train)
)
(src_attn): MultiHeadedAttention(
(linear_q): Linear(in_features=512, out_features=512, dtype=float32)
(linear_k): Linear(in_features=512, out_features=512, dtype=float32)
(linear_v): Linear(in_features=512, out_features=512, dtype=float32)
(linear_out): Linear(in_features=512, out_features=512, dtype=float32)
(dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train)
)
(feed_forward): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): ReLU()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(norm1): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm2): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm3): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(concat_linear1): Linear(in_features=1024, out_features=512, dtype=float32)
(concat_linear2): Linear(in_features=1024, out_features=512, dtype=float32)
)
(1): DecoderLayer(
(self_attn): MultiHeadedAttention(
(linear_q): Linear(in_features=512, out_features=512, dtype=float32)
(linear_k): Linear(in_features=512, out_features=512, dtype=float32)
(linear_v): Linear(in_features=512, out_features=512, dtype=float32)
(linear_out): Linear(in_features=512, out_features=512, dtype=float32)
(dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train)
)
(src_attn): MultiHeadedAttention(
(linear_q): Linear(in_features=512, out_features=512, dtype=float32)
(linear_k): Linear(in_features=512, out_features=512, dtype=float32)
(linear_v): Linear(in_features=512, out_features=512, dtype=float32)
(linear_out): Linear(in_features=512, out_features=512, dtype=float32)
(dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train)
)
(feed_forward): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): ReLU()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(norm1): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm2): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm3): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(concat_linear1): Linear(in_features=1024, out_features=512, dtype=float32)
(concat_linear2): Linear(in_features=1024, out_features=512, dtype=float32)
)
(2): DecoderLayer(
(self_attn): MultiHeadedAttention(
(linear_q): Linear(in_features=512, out_features=512, dtype=float32)
(linear_k): Linear(in_features=512, out_features=512, dtype=float32)
(linear_v): Linear(in_features=512, out_features=512, dtype=float32)
(linear_out): Linear(in_features=512, out_features=512, dtype=float32)
(dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train)
)
(src_attn): MultiHeadedAttention(
(linear_q): Linear(in_features=512, out_features=512, dtype=float32)
(linear_k): Linear(in_features=512, out_features=512, dtype=float32)
(linear_v): Linear(in_features=512, out_features=512, dtype=float32)
(linear_out): Linear(in_features=512, out_features=512, dtype=float32)
(dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train)
)
(feed_forward): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): ReLU()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(norm1): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm2): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm3): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(concat_linear1): Linear(in_features=1024, out_features=512, dtype=float32)
(concat_linear2): Linear(in_features=1024, out_features=512, dtype=float32)
)
(3): DecoderLayer(
(self_attn): MultiHeadedAttention(
(linear_q): Linear(in_features=512, out_features=512, dtype=float32)
(linear_k): Linear(in_features=512, out_features=512, dtype=float32)
(linear_v): Linear(in_features=512, out_features=512, dtype=float32)
(linear_out): Linear(in_features=512, out_features=512, dtype=float32)
(dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train)
)
(src_attn): MultiHeadedAttention(
(linear_q): Linear(in_features=512, out_features=512, dtype=float32)
(linear_k): Linear(in_features=512, out_features=512, dtype=float32)
(linear_v): Linear(in_features=512, out_features=512, dtype=float32)
(linear_out): Linear(in_features=512, out_features=512, dtype=float32)
(dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train)
)
(feed_forward): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): ReLU()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(norm1): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm2): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm3): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(concat_linear1): Linear(in_features=1024, out_features=512, dtype=float32)
(concat_linear2): Linear(in_features=1024, out_features=512, dtype=float32)
)
(4): DecoderLayer(
(self_attn): MultiHeadedAttention(
(linear_q): Linear(in_features=512, out_features=512, dtype=float32)
(linear_k): Linear(in_features=512, out_features=512, dtype=float32)
(linear_v): Linear(in_features=512, out_features=512, dtype=float32)
(linear_out): Linear(in_features=512, out_features=512, dtype=float32)
(dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train)
)
(src_attn): MultiHeadedAttention(
(linear_q): Linear(in_features=512, out_features=512, dtype=float32)
(linear_k): Linear(in_features=512, out_features=512, dtype=float32)
(linear_v): Linear(in_features=512, out_features=512, dtype=float32)
(linear_out): Linear(in_features=512, out_features=512, dtype=float32)
(dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train)
)
(feed_forward): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): ReLU()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(norm1): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm2): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm3): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(concat_linear1): Linear(in_features=1024, out_features=512, dtype=float32)
(concat_linear2): Linear(in_features=1024, out_features=512, dtype=float32)
)
(5): DecoderLayer(
(self_attn): MultiHeadedAttention(
(linear_q): Linear(in_features=512, out_features=512, dtype=float32)
(linear_k): Linear(in_features=512, out_features=512, dtype=float32)
(linear_v): Linear(in_features=512, out_features=512, dtype=float32)
(linear_out): Linear(in_features=512, out_features=512, dtype=float32)
(dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train)
)
(src_attn): MultiHeadedAttention(
(linear_q): Linear(in_features=512, out_features=512, dtype=float32)
(linear_k): Linear(in_features=512, out_features=512, dtype=float32)
(linear_v): Linear(in_features=512, out_features=512, dtype=float32)
(linear_out): Linear(in_features=512, out_features=512, dtype=float32)
(dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train)
)
(feed_forward): PositionwiseFeedForward(
(w_1): Linear(in_features=512, out_features=2048, dtype=float32)
(activation): ReLU()
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(w_2): Linear(in_features=2048, out_features=512, dtype=float32)
)
(norm1): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm2): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(norm3): LayerNorm(normalized_shape=[512], epsilon=1e-12)
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
(concat_linear1): Linear(in_features=1024, out_features=512, dtype=float32)
(concat_linear2): Linear(in_features=1024, out_features=512, dtype=float32)
)
)
)
(ctc): CTCDecoderBase(
(dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train)
(ctc_lo): Linear(in_features=512, out_features=5537, dtype=float32)
(criterion): CTCLoss(
(loss): CTCLoss()
)
)
(criterion_att): LabelSmoothingLoss(
(criterion): KLDivLoss()
)
)
这个网络非常长，所以需要拆开慢慢讲解，下面我们先讲这一层，然后这一层，主要是两部分：一个是卷积，一个是Linear，然后我们来手工实现。
Conv2dSubsampling4(
(pos_enc): RelPositionalEncoding(
(dropout): Dropout(p=0.1, axis=None, mode=upscale_in_train)
)
(conv): Sequential(
(0): Conv2D(1, 512, kernel_size=[3, 3], stride=[2, 2], data_format=NCHW)
(1): ReLU()
(2): Conv2D(512, 512, kernel_size=[3, 3], stride=[2, 2], data_format=NCHW)
(3): ReLU()
)
(out): Sequential(
(0): Linear(in_features=9728, out_features=512, dtype=float32)
)
)
（2）搭建网络
import paddle
class Model(paddle.nn.Layer):
def __init__(self):
super(Model,self).__init__()
self.conv2d_1 = paddle.nn.Conv2D(1, 512, kernel_size=[3, 3], stride=[2, 2], data_format="NCHW")
self.conv2d_2 = paddle.nn.Conv2D(512, 512, kernel_size=[3, 3], stride=[2, 2], data_format="NCHW")
self.lineone = paddle.nn.Linear(9728,512)
def forward(self,inputs):
y = self.conv2d_1(inputs)
y = paddle.nn.functional.relu(y)
y = self.conv2d_2(y)
y = paddle.nn.functional.relu(y)
b, c, t, f = paddle.shape(y)
y = y.transpose([0, 2, 1, 3]).reshape([b,t,c*f])
y = self.lineone(y)
print(y.shape)
print(y)
return y
上面这是使用paddle框架，根据最终的summary搭建出来的网络，具体为何如此搭建，请看下图。
可以清楚的看到，在forward过程中，先是卷积，然后卷积完以后，进行了transpose，reshape才怼入Linear，所以咱们也得这么写。
model = Model()
audio1 = audio.unsqueeze(1) # (b, c=1, t, f)
x = model(audio1)
可以看到，这个时候，咱们自己写的网络和源代码里面的都对上了，输出shape也一样。
（3）embedding
这一部分其实就是一个lookup表，不写代码了，直接看源码，不过这个地方主要是position_embed，最终是多个结果一起输出
完成位置嵌入后，其实后面还有很多处理，这个下次再讲了。
3、总结
好了，这里讲了这么多，其实只是完成了网络的第一部分，后面还有很多，下期继续讲。
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