From cf7ded409ea6a60920db468cc69267367fc488bf Mon Sep 17 00:00:00 2001 From: Ming Date: Mon, 11 Mar 2019 15:45:38 +0800 Subject: [PATCH] Add files via upload --- layerNormedGRU.py | 78 + model901.py | 87 + model902.py | 89 + model903.py | 89 + subtitle_demo.ipynb | 4363 +++++++++++++++++++++++++++++++++++++++++++ subtitle_demo.py | 57 + test_audio.wav | Bin 0 -> 244748 bytes test_audio2.wav | Bin 0 -> 102508 bytes train901.py | 74 + train902.py | 74 + train903.py | 74 + 11 files changed, 4985 insertions(+) create mode 100644 layerNormedGRU.py create mode 100644 model901.py create mode 100644 model902.py create mode 100644 model903.py create mode 100644 subtitle_demo.ipynb create mode 100644 subtitle_demo.py create mode 100644 test_audio.wav create mode 100644 test_audio2.wav create mode 100644 train901.py create mode 100644 train902.py create mode 100644 train903.py diff --git a/layerNormedGRU.py b/layerNormedGRU.py new file mode 100644 index 0000000..1010836 --- /dev/null +++ b/layerNormedGRU.py @@ -0,0 +1,78 @@ +import numpy as np +import tensorflow as tf + + +class layerNormedGRU(tf.contrib.rnn.RNNCell): + + def __init__( + self, size, activation=tf.tanh, reuse=None, + normalizer=tf.contrib.layers.layer_norm, + initializer=tf.contrib.layers.xavier_initializer()): + super(layerNormedGRU, self).__init__(_reuse=reuse) + self._size = size + self._activation = activation + self._normalizer = normalizer + self._initializer = initializer + + @property + def state_size(self): + return self._size + + @property + def output_size(self): + return self._size + + def call(self, input_, state): + update, reset = tf.split(self._forward( + 'update_reset', [state, input_], 2 * self._size, tf.nn.sigmoid, + bias_initializer=tf.constant_initializer(-1.)), 2, 1) + candidate = self._forward( + 'candidate', [reset * state, input_], self._size, self._activation) + state = (1 - update) * state + update * candidate + return state, state + + def _forward(self, name, inputs, size, activation, **kwargs): + with tf.variable_scope(name): + return _forward( + inputs, size, activation, normalizer=self._normalizer, + weight_initializer=self._initializer, **kwargs) + + +def _forward( + inputs, size, activation, normalizer=tf.contrib.layers.layer_norm, + weight_initializer=tf.contrib.layers.xavier_initializer(), + bias_initializer=tf.zeros_initializer()): + if not isinstance(inputs, (tuple, list)): + inputs = (inputs,) + shapes = [] + outputs = [] + # Map each input to individually normalize their outputs. + for index, input_ in enumerate(inputs): + shapes.append(input_.shape[1: -1].as_list()) + input_ = tf.contrib.layers.flatten(input_) + weight = tf.get_variable( + 'weight_{}'.format(index + 1), (int(input_.shape[1]), size), + tf.float32, weight_initializer) + output = tf.matmul(input_, weight) + if normalizer: + output = normalizer(output) + outputs.append(output) + output = tf.reduce_mean(outputs, 0) + # Add bias after normalization. + bias = tf.get_variable( + 'weight', (size,), tf.float32, bias_initializer) + output += bias + # Activation function. + if activation: + output = activation(output) + # Restore shape dimensions that are consistent among inputs. + min_dim = min(len(shape[1:]) for shape in shapes) + dim_shapes = [[shape[dim] for shape in shapes] for dim in range(min_dim)] + matching_dims = ''.join('NY'[len(set(x)) == 1] for x in dim_shapes) + 'N' + agreement = matching_dims.index('N') + remaining = sum(np.prod(shape[agreement:]) for shape in shapes) + if agreement: + batch_size = output.shape[0].value or -1 + shape = [batch_size] + shapes[:agreement] + [remaining] + output = tf.reshape(output, shape) + return output diff --git a/model901.py b/model901.py new file mode 100644 index 0000000..9467734 --- /dev/null +++ b/model901.py @@ -0,0 +1,87 @@ +import tensorflow as tf +import numpy as np +from layerNormedGRU import layerNormedGRU + +class model: + + def __init__(self, num_class, topk_paths = 10): + self.xs = tf.placeholder(tf.float32, [None, 1000, 161]) + self.ys = tf.sparse_placeholder(tf.int32) + self.learning_rate = tf.placeholder(tf.float32) + self.seq_len = tf.placeholder(tf.int32, [None]) + self.isTrain = tf.placeholder(tf.bool, name='phase') + + xs_input = tf.expand_dims(self.xs, 3) + + conv1 = self._nn_conv_bn_layer(xs_input, 'conv_1', [11, 41, 1, 32], [3, 2]) + conv2 = self._nn_conv_bn_layer(conv1, 'conv_2', [11, 21, 32, 32], [1, 2]) + conv_out = tf.reshape(conv2, [-1, 334, 41*32]) + biRNN1 = self._biRNN_bn_layer(conv_out, 'biRNN_1', 256) + biRNN2 = self._biRNN_bn_layer(biRNN1, 'biRNN_2', 256) + biRNN3 = self._biRNN_bn_layer(biRNN2, 'biRNN_3', 256) + + self.phonemes = tf.layers.dense(biRNN3, num_class) + + # Notes: tf.nn.ctc_loss performs the softmax operation for you, so + # inputs should be e.g. linear projections of outputs by an LSTM. + self.loss = tf.reduce_mean(tf.nn.ctc_loss(labels=self.ys, inputs=self.phonemes, sequence_length=self.seq_len, + ignore_longer_outputs_than_inputs=True, time_major=False)) + + optimizer = tf.train.AdamOptimizer(self.learning_rate, beta1 = 0.6, beta2 = 0.8) + update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) + with tf.control_dependencies(update_ops): + gvs = optimizer.compute_gradients(self.loss) + capped_gvs = [(tf.clip_by_value(grad, -400., 400.), var) for grad, var in gvs if grad is not None] + self.train_op = optimizer.apply_gradients(capped_gvs) + + self.prediction, log_prob = tf.nn.ctc_beam_search_decoder(tf.transpose(self.phonemes,[1,0,2]), self.seq_len, top_paths=topk_paths, merge_repeated=False) + + self.loss_summary = tf.summary.scalar("loss", self.loss) + self.merged = tf.summary.merge_all() + + def _nn_conv_bn_layer(self, inputs, scope, shape, strides): + with tf.variable_scope(scope): + W_conv = tf.get_variable("W", shape=shape, initializer=tf.contrib.layers.xavier_initializer()) + h_conv = tf.nn.conv2d(inputs, W_conv, strides=[1, strides[0], strides[1], 1], padding='SAME', name="conv2d") + b = tf.get_variable("bias" , shape=[shape[3]], initializer=tf.contrib.layers.xavier_initializer()) + h_bn = tf.layers.batch_normalization(h_conv+b, training = self.isTrain) + h_relu = tf.nn.relu6(h_bn, name="relu6") + return h_relu + + def _biRNN_bn_layer(self, input, scope, hidden_units, cell = "LayerNormedGRU"): + with tf.variable_scope(scope): + if cell == 'GRU': + fw_cell = tf.nn.rnn_cell.GRUCell(hidden_units, activation=tf.nn.relu, name = 'fw_cell') + bw_cell = tf.nn.rnn_cell.GRUCell(hidden_units, activation=tf.nn.relu, name = 'bw_cell') + elif cell == 'LSTM': + fw_cell = tf.nn.rnn_cell.BasicLSTMCell(hidden_units, activation=tf.nn.relu, name = 'fw_cell') + bw_cell = tf.nn.rnn_cell.BasicLSTMCell(hidden_units, activation=tf.nn.relu, name = 'bw_cell') + elif cell == 'vanila': + fw_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_units, activation=tf.nn.relu, name = 'fw_cell') + bw_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_units, activation=tf.nn.relu, name = 'bw_cell') + elif cell == 'LayerNormedGRU': + with tf.variable_scope('fw_cell'): + fw_cell = layerNormedGRU(hidden_units, activation=tf.nn.relu) + with tf.variable_scope('bw_cell'): + bw_cell = layerNormedGRU(hidden_units, activation=tf.nn.relu) + else: + raise ValueError("Invalid cell type: "+str(cell)) + + (output_fw, output_bw), _ = tf.nn.bidirectional_dynamic_rnn(fw_cell, bw_cell, input, dtype=tf.float32, scope="bi_dynamic_rnn") + # output_fw_bn = tf.layers.batch_normalization(output_fw, training = self.isTrain, name = 'output_fw_bn') + # output_bw_bn = tf.layers.batch_normalization(output_bw, training = self.isTrain, name = 'output_bw_bn') + # bilstm_outputs_concat_1 = tf.concat([output_fw_bn, output_bw_bn], 2) + bilstm_outputs_concat_1 = tf.concat([output_fw, output_bw], 2) + return bilstm_outputs_concat_1 + + def train(self, sess, learning_rate, xs, ys): + _, loss, summary = sess.run([self.train_op, self.loss, self.merged], feed_dict = {self.isTrain: True, self.learning_rate: learning_rate, self.seq_len: np.ones(xs.shape[0])*334, self.xs: xs, self.ys: ys}) + return loss, summary + + def get_loss(self, sess, xs, ys): + loss = sess.run(self.loss, feed_dict = {self.isTrain: False, self.seq_len: np.ones(xs.shape[0])*334, self.xs: xs, self.ys: ys}) + return loss + + def predict(self, sess, xs): + prediction = sess.run(self.prediction, feed_dict = {self.isTrain: False, self.seq_len: np.ones(xs.shape[0])*334, self.xs: xs}) + return prediction diff --git a/model902.py b/model902.py new file mode 100644 index 0000000..a903206 --- /dev/null +++ b/model902.py @@ -0,0 +1,89 @@ +import tensorflow as tf +import numpy as np +from layerNormedGRU import layerNormedGRU + +class model: + + def __init__(self, num_class, topk_paths = 10): + self.xs = tf.placeholder(tf.float32, [None, 1000, 161]) + self.ys = tf.sparse_placeholder(tf.int32) + self.learning_rate = tf.placeholder(tf.float32) + self.seq_len = tf.placeholder(tf.int32, [None]) + self.isTrain = tf.placeholder(tf.bool, name='phase') + + xs_input = tf.expand_dims(self.xs, 3) + + conv1 = self._nn_conv_bn_layer(xs_input, 'conv_1', [11, 41, 1, 32], [3, 2]) + conv2 = self._nn_conv_bn_layer(conv1, 'conv_2', [11, 21, 32, 64], [1, 2]) + conv_out = tf.reshape(conv2, [-1, 334, 41*64]) + biRNN1 = self._biRNN_bn_layer(conv_out, 'biRNN_1', 256) + biRNN2 = self._biRNN_bn_layer(biRNN1, 'biRNN_2', 256) + biRNN3 = self._biRNN_bn_layer(biRNN2, 'biRNN_3', 256) + biRNN4 = self._biRNN_bn_layer(biRNN3, 'biRNN_4', 256) + biRNN5 = self._biRNN_bn_layer(biRNN4, 'biRNN_5', 256) + + self.phonemes = tf.layers.dense(biRNN5, num_class) + + # Notes: tf.nn.ctc_loss performs the softmax operation for you, so + # inputs should be e.g. linear projections of outputs by an LSTM. + self.loss = tf.reduce_mean(tf.nn.ctc_loss(labels=self.ys, inputs=self.phonemes, sequence_length=self.seq_len, + ignore_longer_outputs_than_inputs=True, time_major=False)) + + optimizer = tf.train.AdamOptimizer(self.learning_rate, beta1=0.7, beta2=0.9) + update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) + with tf.control_dependencies(update_ops): + gvs = optimizer.compute_gradients(self.loss) + capped_gvs = [(tf.clip_by_value(grad, -400., 400.), var) for grad, var in gvs if grad is not None] + self.train_op = optimizer.apply_gradients(capped_gvs) + + self.prediction, log_prob = tf.nn.ctc_beam_search_decoder(tf.transpose(self.phonemes,[1,0,2]), self.seq_len, top_paths=topk_paths, merge_repeated=False) + + self.loss_summary = tf.summary.scalar("loss", self.loss) + self.merged = tf.summary.merge_all() + + def _nn_conv_bn_layer(self, inputs, scope, shape, strides): + with tf.variable_scope(scope): + W_conv = tf.get_variable("W", shape=shape, initializer=tf.contrib.layers.xavier_initializer()) + h_conv = tf.nn.conv2d(inputs, W_conv, strides=[1, strides[0], strides[1], 1], padding='SAME', name="conv2d") + b = tf.get_variable("bias" , shape=[shape[3]], initializer=tf.contrib.layers.xavier_initializer()) + h_bn = tf.layers.batch_normalization(h_conv+b, training = self.isTrain) + h_relu = tf.nn.relu6(h_bn, name="relu6") + return h_relu + + def _biRNN_bn_layer(self, input, scope, hidden_units, cell = "LayerNormedGRU"): + with tf.variable_scope(scope): + if cell == 'GRU': + fw_cell = tf.nn.rnn_cell.GRUCell(hidden_units, activation=tf.nn.relu, name = 'fw_cell') + bw_cell = tf.nn.rnn_cell.GRUCell(hidden_units, activation=tf.nn.relu, name = 'bw_cell') + elif cell == 'LSTM': + fw_cell = tf.nn.rnn_cell.BasicLSTMCell(hidden_units, activation=tf.nn.relu, name = 'fw_cell') + bw_cell = tf.nn.rnn_cell.BasicLSTMCell(hidden_units, activation=tf.nn.relu, name = 'bw_cell') + elif cell == 'vanila': + fw_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_units, activation=tf.nn.relu, name = 'fw_cell') + bw_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_units, activation=tf.nn.relu, name = 'bw_cell') + elif cell == 'LayerNormedGRU': + with tf.variable_scope('fw_cell'): + fw_cell = layerNormedGRU(hidden_units, activation=tf.nn.relu) + with tf.variable_scope('bw_cell'): + bw_cell = layerNormedGRU(hidden_units, activation=tf.nn.relu) + else: + raise ValueError("Invalid cell type: "+str(cell)) + + (output_fw, output_bw), _ = tf.nn.bidirectional_dynamic_rnn(fw_cell, bw_cell, input, dtype=tf.float32, scope="bi_dynamic_rnn") + # output_fw_bn = tf.layers.batch_normalization(output_fw, training = self.isTrain, name = 'output_fw_bn') + # output_bw_bn = tf.layers.batch_normalization(output_bw, training = self.isTrain, name = 'output_bw_bn') + # bilstm_outputs_concat_1 = tf.concat([output_fw_bn, output_bw_bn], 2) + bilstm_outputs_concat_1 = tf.concat([output_fw, output_bw], 2) + return bilstm_outputs_concat_1 + + def train(self, sess, learning_rate, xs, ys): + _, loss, summary = sess.run([self.train_op, self.loss, self.merged], feed_dict = {self.isTrain: True, self.learning_rate: learning_rate, self.seq_len: np.ones(xs.shape[0])*334, self.xs: xs, self.ys: ys}) + return loss, summary + + def get_loss(self, sess, xs, ys): + loss = sess.run(self.loss, feed_dict = {self.isTrain: False, self.seq_len: np.ones(xs.shape[0])*334, self.xs: xs, self.ys: ys}) + return loss + + def predict(self, sess, xs): + prediction = sess.run(self.prediction, feed_dict = {self.isTrain: False, self.seq_len: np.ones(xs.shape[0])*334, self.xs: xs}) + return prediction diff --git a/model903.py b/model903.py new file mode 100644 index 0000000..435975a --- /dev/null +++ b/model903.py @@ -0,0 +1,89 @@ +import tensorflow as tf +import numpy as np +from layerNormedGRU import layerNormedGRU + +class model: + + def __init__(self, num_class, topk_paths = 10): + self.xs = tf.placeholder(tf.float32, [None, 1000, 161]) + self.ys = tf.sparse_placeholder(tf.int32) + self.learning_rate = tf.placeholder(tf.float32) + self.seq_len = tf.placeholder(tf.int32, [None]) + self.isTrain = tf.placeholder(tf.bool, name='phase') + + xs_input = tf.expand_dims(self.xs, 3) + + conv1 = self._nn_conv_bn_layer(xs_input, 'conv_1', [11, 41, 1, 32], [3, 2]) + conv2 = self._nn_conv_bn_layer(conv1, 'conv_2', [11, 21, 32, 64], [1, 2]) + conv_out = tf.reshape(conv2, [-1, 334, 41*64]) + biRNN1 = self._biRNN_bn_layer(conv_out, 'biRNN_1', 1024) + biRNN2 = self._biRNN_bn_layer(biRNN1, 'biRNN_2', 1024) + biRNN3 = self._biRNN_bn_layer(biRNN2, 'biRNN_3', 1024) + biRNN4 = self._biRNN_bn_layer(biRNN3, 'biRNN_4', 1024) + biRNN5 = self._biRNN_bn_layer(biRNN4, 'biRNN_5', 1024) + + self.phonemes = tf.layers.dense(biRNN5, num_class) + + # Notes: tf.nn.ctc_loss performs the softmax operation for you, so + # inputs should be e.g. linear projections of outputs by an LSTM. + self.loss = tf.reduce_mean(tf.nn.ctc_loss(labels=self.ys, inputs=self.phonemes, sequence_length=self.seq_len, + ignore_longer_outputs_than_inputs=True, time_major=False)) + + optimizer = tf.train.AdamOptimizer(self.learning_rate, beta1=0.7, beta2=0.9) + update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) + with tf.control_dependencies(update_ops): + gvs = optimizer.compute_gradients(self.loss) + capped_gvs = [(tf.clip_by_value(grad, -400., 400.), var) for grad, var in gvs if grad is not None] + self.train_op = optimizer.apply_gradients(capped_gvs) + + self.prediction, log_prob = tf.nn.ctc_beam_search_decoder(tf.transpose(self.phonemes,[1,0,2]), self.seq_len, top_paths=topk_paths, merge_repeated=False) + + self.loss_summary = tf.summary.scalar("loss", self.loss) + self.merged = tf.summary.merge_all() + + def _nn_conv_bn_layer(self, inputs, scope, shape, strides): + with tf.variable_scope(scope): + W_conv = tf.get_variable("W", shape=shape, initializer=tf.contrib.layers.xavier_initializer()) + h_conv = tf.nn.conv2d(inputs, W_conv, strides=[1, strides[0], strides[1], 1], padding='SAME', name="conv2d") + b = tf.get_variable("bias" , shape=[shape[3]], initializer=tf.contrib.layers.xavier_initializer()) + h_bn = tf.layers.batch_normalization(h_conv+b, training = self.isTrain) + h_relu = tf.nn.relu6(h_bn, name="relu6") + return h_relu + + def _biRNN_bn_layer(self, input, scope, hidden_units, cell = "LayerNormedGRU"): + with tf.variable_scope(scope): + if cell == 'GRU': + fw_cell = tf.nn.rnn_cell.GRUCell(hidden_units, activation=tf.nn.relu, name = 'fw_cell') + bw_cell = tf.nn.rnn_cell.GRUCell(hidden_units, activation=tf.nn.relu, name = 'bw_cell') + elif cell == 'LSTM': + fw_cell = tf.nn.rnn_cell.BasicLSTMCell(hidden_units, activation=tf.nn.relu, name = 'fw_cell') + bw_cell = tf.nn.rnn_cell.BasicLSTMCell(hidden_units, activation=tf.nn.relu, name = 'bw_cell') + elif cell == 'vanila': + fw_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_units, activation=tf.nn.relu, name = 'fw_cell') + bw_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_units, activation=tf.nn.relu, name = 'bw_cell') + elif cell == 'LayerNormedGRU': + with tf.variable_scope('fw_cell'): + fw_cell = layerNormedGRU(hidden_units, activation=tf.nn.relu) + with tf.variable_scope('bw_cell'): + bw_cell = layerNormedGRU(hidden_units, activation=tf.nn.relu) + else: + raise ValueError("Invalid cell type: "+str(cell)) + + (output_fw, output_bw), _ = tf.nn.bidirectional_dynamic_rnn(fw_cell, bw_cell, input, dtype=tf.float32, scope="bi_dynamic_rnn") + # output_fw_bn = tf.layers.batch_normalization(output_fw, training = self.isTrain, name = 'output_fw_bn') + # output_bw_bn = tf.layers.batch_normalization(output_bw, training = self.isTrain, name = 'output_bw_bn') + # bilstm_outputs_concat_1 = tf.concat([output_fw_bn, output_bw_bn], 2) + bilstm_outputs_concat_1 = tf.concat([output_fw, output_bw], 2) + return bilstm_outputs_concat_1 + + def train(self, sess, learning_rate, xs, ys): + _, loss, summary = sess.run([self.train_op, self.loss, self.merged], feed_dict = {self.isTrain: True, self.learning_rate: learning_rate, self.seq_len: np.ones(xs.shape[0])*334, self.xs: xs, self.ys: ys}) + return loss, summary + + def get_loss(self, sess, xs, ys): + loss = sess.run(self.loss, feed_dict = {self.isTrain: False, self.seq_len: np.ones(xs.shape[0])*334, self.xs: xs, self.ys: ys}) + return loss + + def predict(self, sess, xs): + prediction = sess.run(self.prediction, feed_dict = {self.isTrain: False, self.seq_len: np.ones(xs.shape[0])*334, self.xs: xs}) + return prediction diff --git a/subtitle_demo.ipynb b/subtitle_demo.ipynb new file mode 100644 index 0000000..592f07e --- /dev/null +++ b/subtitle_demo.ipynb @@ -0,0 +1,4363 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import time\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n", + "warnings.filterwarnings(\"ignore\", message=\"numpy.ufunc size changed\")\n", + "with warnings.catch_warnings():\n", + " warnings.simplefilter(\"ignore\")\n", + " import tensorflow as tf\n", + "import numpy as np\n", + "from urllib.request import urlopen\n", + "\n", + "from lib.tools_batch import *\n", + "from lib.tools_math import *\n", + "from lib.tools_sparse import *\n", + "from lib.tools_audio import *\n", + "from lib.contrib.audio_featurizer import AudioFeaturizer\n", + "from lib.contrib.audio import AudioSegment\n", + "from model903 import *\n", + "model_name = \"v903\"\n", + "\n", + "def timeStamp2Num(timeStamp, rate):\n", + " \"\"\"\n", + " timeStamp str: 00:00:01,879\n", + " rate int: the sampling rate\n", + " return int\n", + " \"\"\"\n", + " secs, millisec = timeStamp.split(\",\")\n", + " hour, minute, sec = secs.split(\":\")\n", + " millisec = float(millisec)*0.001\n", + " sec = float(hour)*3600+float(minute)*60+float(sec)\n", + " num = int(rate*(sec+millisec))\n", + " return num\n", + "\n", + "pyParser = pinyinParser(\"lib/pinyinDictNoTone.pickle\")\n", + "model = model(409)\n", + "af = AudioFeaturizer()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "sess = tf.Session()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from models/v903/v903_0.ckpt\n" + ] + } + ], + "source": [ + "saver = tf.train.Saver()\n", + "saver.restore(sess, \"models/\"+model_name+\"/\"+model_name+\"_0.ckpt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tamenxiangmusuodingdezhuyaokehuqunjiushiyouhaiwaizhiyexuqiudehuaren\n", + "他们项目锁定的主要客户群就是有海外置业需求的华人\n" + ] + } + ], + "source": [ + "rate, data = read_wav(\"data/test.wav\")\n", + "data = mergeChannels(data)\n", + "data = zero_padding_1d(data, 160240)\n", + "a_seg = AudioSegment(data, rate)\n", + "xs = np.transpose(np.array([af.featurize(a_seg)]), [0,2,1])\n", + "\n", + "pred = model.predict(sess, xs)[0]\n", + "pred_dense = sparseTuples2dense(pred)\n", + "detected_line = []\n", + "for stuff in pred_dense[0]:\n", + " if stuff!=-1:\n", + " detected_line.append(stuff)\n", + "pinyin = pyParser.decodeIndices(detected_line, useUnderline = False)\n", + "print(pinyin)\n", + "response = urlopen(\"https://www.google.com/inputtools/request?ime=pinyin&ie=utf-8&oe=utf-8&app=translate&num=10&text=\"+pinyin)\n", + "html = response.read()\n", + "result = (html.decode('utf8')).split(\",\")[2][2:-1]\n", + "print(result)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0/2707\n", + "1/2707\n", + "Audio time = 3.0 sec.\n", + "2/2707\n", + "3/2707\n", + "4/2707\n", + "5/2707\n", + "6/2707\n", + "Audio time = 1.93 sec.\n", + "Recognition time = 8.552099704742432 sec.\n", + "大家好\n", + "大家好\n", + "7/2707\n", + "8/2707\n", + "9/2707\n", + "10/2707\n", + "Audio time = 3.5 sec.\n", + "Recognition time = 11.099024057388306 sec.\n", + "或持来自华中科学大学的章洛伊\n", + "我是来自华中科技大学的张珞颖\n", + "11/2707\n", + "12/2707\n", + "13/2707\n", + "14/2707\n", + "Audio time = 2.7 sec.\n", + "Recognition time = 11.181823015213013 sec.\n", + "我今天给伊达家呆了一个官\n", + "我今天给大家带来一个\n", + "15/2707\n", + "16/2707\n", + "17/2707\n", + "18/2707\n", + "Audio time = 3.85 sec.\n", + "Recognition time = 11.755331039428711 sec.\n", + "在一个关于我们为什么要睡觉的演讲\n", + "关于我们为什么要睡觉的演讲\n", + "19/2707\n", + "20/2707\n", + "21/2707\n", + "22/2707\n", + "Audio time = 3.6 sec.\n", + "Recognition time = 9.625288963317871 sec.\n", + "我们已身中带来有三分之一的时间\n", + "我们一生中大概有三分之一的时间\n", + "23/2707\n", + "24/2707\n", + "25/2707\n", + "26/2707\n", + "Audio time = 2.7 sec.\n", + "Recognition time = 9.863281965255737 sec.\n", + "是在睡眠中度过的\n", + "是在睡眠中度过的\n", + "27/2707\n", + "28/2707\n", + "29/2707\n", + "30/2707\n", + "31/2707\n", + "Audio time = 2.87 sec.\n", + "Recognition time = 8.321897983551025 sec.\n", + "你从出生到现在\n", + "那么你从出生到现在\n", + "32/2707\n", + "33/2707\n", + "34/2707\n", + "35/2707\n", + "Audio time = 2.83 sec.\n", + "Recognition time = 8.540001153945923 sec.\n", + "先大概睡了能有十年的叫了\n", + "大概睡了能有10年的觉了\n", + "36/2707\n", + "37/2707\n", + "38/2707\n", + "39/2707\n", + "Audio time = 5.9 sec.\n", + "Recognition time = 8.908971071243286 sec.\n", + "如果从一个跟宏大的岩画的背景上面来看的话\n", + "如果从一个更宏大的演化的背景上面来看的话\n", + "40/2707\n", + "41/2707\n", + "42/2707\n", + "43/2707\n", + "Audio time = 2.65 sec.\n", + "Recognition time = 9.589301109313965 sec.\n", + "其实睡眠这个现象\n", + "其实睡眠这个现象\n", + "44/2707\n", + "45/2707\n", + "46/2707\n", + "47/2707\n", + "Audio time = 3.85 sec.\n", + "Recognition time = 9.76643681526184 sec.\n", + "在地球上出现了已经有数亿年了\n", + "在地球上出现了已经有数亿年了\n", + "48/2707\n", + "49/2707\n", + "50/2707\n", + "51/2707\n", + "Audio time = 3.85 sec.\n", + "Recognition time = 10.031269073486328 sec.\n", + "就是地球上的动物均水了基因链的角恶\n", + "就是地球上的动物已经睡了几亿年的觉了\n", + "52/2707\n", + "53/2707\n", + "54/2707\n", + "55/2707\n", + "Audio time = 5.18 sec.\n", + "Recognition time = 9.635947942733765 sec.\n", + "从一个最简单的蠕虫类的东贤重\n", + "从一个最简单的蠕虫类的动物 线虫\n", + "56/2707\n", + "57/2707\n", + "58/2707\n", + "59/2707\n", + "Audio time = 3.0 sec.\n", + "Recognition time = 9.9219069480896 sec.\n", + "老根复杂的生物\n", + "到更复杂的生物\n", + "60/2707\n", + "61/2707\n", + "62/2707\n", + "63/2707\n", + "Audio time = 2.35 sec.\n", + "Recognition time = 10.111473798751831 sec.\n", + "比如说昆虫\n", + "比如说昆虫\n", + "64/2707\n", + "65/2707\n", + "66/2707\n", + "67/2707\n", + "Audio time = 2.9 sec.\n", + "Recognition time = 9.876278162002563 sec.\n", + "然后还有一些鱼类\n", + "然后还有一些鱼类\n", + "68/2707\n", + "69/2707\n", + "70/2707\n", + "71/2707\n", + "Audio time = 3.3 sec.\n", + "Recognition time = 9.803333759307861 sec.\n", + "还有一些哺乳动物\n", + "还有一些哺乳动物\n", + "72/2707\n", + "73/2707\n", + "74/2707\n", + "75/2707\n", + "Audio time = 5.3 sec.\n", + "Recognition time = 9.907963037490845 sec.\n", + "就又跟我们人类跟接近的这种类似睡眠的状态\n", + "就有跟我们人类更接近的这种类似睡眠的状态\n", + "76/2707\n", + "77/2707\n", + "78/2707\n", + "79/2707\n", + "Audio time = 3.08 sec.\n", + "Recognition time = 9.725907802581787 sec.\n", + "所以试了很多年的交\n", + "所以睡了很多年的觉\n", + "80/2707\n", + "81/2707\n", + "82/2707\n", + "83/2707\n", + "Audio time = 3.92 sec.\n", + "Recognition time = 9.657794713973999 sec.\n", + "其实我们并不是特别清楚\n", + "其实我们并不是特别清楚\n", + "84/2707\n", + "85/2707\n", + "86/2707\n", + "87/2707\n", + "Audio time = 3.1 sec.\n", + "Recognition time = 9.42478609085083 sec.\n", + "我们到底是怎么睡觉或者说\n", + "我们到底是怎么睡觉的\n", + "88/2707\n", + "89/2707\n", + "90/2707\n", + "91/2707\n", + "Audio time = 2.53 sec.\n", + "Recognition time = 8.29967713356018 sec.\n", + "睡眠是怎么发生的\n", + "或者说睡眠是怎么发生的\n", + "92/2707\n", + "93/2707\n", + "94/2707\n", + "95/2707\n", + "Audio time = 4.42 sec.\n", + "Recognition time = 10.434919118881226 sec.\n", + "要研究这个问题的话\n", + "要研究这个问题的话\n", + "96/2707\n", + "97/2707\n", + "98/2707\n", + "99/2707\n", + "Audio time = 4.67 sec.\n", + "Recognition time = 11.414813995361328 sec.\n", + "我们首先要搞清楚到底什么是睡眠\n", + "我们首先要搞清楚到底什么是睡眠\n", + "100/2707\n", + "101/2707\n", + "102/2707\n", + "103/2707\n", + "Audio time = 3.3 sec.\n", + "Recognition time = 10.734432935714722 sec.\n", + "我们睡眠从第一章来说\n", + "睡眠从定义上来说\n", + "104/2707\n", + "105/2707\n", + "106/2707\n", + "107/2707\n", + "Audio time = 6.05 sec.\n", + "Recognition time = 10.924272060394287 sec.\n", + "它是一种行为上禁止而且觉醒阈值身高的状态\n", + "它是一种行为上静止而且觉醒阈值升高的状态\n", + "108/2707\n", + "109/2707\n", + "110/2707\n", + "111/2707\n", + "Audio time = 3.18 sec.\n", + "Recognition time = 11.025686979293823 sec.\n", + "这个觉醒阈值是什么意思呢\n", + "那这个觉醒阈值是什么意思呢\n", + "112/2707\n", + "113/2707\n", + "114/2707\n", + "115/2707\n", + "Audio time = 4.77 sec.\n", + "Recognition time = 10.317650079727173 sec.\n", + "就是说当动物或者我们人进入到睡眠的状态以后\n", + "就是说当动物或者我们人进入到睡眠的状态以后\n", + "116/2707\n", + "117/2707\n", + "118/2707\n", + "119/2707\n", + "Audio time = 5.55 sec.\n", + "Recognition time = 9.628606081008911 sec.\n", + "需要更强的刺激才能让我们或者让动物做出反应\n", + "需要更强的刺激才能让我们或者让动物作出反应\n", + "120/2707\n", + "121/2707\n", + "122/2707\n", + "123/2707\n", + "Audio time = 4.1 sec.\n", + "Recognition time = 9.798579216003418 sec.\n", + "这个是一个周边邻的老电影的学段\n", + "这个是一个卓别林的老电影的选段\n", + "124/2707\n", + "125/2707\n", + "126/2707\n", + "127/2707\n", + "Audio time = 3.32 sec.\n", + "Recognition time = 10.634912014007568 sec.\n", + "那从这个里面我们可以看到\n", + "从这个里面我们可以看到\n", + "128/2707\n", + "129/2707\n", + "130/2707\n", + "131/2707\n", + "Audio time = 3.05 sec.\n", + "Recognition time = 10.852342128753662 sec.\n", + "一只狗在他醒酒的时候\n", + "一只狗在它醒着的时候\n", + "132/2707\n", + "133/2707\n", + "134/2707\n", + "135/2707\n", + "Audio time = 3.7 sec.\n", + "Recognition time = 10.997154235839844 sec.\n", + "我们只要接近他他就会有反映\n", + "我们只要接近它 它就会有反应\n", + "136/2707\n", + "137/2707\n", + "138/2707\n", + "139/2707\n", + "Audio time = 2.78 sec.\n", + "Recognition time = 10.418154001235962 sec.\n", + "但是当这个狗睡觉了\n", + "但是当这个狗睡着了\n", + "140/2707\n", + "141/2707\n", + "142/2707\n", + "143/2707\n", + "Audio time = 4.15 sec.\n", + "Recognition time = 11.395659923553467 sec.\n", + "我们临他到耳朵根他的一把\n", + "我们拎它的耳朵 拎它的尾巴\n", + "144/2707\n", + "145/2707\n", + "146/2707\n", + "147/2707\n", + "Audio time = 1.88 sec.\n", + "Recognition time = 9.629563093185425 sec.\n", + "他都没有反应\n", + "它都没有反应\n", + "148/2707\n", + "149/2707\n", + "150/2707\n", + "151/2707\n", + "Audio time = 3.15 sec.\n", + "Recognition time = 10.90805196762085 sec.\n", + "这就是决心与纸的身高\n", + "这就是觉醒阈值的升高\n", + "152/2707\n", + "153/2707\n", + "154/2707\n", + "155/2707\n", + "Audio time = 3.18 sec.\n", + "Recognition time = 11.129576921463013 sec.\n", + "如果要研究睡眠的话\n", + "如果要研究睡眠的话\n", + "156/2707\n", + "157/2707\n", + "158/2707\n", + "159/2707\n", + "Audio time = 3.42 sec.\n", + "Recognition time = 11.296210050582886 sec.\n", + "我是边还有两个基本的特就\n", + "睡眠还有两个基本的特征\n", + "160/2707\n", + "161/2707\n", + "162/2707\n", + "163/2707\n", + "Audio time = 3.98 sec.\n", + "Recognition time = 10.546897888183594 sec.\n", + "一个就是睡眠再会积累\n", + "一个就是睡眠债会积累\n", + "164/2707\n", + "165/2707\n", + "166/2707\n", + "167/2707\n", + "Audio time = 2.37 sec.\n", + "Recognition time = 9.545593023300171 sec.\n", + "去了较北部\n", + "缺了觉得补\n", + "168/2707\n", + "169/2707\n", + "170/2707\n", + "171/2707\n", + "Audio time = 3.5 sec.\n", + "Recognition time = 10.384339809417725 sec.\n", + "这个再生非常有一个专有的名称\n", + "这个在生物学上有一个专有的名称\n", + "172/2707\n", + "173/2707\n", + "174/2707\n", + "175/2707\n", + "Audio time = 3.47 sec.\n", + "Recognition time = 9.970831871032715 sec.\n", + "我们把它叫做睡眠的稳态平衡\n", + "我们把它叫做睡眠的稳态平衡\n", + "176/2707\n", + "177/2707\n", + "178/2707\n", + "179/2707\n", + "Audio time = 2.45 sec.\n", + "Recognition time = 9.712466955184937 sec.\n", + "然后另外一点就是水\n", + "另外一点就是\n", + "180/2707\n", + "181/2707\n", + "182/2707\n", + "183/2707\n", + "Audio time = 4.12 sec.\n", + "Recognition time = 10.458830118179321 sec.\n", + "就是睡眠一般发生在一天中相对固定的时段\n", + "睡眠一般发生在一天中相对固定的时段\n", + "184/2707\n", + "185/2707\n", + "186/2707\n", + "187/2707\n", + "Audio time = 3.98 sec.\n", + "Recognition time = 9.82994270324707 sec.\n", + "对宇宙性动物就是白天活动的动物\n", + "对于昼行动物 就是白天活动的动物\n", + "188/2707\n", + "189/2707\n", + "190/2707\n", + "191/2707\n", + "Audio time = 2.02 sec.\n", + "Recognition time = 10.913809061050415 sec.\n", + "比如说我们人类\n", + "比如说我们人类\n", + "192/2707\n", + "193/2707\n", + "194/2707\n", + "195/2707\n", + "Audio time = 3.02 sec.\n", + "Recognition time = 11.307654857635498 sec.\n", + "睡眠通常发生在夜间\n", + "睡眠通常发生在夜间\n", + "196/2707\n", + "197/2707\n", + "198/2707\n", + "199/2707\n", + "Audio time = 3.27 sec.\n", + "Recognition time = 10.648503065109253 sec.\n", + "而对于夜行动物比如说小数\n", + "而对于夜行动物 比如说小鼠\n", + "200/2707\n", + "201/2707\n", + "202/2707\n", + "203/2707\n", + "Audio time = 2.88 sec.\n", + "Recognition time = 9.821253776550293 sec.\n", + "那睡眠通常法人在白天\n", + "睡眠通常发生在白天\n", + "204/2707\n", + "205/2707\n", + "206/2707\n", + "207/2707\n", + "Audio time = 4.77 sec.\n", + "Recognition time = 11.648120880126953 sec.\n", + "能么要验证睡眠的话我们首先要能够测量睡眠\n", + "要研究睡眠的话我们首先要能够测量睡眠\n", + "208/2707\n", + "209/2707\n", + "210/2707\n", + "211/2707\n", + "Audio time = 3.53 sec.\n", + "Recognition time = 10.890952825546265 sec.\n", + "最标准的测量睡眠的方法\n", + "最标准的测量睡眠的方法\n", + "212/2707\n", + "213/2707\n", + "214/2707\n", + "215/2707\n", + "Audio time = 2.65 sec.\n", + "Recognition time = 10.134207010269165 sec.\n", + "就是通过监测腦電波\n", + "就是通过检测脑电波\n", + "216/2707\n", + "217/2707\n", + "218/2707\n", + "219/2707\n", + "Audio time = 4.45 sec.\n", + "Recognition time = 10.546654224395752 sec.\n", + "老电波是由我们脑内的神经元产生的\n", + "脑电波是由我们脑内的神经元产生的\n", + "220/2707\n", + "221/2707\n", + "222/2707\n", + "223/2707\n", + "Audio time = 5.67 sec.\n", + "Recognition time = 10.137505769729614 sec.\n", + "然后神经元是我们脑内最重要的一种神经细胞\n", + "神经元是我们脑内最重要的一种神经细胞\n", + "224/2707\n", + "225/2707\n", + "226/2707\n", + "227/2707\n", + "Audio time = 5.3 sec.\n", + "Recognition time = 10.029753684997559 sec.\n", + "他他在齿形它的功能的时候\n", + "它在执行它的功能的时候\n", + "228/2707\n", + "229/2707\n", + "230/2707\n", + "231/2707\n", + "232/2707\n", + "Audio time = 5.05 sec.\n", + "Recognition time = 10.653613805770874 sec.\n", + "这种方便体现在整个脑的层面竟是脑电波\n", + "这种放电体现在整个脑的层面就是脑电波\n", + "233/2707\n", + "234/2707\n", + "235/2707\n", + "236/2707\n", + "Audio time = 3.22 sec.\n", + "Recognition time = 9.727614879608154 sec.\n", + "在我们醒着和睡着的时候\n", + "在我们醒着和睡着的时候\n", + "237/2707\n", + "238/2707\n", + "239/2707\n", + "240/2707\n", + "Audio time = 4.25 sec.\n", + "Recognition time = 11.570410966873169 sec.\n", + "脑电波发放的平率和模式是不一样的\n", + "脑电波发放的频率和模式是不一样的\n", + "241/2707\n", + "242/2707\n", + "243/2707\n", + "244/2707\n", + "Audio time = 3.07 sec.\n", + "Recognition time = 11.969449996948242 sec.\n", + "所以通过这种方式\n", + "所以通过这种方式\n", + "245/2707\n", + "246/2707\n", + "247/2707\n", + "248/2707\n", + "Audio time = 3.35 sec.\n", + "Recognition time = 10.143391370773315 sec.\n", + "就可以判断这个人或者这个动物\n", + "就可以判断这个人或者这个动物\n", + "249/2707\n", + "250/2707\n", + "251/2707\n", + "252/2707\n", + "Audio time = 4.03 sec.\n", + "Recognition time = 11.102733850479126 sec.\n", + "动物是处于清醒的状态还是睡眠的状态\n", + "是处于清醒的状态还是睡眠的状态\n", + "253/2707\n", + "254/2707\n", + "255/2707\n", + "256/2707\n", + "Audio time = 4.78 sec.\n", + "Recognition time = 11.26534104347229 sec.\n", + "们这边又变得就有一些典型的脑电波的代表土\n", + "右边就有一些典型的脑电波的代表图\n", + "257/2707\n", + "258/2707\n", + "259/2707\n", + "260/2707\n", + "Audio time = 4.0 sec.\n", + "Recognition time = 11.73793888092041 sec.\n", + "对对于色地使我们清醒的时候的闹过\n", + "绿色是我们清醒时候的脑波\n", + "261/2707\n", + "262/2707\n", + "263/2707\n", + "264/2707\n", + "Audio time = 4.4 sec.\n", + "Recognition time = 11.047256231307983 sec.\n", + "根据睡眠状态的时候我们的眼球是否转动\n", + "根据睡眠状态的时候我们的眼球是否转动\n", + "265/2707\n", + "266/2707\n", + "267/2707\n", + "268/2707\n", + "Audio time = 5.1 sec.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Recognition time = 10.043828964233398 sec.\n", + "睡眠又会被分为动眼睡眠和不动眼睡眠\n", + "睡眠又会被分为动眼睡眠和不动眼睡眠\n", + "269/2707\n", + "270/2707\n", + "271/2707\n", + "272/2707\n", + "Audio time = 3.13 sec.\n", + "Recognition time = 9.584776163101196 sec.\n", + "动睡眠不说我们人类可有的\n", + "动眼睡眠不光是我们人类特有的\n", + "273/2707\n", + "274/2707\n", + "275/2707\n", + "276/2707\n", + "Audio time = 3.05 sec.\n", + "Recognition time = 10.095137119293213 sec.\n", + "我们通常我所说的几个做梦\n", + "我们通常所说的做梦\n", + "277/2707\n", + "278/2707\n", + "279/2707\n", + "280/2707\n", + "281/2707\n", + "Audio time = 4.1 sec.\n", + "Recognition time = 9.666446208953857 sec.\n", + "大家可能觉得做工很高阶的中线向\n", + "大家可能觉得做梦是很高级的一种现象\n", + "282/2707\n", + "283/2707\n", + "284/2707\n", + "285/2707\n", + "Audio time = 3.43 sec.\n", + "Recognition time = 9.769724130630493 sec.\n", + "但是在骑士东野的水里面这个阶段\n", + "但是其实动眼睡眠这个阶段\n", + "286/2707\n", + "287/2707\n", + "288/2707\n", + "289/2707\n", + "Audio time = 4.15 sec.\n", + "Recognition time = 10.027660131454468 sec.\n", + "在一些爬行类动物在一些鱼类究竟\n", + "在一些爬行类动物 在一些鱼类\n", + "290/2707\n", + "291/2707\n", + "292/2707\n", + "293/2707\n", + "Audio time = 2.18 sec.\n", + "Recognition time = 11.577821254730225 sec.\n", + "就已经拍时出现了\n", + "就已经开始出现了\n", + "294/2707\n", + "295/2707\n", + "296/2707\n", + "297/2707\n", + "Audio time = 2.95 sec.\n", + "Recognition time = 11.365415811538696 sec.\n", + "所以可能不止我们人会做梦\n", + "所以可能不只我们人会做梦\n", + "298/2707\n", + "299/2707\n", + "300/2707\n", + "301/2707\n", + "Audio time = 2.43 sec.\n", + "Recognition time = 10.037224054336548 sec.\n", + "包括小刷\n", + "包括小鼠\n", + "302/2707\n", + "303/2707\n", + "304/2707\n", + "305/2707\n", + "Audio time = 5.47 sec.\n", + "Recognition time = 10.851239204406738 sec.\n", + "然后写鱼鸟可能都可以做梦\n", + "然后一些鱼 鸟可能都可以做梦\n", + "306/2707\n", + "307/2707\n", + "308/2707\n", + "309/2707\n", + "Audio time = 4.53 sec.\n", + "Recognition time = 11.36710786819458 sec.\n", + "我动员睡眠的根据台的脑电波发放模式的不同\n", + "不动眼睡眠根据它的脑电波发放模式的不同\n", + "310/2707\n", + "311/2707\n", + "312/2707\n", + "313/2707\n", + "Audio time = 4.32 sec.\n", + "Recognition time = 10.881148099899292 sec.\n", + "不会被分为一根儿和恩善散的阶段\n", + "会被分为N1 N2和N3三个阶段\n", + "314/2707\n", + "315/2707\n", + "316/2707\n", + "317/2707\n", + "Audio time = 4.62 sec.\n", + "Recognition time = 10.397065877914429 sec.\n", + "这个红色就是跟完阶段的脑电波\n", + "这个红色的就是N2阶段的脑电波\n", + "318/2707\n", + "319/2707\n", + "320/2707\n", + "321/2707\n", + "Audio time = 3.2 sec.\n", + "Recognition time = 10.470474004745483 sec.\n", + "一阶段因为跟清醒的时候比较像\n", + "N1阶段因为跟清醒的时候比较像\n", + "322/2707\n", + "323/2707\n", + "324/2707\n", + "325/2707\n", + "Audio time = 2.3499375 sec.\n", + "Recognition time = 10.86468243598938 sec.\n", + "这里没有展示出来\n", + "这里没有展示出来\n", + "326/2707\n", + "327/2707\n", + "328/2707\n", + "329/2707\n", + "Audio time = 4.6699375 sec.\n", + "Recognition time = 10.574501991271973 sec.\n", + "然后下面这个蓝色的呢是本三结对\n", + "然后下面这个蓝色的是N3阶段\n", + "330/2707\n", + "331/2707\n", + "332/2707\n", + "333/2707\n", + "334/2707\n", + "Audio time = 7.1 sec.\n", + "Recognition time = 11.44644021987915 sec.\n", + "这个是一个健康的成年人一晚上的睡眠分布图\n", + "这个是一个健康的成年人一晚上的睡眠分布图\n", + "335/2707\n", + "336/2707\n", + "337/2707\n", + "338/2707\n", + "Audio time = 4.38 sec.\n", + "Recognition time = 9.783345222473145 sec.\n", + "我可以看到首先会来到这个另一阶段\n", + "我们可以看到首先会来到这个N1阶段\n", + "339/2707\n", + "340/2707\n", + "341/2707\n", + "342/2707\n", + "Audio time = 5.48 sec.\n", + "Recognition time = 9.822526931762695 sec.\n", + "然后一阶段大概很多只有两三分钟就结束了\n", + "N1阶段大概很短 只有两三分钟就结束了\n", + "343/2707\n", + "344/2707\n", + "345/2707\n", + "346/2707\n", + "Audio time = 5.18 sec.\n", + "Recognition time = 9.965109825134277 sec.\n", + "然后之后会进入到不动员睡眠的恶骂二阶段\n", + "之后会进入到不动眼睡眠的N2阶段\n", + "347/2707\n", + "348/2707\n", + "349/2707\n", + "350/2707\n", + "Audio time = 5.07 sec.\n", + "Recognition time = 10.915495157241821 sec.\n", + "让恩而切断一般会持续二十到三十分钟\n", + "N2阶段一般会持续20到30分钟\n", + "351/2707\n", + "352/2707\n", + "353/2707\n", + "354/2707\n", + "Audio time = 3.72 sec.\n", + "Recognition time = 10.996959924697876 sec.\n", + "然后就进又到了深度深绵恩三阶段\n", + "然后就进入到了深度睡眠N3阶段\n", + "355/2707\n", + "356/2707\n", + "357/2707\n", + "358/2707\n", + "Audio time = 5.37 sec.\n", + "Recognition time = 11.775229930877686 sec.\n", + "等三节大概也会有摆个小时到四十分钟\n", + "N3阶段大概会有半个小时到40分钟\n", + "359/2707\n", + "360/2707\n", + "361/2707\n", + "362/2707\n", + "Audio time = 3.08 sec.\n", + "Recognition time = 10.342374801635742 sec.\n", + "然后之后又进入到了华尔街段\n", + "之后又进入到N2阶段\n", + "363/2707\n", + "364/2707\n", + "365/2707\n", + "366/2707\n", + "Audio time = 3.97 sec.\n", + "Recognition time = 9.932070016860962 sec.\n", + "然后完了以后就会经入道这个图上红色标记的\n", + "完了以后就会进入到图上红色标记的\n", + "367/2707\n", + "368/2707\n", + "369/2707\n", + "370/2707\n", + "Audio time = 2.48 sec.\n", + "Recognition time = 9.448712348937988 sec.\n", + "动眼睡眠阶段\n", + "动眼睡眠阶段\n", + "371/2707\n", + "372/2707\n", + "373/2707\n", + "374/2707\n", + "Audio time = 3.22 sec.\n", + "Recognition time = 9.702091932296753 sec.\n", + "这个时候就开始做梦了\n", + "这个时候就开始做梦了\n", + "375/2707\n", + "376/2707\n", + "377/2707\n", + "378/2707\n", + "Audio time = 3.25 sec.\n", + "Recognition time = 9.725405931472778 sec.\n", + "我们夜间第一次说梦网时间很短\n", + "我们夜间第一次做梦时间很短\n", + "379/2707\n", + "380/2707\n", + "381/2707\n", + "382/2707\n", + "Audio time = 2.63 sec.\n", + "Recognition time = 9.727473020553589 sec.\n", + "他该球季分钟就解说了\n", + "大概几分钟就结束了\n", + "383/2707\n", + "384/2707\n", + "385/2707\n", + "386/2707\n", + "Audio time = 5.02 sec.\n", + "Recognition time = 9.932792901992798 sec.\n", + "然后完了以后就会进入到不懂夜睡眠的恶化而本三\n", + "完了以后又会进入到不动眼睡眠的N2 N3\n", + "387/2707\n", + "388/2707\n", + "389/2707\n", + "390/2707\n", + "Audio time = 2.45 sec.\n", + "Recognition time = 9.547476053237915 sec.\n", + "然后问三赛道德观\n", + "然后N3再到N2\n", + "391/2707\n", + "392/2707\n", + "393/2707\n", + "394/2707\n", + "Audio time = 3.7 sec.\n", + "Recognition time = 9.805496215820312 sec.\n", + "然后再到这个动眼睡眠的琢磨了阶段\n", + "然后再到动眼睡眠的做梦的阶段\n", + "395/2707\n", + "396/2707\n", + "397/2707\n", + "398/2707\n", + "Audio time = 3.07 sec.\n", + "Recognition time = 9.727619886398315 sec.\n", + "中间可能他会玩醒来几次\n", + "中间可能还会偶尔醒来几次\n", + "399/2707\n", + "400/2707\n", + "401/2707\n", + "402/2707\n", + "Audio time = 2.5 sec.\n", + "Recognition time = 9.62583303451538 sec.\n", + "所以我们以诚月的睡眠\n", + "所以我们一整夜的睡眠\n", + "403/2707\n", + "404/2707\n", + "405/2707\n", + "406/2707\n", + "Audio time = 3.95 sec.\n", + "Recognition time = 10.813950777053833 sec.\n", + "大概就是在东衍义补洞沿水面舰\n", + "大概就是在动眼与不动眼睡眠间\n", + "407/2707\n", + "408/2707\n", + "409/2707\n", + "410/2707\n", + "Audio time = 2.8 sec.\n", + "Recognition time = 9.664967775344849 sec.\n", + "交替我付进行的\n", + "交替往复进行的\n", + "411/2707\n", + "412/2707\n", + "413/2707\n", + "414/2707\n", + "Audio time = 4.1 sec.\n", + "Recognition time = 9.705291032791138 sec.\n", + "但是总过再说我们从初上可以看出来一个趋势\n", + "但是总的来说我们从图上可以看出来一个趋势\n", + "415/2707\n", + "416/2707\n", + "417/2707\n", + "418/2707\n", + "Audio time = 4.75 sec.\n", + "Recognition time = 9.852061986923218 sec.\n", + "就是不动员睡眠恩三阶段的这个深冬睡眠\n", + "就是不动眼睡眠N3阶段的深度睡眠\n", + "419/2707\n", + "420/2707\n", + "421/2707\n", + "422/2707\n", + "Audio time = 4.33 sec.\n", + "Recognition time = 9.65317416191101 sec.\n", + "主要发生在前半夜就是铅似的消失\n", + "主要发生在前半夜 就是前4个小时\n", + "423/2707\n", + "424/2707\n", + "425/2707\n", + "426/2707\n", + "Audio time = 3.97 sec.\n", + "Recognition time = 9.98378324508667 sec.\n", + "然后动眼睡眠做梦主要发生在后半叶\n", + "动眼睡眠 做梦 主要发生在后半夜\n", + "427/2707\n", + "428/2707\n", + "429/2707\n", + "430/2707\n", + "431/2707\n", + "Audio time = 3.77 sec.\n", + "Recognition time = 9.622798204421997 sec.\n", + "因为我觉得这个厂子还挺适合搭客岁的\n", + "因为我觉得这个场子还挺适合打瞌睡的\n", + "432/2707\n", + "433/2707\n", + "434/2707\n", + "435/2707\n", + "Audio time = 5.0 sec.\n", + "Recognition time = 9.85256028175354 sec.\n", + "如果说我在演讲的时候你们睡着了就很正常\n", + "如果说我在演讲的时候你们睡着了这很正常\n", + "436/2707\n", + "437/2707\n", + "438/2707\n", + "439/2707\n", + "Audio time = 3.83 sec.\n", + "Recognition time = 11.778626203536987 sec.\n", + "但是我在演讲的时候我睡着了\n", + "但如果我在演讲的时候我睡着了\n", + "440/2707\n", + "441/2707\n", + "442/2707\n", + "443/2707\n", + "Audio time = 2.87 sec.\n", + "Recognition time = 10.750944137573242 sec.\n", + "这个可能就是使水镇了\n", + "这个可能就是嗜睡症了\n", + "444/2707\n", + "445/2707\n", + "446/2707\n", + "447/2707\n", + "Audio time = 3.68 sec.\n", + "Recognition time = 9.830672025680542 sec.\n", + "是水生患者可以在任何的时候\n", + "嗜睡症患者可以在任何时候\n", + "448/2707\n", + "449/2707\n", + "450/2707\n", + "451/2707\n", + "Audio time = 3.87 sec.\n", + "Recognition time = 9.829492807388306 sec.\n", + "比如说说话的时候站着走路的时候\n", + "比如说说话的时候 站着 走路的时候\n", + "452/2707\n", + "453/2707\n", + "454/2707\n", + "455/2707\n", + "Audio time = 2.4 sec.\n", + "Recognition time = 9.539852857589722 sec.\n", + "都有可能随时睡着\n", + "都有可能随时睡着\n", + "456/2707\n", + "457/2707\n", + "458/2707\n", + "459/2707\n", + "Audio time = 4.25 sec.\n", + "Recognition time = 10.017771005630493 sec.\n", + "比如说这个左边这个视频就位小朋友他正在大嚼\n", + "比如说左边这个视频里这位小朋友他正在大叫\n", + "460/2707\n", + "461/2707\n", + "462/2707\n", + "463/2707\n", + "Audio time = 2.65 sec.\n", + "Recognition time = 9.604837894439697 sec.\n", + "然后突然就睡着了\n", + "然后突然就睡着了\n", + "464/2707\n", + "465/2707\n", + "466/2707\n", + "467/2707\n", + "Audio time = 2.75 sec.\n", + "Recognition time = 9.748177766799927 sec.\n", + "然不光是人有时睡着\n", + "不光是人有嗜睡症\n", + "468/2707\n", + "469/2707\n", + "470/2707\n", + "471/2707\n", + "Audio time = 1.85 sec.\n", + "Recognition time = 9.727482080459595 sec.\n", + "这种动物也有\n", + "动物也有\n", + "472/2707\n", + "473/2707\n", + "474/2707\n", + "475/2707\n", + "Audio time = 3.9 sec.\n", + "Recognition time = 10.34311318397522 sec.\n", + "比如说右边的视频里了举止够\n", + "比如说右边的视频里的这只狗\n", + "476/2707\n", + "477/2707\n", + "478/2707\n", + "479/2707\n", + "Audio time = 3.23 sec.\n", + "Recognition time = 9.853617906570435 sec.\n", + "他跑着袍子然后就高了睡觉了\n", + "它跑着跑着然后就倒了 睡着了\n", + "480/2707\n", + "481/2707\n", + "482/2707\n", + "483/2707\n", + "Audio time = 1.97 sec.\n", + "Recognition time = 10.190370082855225 sec.\n", + "还有个特写\n", + "还有个特写\n", + "484/2707\n", + "485/2707\n", + "486/2707\n", + "487/2707\n", + "Audio time = 2.4 sec.\n", + "Recognition time = 10.981247186660767 sec.\n", + "不要解释是随证\n", + "要解释嗜睡症\n", + "488/2707\n", + "489/2707\n", + "490/2707\n", + "491/2707\n", + "Audio time = 4.35 sec.\n", + "Recognition time = 10.111642122268677 sec.\n", + "我们就要先解释一下水面到底是怎么产生的\n", + "我们就要先解释一下睡眠到底是怎么产生的\n", + "492/2707\n", + "493/2707\n", + "494/2707\n", + "495/2707\n", + "Audio time = 4.62 sec.\n", + "Recognition time = 10.934529781341553 sec.\n", + "从环路的从一个宏观的环的层面来说\n", + "从一个宏观的环路的层面来说\n", + "496/2707\n", + "497/2707\n", + "498/2707\n", + "499/2707\n", + "Audio time = 2.48 sec.\n", + "Recognition time = 9.775462865829468 sec.\n", + "睡眠主要是因为\n", + "睡眠主要是因为\n", + "500/2707\n", + "501/2707\n", + "502/2707\n", + "503/2707\n", + "Audio time = 3.6 sec.\n", + "Recognition time = 9.83092212677002 sec.\n", + "因为我们脑内有一些促进睡眠的脑区\n", + "我们脑内有一些促进睡眠的脑区\n", + "504/2707\n", + "505/2707\n", + "506/2707\n", + "507/2707\n", + "Audio time = 1.9 sec.\n", + "Recognition time = 9.625238180160522 sec.\n", + "他活跃了\n", + "它活跃了\n", + "508/2707\n", + "509/2707\n", + "510/2707\n", + "511/2707\n", + "Audio time = 4.2 sec.\n", + "Recognition time = 9.93152403831482 sec.\n", + "然后抑制了一些促进清醒的脑区的活动\n", + "然后抑制了一些促进清醒的脑区的活动\n", + "512/2707\n", + "513/2707\n", + "514/2707\n", + "515/2707\n", + "Audio time = 2.65 sec.\n", + "Recognition time = 9.728116035461426 sec.\n", + "这样子睡眠就发生了\n", + "这样子睡眠就发生了\n", + "516/2707\n", + "517/2707\n", + "518/2707\n", + "519/2707\n", + "Audio time = 4.85 sec.\n", + "Recognition time = 9.830097913742065 sec.\n", + "然后在我们脑内有一个叫做下丘脑的部位\n", + "然后在我们脑内有一个叫做下丘脑的部位\n", + "520/2707\n", + "521/2707\n", + "522/2707\n", + "523/2707\n", + "Audio time = 3.41 sec.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Recognition time = 9.830487966537476 sec.\n", + "它分泌右边这个图里的这种叫做\n", + "它分泌右边这个图里的这种\n", + "524/2707\n", + "525/2707\n", + "526/2707\n", + "527/2707\n", + "Audio time = 2.64 sec.\n", + "Recognition time = 9.749634981155396 sec.\n", + "焦作市欲诉的起诉\n", + "叫做食欲素的激素\n", + "528/2707\n", + "529/2707\n", + "530/2707\n", + "531/2707\n", + "Audio time = 2.9 sec.\n", + "Recognition time = 9.705573797225952 sec.\n", + "这个时日苏轼一个堕胎\n", + "这个食欲素是一个多肽\n", + "532/2707\n", + "533/2707\n", + "534/2707\n", + "535/2707\n", + "Audio time = 2.98 sec.\n", + "Recognition time = 9.728498935699463 sec.\n", + "然后他又出轻型的作用\n", + "然后它有促清醒的作用\n", + "536/2707\n", + "537/2707\n", + "538/2707\n", + "539/2707\n", + "Audio time = 5.42 sec.\n", + "Recognition time = 9.726438999176025 sec.\n", + "所以在我们脑内分别是语素的脑区\n", + "所以在我们脑内分泌这个食欲素的脑区\n", + "540/2707\n", + "541/2707\n", + "542/2707\n", + "543/2707\n", + "Audio time = 2.28 sec.\n", + "Recognition time = 9.878099918365479 sec.\n", + "如果出现了问题\n", + "如果出现了问题\n", + "544/2707\n", + "545/2707\n", + "546/2707\n", + "547/2707\n", + "Audio time = 5.27 sec.\n", + "Recognition time = 9.774406909942627 sec.\n", + "让这个是匀速不能正常分离了就回的嗜睡症\n", + "让这个食欲素不能正常分泌了就会得嗜睡症\n", + "548/2707\n", + "549/2707\n", + "550/2707\n", + "551/2707\n", + "Audio time = 5.25 sec.\n", + "Recognition time = 9.71791696548462 sec.\n", + "如果反过来内邪从水面的脑区出现问题的话\n", + "如果反过来那些促睡眠的脑区出现问题的话\n", + "552/2707\n", + "553/2707\n", + "554/2707\n", + "555/2707\n", + "Audio time = 2.37 sec.\n", + "Recognition time = 9.643173933029175 sec.\n", + "那就可能会导致失眠\n", + "那就可能会导致失眠\n", + "556/2707\n", + "557/2707\n", + "558/2707\n", + "559/2707\n", + "Audio time = 4.0 sec.\n", + "Recognition time = 9.830233812332153 sec.\n", + "这个是从一个宏观的关东的层面来\n", + "这个是从一个宏观的环路的层面\n", + "560/2707\n", + "561/2707\n", + "562/2707\n", + "563/2707\n", + "Audio time = 2.75 sec.\n", + "Recognition time = 10.698323965072632 sec.\n", + "曾来解释睡眠是怎么产生的\n", + "来解释睡眠是怎么产生的\n", + "564/2707\n", + "565/2707\n", + "566/2707\n", + "567/2707\n", + "Audio time = 4.65 sec.\n", + "Recognition time = 9.882713079452515 sec.\n", + "下面我再介绍一下从更微观的分子的层面\n", + "下面我再介绍一下从更微观的分子层面\n", + "568/2707\n", + "569/2707\n", + "570/2707\n", + "571/2707\n", + "Audio time = 2.4 sec.\n", + "Recognition time = 9.650267124176025 sec.\n", + "睡眠是怎么产生的\n", + "睡眠是怎么产生的\n", + "572/2707\n", + "573/2707\n", + "574/2707\n", + "575/2707\n", + "Audio time = 3.7 sec.\n", + "Recognition time = 9.773362874984741 sec.\n", + "这就要回到我前面说的一个概念了\n", + "这就要回到我前面说的一个概念\n", + "576/2707\n", + "577/2707\n", + "578/2707\n", + "579/2707\n", + "Audio time = 3.33 sec.\n", + "Recognition time = 10.030200958251953 sec.\n", + "就是睡眠的稳态平衡\n", + "就是睡眠的稳态平衡\n", + "580/2707\n", + "581/2707\n", + "582/2707\n", + "583/2707\n", + "Audio time = 3.75 sec.\n", + "Recognition time = 10.664466142654419 sec.\n", + "这个稳态平衡其实一直都在进行\n", + "这个稳态平衡其实一直都在进行\n", + "584/2707\n", + "585/2707\n", + "586/2707\n", + "587/2707\n", + "Audio time = 2.96 sec.\n", + "Recognition time = 10.670705795288086 sec.\n", + "在我们现在醒着的时候\n", + "在我们现在醒着的时候\n", + "588/2707\n", + "589/2707\n", + "590/2707\n", + "591/2707\n", + "Audio time = 2.46 sec.\n", + "Recognition time = 9.720854043960571 sec.\n", + "是时刻个都在进行\n", + "时时刻刻都在进行\n", + "592/2707\n", + "593/2707\n", + "594/2707\n", + "595/2707\n", + "Audio time = 3.82 sec.\n", + "Recognition time = 9.638154983520508 sec.\n", + "然后它的作用就是他会积累睡眠债\n", + "它的作用就是它会积累睡眠债\n", + "596/2707\n", + "597/2707\n", + "598/2707\n", + "599/2707\n", + "Audio time = 2.23 sec.\n", + "Recognition time = 9.72170901298523 sec.\n", + "或者说睡眠压力\n", + "或者说睡眠压力\n", + "600/2707\n", + "601/2707\n", + "602/2707\n", + "603/2707\n", + "Audio time = 3.55 sec.\n", + "Recognition time = 9.701176881790161 sec.\n", + "大家现在听到这的已经是与领悟力的心\n", + "大家现在听到这里已经是云里雾里了\n", + "604/2707\n", + "605/2707\n", + "606/2707\n", + "607/2707\n", + "Audio time = 3.6 sec.\n", + "Recognition time = 9.857541799545288 sec.\n", + "新田就像这位专家请说扔花\n", + "心里就想 这位专家 请说人话\n", + "608/2707\n", + "609/2707\n", + "610/2707\n", + "611/2707\n", + "Audio time = 7.17 sec.\n", + "Recognition time = 9.861650228500366 sec.\n", + "通俗地说着稳态平衡会持续的积累困意\n", + "通俗地说这个稳态平衡它会持续地积累困意\n", + "612/2707\n", + "613/2707\n", + "614/2707\n", + "615/2707\n", + "Audio time = 3.45 sec.\n", + "Recognition time = 9.621397018432617 sec.\n", + "就是我们行得越久我们会觉得越困\n", + "就是我们醒得越久我们会觉得越困\n", + "616/2707\n", + "617/2707\n", + "618/2707\n", + "619/2707\n", + "Audio time = 3.19 sec.\n", + "Recognition time = 9.552765130996704 sec.\n", + "这种困意其实就是\n", + "这种困意其实就是\n", + "620/2707\n", + "621/2707\n", + "622/2707\n", + "623/2707\n", + "Audio time = 4.26 sec.\n", + "Recognition time = 9.753791093826294 sec.\n", + "稳态平衡机制金磊睡眠压力的体现\n", + "稳态平衡机制积累睡眠压力的体现\n", + "624/2707\n", + "625/2707\n", + "626/2707\n", + "627/2707\n", + "Audio time = 3.38 sec.\n", + "Recognition time = 9.642898321151733 sec.\n", + "那么这个坤到底是什么呢\n", + "这个困到底是什么呢\n", + "628/2707\n", + "629/2707\n", + "630/2707\n", + "631/2707\n", + "Audio time = 5.27 sec.\n", + "Recognition time = 10.137492895126343 sec.\n", + "在我们的脑内有一种叫做腺甘的化学物质\n", + "在我们的脑内有一种叫做腺苷的化学物质\n", + "632/2707\n", + "633/2707\n", + "634/2707\n", + "635/2707\n", + "Audio time = 2.52 sec.\n", + "Recognition time = 9.99905800819397 sec.\n", + "在我们醒着的时候\n", + "在我们醒着的时候\n", + "636/2707\n", + "637/2707\n", + "638/2707\n", + "639/2707\n", + "Audio time = 5.1 sec.\n", + "Recognition time = 10.277492046356201 sec.\n", + "这项安徽在脑内移植及泪不停的积累\n", + "这个腺苷会在脑内一直积累 不停地积累\n", + "640/2707\n", + "641/2707\n", + "642/2707\n", + "643/2707\n", + "Audio time = 3.8 sec.\n", + "Recognition time = 9.665159940719604 sec.\n", + "然后他会有一个促进睡眠的作用\n", + "然后它会有一个促进睡眠的作用\n", + "644/2707\n", + "645/2707\n", + "646/2707\n", + "647/2707\n", + "Audio time = 2.4 sec.\n", + "Recognition time = 9.89172887802124 sec.\n", + "如果我们这样想\n", + "如果我们这样想\n", + "648/2707\n", + "649/2707\n", + "650/2707\n", + "651/2707\n", + "Audio time = 4.4 sec.\n", + "Recognition time = 9.931124925613403 sec.\n", + "如果有一种方法可以对抗着相爱的作用\n", + "如果有一种方法可以对抗这个腺苷的作用\n", + "652/2707\n", + "653/2707\n", + "654/2707\n", + "655/2707\n", + "Audio time = 5.22 sec.\n", + "Recognition time = 9.63114595413208 sec.\n", + "但是不是我们就我可以不不觉得困难\n", + "那是不是我们就可以不觉得困了\n", + "656/2707\n", + "657/2707\n", + "658/2707\n", + "659/2707\n", + "660/2707\n", + "Audio time = 2.92 sec.\n", + "Recognition time = 9.92594313621521 sec.\n", + "后那确实是有这样的方法的\n", + "确实是有这样的方法的\n", + "661/2707\n", + "662/2707\n", + "663/2707\n", + "664/2707\n", + "Audio time = 6.63 sec.\n", + "Recognition time = 9.932551145553589 sec.\n", + "最常用的一种可以对抗腺苷的物质就是咖啡应\n", + "最常用的一种可以对抗腺苷的物质就是咖啡因\n", + "665/2707\n", + "666/2707\n", + "667/2707\n", + "668/2707\n", + "Audio time = 3.3 sec.\n", + "Recognition time = 9.727835893630981 sec.\n", + "它存在于图上这些我们\n", + "它存在于图上这些\n", + "669/2707\n", + "670/2707\n", + "671/2707\n", + "672/2707\n", + "Audio time = 4.68 sec.\n", + "Recognition time = 9.776414394378662 sec.\n", + "我们每天可能都在应用的应聘当中\n", + "我们每天可能都在饮用的饮品当中\n", + "673/2707\n", + "674/2707\n", + "675/2707\n", + "676/2707\n", + "Audio time = 7.0999375 sec.\n", + "Recognition time = 9.667120933532715 sec.\n", + "包括像咖啡红牛各种茶还有可能等等\n", + "包括像咖啡 红牛 各种茶 还有可乐等等\n", + "677/2707\n", + "678/2707\n", + "679/2707\n", + "680/2707\n", + "Audio time = 6.071 sec.\n", + "Recognition time = 9.94428014755249 sec.\n", + "这个像安踏在我们脑内是可以跟它的受体结合\n", + "这个腺苷它在我们脑内是可以跟它的受体结合\n", + "681/2707\n", + "682/2707\n", + "683/2707\n", + "684/2707\n", + "Audio time = 2.3 sec.\n", + "Recognition time = 9.938446998596191 sec.\n", + "从而促进睡眠\n", + "从而促进睡眠\n", + "685/2707\n", + "686/2707\n", + "687/2707\n", + "688/2707\n", + "Audio time = 2.5 sec.\n", + "Recognition time = 10.423325061798096 sec.\n", + "那咖啡人作用就是看\n", + "咖啡因的作用就是\n", + "689/2707\n", + "690/2707\n", + "691/2707\n", + "692/2707\n", + "Audio time = 3.15 sec.\n", + "Recognition time = 10.562416791915894 sec.\n", + "就是咖啡因会跟线杆的受体结合\n", + "咖啡因会跟腺苷的受体结合\n", + "693/2707\n", + "694/2707\n", + "695/2707\n", + "696/2707\n", + "Audio time = 3.4 sec.\n", + "Recognition time = 10.547760963439941 sec.\n", + "让岘港不能跟他自己的手地结合\n", + "让腺苷不能跟它自己的受体结合\n", + "697/2707\n", + "698/2707\n", + "699/2707\n", + "700/2707\n", + "Audio time = 4.25 sec.\n", + "Recognition time = 9.930874109268188 sec.\n", + "这样就可以一直睡眠促进清晰\n", + "这样就可以抑制睡眠 促进清醒\n", + "701/2707\n", + "702/2707\n", + "703/2707\n", + "704/2707\n", + "Audio time = 4.45 sec.\n", + "Recognition time = 9.728864908218384 sec.\n", + "相爱是我前面提到的各\n", + "腺苷是我前面提到的那个\n", + "705/2707\n", + "706/2707\n", + "707/2707\n", + "708/2707\n", + "709/2707\n", + "Audio time = 2.83 sec.\n", + "Recognition time = 9.697293758392334 sec.\n", + "但是他不是唯一的媒介\n", + "但是它不是唯一的媒介\n", + "710/2707\n", + "711/2707\n", + "712/2707\n", + "713/2707\n", + "Audio time = 4.82 sec.\n", + "Recognition time = 10.371662139892578 sec.\n", + "还有其他许多粉丝都参与到了这个调控过程中\n", + "还有其他许多分子都参与到了这个调控过程中\n", + "714/2707\n", + "715/2707\n", + "716/2707\n", + "717/2707\n", + "Audio time = 4.0 sec.\n", + "Recognition time = 10.035088777542114 sec.\n", + "我们可以把稳态平衡机制肖湘成一个杀戮\n", + "我们可以把稳态平衡机制想象成一个沙漏\n", + "718/2707\n", + "719/2707\n", + "720/2707\n", + "721/2707\n", + "Audio time = 2.77 sec.\n", + "Recognition time = 9.7845139503479 sec.\n", + "在我们醒着的时候\n", + "在我们醒着的时候\n", + "722/2707\n", + "723/2707\n", + "724/2707\n", + "725/2707\n", + "Audio time = 5.1 sec.\n", + "Recognition time = 9.939879894256592 sec.\n", + "个我太平衡其实它会一直的积累我们的睡眠压力\n", + "这个稳态平衡机制它会一直地积累我们的睡眠压力\n", + "726/2707\n", + "727/2707\n", + "728/2707\n", + "729/2707\n", + "Audio time = 3.32 sec.\n", + "Recognition time = 11.504558086395264 sec.\n", + "然后到我们进入到睡眠的时候\n", + "然后到我们进入到睡眠的时候\n", + "730/2707\n", + "731/2707\n", + "732/2707\n", + "733/2707\n", + "734/2707\n", + "Audio time = 3.23 sec.\n", + "Recognition time = 10.95905089378357 sec.\n", + "但这个水边压力释放关闭以后\n", + "当这个睡眠压力释放完毕以后\n", + "735/2707\n", + "736/2707\n", + "737/2707\n", + "738/2707\n", + "Audio time = 1.9 sec.\n", + "Recognition time = 9.931496858596802 sec.\n", + "睡眠就会结束\n", + "睡眠就会结束\n", + "739/2707\n", + "740/2707\n", + "741/2707\n", + "742/2707\n", + "Audio time = 1.95 sec.\n", + "Recognition time = 9.603691101074219 sec.\n", + "我们会醒来\n", + "我们会醒来\n", + "743/2707\n", + "744/2707\n", + "745/2707\n", + "746/2707\n", + "Audio time = 3.32 sec.\n", + "Recognition time = 9.71631908416748 sec.\n", + "我们可以想象如果在醒着的时候\n", + "我们可以想象如果在醒着的时候\n", + "747/2707\n", + "748/2707\n", + "749/2707\n", + "750/2707\n", + "Audio time = 2.7 sec.\n", + "Recognition time = 9.863147974014282 sec.\n", + "我们挤在越多的睡眠压力\n", + "我们积累越多的睡眠压力\n", + "751/2707\n", + "752/2707\n", + "753/2707\n", + "754/2707\n", + "Audio time = 3.0 sec.\n", + "Recognition time = 9.725075960159302 sec.\n", + "也就意味着说进入睡眠以后\n", + "那就意味着说进入睡眠以后\n", + "755/2707\n", + "756/2707\n", + "757/2707\n", + "758/2707\n", + "Audio time = 2.42 sec.\n", + "Recognition time = 9.627044200897217 sec.\n", + "需要更长的时间\n", + "需要更长的时间\n", + "759/2707\n", + "760/2707\n", + "761/2707\n", + "762/2707\n", + "Audio time = 3.22 sec.\n", + "Recognition time = 11.06000804901123 sec.\n", + "才能把这些水被压力释放干净\n", + "才能把这些睡眠压力释放干净\n", + "763/2707\n", + "764/2707\n", + "765/2707\n", + "766/2707\n", + "Audio time = 2.55 sec.\n", + "Recognition time = 9.931901931762695 sec.\n", + "那也就意味的是我们可能\n", + "那也就意味着说\n", + "767/2707\n", + "768/2707\n", + "769/2707\n", + "770/2707\n", + "Audio time = 3.3 sec.\n", + "Recognition time = 9.831027269363403 sec.\n", + "我们可能会睡得更就是一个跟神\n", + "我们可能会睡得更久 睡得更深\n", + "771/2707\n", + "772/2707\n", + "773/2707\n", + "774/2707\n", + "Audio time = 3.47 sec.\n", + "Recognition time = 10.13632583618164 sec.\n", + "我太平衡晶石的左右其实就在这里\n", + "稳态平衡机制的作用其实就在这里\n", + "775/2707\n", + "776/2707\n", + "777/2707\n", + "778/2707\n", + "Audio time = 3.38 sec.\n", + "Recognition time = 10.238585948944092 sec.\n", + "它是决定我们睡多久和水多深\n", + "它是决定我们睡多久和睡多深\n", + "779/2707\n", + "780/2707\n", + "781/2707\n", + "782/2707\n", + "Audio time = 3.3 sec.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Recognition time = 9.539638996124268 sec.\n", + "所以如果你一夜没睡熬夜了\n", + "所以如果你一夜没睡 熬夜了\n", + "783/2707\n", + "784/2707\n", + "785/2707\n", + "786/2707\n", + "Audio time = 3.87 sec.\n", + "Recognition time = 9.81445574760437 sec.\n", + "离第二天就会睡得比平时要酒喝声\n", + "你第二天就会睡得比平时要久和深\n", + "787/2707\n", + "788/2707\n", + "789/2707\n", + "790/2707\n", + "Audio time = 3.68 sec.\n", + "Recognition time = 9.829972982406616 sec.\n", + "然后如果说这个工作日睡的比较少\n", + "如果说你工作日睡得比较少\n", + "791/2707\n", + "792/2707\n", + "793/2707\n", + "794/2707\n", + "Audio time = 3.4 sec.\n", + "Recognition time = 9.848114728927612 sec.\n", + "那周末可能就会要不教会\n", + "那周末可能就会要补觉\n", + "795/2707\n", + "796/2707\n", + "797/2707\n", + "798/2707\n", + "Audio time = 3.52 sec.\n", + "Recognition time = 10.671857118606567 sec.\n", + "回水到特别的就特别的深\n", + "会睡得特别地久 特别地深\n", + "799/2707\n", + "800/2707\n", + "801/2707\n", + "802/2707\n", + "Audio time = 3.15 sec.\n", + "Recognition time = 11.05112338066101 sec.\n", + "可以所以从这里我们可以看出来\n", + "所以从这里我们可以看出来\n", + "803/2707\n", + "804/2707\n", + "805/2707\n", + "806/2707\n", + "Audio time = 3.93 sec.\n", + "Recognition time = 11.739706039428711 sec.\n", + "七十个睡眠呀离婚者所睡眠债一个\n", + "其实这个睡眠压力或者说睡眠债\n", + "807/2707\n", + "808/2707\n", + "809/2707\n", + "810/2707\n", + "Audio time = 3.92 sec.\n", + "Recognition time = 11.164644002914429 sec.\n", + "再以跟我们签的所有其他债一样都是要还的\n", + "跟我们欠的所有其他债一样 都是要还的\n", + "811/2707\n", + "812/2707\n", + "813/2707\n", + "814/2707\n", + "Audio time = 3.43 sec.\n", + "Recognition time = 11.262641906738281 sec.\n", + "对我前面讲到了水面这两大特征\n", + "我前面讲到的睡眠的两大特征\n", + "815/2707\n", + "816/2707\n", + "817/2707\n", + "818/2707\n", + "Audio time = 4.15 sec.\n", + "Recognition time = 11.49067497253418 sec.\n", + "一个是他收到我太平和机制的调控\n", + "一个是它受到稳态平衡机制的调控\n", + "819/2707\n", + "820/2707\n", + "821/2707\n", + "822/2707\n", + "Audio time = 2.5 sec.\n", + "Recognition time = 9.9706711769104 sec.\n", + "然后另外一点就是她还\n", + "另外一点就是\n", + "823/2707\n", + "824/2707\n", + "825/2707\n", + "826/2707\n", + "Audio time = 3.47 sec.\n", + "Recognition time = 10.385109901428223 sec.\n", + "他还会发展在一天中相对固定的时段\n", + "它还会发生在一天中相对固定的时段\n", + "827/2707\n", + "828/2707\n", + "829/2707\n", + "830/2707\n", + "Audio time = 3.4 sec.\n", + "Recognition time = 10.632283210754395 sec.\n", + "对于我们人类来说主要是在夜间\n", + "对我们人类来说主要是在夜间\n", + "831/2707\n", + "832/2707\n", + "833/2707\n", + "834/2707\n", + "Audio time = 2.8 sec.\n", + "Recognition time = 10.150068044662476 sec.\n", + "但是基本说在液晶睡觉\n", + "但是即便说在夜间睡觉\n", + "835/2707\n", + "836/2707\n", + "837/2707\n", + "838/2707\n", + "Audio time = 3.75 sec.\n", + "Recognition time = 11.003652095794678 sec.\n", + "谁叫我相信在座的大家可能作息时间\n", + "我相信在座的大家可能作息时间\n", + "839/2707\n", + "840/2707\n", + "841/2707\n", + "842/2707\n", + "Audio time = 2.2 sec.\n", + "Recognition time = 11.117875099182129 sec.\n", + "都还挺不一样的\n", + "都还挺不一样的\n", + "843/2707\n", + "844/2707\n", + "845/2707\n", + "846/2707\n", + "Audio time = 5.9 sec.\n", + "Recognition time = 11.228707075119019 sec.\n", + "比如说这个在座的可能有这种早睡早起的每天\n", + "比如说在座的可能有这种早睡早起的\n", + "847/2707\n", + "848/2707\n", + "849/2707\n", + "850/2707\n", + "Audio time = 2.85 sec.\n", + "Recognition time = 10.889628887176514 sec.\n", + "每天教廷我的不是道中\n", + "每天叫醒我的不是闹钟\n", + "851/2707\n", + "852/2707\n", + "853/2707\n", + "854/2707\n", + "855/2707\n", + "Audio time = 3.52 sec.\n", + "Recognition time = 10.766643047332764 sec.\n", + "最终我们叫做白领聊性的坐骑\n", + "这种我们叫做百灵鸟型的作息\n", + "856/2707\n", + "857/2707\n", + "858/2707\n", + "859/2707\n", + "Audio time = 3.17 sec.\n", + "Recognition time = 10.757474184036255 sec.\n", + "然后也会有晚睡晚起的\n", + "然后也会有晚睡晚起的\n", + "860/2707\n", + "861/2707\n", + "862/2707\n", + "863/2707\n", + "Audio time = 3.55 sec.\n", + "Recognition time = 11.207590818405151 sec.\n", + "每天最大的越王可能就是睡到十二点的\n", + "每天最大的愿望可能就是睡到12点的\n", + "864/2707\n", + "865/2707\n", + "866/2707\n", + "867/2707\n", + "Audio time = 2.9 sec.\n", + "Recognition time = 10.9791841506958 sec.\n", + "这就毛佗硬性的做些\n", + "这种猫头鹰型的作息\n", + "868/2707\n", + "869/2707\n", + "870/2707\n", + "871/2707\n", + "Audio time = 2.4 sec.\n", + "Recognition time = 10.691560983657837 sec.\n", + "就是我个人而言\n", + "就我个人而言\n", + "872/2707\n", + "873/2707\n", + "874/2707\n", + "875/2707\n", + "Audio time = 3.75 sec.\n", + "Recognition time = 11.438000917434692 sec.\n", + "如果我不是我外界因素的干扰\n", + "如果我不受外界因素的干扰\n", + "876/2707\n", + "877/2707\n", + "878/2707\n", + "879/2707\n", + "Audio time = 4.8 sec.\n", + "Recognition time = 10.490594148635864 sec.\n", + "那我大概是凌晨三点睡到上午十点异性\n", + "那我大概是凌晨3点睡 然后上午10点醒\n", + "880/2707\n", + "881/2707\n", + "882/2707\n", + "883/2707\n", + "Audio time = 3.83 sec.\n", + "Recognition time = 8.91028118133545 sec.\n", + "所以我也是一个典型的檐帽子型的\n", + "所以我也是一个典型的夜猫子型\n", + "884/2707\n", + "885/2707\n", + "886/2707\n", + "887/2707\n", + "Audio time = 2.46 sec.\n", + "Recognition time = 11.33391809463501 sec.\n", + "毛陀硬性的做些\n", + "猫头鹰型的作息\n", + "888/2707\n", + "889/2707\n", + "890/2707\n", + "891/2707\n", + "Audio time = 4.68 sec.\n", + "Recognition time = 9.400279760360718 sec.\n", + "从我们从小到大受的教育可能会觉得\n", + "我们从小到大受的教育可能会觉得\n", + "892/2707\n", + "893/2707\n", + "894/2707\n", + "895/2707\n", + "Audio time = 2.75 sec.\n", + "Recognition time = 10.655029058456421 sec.\n", + "这种不同的作息制度\n", + "这种不同的作息制度\n", + "896/2707\n", + "897/2707\n", + "898/2707\n", + "899/2707\n", + "Audio time = 3.27 sec.\n", + "Recognition time = 9.58838677406311 sec.\n", + "跟你是情分还是懒惰\n", + "跟你是勤奋还是懒惰\n", + "900/2707\n", + "901/2707\n", + "902/2707\n", + "903/2707\n", + "Audio time = 3.73 sec.\n", + "Recognition time = 12.32562804222107 sec.\n", + "你们有一个好的生活和学习习惯有关系\n", + "你有没有一个好的生活和学习习惯有关系\n", + "904/2707\n", + "905/2707\n", + "906/2707\n", + "907/2707\n", + "Audio time = 3.62 sec.\n", + "Recognition time = 11.26798677444458 sec.\n", + "但是其实也不一定吃这样\n", + "但是其实也不一定是这样\n", + "908/2707\n", + "909/2707\n", + "910/2707\n", + "911/2707\n", + "Audio time = 5.72 sec.\n", + "Recognition time = 11.97499394416809 sec.\n", + "在上个世纪的九十年代\n", + "在上个世纪的90年代\n", + "912/2707\n", + "913/2707\n", + "914/2707\n", + "915/2707\n", + "Audio time = 5.17 sec.\n", + "Recognition time = 10.610878944396973 sec.\n", + "在美国有个犹他大学的睡眠尊属\n", + "在美国有个犹他大学的睡眠诊所\n", + "916/2707\n", + "917/2707\n", + "918/2707\n", + "919/2707\n", + "Audio time = 3.23 sec.\n", + "Recognition time = 12.025944709777832 sec.\n", + "然后有一天有一位睡眠医生迎来\n", + "有一天有一位睡眠医生\n", + "920/2707\n", + "921/2707\n", + "922/2707\n", + "923/2707\n", + "Audio time = 4.7 sec.\n", + "Recognition time = 11.564014196395874 sec.\n", + "艺声引来了一个老太太六十多岁老太太叫做代替\n", + "迎来了一个60多岁的老太太 叫做Becky\n", + "924/2707\n", + "925/2707\n", + "926/2707\n", + "927/2707\n", + "Audio time = 3.81 sec.\n", + "Recognition time = 8.943893909454346 sec.\n", + "这个白厅就跟着渭水边医生说\n", + "这个Becky就跟这位睡眠医生说\n", + "928/2707\n", + "929/2707\n", + "930/2707\n", + "931/2707\n", + "Audio time = 3.89 sec.\n", + "Recognition time = 10.408736944198608 sec.\n", + "他说卧床小到大都早睡早起\n", + "她说我从小到大都早睡早起\n", + "932/2707\n", + "933/2707\n", + "934/2707\n", + "935/2707\n", + "Audio time = 2.42 sec.\n", + "Recognition time = 8.908719301223755 sec.\n", + "然后这个沈湎医生说\n", + "然后这个睡眠医生说\n", + "936/2707\n", + "937/2707\n", + "938/2707\n", + "939/2707\n", + "Audio time = 3.58 sec.\n", + "Recognition time = 10.541115283966064 sec.\n", + "那不是挺好的吗早非早起身体好\n", + "那不是挺好的吗 早睡早起身体好\n", + "940/2707\n", + "941/2707\n", + "942/2707\n", + "943/2707\n", + "Audio time = 2.52 sec.\n", + "Recognition time = 9.929500341415405 sec.\n", + "然后老太来说\n", + "然后老太太说\n", + "944/2707\n", + "945/2707\n", + "946/2707\n", + "947/2707\n", + "Audio time = 3.45 sec.\n", + "Recognition time = 10.349467992782593 sec.\n", + "不是你想象那样早睡也早起\n", + "不是你想象那样早睡早起\n", + "948/2707\n", + "949/2707\n", + "950/2707\n", + "951/2707\n", + "Audio time = 5.12 sec.\n", + "Recognition time = 10.21029782295227 sec.\n", + "我是每天晚上祁烈我就困了时睡觉了\n", + "我是每天晚上7点多就困了 然后需要睡觉了\n", + "952/2707\n", + "953/2707\n", + "954/2707\n", + "955/2707\n", + "Audio time = 3.52 sec.\n", + "Recognition time = 10.267696142196655 sec.\n", + "凌晨每天凌晨撕裂多就会醒来\n", + "每天凌晨4点多就会醒来\n", + "956/2707\n", + "957/2707\n", + "958/2707\n", + "959/2707\n", + "Audio time = 2.33 sec.\n", + "Recognition time = 9.497772932052612 sec.\n", + "在我年轻的时候呢\n", + "她说在我年轻的时候\n", + "960/2707\n", + "961/2707\n", + "962/2707\n", + "963/2707\n", + "Audio time = 3.82 sec.\n", + "Recognition time = 10.189707040786743 sec.\n", + "他说这个对我的生活造成了不小的困扰\n", + "这个也对我的生活造成了不小的困扰\n", + "964/2707\n", + "965/2707\n", + "966/2707\n", + "967/2707\n", + "968/2707\n", + "Audio time = 2.73 sec.\n", + "Recognition time = 10.070319175720215 sec.\n", + "这我都没有办法才加\n", + "我都没有办法参加\n", + "969/2707\n", + "970/2707\n", + "971/2707\n", + "972/2707\n", + "973/2707\n", + "Audio time = 3.9 sec.\n", + "Recognition time = 9.709878206253052 sec.\n", + "然后他说不仅是他是这样\n", + "然后她说不仅是她是这样\n", + "974/2707\n", + "975/2707\n", + "976/2707\n", + "977/2707\n", + "Audio time = 4.5 sec.\n", + "Recognition time = 10.216222047805786 sec.\n", + "他的母亲他的外祖父他的兄弟\n", + "她的母亲 她的外祖父 她的兄弟\n", + "978/2707\n", + "979/2707\n", + "980/2707\n", + "981/2707\n", + "Audio time = 3.5 sec.\n", + "Recognition time = 9.88833498954773 sec.\n", + "他的女儿甚至是她的外孙女儿\n", + "她的女儿 甚至是她的外孙女\n", + "982/2707\n", + "983/2707\n", + "984/2707\n", + "985/2707\n", + "Audio time = 2.13 sec.\n", + "Recognition time = 9.780324220657349 sec.\n", + "都是这个样子\n", + "都是这个样子\n", + "986/2707\n", + "987/2707\n", + "988/2707\n", + "989/2707\n", + "Audio time = 3.35 sec.\n", + "Recognition time = 10.5138680934906 sec.\n", + "他也为这个事情看过很多医生\n", + "她也为这个事情看过很多医生\n", + "990/2707\n", + "991/2707\n", + "992/2707\n", + "993/2707\n", + "Audio time = 3.73 sec.\n", + "Recognition time = 9.933246850967407 sec.\n", + "内些一身都跟他说他有精神问题\n", + "那些医生都跟她说她有精神问题\n", + "994/2707\n", + "995/2707\n", + "996/2707\n", + "997/2707\n", + "Audio time = 2.65 sec.\n", + "Recognition time = 10.4442138671875 sec.\n", + "所以他也挺困惑的\n", + "所以她也挺困惑的\n", + "998/2707\n", + "999/2707\n", + "1000/2707\n", + "1001/2707\n", + "Audio time = 2.75 sec.\n", + "Recognition time = 9.666093111038208 sec.\n", + "因为他觉得子宇亲身挺正常\n", + "因为她觉得自己精神挺正常的\n", + "1002/2707\n", + "1003/2707\n", + "1004/2707\n", + "1005/2707\n", + "Audio time = 3.58 sec.\n", + "Recognition time = 9.941423654556274 sec.\n", + "这个睡眠也生听到了这个事情以后\n", + "这个睡眠医生听到了这个事情以后\n", + "1006/2707\n", + "1007/2707\n", + "1008/2707\n", + "1009/2707\n", + "Audio time = 3.05 sec.\n", + "Recognition time = 9.780264139175415 sec.\n", + "他想这个家族留这么多成员\n", + "他想这个家族有这么多成员\n", + "1010/2707\n", + "1011/2707\n", + "1012/2707\n", + "1013/2707\n", + "Audio time = 3.2 sec.\n", + "Recognition time = 10.034083127975464 sec.\n", + "于是他就进行了一个系统的调查\n", + "于是他就进行了一个系统的调查\n", + "1014/2707\n", + "1015/2707\n", + "1016/2707\n", + "1017/2707\n", + "Audio time = 4.08 sec.\n", + "Recognition time = 9.687443971633911 sec.\n", + "发现这个家族中确实有二十九过程远\n", + "发现这个家族中确实有29个成员\n", + "1018/2707\n", + "1019/2707\n", + "1020/2707\n", + "1021/2707\n", + "Audio time = 3.92 sec.\n", + "Recognition time = 9.92458701133728 sec.\n", + "都有这种吸毒的早睡早起的现象\n", + "都有这种极度的早睡早起的现象\n", + "1022/2707\n", + "1023/2707\n", + "1024/2707\n", + "1025/2707\n", + "Audio time = 5.33 sec.\n", + "Recognition time = 10.65902590751648 sec.\n", + "其中他又让张国成员到她的歌睡眠中心的因为\n", + "其中他又让6个成员到他的睡眠中心来\n", + "1026/2707\n", + "1027/2707\n", + "1028/2707\n", + "1029/2707\n", + "Audio time = 2.13 sec.\n", + "Recognition time = 11.199529886245728 sec.\n", + "这个早非早起呢\n", + "因为这个早睡早起\n", + "1030/2707\n", + "1031/2707\n", + "1032/2707\n", + "1033/2707\n", + "Audio time = 3.48 sec.\n", + "Recognition time = 10.41060996055603 sec.\n", + "不是你说早而造情酒浓酸的\n", + "不是你说早睡早起就能算的\n", + "1034/2707\n", + "1035/2707\n", + "1036/2707\n", + "1037/2707\n", + "Audio time = 3.88 sec.\n", + "Recognition time = 10.478367328643799 sec.\n", + "对缺失的用脑电波去测量它的睡眠\n", + "得确实地用脑电波去测量他的睡眠\n", + "1038/2707\n", + "1039/2707\n", + "1040/2707\n", + "1041/2707\n", + "Audio time = 2.75 sec.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Recognition time = 8.69693899154663 sec.\n", + "开始不是真的早睡早起了\n", + "看是不是真的早睡早起了\n", + "1042/2707\n", + "1043/2707\n", + "1044/2707\n", + "1045/2707\n", + "Audio time = 3.78 sec.\n", + "Recognition time = 10.330029964447021 sec.\n", + "所以究竟写了用脑电波的对睡眠的测量\n", + "所以就进行了用脑电波对睡眠的测量\n", + "1046/2707\n", + "1047/2707\n", + "1048/2707\n", + "1049/2707\n", + "Audio time = 5.17 sec.\n", + "Recognition time = 9.376667737960815 sec.\n", + "然后发现这些人他们的睡眠确实如白婷诉说\n", + "然后发现这些人他们的睡眠确实如Becky所说\n", + "1050/2707\n", + "1051/2707\n", + "1052/2707\n", + "1053/2707\n", + "Audio time = 5.05 sec.\n", + "Recognition time = 10.506406307220459 sec.\n", + "然后这个是比正常的人要造了带盖三个半到四个小时\n", + "这个是比正常的人要早大概3个半到4个小时\n", + "1054/2707\n", + "1055/2707\n", + "1056/2707\n", + "1057/2707\n", + "Audio time = 4.35 sec.\n", + "Recognition time = 9.216369152069092 sec.\n", + "因为这个现象是这个家族都有\n", + "因为这个现象是整个家族都有\n", + "1058/2707\n", + "1059/2707\n", + "1060/2707\n", + "1061/2707\n", + "Audio time = 3.5 sec.\n", + "Recognition time = 11.876338958740234 sec.\n", + "所以你就是说它是一个可以溢出的现象\n", + "所以就是说它是一个可以遗传的现象\n", + "1062/2707\n", + "1063/2707\n", + "1064/2707\n", + "1065/2707\n", + "Audio time = 4.98 sec.\n", + "Recognition time = 10.549348831176758 sec.\n", + "这位睡眠一生的就决定进一步的去调查这个事情\n", + "这位睡眠医生就决定进一步地去调查这个事情\n", + "1066/2707\n", + "1067/2707\n", + "1068/2707\n", + "1069/2707\n", + "Audio time = 2.92 sec.\n", + "Recognition time = 10.03463888168335 sec.\n", + "他和一位遗传学家\n", + "他和一位遗传学家\n", + "1070/2707\n", + "1071/2707\n", + "1072/2707\n", + "1073/2707\n", + "Audio time = 3.93 sec.\n", + "Recognition time = 9.010717868804932 sec.\n", + "也就是我在美国博士后的导师合作\n", + "也就是我在美国博士后的导师合作\n", + "1074/2707\n", + "1075/2707\n", + "1076/2707\n", + "1077/2707\n", + "Audio time = 2.53 sec.\n", + "Recognition time = 8.68841004371643 sec.\n", + "然后他们就发现了\n", + "他们就发现了\n", + "1078/2707\n", + "1079/2707\n", + "1080/2707\n", + "1081/2707\n", + "Audio time = 3.7 sec.\n", + "Recognition time = 9.936977863311768 sec.\n", + "们这些特别早睡早起的人\n", + "这些特别早睡早起的人\n", + "1082/2707\n", + "1083/2707\n", + "1084/2707\n", + "1085/2707\n", + "Audio time = 3.65 sec.\n", + "Recognition time = 8.949090242385864 sec.\n", + "其实是带了一个罕见的基因变异\n", + "其实是带了一个罕见的基因变异\n", + "1086/2707\n", + "1087/2707\n", + "1088/2707\n", + "1089/2707\n", + "Audio time = 4.27 sec.\n", + "Recognition time = 9.697094917297363 sec.\n", + "这个变异发生在一个叫做裴瑞驰的基因里面\n", + "这个变异发生在一个叫做PERIOD2的基因里面\n", + "1090/2707\n", + "1091/2707\n", + "1092/2707\n", + "1093/2707\n", + "Audio time = 2.92 sec.\n", + "Recognition time = 10.638010025024414 sec.\n", + "然后这个编译器时就导致了\n", + "这个变异其实就导致了\n", + "1094/2707\n", + "1095/2707\n", + "1096/2707\n", + "1097/2707\n", + "Audio time = 3.78 sec.\n", + "Recognition time = 10.272983074188232 sec.\n", + "胚乳球的点黑须那上有\n", + "PERIOD2的的DNA序列上\n", + "1098/2707\n", + "1099/2707\n", + "1100/2707\n", + "1101/2707\n", + "Audio time = 2.5 sec.\n", + "Recognition time = 10.458916902542114 sec.\n", + "有一个阶级的改变\n", + "有一个碱基对的改变\n", + "1102/2707\n", + "1103/2707\n", + "1104/2707\n", + "1105/2707\n", + "Audio time = 4.15 sec.\n", + "Recognition time = 10.171255588531494 sec.\n", + "后一个碱基的改变了导致他所编码的基因\n", + "然后一个碱基对的改变导致它所编码的基因\n", + "1106/2707\n", + "1107/2707\n", + "1108/2707\n", + "1109/2707\n", + "Audio time = 4.33 sec.\n", + "Recognition time = 10.784722328186035 sec.\n", + "渴而除蛋白有一个案子酸的改变\n", + "PERIOD2蛋白有一个氨基酸的改变\n", + "1110/2707\n", + "1111/2707\n", + "1112/2707\n", + "1113/2707\n", + "Audio time = 2.48 sec.\n", + "Recognition time = 10.316540956497192 sec.\n", + "这么的一个改变\n", + "这么一个改变\n", + "1114/2707\n", + "1115/2707\n", + "1116/2707\n", + "1117/2707\n", + "Audio time = 3.92 sec.\n", + "Recognition time = 10.117632150650024 sec.\n", + "因为科尔秋瑾是一个生物中的精\n", + "因为PERIOD2基因是一个生物钟的基因\n", + "1118/2707\n", + "1119/2707\n", + "1120/2707\n", + "1121/2707\n", + "Audio time = 4.15 sec.\n", + "Recognition time = 10.107079029083252 sec.\n", + "所以这么一个改变就影响了这些人的生物钟\n", + "所以这么一个改变就影响了这些人的生物钟\n", + "1122/2707\n", + "1123/2707\n", + "1124/2707\n", + "1125/2707\n", + "Audio time = 4.72 sec.\n", + "Recognition time = 10.416341066360474 sec.\n", + "生物钟我们可大家听说过都听说过名称\n", + "生物钟大家可能都听说过这个名称\n", + "1126/2707\n", + "1127/2707\n", + "1128/2707\n", + "1129/2707\n", + "1130/2707\n", + "Audio time = 4.2 sec.\n", + "Recognition time = 10.445109128952026 sec.\n", + "他其实是我们体内的一种计时机制\n", + "它其实是我们体内的一种计时机制\n", + "1131/2707\n", + "1132/2707\n", + "1133/2707\n", + "1134/2707\n", + "Audio time = 4.65 sec.\n", + "Recognition time = 10.649009227752686 sec.\n", + "然后它会使我们的各种行为和生理过程\n", + "它会使我们的各种行为和生理过程\n", + "1135/2707\n", + "1136/2707\n", + "1137/2707\n", + "1138/2707\n", + "Audio time = 3.38 sec.\n", + "Recognition time = 10.23893690109253 sec.\n", + "表现出一种啊是四小时的戒律\n", + "表现出一种24小时的节律\n", + "1139/2707\n", + "1140/2707\n", + "1141/2707\n", + "1142/2707\n", + "Audio time = 3.17 sec.\n", + "Recognition time = 9.915915966033936 sec.\n", + "生活中不致影响我们所需时间\n", + "生物钟不只影响我们的作息时间\n", + "1143/2707\n", + "1144/2707\n", + "1145/2707\n", + "1146/2707\n", + "Audio time = 2.52 sec.\n", + "Recognition time = 10.053148984909058 sec.\n", + "还有我们的方方面面\n", + "还有我们的方方面面\n", + "1147/2707\n", + "1148/2707\n", + "1149/2707\n", + "1150/2707\n", + "Audio time = 3.63 sec.\n", + "Recognition time = 10.035830974578857 sec.\n", + "比如说我们的体温心率\n", + "比如说我们的体温 心率\n", + "1151/2707\n", + "1152/2707\n", + "1153/2707\n", + "1154/2707\n", + "Audio time = 2.22 sec.\n", + "Recognition time = 9.93106198310852 sec.\n", + "我们的这个血压\n", + "我们的血压\n", + "1155/2707\n", + "1156/2707\n", + "1157/2707\n", + "1158/2707\n", + "1159/2707\n", + "Audio time = 2.75 sec.\n", + "Recognition time = 10.936872959136963 sec.\n", + "都有二零四小时的戒律\n", + "都有24小时的节律\n", + "1160/2707\n", + "1161/2707\n", + "1162/2707\n", + "1163/2707\n", + "Audio time = 3.25 sec.\n", + "Recognition time = 9.996380805969238 sec.\n", + "生物钟索取中我就重按十四小时在竭力\n", + "生物钟所驱动的这种24小时的节律\n", + "1164/2707\n", + "1165/2707\n", + "1166/2707\n", + "1167/2707\n", + "Audio time = 3.2 sec.\n", + "Recognition time = 10.605979919433594 sec.\n", + "我们把它称为今日戒律\n", + "我们把它称为近日节律\n", + "1168/2707\n", + "1169/2707\n", + "1170/2707\n", + "1171/2707\n", + "Audio time = 4.12 sec.\n", + "Recognition time = 9.11269497871399 sec.\n", + "就是大约翌日大约二里四小时的个意思\n", + "就是大约一日 大约24小时的意思\n", + "1172/2707\n", + "1173/2707\n", + "1174/2707\n", + "1175/2707\n", + "Audio time = 3.2 sec.\n", + "Recognition time = 9.989053964614868 sec.\n", + "他还有一个更为同属\n", + "它还有一个更为通俗\n", + "1176/2707\n", + "1177/2707\n", + "1178/2707\n", + "1179/2707\n", + "Audio time = 2.65 sec.\n", + "Recognition time = 11.103986263275146 sec.\n", + "但是不那么准确的称呼\n", + "但是不那么准确的称呼\n", + "1180/2707\n", + "1181/2707\n", + "1182/2707\n", + "1183/2707\n", + "Audio time = 2.02 sec.\n", + "Recognition time = 9.995306968688965 sec.\n", + "我叫做昼夜节律\n", + "叫做昼夜节律\n", + "1184/2707\n", + "1185/2707\n", + "1186/2707\n", + "1187/2707\n", + "Audio time = 3.55 sec.\n", + "Recognition time = 10.177406072616577 sec.\n", + "我们生物钟与今日戒律在地球上\n", + "生物钟与近日节律在地球上\n", + "1188/2707\n", + "1189/2707\n", + "1190/2707\n", + "1191/2707\n", + "Audio time = 3.52 sec.\n", + "Recognition time = 9.945289134979248 sec.\n", + "大概七十一年词可能就出现了\n", + "大概几十亿年前可能就出现了\n", + "1192/2707\n", + "1193/2707\n", + "1194/2707\n", + "1195/2707\n", + "Audio time = 4.4 sec.\n", + "Recognition time = 9.958027839660645 sec.\n", + "从最肩带的意中人和代谢宝生物\n", + "从最简单的一种单细胞生物\n", + "1196/2707\n", + "1197/2707\n", + "1198/2707\n", + "1199/2707\n", + "Audio time = 1.85 sec.\n", + "Recognition time = 9.92228889465332 sec.\n", + "比如来细菌\n", + "比如蓝细菌\n", + "1200/2707\n", + "1201/2707\n", + "1202/2707\n", + "1203/2707\n", + "Audio time = 2.73 sec.\n", + "Recognition time = 9.189191818237305 sec.\n", + "就是图上这个绿绿的东西\n", + "就是图上这个绿绿的东西\n", + "1204/2707\n", + "1205/2707\n", + "1206/2707\n", + "1207/2707\n", + "Audio time = 4.3 sec.\n", + "Recognition time = 9.890074253082275 sec.\n", + "一直到各种复杂的植物动物\n", + "一直到各种复杂的植物 动物\n", + "1208/2707\n", + "1209/2707\n", + "1210/2707\n", + "1211/2707\n", + "Audio time = 3.17 sec.\n", + "Recognition time = 10.07413911819458 sec.\n", + "都能生物钟和今日戒律\n", + "都有生物钟和近日节律\n", + "1212/2707\n", + "1213/2707\n", + "1214/2707\n", + "1215/2707\n", + "Audio time = 4.47 sec.\n", + "Recognition time = 10.444883108139038 sec.\n", + "所以说它其实是一种比睡眠要古老得多的现象\n", + "所以它其实是一种比睡眠要古老得多的现象\n", + "1216/2707\n", + "1217/2707\n", + "1218/2707\n", + "1219/2707\n", + "Audio time = 3.67 sec.\n", + "Recognition time = 10.546808004379272 sec.\n", + "这个其实也挺合理挺容易理解的因为\n", + "这个其实也挺合理 挺容易理解的\n", + "1220/2707\n", + "1221/2707\n", + "1222/2707\n", + "1223/2707\n", + "Audio time = 3.06 sec.\n", + "Recognition time = 10.138069868087769 sec.\n", + "因睡眠只是生活中调控的\n", + "因为睡眠只是生物钟调控的\n", + "1224/2707\n", + "1225/2707\n", + "1226/2707\n", + "1227/2707\n", + "Audio time = 3.87 sec.\n", + "Recognition time = 11.620110034942627 sec.\n", + "帮户数个生命过程中间的一个\n", + "无数个生命过程中间的一个\n", + "1228/2707\n", + "1229/2707\n", + "1230/2707\n", + "1231/2707\n", + "Audio time = 2.6 sec.\n", + "Recognition time = 10.32073712348938 sec.\n", + "在分子呈面道声中\n", + "在分子层面\n", + "1232/2707\n", + "1233/2707\n", + "1234/2707\n", + "1235/2707\n", + "Audio time = 4.8 sec.\n", + "Recognition time = 10.453410148620605 sec.\n", + "生物钟是有大概十几个基因组成的一个反馈环路\n", + "生物钟是由大概十几个基因组成的一个反馈环路\n", + "1236/2707\n", + "1237/2707\n", + "1238/2707\n", + "1239/2707\n", + "Audio time = 3.25 sec.\n", + "Recognition time = 9.99799394607544 sec.\n", + "去年的诺被生理医学奖\n", + "去年的诺贝尔生理医学奖\n", + "1240/2707\n", + "1241/2707\n", + "1242/2707\n", + "1243/2707\n", + "Audio time = 4.47 sec.\n", + "Recognition time = 10.13641095161438 sec.\n", + "就颁给了三位再过瘾克隆出\n", + "就颁给了三位在果蝇里克隆出\n", + "1244/2707\n", + "1245/2707\n", + "1246/2707\n", + "1247/2707\n", + "Audio time = 2.9 sec.\n", + "Recognition time = 10.239814043045044 sec.\n", + "除第一个生物中浸淫的科学家\n", + "第一个生物钟基因的科学家\n", + "1248/2707\n", + "1249/2707\n", + "1250/2707\n", + "1251/2707\n", + "Audio time = 3.13 sec.\n", + "Recognition time = 10.1371910572052 sec.\n", + "国营改天轮仅在哺乳动物\n", + "果蝇的period基因在哺乳动物\n", + "1252/2707\n", + "1253/2707\n", + "1254/2707\n", + "1255/2707\n", + "Audio time = 2.67 sec.\n", + "Recognition time = 10.2382173538208 sec.\n", + "动物包括我们人类便是由三个\n", + "包括我们人里面是有3个\n", + "1256/2707\n", + "1257/2707\n", + "1258/2707\n", + "1259/2707\n", + "Audio time = 4.27 sec.\n", + "Recognition time = 10.138260841369629 sec.\n", + "分别叫做平人玩味秋荷亭为思维\n", + "分别叫做PER1 PER2和PER3\n", + "1260/2707\n", + "1261/2707\n", + "1262/2707\n", + "1263/2707\n", + "Audio time = 3.48 sec.\n", + "Recognition time = 10.067363977432251 sec.\n", + "这个就是我前面讲到的\n", + "PER2这个就是我前面讲到的\n", + "1264/2707\n", + "1265/2707\n", + "1266/2707\n", + "1267/2707\n", + "Audio time = 4.62 sec.\n", + "Recognition time = 10.411110162734985 sec.\n", + "他的便也导致了大家出入的气度的早睡早起\n", + "它的变异导致了那个大家族极度的早睡早起\n", + "1268/2707\n", + "1269/2707\n", + "1270/2707\n", + "1271/2707\n", + "Audio time = 4.8 sec.\n", + "Recognition time = 10.434869050979614 sec.\n", + "然后我们实验室研究的方向是这个可司机\n", + "我们实验室研究的方向是PER3基因\n", + "1272/2707\n", + "1273/2707\n", + "1274/2707\n", + "1275/2707\n", + "Audio time = 4.32 sec.\n", + "Recognition time = 10.863201141357422 sec.\n", + "我们发现会碎金的汉奸便也\n", + "我们发现PER3基因的罕见变异\n", + "1276/2707\n", + "1277/2707\n", + "1278/2707\n", + "1279/2707\n", + "Audio time = 4.1 sec.\n", + "Recognition time = 10.445040225982666 sec.\n", + "爷爷可以捯饬人气度的早睡早起\n", + "也可以导致人极度地早睡早起\n", + "1280/2707\n", + "1281/2707\n", + "1282/2707\n", + "1283/2707\n", + "Audio time = 2.35 sec.\n", + "Recognition time = 9.75477385520935 sec.\n", + "除了早睡早起\n", + "除了早睡早起\n", + "1284/2707\n", + "1285/2707\n", + "1286/2707\n", + "1287/2707\n", + "Audio time = 3.1 sec.\n", + "Recognition time = 10.007096767425537 sec.\n", + "他们还有一种情绪病\n", + "他们还有一种情绪病\n", + "1288/2707\n", + "1289/2707\n", + "1290/2707\n", + "1291/2707\n", + "Audio time = 3.08 sec.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Recognition time = 10.24026894569397 sec.\n", + "叫做季节性情感障碍\n", + "叫做季节性情感障碍\n", + "1292/2707\n", + "1293/2707\n", + "1294/2707\n", + "1295/2707\n", + "Audio time = 2.52 sec.\n", + "Recognition time = 9.906393051147461 sec.\n", + "俗称东徙倚欲\n", + "俗称冬季抑郁\n", + "1296/2707\n", + "1297/2707\n", + "1298/2707\n", + "1299/2707\n", + "Audio time = 3.5 sec.\n", + "Recognition time = 10.47254991531372 sec.\n", + "这个病的主要特征就是\n", + "这个病的主要特征就是\n", + "1300/2707\n", + "1301/2707\n", + "1302/2707\n", + "1303/2707\n", + "Audio time = 5.93 sec.\n", + "Recognition time = 10.54602599143982 sec.\n", + "就是患者在每年的秋天会开始出现一些抑郁的症状\n", + "患者在每年的秋天会开始出现一些抑郁的症状\n", + "1304/2707\n", + "1305/2707\n", + "1306/2707\n", + "1307/2707\n", + "Audio time = 4.45 sec.\n", + "Recognition time = 9.978378057479858 sec.\n", + "然后到冬天冻气的时候是最严重的\n", + "然后到冬季的时候是最严重的\n", + "1308/2707\n", + "1309/2707\n", + "1310/2707\n", + "1311/2707\n", + "Audio time = 3.72 sec.\n", + "Recognition time = 10.40015697479248 sec.\n", + "渠道每年十二月一月二月的时候是随眼中的\n", + "就到每年12月 1月 2月的时候是最严重的\n", + "1312/2707\n", + "1313/2707\n", + "1314/2707\n", + "1315/2707\n", + "Audio time = 3.75 sec.\n", + "Recognition time = 10.24106478691101 sec.\n", + "然后到次年的春夏优惠此法的好准\n", + "然后到次年的春夏又会自发地好转\n", + "1316/2707\n", + "1317/2707\n", + "1318/2707\n", + "1319/2707\n", + "Audio time = 4.06 sec.\n", + "Recognition time = 10.237606048583984 sec.\n", + "就是这一些性感障碍她在人群中的发病率\n", + "季节性情感障碍它在人群中的发病率\n", + "1320/2707\n", + "1321/2707\n", + "1322/2707\n", + "1323/2707\n", + "Audio time = 2.98 sec.\n", + "Recognition time = 10.341224908828735 sec.\n", + "并屡遇大概是百分之一到百分之十\n", + "大概是1%到10%\n", + "1324/2707\n", + "1325/2707\n", + "1326/2707\n", + "1327/2707\n", + "Audio time = 3.93 sec.\n", + "Recognition time = 10.03522515296936 sec.\n", + "所以这些这个现象就提示我们\n", + "这个现象就提示我们\n", + "1328/2707\n", + "1329/2707\n", + "1330/2707\n", + "1331/2707\n", + "Audio time = 3.31 sec.\n", + "Recognition time = 10.137287855148315 sec.\n", + "我们生物钟和今日竭力的紊乱\n", + "生物钟和近日节律的紊乱\n", + "1332/2707\n", + "1333/2707\n", + "1334/2707\n", + "1335/2707\n", + "Audio time = 3.74 sec.\n", + "Recognition time = 9.831539869308472 sec.\n", + "卵可能与精神疾病也有关联\n", + "可能与精神疾病也有关联\n", + "1336/2707\n", + "1337/2707\n", + "1338/2707\n", + "1339/2707\n", + "Audio time = 2.85 sec.\n", + "Recognition time = 10.23816204071045 sec.\n", + "前面我给大家讲了两个都是\n", + "前面我给大家讲了两个\n", + "1340/2707\n", + "1341/2707\n", + "1342/2707\n", + "1343/2707\n", + "Audio time = 4.55 sec.\n", + "Recognition time = 10.233273983001709 sec.\n", + "两个都是这种白领聊性的扫雪早期的别一\n", + "都是这种百灵鸟型的早睡早起的变异\n", + "1344/2707\n", + "1345/2707\n", + "1346/2707\n", + "1347/2707\n", + "Audio time = 5.33 sec.\n", + "Recognition time = 9.019546747207642 sec.\n", + "还有这种变异人是可以导致有夜猫子型的作息\n", + "还有变异是可以导致夜猫子型的作息\n", + "1348/2707\n", + "1349/2707\n", + "1350/2707\n", + "1351/2707\n", + "Audio time = 3.64 sec.\n", + "Recognition time = 8.906440734863281 sec.\n", + "比如说美国的科研人员发现在这个叫做\n", + "比如说美国的科研人员发现在这个\n", + "1352/2707\n", + "1353/2707\n", + "1354/2707\n", + "1355/2707\n", + "Audio time = 4.76 sec.\n", + "Recognition time = 10.03446912765503 sec.\n", + "在这个叫做头颇不安的生活中基因里面的变异\n", + "叫做CRYPTOCHROME1生物钟基因里面的变异\n", + "1356/2707\n", + "1357/2707\n", + "1358/2707\n", + "1359/2707\n", + "Audio time = 3.35 sec.\n", + "Recognition time = 10.754530191421509 sec.\n", + "就可以逃至人嫉妒的顽石晚期\n", + "就可以导致人极度地晚睡晚起\n", + "1360/2707\n", + "1361/2707\n", + "1362/2707\n", + "1363/2707\n", + "Audio time = 4.87 sec.\n", + "Recognition time = 9.2134850025177 sec.\n", + "其实人群中夜猫子型的作息是比白领狼性的要多的\n", + "其实人群中夜猫子型的作息是比百灵鸟型的要多的\n", + "1364/2707\n", + "1365/2707\n", + "1366/2707\n", + "1367/2707\n", + "Audio time = 2.87 sec.\n", + "Recognition time = 9.932822942733765 sec.\n", + "在这其中了有一部分事项\n", + "但是其中有一部分是像\n", + "1368/2707\n", + "1369/2707\n", + "1370/2707\n", + "1371/2707\n", + "Audio time = 3.83 sec.\n", + "Recognition time = 9.010280847549438 sec.\n", + "带了个多矿完井便衣的人一样\n", + "带了CRYPTOCHROME1基因变异的人一样\n", + "1372/2707\n", + "1373/2707\n", + "1374/2707\n", + "1375/2707\n", + "Audio time = 3.37 sec.\n", + "Recognition time = 9.039043664932251 sec.\n", + "一样是因为一些起因变异导致的\n", + "是因为一些基因变异导致的\n", + "1376/2707\n", + "1377/2707\n", + "1378/2707\n", + "1379/2707\n", + "Audio time = 4.03 sec.\n", + "Recognition time = 10.82593584060669 sec.\n", + "但是还有一些可能是为环境因素导致的\n", + "但是还有一些可能是因为环境因素导致的\n", + "1380/2707\n", + "1381/2707\n", + "1382/2707\n", + "1383/2707\n", + "Audio time = 2.7 sec.\n", + "Recognition time = 8.912533044815063 sec.\n", + "比如说你晚上七时一定很困了\n", + "比如说你晚上其实已经很困了\n", + "1384/2707\n", + "1385/2707\n", + "1386/2707\n", + "1387/2707\n", + "Audio time = 4.25 sec.\n", + "Recognition time = 9.007572889328003 sec.\n", + "但是还想玩有心想看片不想睡觉\n", + "但是还想玩游戏 想看片 不想睡觉\n", + "1388/2707\n", + "1389/2707\n", + "1390/2707\n", + "1391/2707\n", + "Audio time = 4.38 sec.\n", + "Recognition time = 8.792241334915161 sec.\n", + "这也是这个椰汁晚睡晚起的檐帽的星座性\n", + "这个也是晚睡晚起的夜猫子型作息\n", + "1392/2707\n", + "1393/2707\n", + "1394/2707\n", + "1395/2707\n", + "Audio time = 2.72 sec.\n", + "Recognition time = 8.819468021392822 sec.\n", + "作息但是并不是天生的\n", + "但是并不是天生的\n", + "1396/2707\n", + "1397/2707\n", + "1398/2707\n", + "1399/2707\n", + "Audio time = 3.52 sec.\n", + "Recognition time = 9.148534059524536 sec.\n", + "虽然说生物钟天生的变异\n", + "虽然说生物钟天生的变异\n", + "1400/2707\n", + "1401/2707\n", + "1402/2707\n", + "1403/2707\n", + "Audio time = 4.23 sec.\n", + "Recognition time = 8.976423978805542 sec.\n", + "可以导致我们个体之间生活中的差异\n", + "可以导致我们个体之间生物钟的差异\n", + "1404/2707\n", + "1405/2707\n", + "1406/2707\n", + "1407/2707\n", + "Audio time = 4.25 sec.\n", + "Recognition time = 10.136827945709229 sec.\n", + "但是生活中的差异的也不全是天生的\n", + "但是生物钟的差异也不全是天生的\n", + "1408/2707\n", + "1409/2707\n", + "1410/2707\n", + "1411/2707\n", + "Audio time = 2.18 sec.\n", + "Recognition time = 9.944804906845093 sec.\n", + "就是同一个人\n", + "就是同一个人\n", + "1412/2707\n", + "1413/2707\n", + "1414/2707\n", + "1415/2707\n", + "Audio time = 3.1199375 sec.\n", + "Recognition time = 10.124817848205566 sec.\n", + "一人他在年轻和年老的时候\n", + "他在年轻和年老的时候\n", + "1416/2707\n", + "1417/2707\n", + "1418/2707\n", + "1419/2707\n", + "Audio time = 2.5799375 sec.\n", + "Recognition time = 10.136040925979614 sec.\n", + "后海的生活中也是不一样的\n", + "他的生物钟也是不一样的\n", + "1420/2707\n", + "1421/2707\n", + "1422/2707\n", + "1423/2707\n", + "Audio time = 3.7 sec.\n", + "Recognition time = 10.605434894561768 sec.\n", + "新恋人特别是十几岁的青少年\n", + "青年人 特别是十几岁的青少年\n", + "1424/2707\n", + "1425/2707\n", + "1426/2707\n", + "1427/2707\n", + "Audio time = 4.4000625 sec.\n", + "Recognition time = 9.873936891555786 sec.\n", + "而这生物钟是更加倾向于晚睡晚起的\n", + "他的生物钟是更加倾向于晚睡晚起的\n", + "1428/2707\n", + "1429/2707\n", + "1430/2707\n", + "1431/2707\n", + "Audio time = 3.15 sec.\n", + "Recognition time = 9.713008165359497 sec.\n", + "由于也就是大家可能会想到的\n", + "有一点就是大家可能会想到的\n", + "1432/2707\n", + "1433/2707\n", + "1434/2707\n", + "1435/2707\n", + "Audio time = 4.4499375 sec.\n", + "Recognition time = 10.459751844406128 sec.\n", + "其实青少年他每天早上上学的时间还挺早的\n", + "其实青少年每天早上上学的时间还挺早的\n", + "1436/2707\n", + "1437/2707\n", + "1438/2707\n", + "1439/2707\n", + "Audio time = 3.85 sec.\n", + "Recognition time = 10.139230012893677 sec.\n", + "把电动稳婆的是七点半深圳市更早\n", + "8点钟或者是7点半 甚至是更早\n", + "1440/2707\n", + "1441/2707\n", + "1442/2707\n", + "1443/2707\n", + "Audio time = 4.3 sec.\n", + "Recognition time = 13.473580837249756 sec.\n", + "所以其实就是说现在目前我们这种上学的事\n", + "这其实就是说现在目前这种上学的时间\n", + "1444/2707\n", + "1445/2707\n", + "1446/2707\n", + "1447/2707\n", + "Audio time = 5.2 sec.\n", + "Recognition time = 11.407232999801636 sec.\n", + "事事要强薄衣裙夜猫子型天生的夜猫子型的青年人\n", + "是要强迫一群天生的夜猫子型的青年人\n", + "1448/2707\n", + "1449/2707\n", + "1450/2707\n", + "1451/2707\n", + "Audio time = 3.23 sec.\n", + "Recognition time = 10.145721197128296 sec.\n", + "去要强过他们早学早七\n", + "要强迫他们早睡早起\n", + "1452/2707\n", + "1453/2707\n", + "1454/2707\n", + "1455/2707\n", + "Audio time = 2.95 sec.\n", + "Recognition time = 9.852151870727539 sec.\n", + "这对于他们其实是挺痛苦的\n", + "这对于他们其实是挺痛苦的\n", + "1456/2707\n", + "1457/2707\n", + "1458/2707\n", + "1459/2707\n", + "Audio time = 4.13 sec.\n", + "Recognition time = 10.926384925842285 sec.\n", + "而且对他们的健康也不是太有利\n", + "而且对他们的健康也不是太有利\n", + "1460/2707\n", + "1461/2707\n", + "1462/2707\n", + "1463/2707\n", + "Audio time = 5.1 sec.\n", + "Recognition time = 10.752143144607544 sec.\n", + "所以现在国外的一些学校\n", + "所以现在国外的一些学校\n", + "1464/2707\n", + "1465/2707\n", + "1466/2707\n", + "1467/2707\n", + "Audio time = 8.1 sec.\n", + "Recognition time = 10.927308082580566 sec.\n", + "就是开始时请让中学更晚的开课的时间看是上课的这件更管\n", + "开始试行让中学开始上课的时间更晚\n", + "1468/2707\n", + "1469/2707\n", + "1470/2707\n", + "1471/2707\n", + "Audio time = 4.58 sec.\n", + "Recognition time = 11.397500038146973 sec.\n", + "他们认为这个可能会更有利于这些学生的健康\n", + "他们认为这个可能会更有利于这些学生的健康\n", + "1472/2707\n", + "1473/2707\n", + "1474/2707\n", + "1475/2707\n", + "Audio time = 3.35 sec.\n", + "Recognition time = 10.715612888336182 sec.\n", + "以期他们的认知表现\n", + "以及他们的认知表现\n", + "1476/2707\n", + "1477/2707\n", + "1478/2707\n", + "1479/2707\n", + "Audio time = 2.45 sec.\n", + "Recognition time = 9.560259103775024 sec.\n", + "就是说他们在学习长街\n", + "也就是说他们的学习成绩\n", + "1480/2707\n", + "1481/2707\n", + "1482/2707\n", + "1483/2707\n", + "Audio time = 5.17 sec.\n", + "Recognition time = 11.77139401435852 sec.\n", + "所以如果你的孩子在学校上课的时候谁笑了\n", + "所以如果你的孩子在学校上课的时候睡着了\n", + "1484/2707\n", + "1485/2707\n", + "1486/2707\n", + "1487/2707\n", + "Audio time = 2.2 sec.\n", + "Recognition time = 9.419704914093018 sec.\n", + "清理原谅他\n", + "请你原谅他\n", + "1488/2707\n", + "1489/2707\n", + "1490/2707\n", + "1491/2707\n", + "Audio time = 3.03 sec.\n", + "Recognition time = 11.073076009750366 sec.\n", + "这个也不一定她就是不认真\n", + "这个也不一定他就是不认真\n", + "1492/2707\n", + "1493/2707\n", + "1494/2707\n", + "1495/2707\n", + "Audio time = 3.55 sec.\n", + "Recognition time = 10.228187799453735 sec.\n", + "它也可能是因为他的生物钟中导致的\n", + "它也可能是因为他的生物钟导致的\n", + "1496/2707\n", + "1497/2707\n", + "1498/2707\n", + "1499/2707\n", + "Audio time = 3.5 sec.\n", + "Recognition time = 10.161854982376099 sec.\n", + "到这里我就小结了一下\n", + "到这里我就小结一下\n", + "1500/2707\n", + "1501/2707\n", + "1502/2707\n", + "1503/2707\n", + "Audio time = 2.57 sec.\n", + "Recognition time = 10.001413822174072 sec.\n", + "我倒不亲卫一直讲的内容\n", + "我到目前为止讲的内容\n", + "1504/2707\n", + "1505/2707\n", + "1506/2707\n", + "1507/2707\n", + "Audio time = 4.45 sec.\n", + "Recognition time = 8.813225984573364 sec.\n", + "所以睡眠其实是主要通过两个同入\n", + "睡眠其实是主要通过两个通路\n", + "1508/2707\n", + "1509/2707\n", + "1510/2707\n", + "1511/2707\n", + "Audio time = 3.35 sec.\n", + "Recognition time = 9.932440042495728 sec.\n", + "或者说两股力量来调控的\n", + "或者说两股力量来调控的\n", + "1512/2707\n", + "1513/2707\n", + "1514/2707\n", + "1515/2707\n", + "Audio time = 2.73 sec.\n", + "Recognition time = 9.561797142028809 sec.\n", + "一个就是我们的生活中\n", + "一个就是我们的生物钟\n", + "1516/2707\n", + "1517/2707\n", + "1518/2707\n", + "1519/2707\n", + "Audio time = 3.25 sec.\n", + "Recognition time = 9.894231796264648 sec.\n", + "还有一个就是我前面提到了我太平衡\n", + "还有一个就是我前面提到的稳态平衡\n", + "1520/2707\n", + "1521/2707\n", + "1522/2707\n", + "1523/2707\n", + "Audio time = 3.3 sec.\n", + "Recognition time = 8.629566431045532 sec.\n", + "生活种种决定的是我们什么时候睡\n", + "生物钟决定的是我们什么时候睡\n", + "1524/2707\n", + "1525/2707\n", + "1526/2707\n", + "1527/2707\n", + "Audio time = 5.03 sec.\n", + "Recognition time = 9.865908861160278 sec.\n", + "而我太平和决定的是我们睡多久睡多深\n", + "而稳态平衡决定的是我们睡多久 睡多深\n", + "1528/2707\n", + "1529/2707\n", + "1530/2707\n", + "1531/2707\n", + "Audio time = 3.67 sec.\n", + "Recognition time = 10.085025072097778 sec.\n", + "听到这里可能会有观众想\n", + "听到这里可能会有观众想\n", + "1532/2707\n", + "1533/2707\n", + "1534/2707\n", + "1535/2707\n", + "Audio time = 4.43 sec.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Recognition time = 9.738590955734253 sec.\n", + "试听了很多道理但是还是睡不了一个好觉\n", + "是听了很多道理 但是还是睡不了一个好觉\n", + "1536/2707\n", + "1537/2707\n", + "1538/2707\n", + "1539/2707\n", + "Audio time = 6.68 sec.\n", + "Recognition time = 11.137609958648682 sec.\n", + "所以就接下来的和大家聊一下一些广为流传的盖上\n", + "所以接下来和大家聊一下一些广为流传的\n", + "1540/2707\n", + "1541/2707\n", + "1542/2707\n", + "1543/2707\n", + "Audio time = 2.97 sec.\n", + "Recognition time = 10.444470167160034 sec.\n", + "改善或者促进睡眠的一些方法\n", + "改善或者促进睡眠的一些方法\n", + "1544/2707\n", + "1545/2707\n", + "1546/2707\n", + "1547/2707\n", + "Audio time = 2.4 sec.\n", + "Recognition time = 9.709181070327759 sec.\n", + "他们是不是真的有效\n", + "它们是不是真的有效\n", + "1548/2707\n", + "1549/2707\n", + "1550/2707\n", + "1551/2707\n", + "Audio time = 5.37 sec.\n", + "Recognition time = 10.36344313621521 sec.\n", + "大家可能都是听说过就是喝牛奶可以促进睡眠\n", + "大家可能都听说过喝牛奶可以促进睡眠\n", + "1552/2707\n", + "1553/2707\n", + "1554/2707\n", + "1555/2707\n", + "Audio time = 3.68 sec.\n", + "Recognition time = 9.31540298461914 sec.\n", + "喝着来是不是真的可以促进睡眠呢\n", + "喝牛奶是不是真的可以促进睡眠呢\n", + "1556/2707\n", + "1557/2707\n", + "1558/2707\n", + "1559/2707\n", + "Audio time = 3.87 sec.\n", + "Recognition time = 10.648298978805542 sec.\n", + "牛奶以及其它的一切乳制品里面\n", + "牛奶以及其他的一些乳制品里面\n", + "1560/2707\n", + "1561/2707\n", + "1562/2707\n", + "1563/2707\n", + "Audio time = 3.08 sec.\n", + "Recognition time = 9.419909954071045 sec.\n", + "还有一种叫做四爱酸的物质\n", + "含有一种叫做色氨酸的物质\n", + "1564/2707\n", + "1565/2707\n", + "1566/2707\n", + "1567/2707\n", + "Audio time = 5.3 sec.\n", + "Recognition time = 10.75905990600586 sec.\n", + "后者中文字一点程度上是可以改善和促进睡眠的\n", + "这种物质一定程度上是可以改善和促进睡眠的\n", + "1568/2707\n", + "1569/2707\n", + "1570/2707\n", + "1571/2707\n", + "Audio time = 3.2 sec.\n", + "Recognition time = 10.62932014465332 sec.\n", + "但是他不知纽带利率诶有\n", + "但是它不只牛奶里面有\n", + "1572/2707\n", + "1573/2707\n", + "1574/2707\n", + "1575/2707\n", + "Audio time = 6.5 sec.\n", + "Recognition time = 11.174374103546143 sec.\n", + "在蛋类鱼类肉类打动类产品中也都富含色胺孙\n", + "在蛋类 鱼类 肉类 大豆类产品中也都富含色氨酸\n", + "1576/2707\n", + "1577/2707\n", + "1578/2707\n", + "1579/2707\n", + "Audio time = 4.43 sec.\n", + "Recognition time = 10.775142908096313 sec.\n", + "然后经典流行一种叫做水便溏的东东\n", + "近年流行一种叫做睡眠糖的东东\n", + "1580/2707\n", + "1581/2707\n", + "1582/2707\n", + "1583/2707\n", + "Audio time = 3.1 sec.\n", + "Recognition time = 10.631054162979126 sec.\n", + "然后据说是可以促进睡眠\n", + "据说是可以促进睡眠\n", + "1584/2707\n", + "1585/2707\n", + "1586/2707\n", + "1587/2707\n", + "Audio time = 2.5 sec.\n", + "Recognition time = 11.988529205322266 sec.\n", + "这个睡眠唐诗什么的\n", + "这个睡眠糖是什么呢\n", + "1588/2707\n", + "1589/2707\n", + "1590/2707\n", + "1591/2707\n", + "Audio time = 4.48 sec.\n", + "Recognition time = 8.991067886352539 sec.\n", + "它的主要成分其实就是推诿苏\n", + "它的主要成分其实就是褪黑素\n", + "1592/2707\n", + "1593/2707\n", + "1594/2707\n", + "1595/2707\n", + "Audio time = 5.13 sec.\n", + "Recognition time = 10.789504051208496 sec.\n", + "然后推着此事当中我们条件生物钟的\n", + "褪黑素是帮助我们调节生物钟的\n", + "1596/2707\n", + "1597/2707\n", + "1598/2707\n", + "1599/2707\n", + "1600/2707\n", + "Audio time = 3.37 sec.\n", + "Recognition time = 10.617730140686035 sec.\n", + "他可能以丁城都可以帮助改善睡眠\n", + "它可能一定程度可以帮助改善睡眠\n", + "1601/2707\n", + "1602/2707\n", + "1603/2707\n", + "1604/2707\n", + "Audio time = 4.7 sec.\n", + "Recognition time = 10.75219202041626 sec.\n", + "但是它最主要的功效是保住我们倒时差\n", + "但是它最主要的功效是帮助我们倒时差\n", + "1605/2707\n", + "1606/2707\n", + "1607/2707\n", + "1608/2707\n", + "Audio time = 3.75 sec.\n", + "Recognition time = 10.23914909362793 sec.\n", + "而且这个效果也还是因人而低的\n", + "而且这个效果也还是因人而异的\n", + "1609/2707\n", + "1610/2707\n", + "1611/2707\n", + "1612/2707\n", + "Audio time = 2.37 sec.\n", + "Recognition time = 11.262695074081421 sec.\n", + "在我们小的时候睡不消\n", + "在我们小的时候睡不着\n", + "1613/2707\n", + "1614/2707\n", + "1615/2707\n", + "1616/2707\n", + "Audio time = 3.7 sec.\n", + "Recognition time = 10.642468690872192 sec.\n", + "把妈妈都会说谁不知可以属羊\n", + "爸爸妈妈都会说睡不着可以数羊\n", + "1617/2707\n", + "1618/2707\n", + "1619/2707\n", + "1620/2707\n", + "Audio time = 3.3 sec.\n", + "Recognition time = 11.168534994125366 sec.\n", + "数量神不是真的可以帮助你入睡的\n", + "数羊是不是真的可以帮助你入睡呢\n", + "1621/2707\n", + "1622/2707\n", + "1623/2707\n", + "1624/2707\n", + "Audio time = 3.33 sec.\n", + "Recognition time = 9.928301095962524 sec.\n", + "如果说你在熟料的过程中\n", + "如果说你在数羊的过程中\n", + "1625/2707\n", + "1626/2707\n", + "1627/2707\n", + "1628/2707\n", + "Audio time = 2.87 sec.\n", + "Recognition time = 8.661370038986206 sec.\n", + "他确实会让你干变到更放松\n", + "它确实会让你感觉到更放松\n", + "1629/2707\n", + "1630/2707\n", + "1631/2707\n", + "1632/2707\n", + "Audio time = 2.68 sec.\n", + "Recognition time = 9.568925142288208 sec.\n", + "帮助你的肌肉放松\n", + "帮助你的肌肉放松\n", + "1633/2707\n", + "1634/2707\n", + "1635/2707\n", + "1636/2707\n", + "1637/2707\n", + "Audio time = 3.4 sec.\n", + "Recognition time = 9.830211162567139 sec.\n", + "它一定程度上是可以促进睡眠\n", + "那它一定程度上是可以促进睡眠的\n", + "1638/2707\n", + "1639/2707\n", + "1640/2707\n", + "1641/2707\n", + "1642/2707\n", + "Audio time = 4.7 sec.\n", + "Recognition time = 9.76432991027832 sec.\n", + "大家很多人是越数越兴奋\n", + "很多人是越数越兴奋\n", + "1643/2707\n", + "1644/2707\n", + "1645/2707\n", + "1646/2707\n", + "Audio time = 2.45 sec.\n", + "Recognition time = 8.565114974975586 sec.\n", + "或者说越数越焦虑\n", + "或者说越数越焦虑\n", + "1647/2707\n", + "1648/2707\n", + "1649/2707\n", + "1650/2707\n", + "Audio time = 2.5 sec.\n", + "Recognition time = 9.838749885559082 sec.\n", + "来其实会侵犯作用\n", + "其实会起反作用\n", + "1651/2707\n", + "1652/2707\n", + "1653/2707\n", + "1654/2707\n", + "Audio time = 3.6 sec.\n", + "Recognition time = 9.849634170532227 sec.\n", + "然后还有一种大家都应该有听说过的\n", + "还有一种大家应该都听说过的\n", + "1655/2707\n", + "1656/2707\n", + "1657/2707\n", + "1658/2707\n", + "Audio time = 3.72 sec.\n", + "Recognition time = 9.800726175308228 sec.\n", + "可以改善睡眠的方法就是安眠药\n", + "可以改善睡眠的方法就是安眠药\n", + "1659/2707\n", + "1660/2707\n", + "1661/2707\n", + "1662/2707\n", + "Audio time = 3.65 sec.\n", + "Recognition time = 9.522045135498047 sec.\n", + "安眠药确实是可以改善我们的睡眠\n", + "安眠药确实是可以改善我们的睡眠\n", + "1663/2707\n", + "1664/2707\n", + "1665/2707\n", + "1666/2707\n", + "Audio time = 3.58 sec.\n", + "Recognition time = 9.215401887893677 sec.\n", + "但是我在这里要提醒大家是\n", + "但是我在这里要提醒大家\n", + "1667/2707\n", + "1668/2707\n", + "1669/2707\n", + "1670/2707\n", + "Audio time = 5.15 sec.\n", + "Recognition time = 10.07035493850708 sec.\n", + "使用安眠药的时候一定要小型精神\n", + "使用安眠药的时候一定要小心谨慎\n", + "1671/2707\n", + "1672/2707\n", + "1673/2707\n", + "1674/2707\n", + "1675/2707\n", + "Audio time = 3.6 sec.\n", + "Recognition time = 9.595734119415283 sec.\n", + "因为常见的爱眠药都是作用于\n", + "因为常见的安眠药都是作用于\n", + "1676/2707\n", + "1677/2707\n", + "1678/2707\n", + "1679/2707\n", + "Audio time = 3.12 sec.\n", + "Recognition time = 9.69861388206482 sec.\n", + "与我们脑内的神经递质系统\n", + "我们脑内的神经递质系统\n", + "1680/2707\n", + "1681/2707\n", + "1682/2707\n", + "1683/2707\n", + "Audio time = 3.7 sec.\n", + "Recognition time = 10.467654943466187 sec.\n", + "然后这趟神经递质系统它却是调控睡眠\n", + "这套神经递质系统它确实调控睡眠\n", + "1684/2707\n", + "1685/2707\n", + "1686/2707\n", + "1687/2707\n", + "1688/2707\n", + "Audio time = 4.5 sec.\n", + "Recognition time = 11.265694856643677 sec.\n", + "但是他还负责执行我们闹的各种功能\n", + "但是它还负责执行我们脑的各种功能\n", + "1689/2707\n", + "1690/2707\n", + "1691/2707\n", + "1692/2707\n", + "Audio time = 4.15 sec.\n", + "Recognition time = 10.38594102859497 sec.\n", + "所以安眠药不仅会对你的睡眠产生影响\n", + "所以安眠药不仅会对你的睡眠产生影响\n", + "1693/2707\n", + "1694/2707\n", + "1695/2707\n", + "1696/2707\n", + "Audio time = 3.53 sec.\n", + "Recognition time = 10.750540018081665 sec.\n", + "他还会可能会影响我们的情绪\n", + "它可能还会影响我们的情绪\n", + "1697/2707\n", + "1698/2707\n", + "1699/2707\n", + "1700/2707\n", + "Audio time = 2.88 sec.\n", + "Recognition time = 10.43662428855896 sec.\n", + "认知功能包括学习基业\n", + "认知功能 包括学习记忆\n", + "1701/2707\n", + "1702/2707\n", + "1703/2707\n", + "1704/2707\n", + "Audio time = 3.45 sec.\n", + "Recognition time = 10.306946039199829 sec.\n", + "还有我甚至是运动功能等等\n", + "甚至是运动功能等等\n", + "1705/2707\n", + "1706/2707\n", + "1707/2707\n", + "1708/2707\n", + "Audio time = 3.92 sec.\n", + "Recognition time = 11.87840723991394 sec.\n", + "所以在使用来煤窑的时候一定要小型谨慎\n", + "所以在使用安眠药的时候一定要小心谨慎\n", + "1709/2707\n", + "1710/2707\n", + "1711/2707\n", + "1712/2707\n", + "Audio time = 3.85 sec.\n", + "Recognition time = 11.060353994369507 sec.\n", + "其实除了药物来改成睡眠还有一些\n", + "其实除了药物来改善睡眠\n", + "1713/2707\n", + "1714/2707\n", + "1715/2707\n", + "1716/2707\n", + "Audio time = 3.73 sec.\n", + "Recognition time = 11.671911001205444 sec.\n", + "认知行为疗法也可以改善睡眠\n", + "还有一些认知行为疗法也可以改善睡眠\n", + "1717/2707\n", + "1718/2707\n", + "1719/2707\n", + "1720/2707\n", + "Audio time = 7.25 sec.\n", + "Recognition time = 11.426790952682495 sec.\n", + "比如说把你的睡眠和卧室以及创建里一个关联\n", + "比如说把你的睡眠和卧室以及床建立一个关联\n", + "1721/2707\n", + "1722/2707\n", + "1723/2707\n", + "1724/2707\n", + "Audio time = 5.13 sec.\n", + "Recognition time = 11.306833028793335 sec.\n", + "在卧室里在床上就进行睡眠这个事情\n", + "在卧室里 在床上 就只进行睡眠这个事情\n", + "1725/2707\n", + "1726/2707\n", + "1727/2707\n", + "1728/2707\n", + "1729/2707\n", + "Audio time = 2.82 sec.\n", + "Recognition time = 10.859714031219482 sec.\n", + "就不在卧室和床上进行\n", + "就不要在卧室和床上进行\n", + "1730/2707\n", + "1731/2707\n", + "1732/2707\n", + "1733/2707\n", + "Audio time = 3.25 sec.\n", + "Recognition time = 11.359398126602173 sec.\n", + "如能比较好地建立这个关联的话\n", + "如果能比较好地建立这个关联的话\n", + "1734/2707\n", + "1735/2707\n", + "1736/2707\n", + "1737/2707\n", + "Audio time = 2.63 sec.\n", + "Recognition time = 10.975263118743896 sec.\n", + "花当你进入到这个环境里\n", + "当你进入到这个环境里\n", + "1738/2707\n", + "1739/2707\n", + "1740/2707\n", + "1741/2707\n", + "Audio time = 3.47 sec.\n", + "Recognition time = 12.985117197036743 sec.\n", + "就会能够比较容易睡着\n", + "就会能够比较容易睡着\n", + "1742/2707\n", + "1743/2707\n", + "1744/2707\n", + "1745/2707\n", + "Audio time = 2.32 sec.\n", + "Recognition time = 10.166368961334229 sec.\n", + "所有的科研问题\n", + "所有的科研问题\n", + "1746/2707\n", + "1747/2707\n", + "1748/2707\n", + "1749/2707\n", + "Audio time = 4.0 sec.\n", + "Recognition time = 11.109914064407349 sec.\n", + "我们不但有知其人我还要窒息所以然\n", + "我们不但要知其然我们还要知其所以然\n", + "1750/2707\n", + "1751/2707\n", + "1752/2707\n", + "1753/2707\n", + "Audio time = 6.48 sec.\n", + "Recognition time = 9.967283010482788 sec.\n", + "所以科学家们不带要研究睡眠如何产生的\n", + "所以科学家们不但要研究睡眠如何产生的\n", + "1754/2707\n", + "1755/2707\n", + "1756/2707\n", + "1757/2707\n", + "Audio time = 3.4 sec.\n", + "Recognition time = 10.022706985473633 sec.\n", + "他们还想研究我们为什么要睡觉\n", + "他们还想研究我们为什么要睡觉\n", + "1758/2707\n", + "1759/2707\n", + "1760/2707\n", + "1761/2707\n", + "Audio time = 2.55 sec.\n", + "Recognition time = 10.569955110549927 sec.\n", + "大家可能会觉得\n", + "大家可能会觉得\n", + "1762/2707\n", + "1763/2707\n", + "1764/2707\n", + "1765/2707\n", + "Audio time = 3.68 sec.\n", + "Recognition time = 11.552134275436401 sec.\n", + "我们为什么要睡觉这个问题还需要也中文\n", + "我们为什么要睡觉这个问题还需要研究吗\n", + "1766/2707\n", + "1767/2707\n", + "1768/2707\n", + "1769/2707\n", + "1770/2707\n", + "Audio time = 3.83 sec.\n", + "Recognition time = 9.068635940551758 sec.\n", + "但是我你仔细想想睡眠这个事情的话\n", + "但是如果你仔细想一想睡眠这个事情的话\n", + "1771/2707\n", + "1772/2707\n", + "1773/2707\n", + "1774/2707\n", + "Audio time = 2.9 sec.\n", + "Recognition time = 22.229652166366577 sec.\n", + "他其实也不是那么理所当然\n", + "它其实也不是那么理所当然\n", + "1775/2707\n", + "1776/2707\n", + "1777/2707\n", + "1778/2707\n", + "Audio time = 5.2 sec.\n", + "Recognition time = 10.066240072250366 sec.\n", + "因为未当动物进入到失眠的状态以后\n", + "因为当动物进入到睡眠的状态以后\n", + "1779/2707\n", + "1780/2707\n", + "1781/2707\n", + "1782/2707\n", + "Audio time = 3.5 sec.\n", + "Recognition time = 10.130698919296265 sec.\n", + "他是不能进食不能繁殖\n", + "它是不能进食 不能繁殖\n", + "1783/2707\n", + "1784/2707\n", + "1785/2707\n", + "1786/2707\n", + "Audio time = 2.93 sec.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Recognition time = 10.041284084320068 sec.\n", + "甚至是不能保护太子记得\n", + "甚至是不能保护它自己的\n", + "1787/2707\n", + "1788/2707\n", + "1789/2707\n", + "1790/2707\n", + "Audio time = 2.25 sec.\n", + "Recognition time = 9.727653980255127 sec.\n", + "但是基本是这辆的\n", + "但是即便是这样\n", + "1791/2707\n", + "1792/2707\n", + "1793/2707\n", + "1794/2707\n", + "Audio time = 2.48 sec.\n", + "Recognition time = 9.970431804656982 sec.\n", + "这两根我又睡了记忆点的叫了\n", + "动物也睡了几亿年的觉了\n", + "1795/2707\n", + "1796/2707\n", + "1797/2707\n", + "1798/2707\n", + "Audio time = 2.47 sec.\n", + "Recognition time = 10.636347770690918 sec.\n", + "这么说的话\n", + "这么说的话\n", + "1799/2707\n", + "1800/2707\n", + "1801/2707\n", + "1802/2707\n", + "Audio time = 3.15 sec.\n", + "Recognition time = 9.70245909690857 sec.\n", + "睡眠都不要付出那么大的代价\n", + "动物要付出那么大的代价\n", + "1803/2707\n", + "1804/2707\n", + "1805/2707\n", + "1806/2707\n", + "1807/2707\n", + "Audio time = 3.33 sec.\n", + "Recognition time = 10.953075170516968 sec.\n", + "所以睡眠应该是挺重要的\n", + "所以睡眠应该是挺重要的\n", + "1808/2707\n", + "1809/2707\n", + "1810/2707\n", + "1811/2707\n", + "Audio time = 2.55 sec.\n", + "Recognition time = 10.447063207626343 sec.\n", + "对于我们人类\n", + "对于我们人类\n", + "1812/2707\n", + "1813/2707\n", + "1814/2707\n", + "1815/2707\n", + "Audio time = 2.85 sec.\n", + "Recognition time = 10.32799506187439 sec.\n", + "如果一晚上的睡眠不足\n", + "如果一晚上的睡眠不足\n", + "1816/2707\n", + "1817/2707\n", + "1818/2707\n", + "1819/2707\n", + "Audio time = 3.2 sec.\n", + "Recognition time = 10.055874109268188 sec.\n", + "就会导致说认知功能在下降\n", + "就会导致认知功能的下降\n", + "1820/2707\n", + "1821/2707\n", + "1822/2707\n", + "1823/2707\n", + "Audio time = 2.8 sec.\n", + "Recognition time = 10.898871898651123 sec.\n", + "这个大家可能都有体会\n", + "这个大家可能都有体会\n", + "1824/2707\n", + "1825/2707\n", + "1826/2707\n", + "1827/2707\n", + "Audio time = 3.6 sec.\n", + "Recognition time = 11.943390130996704 sec.\n", + "一晚上没睡好第二天就感觉笨笨的\n", + "一晚上没睡好 第二天就感觉笨笨的\n", + "1828/2707\n", + "1829/2707\n", + "1830/2707\n", + "1831/2707\n", + "Audio time = 2.8 sec.\n", + "Recognition time = 10.328142881393433 sec.\n", + "然后如果长期学教的话\n", + "如果长期缺觉的话\n", + "1832/2707\n", + "1833/2707\n", + "1834/2707\n", + "1835/2707\n", + "Audio time = 4.03 sec.\n", + "Recognition time = 12.464287281036377 sec.\n", + "会增加你患多种疾病的风险包括\n", + "会增加罹患多种疾病的风险\n", + "1836/2707\n", + "1837/2707\n", + "1838/2707\n", + "1839/2707\n", + "Audio time = 5.73 sec.\n", + "Recognition time = 10.342688083648682 sec.\n", + "肥胖症糖尿病癌症精神疾病等等\n", + "包括肥胖症 糖尿病 癌症 精神疾病等等\n", + "1840/2707\n", + "1841/2707\n", + "1842/2707\n", + "1843/2707\n", + "Audio time = 4.2 sec.\n", + "Recognition time = 10.101732015609741 sec.\n", + "如果把大鼠放在向右边的图里面\n", + "如果把大鼠放在像右边的这个图里面\n", + "1844/2707\n", + "1845/2707\n", + "1846/2707\n", + "1847/2707\n", + "Audio time = 2.05 sec.\n", + "Recognition time = 10.227153778076172 sec.\n", + "里这样一个桩身里面\n", + "这样的一个装置里面\n", + "1848/2707\n", + "1849/2707\n", + "1850/2707\n", + "1851/2707\n", + "Audio time = 3.4 sec.\n", + "Recognition time = 9.403329133987427 sec.\n", + "对他进行持续的睡眠剥夺\n", + "对它进行持续的睡眠剥夺\n", + "1852/2707\n", + "1853/2707\n", + "1854/2707\n", + "1855/2707\n", + "Audio time = 3.2 sec.\n", + "Recognition time = 10.332170963287354 sec.\n", + "带来两周的世界大树就会死亡\n", + "大概两周的时间大鼠就会死亡\n", + "1856/2707\n", + "1857/2707\n", + "1858/2707\n", + "1859/2707\n", + "Audio time = 3.33 sec.\n", + "Recognition time = 8.910773992538452 sec.\n", + "但是为什么会这样我们还不太清楚\n", + "但是为什么会这样我们还不太清楚\n", + "1860/2707\n", + "1861/2707\n", + "1862/2707\n", + "1863/2707\n", + "Audio time = 4.7 sec.\n", + "Recognition time = 10.441707134246826 sec.\n", + "我前关于睡眠对脑的影响\n", + "目前关于睡眠对脑的影响\n", + "1864/2707\n", + "1865/2707\n", + "1866/2707\n", + "1867/2707\n", + "1868/2707\n", + "Audio time = 4.55 sec.\n", + "Recognition time = 10.263937950134277 sec.\n", + "睡眠可以在睡眠过程中我们脑内迁\n", + "在睡眠过程中我们脑内\n", + "1869/2707\n", + "1870/2707\n", + "1871/2707\n", + "1872/2707\n", + "Audio time = 3.65 sec.\n", + "Recognition time = 10.17685079574585 sec.\n", + "那前面讲的是警员会得到一些修复\n", + "前面讲的神经元 会得到一些修复\n", + "1873/2707\n", + "1874/2707\n", + "1875/2707\n", + "1876/2707\n", + "Audio time = 6.08 sec.\n", + "Recognition time = 10.446882009506226 sec.\n", + "这个是经人在我们脑内他会神经源直接会形成链结构\n", + "在脑内神经元之间会形成连接\n", + "1877/2707\n", + "1878/2707\n", + "1879/2707\n", + "1880/2707\n", + "Audio time = 2.17 sec.\n", + "Recognition time = 10.647816181182861 sec.\n", + "结构成一个这样的网络\n", + "构成一个这样的网络\n", + "1881/2707\n", + "1882/2707\n", + "1883/2707\n", + "1884/2707\n", + "Audio time = 2.8 sec.\n", + "Recognition time = 9.970585107803345 sec.\n", + "在我们醒着的时候\n", + "在我们醒着的时候\n", + "1885/2707\n", + "1886/2707\n", + "1887/2707\n", + "1888/2707\n", + "Audio time = 3.78 sec.\n", + "Recognition time = 10.700876235961914 sec.\n", + "因为脑接收后大量来自外界的星系\n", + "因为脑接收大量来自外界的信息\n", + "1889/2707\n", + "1890/2707\n", + "1891/2707\n", + "1892/2707\n", + "Audio time = 5.98 sec.\n", + "Recognition time = 11.620858192443848 sec.\n", + "所以这些事警员的需要接受处理传递这些星系\n", + "所以这些神经元需要接收处理传递这些信息\n", + "1893/2707\n", + "1894/2707\n", + "1895/2707\n", + "1896/2707\n", + "Audio time = 3.05 sec.\n", + "Recognition time = 10.85471796989441 sec.\n", + "在这个过程中他们会发生了一些性\n", + "在这个过程中它们会发生一些\n", + "1897/2707\n", + "1898/2707\n", + "1899/2707\n", + "1900/2707\n", + "Audio time = 2.62 sec.\n", + "Recognition time = 10.161369800567627 sec.\n", + "写形态和功能上的改变\n", + "形态和功能上的改变\n", + "1901/2707\n", + "1902/2707\n", + "1903/2707\n", + "1904/2707\n", + "Audio time = 3.2 sec.\n", + "Recognition time = 10.831066846847534 sec.\n", + "然后当我们寻中睡眠以后呢\n", + "当我们进入到睡眠以后\n", + "1905/2707\n", + "1906/2707\n", + "1907/2707\n", + "1908/2707\n", + "Audio time = 4.22 sec.\n", + "Recognition time = 10.85332202911377 sec.\n", + "这些形态和功能的改变可以得到一定的恢复\n", + "这些形态和功能的改变可以得到一定的恢复\n", + "1909/2707\n", + "1910/2707\n", + "1911/2707\n", + "1912/2707\n", + "Audio time = 2.98 sec.\n", + "Recognition time = 11.470728158950806 sec.\n", + "这样一遍又我们再次醒来的时候\n", + "这样以便于我们再次醒来的时候\n", + "1913/2707\n", + "1914/2707\n", + "1915/2707\n", + "1916/2707\n", + "Audio time = 4.53 sec.\n", + "Recognition time = 9.931190013885498 sec.\n", + "这些神经元可以更高效就觉得处理性\n", + "这些神经元可以更高效准确地\n", + "1917/2707\n", + "1918/2707\n", + "1919/2707\n", + "1920/2707\n", + "Audio time = 3.85 sec.\n", + "Recognition time = 10.750570058822632 sec.\n", + "处理新的星系传递性的星系\n", + "处理新的信息 传递新的信息\n", + "1921/2707\n", + "1922/2707\n", + "1923/2707\n", + "1924/2707\n", + "Audio time = 2.53 sec.\n", + "Recognition time = 10.854321241378784 sec.\n", + "但是因为我前面说了\n", + "但是因为我前面说了\n", + "1925/2707\n", + "1926/2707\n", + "1927/2707\n", + "1928/2707\n", + "Audio time = 3.37 sec.\n", + "Recognition time = 10.341864824295044 sec.\n", + "身边不足对我们的危害是防卫免得\n", + "睡眠不足对我们的危害是方方面面的\n", + "1929/2707\n", + "1930/2707\n", + "1931/2707\n", + "1932/2707\n", + "Audio time = 3.65 sec.\n", + "Recognition time = 10.24030590057373 sec.\n", + "所以水边的功能应该不止于此\n", + "所以睡眠的功能应该不止于此\n", + "1933/2707\n", + "1934/2707\n", + "1935/2707\n", + "1936/2707\n", + "Audio time = 4.43 sec.\n", + "Recognition time = 10.546146869659424 sec.\n", + "在升学与有一句广为流出来的话\n", + "在生物学领域有一句广为流传的话\n", + "1937/2707\n", + "1938/2707\n", + "1939/2707\n", + "1940/2707\n", + "1941/2707\n", + "Audio time = 2.75 sec.\n", + "Recognition time = 10.342514038085938 sec.\n", + "这句话原话是这样的\n", + "这句话原话是这样的\n", + "1942/2707\n", + "1943/2707\n", + "1944/2707\n", + "1945/2707\n", + "Audio time = 4.57 sec.\n", + "Recognition time = 9.031803846359253 sec.\n", + "那绯羽白熬了去梅瓶来都比我懂事\n", + "Nothing in biology makes sense except in light of evolution\n", + "1946/2707\n", + "1947/2707\n", + "1948/2707\n", + "1949/2707\n", + "Audio time = 3.82 sec.\n", + "Recognition time = 9.731159210205078 sec.\n", + "大意就是说任何生物学的问题到\n", + "大意就是说任何生物学的问题\n", + "1950/2707\n", + "1951/2707\n", + "1952/2707\n", + "1953/2707\n", + "Audio time = 4.05 sec.\n", + "Recognition time = 10.21561884880066 sec.\n", + "都要从演化的交通去分析才有意义\n", + "都要从演化的角度去分析才有意义\n", + "1954/2707\n", + "1955/2707\n", + "1956/2707\n", + "1957/2707\n", + "Audio time = 3.34 sec.\n", + "Recognition time = 12.388890743255615 sec.\n", + "如果从演化了角度我们想一想\n", + "如果从演化的角度我们想一想\n", + "1958/2707\n", + "1959/2707\n", + "1960/2707\n", + "1961/2707\n", + "Audio time = 1.98 sec.\n", + "Recognition time = 9.58765721321106 sec.\n", + "为什么要睡觉\n", + "为什么要睡觉\n", + "1962/2707\n", + "1963/2707\n", + "1964/2707\n", + "1965/2707\n", + "Audio time = 3.3 sec.\n", + "Recognition time = 10.277790307998657 sec.\n", + "为什么地球上东问你睡了几亿年了\n", + "为什么地球上的动物已经睡了几亿年了\n", + "1966/2707\n", + "1967/2707\n", + "1968/2707\n", + "1969/2707\n", + "Audio time = 3.2 sec.\n", + "Recognition time = 11.172950983047485 sec.\n", + "就如果从这个叫伦分析的话我\n", + "如果从这个角度来分析的话\n", + "1970/2707\n", + "1971/2707\n", + "1972/2707\n", + "1973/2707\n", + "Audio time = 3.42 sec.\n", + "Recognition time = 9.000153303146362 sec.\n", + "我们可能会得到一些新的提示\n", + "我们可能会得到一些新的提示\n", + "1974/2707\n", + "1975/2707\n", + "1976/2707\n", + "1977/2707\n", + "Audio time = 4.95 sec.\n", + "Recognition time = 10.396843671798706 sec.\n", + "虽然涤纶尚东都大多数中午都需要睡觉\n", + "虽然地球上大多数动物都需要睡觉\n", + "1978/2707\n", + "1979/2707\n", + "1980/2707\n", + "1981/2707\n", + "Audio time = 3.05 sec.\n", + "Recognition time = 8.751291990280151 sec.\n", + "他们的睡眠其实还挺不一样的\n", + "它们的睡眠其实还挺不一样的\n", + "1982/2707\n", + "1983/2707\n", + "1984/2707\n", + "1985/2707\n", + "Audio time = 4.6 sec.\n", + "Recognition time = 8.490720987319946 sec.\n", + "比如说一种叫做野猴的动物\n", + "比如说一种叫做夜猴的动物\n", + "1986/2707\n", + "1987/2707\n", + "1988/2707\n", + "1989/2707\n", + "Audio time = 2.58 sec.\n", + "Recognition time = 8.635629177093506 sec.\n", + "他就需要睡的比较久\n", + "它就需要睡得比较久\n", + "1990/2707\n", + "1991/2707\n", + "1992/2707\n", + "1993/2707\n", + "Audio time = 3.45 sec.\n", + "Recognition time = 10.498374938964844 sec.\n", + "他每天可能要睡大约十七个小时\n", + "它每天可能要睡大约17个小时\n", + "1994/2707\n", + "1995/2707\n", + "1996/2707\n", + "1997/2707\n", + "Audio time = 4.1 sec.\n", + "Recognition time = 11.285339117050171 sec.\n", + "你的出五毛该来每天睡十二个半小时\n", + "你的宠物猫大概每天睡12个半小时\n", + "1998/2707\n", + "1999/2707\n", + "2000/2707\n", + "2001/2707\n", + "Audio time = 4.45 sec.\n", + "Recognition time = 10.137523889541626 sec.\n", + "我们人类的一般是需要八个小时所有的睡眠\n", + "我们人类一般需要8个小时左右的睡眠\n", + "2002/2707\n", + "2003/2707\n", + "2004/2707\n", + "2005/2707\n", + "Audio time = 2.87 sec.\n", + "Recognition time = 10.034235954284668 sec.\n", + "而有一些动物就睡的比较少比如\n", + "而有一些动物就睡得比较少\n", + "2006/2707\n", + "2007/2707\n", + "2008/2707\n", + "2009/2707\n", + "Audio time = 3.63 sec.\n", + "Recognition time = 9.728991985321045 sec.\n", + "比如说山羊和马\n", + "比如说山羊和马\n", + "2010/2707\n", + "2011/2707\n", + "2012/2707\n", + "2013/2707\n", + "2014/2707\n", + "Audio time = 3.32 sec.\n", + "Recognition time = 10.138468980789185 sec.\n", + "到这里大家可能会很好奇不\n", + "到这里大家可能会很好奇\n", + "2015/2707\n", + "2016/2707\n", + "2017/2707\n", + "2018/2707\n", + "Audio time = 3.18 sec.\n", + "Recognition time = 10.477406978607178 sec.\n", + "或不还有动物睡得更少\n", + "会不会还有动物睡得更少\n", + "2019/2707\n", + "2020/2707\n", + "2021/2707\n", + "2022/2707\n", + "Audio time = 2.58 sec.\n", + "Recognition time = 11.842370986938477 sec.\n", + "甚至是不用睡觉的\n", + "甚至是不用睡觉呢\n", + "2023/2707\n", + "2024/2707\n", + "2025/2707\n", + "2026/2707\n", + "Audio time = 3.25 sec.\n", + "Recognition time = 8.909492015838623 sec.\n", + "也是可能有这样的动物的\n", + "也是可能有这样的动物的\n", + "2027/2707\n", + "2028/2707\n", + "2029/2707\n", + "2030/2707\n", + "Audio time = 5.08 sec.\n" + ] + } + ], + "source": [ + "subtitles = []\n", + "new_subtitles = []\n", + "srtfile = \"data/yixi_original.srt\"\n", + "srtfile_recognized = \"data/yixi.srt\"\n", + "wavfile = \"data/yixi.wav\"\n", + "\n", + "rate, data = read_wav(wavfile)\n", + "\n", + "with open(srtfile, 'r') as f:\n", + " for line in f:\n", + " subtitles.append(line.strip())\n", + "\n", + "j = 0\n", + "for i in range(len(subtitles)):\n", + " print(str(i)+\"/\"+str(len(subtitles)))\n", + " if \"-->\" in subtitles[i]:\n", + " time1, time2 = [timeStamp2Num(x, rate) for x in subtitles[i].split(\" --> \")]\n", + " time1 = max([0, time1-8000])\n", + " time2 = time2+8000\n", + " print(\"Audio time = \"+str((time2-time1)/16000.0)+\" sec.\")\n", + " \n", + " recog_starting_time = time.time()\n", + " currentdata = data[time1:time2]\n", + " if len(currentdata)<100:\n", + " continue\n", + " currentdata = mergeChannels(currentdata)\n", + " if len(currentdata)>160240:\n", + " j+=1\n", + " new_subtitles.append(str(j))\n", + " new_subtitles.append(subtitles[i])\n", + " new_subtitles.append(\"语句太长，识别失败\")\n", + " new_subtitles.append(\"\")\n", + " continue\n", + " currentdata = zero_padding_1d(currentdata, 160240)\n", + " a_seg = AudioSegment(currentdata, rate)\n", + " xs = np.transpose(np.array([af.featurize(a_seg)]), [0,2,1])\n", + " \n", + " pred = model.predict(sess, xs)[0]\n", + " pred_dense = sparseTuples2dense(pred)\n", + " detected_line = []\n", + " for stuff in pred_dense[0]:\n", + " if stuff!=-1:\n", + " detected_line.append(stuff)\n", + " if len(detected_line)<1:\n", + " continue\n", + " pinyin = pyParser.decodeIndices(detected_line, useUnderline = False)\n", + " response = urlopen(\"https://www.google.com/inputtools/request?ime=pinyin&ie=utf-8&oe=utf-8&app=translate&num=10&text=\"+pinyin)\n", + " html = response.read()\n", + " result = (html.decode('utf8')).split(\",\")[2][2:-1]\n", + " \n", + " print(\"Recognition time = \"+str(time.time()-recog_starting_time)+\" sec.\")\n", + " print(result)\n", + " print(subtitles[i+1])\n", + " \n", + " j+=1\n", + " new_subtitles.append(str(j))\n", + " new_subtitles.append(subtitles[i])\n", + " new_subtitles.append(result)\n", + " new_subtitles.append(\"\")\n", + "\n", + "new_subtitles = new_subtitles[:-1]\n", + "with open(srtfile_recognized, 'w+') as f:\n", + " for line in new_subtitles:\n", + " f.write(line+\"\\n\")\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 去掉空格，改数字，英文，和“他，她，它”\n", + "subtitles = []\n", + "new_subtitles = []\n", + "srtfile = \"data/zongli_original.srt\"\n", + "srtfile_recognized = \"data/zongli.srt\"\n", + "wavfile = \"data/zongli.wav\"\n", + "\n", + "rate, data = read_wav(wavfile)\n", + "\n", + "with open(srtfile, 'r') as f:\n", + " for line in f:\n", + " subtitles.append(line.strip())\n", + "\n", + "j = 0\n", + "for i in range(len(subtitles))):\n", + " print(str(i)+\"/\"+str(len(subtitles)))\n", + " if \"-->\" in subtitles[i]:\n", + " time1, time2 = [timeStamp2Num(x, rate) for x in subtitles[i].split(\" --> \")]\n", + " time1 = max([0, time1-8000])\n", + " time2 = time2+8000\n", + " \n", + " currentdata = data[time1:time2]\n", + " if len(currentdata)<100:\n", + " continue\n", + " currentdata = mergeChannels(currentdata)\n", + " if len(currentdata)>160240:\n", + " j+=1\n", + " new_subtitles.append(str(j))\n", + " new_subtitles.append(subtitles[i])\n", + " new_subtitles.append(subtitles[i+1])\n", + " new_subtitles.append(\"\")\n", + " continue\n", + " currentdata = zero_padding_1d(currentdata, 160240)\n", + " a_seg = AudioSegment(currentdata, rate)\n", + " xs = np.transpose(np.array([af.featurize(a_seg)]), [0,2,1])\n", + " \n", + " pred = model.predict(sess, xs)[0]\n", + " pred_dense = sparseTuples2dense(pred)\n", + " detected_line = []\n", + " for stuff in pred_dense[0]:\n", + " if stuff!=-1:\n", + " detected_line.append(stuff)\n", + " if len(detected_line)<1:\n", + " continue\n", + " pinyin = pyParser.decodeIndices(detected_line, useUnderline = False)\n", + " response = urlopen(\"https://www.google.com/inputtools/request?ime=pinyin&ie=utf-8&oe=utf-8&app=translate&num=10&text=\"+pinyin)\n", + " html = response.read()\n", + " result = (html.decode('utf8')).split(\",\")[2][2:-1]\n", + " \n", + " print(result)\n", + " print(subtitles[i+1])\n", + " \n", + " j+=1\n", + " new_subtitles.append(str(j))\n", + " new_subtitles.append(subtitles[i])\n", + " new_subtitles.append(result)\n", + " new_subtitles.append(\"\")\n", + "\n", + "new_subtitles = new_subtitles[:-1]\n", + "with open(srtfile_recognized, 'w+') as f:\n", + " for line in new_subtitles:\n", + " f.write(line+\"\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/subtitle_demo.py b/subtitle_demo.py new file mode 100644 index 0000000..9a800bf --- /dev/null +++ b/subtitle_demo.py @@ -0,0 +1,57 @@ +import os +import time +import warnings +warnings.filterwarnings("ignore", message="numpy.dtype size changed") +warnings.filterwarnings("ignore", message="numpy.ufunc size changed") +with warnings.catch_warnings(): + warnings.simplefilter("ignore") + import tensorflow as tf +import numpy as np +from urllib.request import urlopen + +from lib.tools_batch import * +from lib.tools_math import * +from lib.tools_sparse import * +from lib.contrib.audio_featurizer import AudioFeaturizer +from lib.contrib.audio import AudioSegment +from model901 import * +model_name = "v901" + +def timeStamp2Num(timeStamp, rate): + """ + timeStamp str: 00:00:01,879 + rate int: the sampling rate + return int + """ + secs, millisec = timeStamp.split(",") + hour, minute, sec = secs.split(":") + millisec = float(millisec)*0.001 + sec = float(hour)*3600+float(minute)*60+float(sec) + num = int(rate*(sec+millisec)) + return num + +pyParser = pinyinParser("lib/pinyinDictNoTone.pickle") +model = model(409) +af = AudioFeaturizer() + +with tf.Session() as sess: + saver = tf.train.Saver() + saver.restore(sess, "models/"+model_name+"/"+model_name+"_0.ckpt") + + rate, data = read_wav("data/test.wav") + data = mergeChannels(data) + data = zero_padding_1d(data, 160240) + a_seg = AudioSegment(data, rate) + xs = np.transpose(np.array([af.featurize(a_seg)]), [0,2,1]) + + pred = model.predict(sess, xs)[0] + pred_dense = sparseTuples2dense(pred) + detected_line = [] + for stuff in pred_dense[0]: + if stuff!=-1: + detected_line.append(stuff) + pinyin = pyParser.decodeIndices(detected_line, useUnderline = False) + print(pinyin) + response = urlopen("https://www.google.com/inputtools/request?ime=pinyin&ie=utf-8&oe=utf-8&app=translate&num=10&text="+pinyin) + html = response.read() + result = html.split(",")[2][2:-1] diff --git a/test_audio.wav b/test_audio.wav new file mode 100644 index 0000000000000000000000000000000000000000..97caa74ffca10d0dfc784cc40c77056a3706d22e GIT binary patch literal 244748 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z-t5$%f@g-LGOk8|CB#9jLPIrcRh07gGc=rrbIOBnPE2KC0Wr*Rsj%hE{thA~wn|9( zAQr=C&MnrZ&&rRB-5Y(QY^01;%spP=EA~Jc+**c5qnCmk-uXdH?>J}nO+n;k_ZqIM zx$Cm$a*1WkMdftlc==X(HW$b?n^CLs#0qO_SU#&i&=6G@%Dk&Kg;lY?=->15Fh1$6 zvh-rgP;^Z>;n>TtRR`@0w?$@gdHi>0+Td#VvR?K(F2$m%8_shQi0nbR9E Nxw`1Gh+@8||9`hD-N66= literal 0 HcmV?d00001 diff --git a/train901.py b/train901.py new file mode 100644 index 0000000..1266e0b --- /dev/null +++ b/train901.py @@ -0,0 +1,74 @@ +import os +os.environ["CUDA_VISIBLE_DEVICES"] = input("Which GPU? ") +import time +import warnings +warnings.filterwarnings("ignore", message="numpy.dtype size changed") +warnings.filterwarnings("ignore", message="numpy.ufunc size changed") +with warnings.catch_warnings(): + warnings.simplefilter("ignore") + import tensorflow as tf +import numpy as np + +from lib.tools_batch import * +from lib.tools_math import * +from model901 import * + +def get_learningRate(step): + # return max(4e-4*(0.99999**step), 2e-5) + return 2e-4 + +TEST_ROUND = 1 +BATCH_SIZE = 128 +TEST_SIZE = 16 +AUGMENTATION = True +NUM_STEP = int(1e7) + +model_name = "v901" +saving_period = 200 +num_labels = 407 +num_class = 409 +bg = BatchGetter("../data/data_aishell/wav", "../data/data_aishell/transcript/aishell_transcript_v0.8.txt", + "lib/pinyinDictNoTone.pickle", "../data/backgrounds/", server = True) +bg2 = BatchGetter("../data/youtube_subtitles/wav", "../data/youtube_subtitles/subs.txt", + "lib/pinyinDictNoTone.pickle", "../data/backgrounds/", server = True) +pyParser = pinyinParser("lib/pinyinDictNoTone.pickle") +model = model(num_class) +if model_name not in os.listdir('models/'): + os.mkdir('models/'+model_name) + +gpu_options = tf.GPUOptions(allow_growth=True) +with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,allow_soft_placement=True,log_device_placement=False)) as sess: + + sess.run(tf.global_variables_initializer()) + saver = tf.train.Saver() + # tensorboard --logdir logs/ + summary_writer = tf.summary.FileWriter(logdir = "logs", graph = tf.get_default_graph()) + saver.restore(sess, "models/"+model_name+"/"+model_name+"_0.ckpt") + + for i in range(1, NUM_STEP+1): + + lr = get_learningRate(i) + if i%2==0: + xs, ys = bg.get_batch(BATCH_SIZE, batch_type = 'train') + else: + xs, ys = bg2.get_batch(BATCH_SIZE, batch_type = 'train') + loss, summary = model.train(sess, lr, xs, ys) + summary_writer.add_summary(summary, i) + print(i, loss) + + if i%saving_period == 0: + print("Learning rate =", lr) + save_path = saver.save(sess, "models/"+model_name+"/"+model_name+"_"+str(int(i/50000))+".ckpt") + print("Model saved in path: "+save_path) + + ave_loss = 0.0 + for i in range(2): + if i==0: + xs, ys = bg.get_batch(TEST_SIZE, batch_type = 'test', augmentation = False) + else: + xs, ys = bg2.get_batch(TEST_SIZE, batch_type = 'test', augmentation = False) + loss = model.get_loss(sess, xs, ys) + pred = model.predict(sess, xs)[0] + report_accuracy(pred, ys, pyParser) + ave_loss+=loss + print("Test Loss = "+str(ave_loss/float(TEST_ROUND))) diff --git a/train902.py b/train902.py new file mode 100644 index 0000000..68dfb37 --- /dev/null +++ b/train902.py @@ -0,0 +1,74 @@ +import os +os.environ["CUDA_VISIBLE_DEVICES"] = input("Which GPU? ") +import time +import warnings +warnings.filterwarnings("ignore", message="numpy.dtype size changed") +warnings.filterwarnings("ignore", message="numpy.ufunc size changed") +with warnings.catch_warnings(): + warnings.simplefilter("ignore") + import tensorflow as tf +import numpy as np + +from lib.tools_batch import * +from lib.tools_math import * +from model902 import * + +def get_learningRate(step): + # return max(4e-4*(0.99999**step), 2e-5) + return 2e-4 + +TEST_ROUND = 1 +BATCH_SIZE = 64 +TEST_SIZE = 16 +AUGMENTATION = True +NUM_STEP = int(1e7) + +model_name = "v902" +saving_period = 200 +num_labels = 407 +num_class = 409 +bg = BatchGetter("../data/data_aishell/wav", "../data/data_aishell/transcript/aishell_transcript_v0.8.txt", + "lib/pinyinDictNoTone.pickle", "../data/backgrounds/", server = True) +bg2 = BatchGetter("../data/youtube_subtitles/wav", "../data/youtube_subtitles/subs.txt", + "lib/pinyinDictNoTone.pickle", "../data/backgrounds/", server = True) +pyParser = pinyinParser("lib/pinyinDictNoTone.pickle") +model = model(num_class) +if model_name not in os.listdir('models/'): + os.mkdir('models/'+model_name) + +gpu_options = tf.GPUOptions(allow_growth=True) +with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,allow_soft_placement=True,log_device_placement=False)) as sess: + + sess.run(tf.global_variables_initializer()) + saver = tf.train.Saver() + # tensorboard --logdir logs/ + summary_writer = tf.summary.FileWriter(logdir = "logs", graph = tf.get_default_graph()) + saver.restore(sess, "models/"+model_name+"/"+model_name+"_0.ckpt") + + for i in range(1, NUM_STEP+1): + + lr = get_learningRate(i) + if i%2==0: + xs, ys = bg.get_batch(BATCH_SIZE, batch_type = 'train') + else: + xs, ys = bg2.get_batch(BATCH_SIZE, batch_type = 'train') + loss, summary = model.train(sess, lr, xs, ys) + summary_writer.add_summary(summary, i) + print(i, loss) + + if i%saving_period == 0: + print("Learning rate =", lr) + save_path = saver.save(sess, "models/"+model_name+"/"+model_name+"_"+str(int(i/50000))+".ckpt") + print("Model saved in path: "+save_path) + + ave_loss = 0.0 + for i in range(2): + if i==0: + xs, ys = bg.get_batch(TEST_SIZE, batch_type = 'test', augmentation = False) + else: + xs, ys = bg2.get_batch(TEST_SIZE, batch_type = 'test', augmentation = False) + loss = model.get_loss(sess, xs, ys) + pred = model.predict(sess, xs)[0] + report_accuracy(pred, ys, pyParser) + ave_loss+=loss + print("Test Loss = "+str(ave_loss/float(TEST_ROUND))) diff --git a/train903.py b/train903.py new file mode 100644 index 0000000..cf46f1a --- /dev/null +++ b/train903.py @@ -0,0 +1,74 @@ +import os +os.environ["CUDA_VISIBLE_DEVICES"] = input("Which GPU? ") +import time +import warnings +warnings.filterwarnings("ignore", message="numpy.dtype size changed") +warnings.filterwarnings("ignore", message="numpy.ufunc size changed") +with warnings.catch_warnings(): + warnings.simplefilter("ignore") + import tensorflow as tf +import numpy as np + +from lib.tools_batch import * +from lib.tools_math import * +from model903 import * + +def get_learningRate(step): + # return max(4e-4*(0.99999**step), 2e-5) + return 2e-4 + +TEST_ROUND = 1 +BATCH_SIZE = 32 +TEST_SIZE = 16 +AUGMENTATION = True +NUM_STEP = int(1e7) + +model_name = "v903" +saving_period = 200 +num_labels = 407 +num_class = 409 +bg = BatchGetter("../data/data_aishell/wav", "../data/data_aishell/transcript/aishell_transcript_v0.8.txt", + "lib/pinyinDictNoTone.pickle", "../data/backgrounds/", server = True) +bg2 = BatchGetter("../data/youtube_subtitles/wav", "../data/youtube_subtitles/subs.txt", + "lib/pinyinDictNoTone.pickle", "../data/backgrounds/", server = True) +pyParser = pinyinParser("lib/pinyinDictNoTone.pickle") +model = model(num_class) +if model_name not in os.listdir('models/'): + os.mkdir('models/'+model_name) + +gpu_options = tf.GPUOptions(allow_growth=True) +with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,allow_soft_placement=True,log_device_placement=False)) as sess: + + sess.run(tf.global_variables_initializer()) + saver = tf.train.Saver() + # tensorboard --logdir logs/ + summary_writer = tf.summary.FileWriter(logdir = "logs", graph = tf.get_default_graph()) + saver.restore(sess, "models/"+model_name+"/"+model_name+"_0.ckpt") + + for i in range(1, NUM_STEP+1): + + lr = get_learningRate(i) + if i%2==0: + xs, ys = bg.get_batch(BATCH_SIZE, batch_type = 'train') + else: + xs, ys = bg2.get_batch(BATCH_SIZE, batch_type = 'train') + loss, summary = model.train(sess, lr, xs, ys) + summary_writer.add_summary(summary, i) + print(i, loss) + + if i%saving_period == 0: + print("Learning rate =", lr) + save_path = saver.save(sess, "models/"+model_name+"/"+model_name+"_"+str(int(i/50000))+".ckpt") + print("Model saved in path: "+save_path) + + ave_loss = 0.0 + for i in range(2): + if i==0: + xs, ys = bg.get_batch(TEST_SIZE, batch_type = 'test', augmentation = False) + else: + xs, ys = bg2.get_batch(TEST_SIZE, batch_type = 'test', augmentation = False) + loss = model.get_loss(sess, xs, ys) + pred = model.predict(sess, xs)[0] + report_accuracy(pred, ys, pyParser) + ave_loss+=loss + print("Test Loss = "+str(ave_loss/float(TEST_ROUND)))