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中文语音识别
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轉載：http://f.dataguru.cn/thread-857009-1-1.html
??????????https://blog.csdn.net/u014365862/article/details/53869701
使用的數據集
THCHS30是Dong Wang, Xuewei Zhang, Zhiyong Zhang這幾位大神發布的開放語音數據集，可用于開發中文語音識別系統。
為了感謝這幾位大神，我是跪在電腦前寫的本帖代碼。
下載中文語音數據集（5G+）：
[python]?view plain?copy
- $ wget?http://data.cslt.org/thchs30/zip/wav.tgz??
- $ wget?http://data.cslt.org/thchs30/zip/doc.tgz??
- $ wget?http://data.cslt.org/thchs30/zip/lm.tgz??
- # 解壓??
- $ tar xvf wav.tgz??
- $ tar xvf doc.tgz??
- $ tar xvf lm.tgz??
????在開始之前，先好好檢視一下數據集。
[python]? view plain copy
??import?tensorflow?as?tf????import?numpy?as?np????import?os????from?collections?import?Counter????import?librosa????????from?joblib?import?Parallel,?delayed????????wav_path?=?'data/wav/train'????label_file?=?'data/doc/trans/train.word.txt'??????def?get_wav_files(wav_path?=?wav_path):????????wav_files?=?[]????????for?(dirpath,?dirnames,?filenames)?in?os.walk(wav_path):????????????for?filename?in?filenames:????????????????if?filename.endswith(".wav")?or?filename.endswith(".WAV"):????????????????????filename_path?=?os.sep.join([dirpath,?filename])????????????????????if?os.stat(filename_path).st_size?<?240000:????????????????????????continue????????????????????wav_files.append(filename_path)????????????return?wav_files????????wav_files?=?get_wav_files()????????def?get_wav_label(wav_files?=?wav_files,?label_file?=?label_file):????????labels_dict?=?{}????????with?open(label_file,?"r",?encoding='utf-8')?as?f:????????????for?label?in?f:????????????????label?=?label.strip("\n")????????????????label_id,?label_text?=?label.split('?',?1)????????????????labels_dict[label_id]?=?label_text????????????labels?=?[]????????new_wav_files?=?[]????????for?wav_file?in?wav_files:????????????wav_id?=?os.path.basename(wav_file).split(".")[0]????????????if?wav_id?in?labels_dict:????????????????labels.append(labels_dict[wav_id])????????????????new_wav_files.append(wav_file)????????????return?new_wav_files,?labels????????def?get_wav_length(wav):????????import?numpy?as?np????????import?librosa????????????print(wav)????????????wav,?sr?=?librosa.load(wav)????????mfcc?=?np.transpose(librosa.feature.mfcc(wav,?sr),?[1,?0])????????return?len(mfcc)????????pointer?=?0????def?get_next_batches(batch_size,?wav_max_len):????????global?pointer????????batches_wavs?=?[]????????batches_labels?=?[]????????for?i?in?range(batch_size):????????????wav,?sr?=?librosa.load(wav_files[pointer])????????????mfcc?=?np.transpose(librosa.feature.mfcc(wav,?sr),?[1,0])????????????batches_wavs.append(mfcc.tolist())????????????batches_labels.append(labels_vector[pointer])????????????pointer?+=?1??????????????????????????????for?mfcc?in?batches_wavs:????????????while?len(mfcc)?<?wav_max_len:????????????????mfcc.append([0]*20)????????for?label?in?batches_labels:????????????while?len(label)?<?label_max_len:????????????????label.append(0)????????????return?batches_wavs,?batches_labels????????conv1d_index?=?0????def?conv1d_layer(input_tensor,?size,?dim,?activation,?scale,?bias):????????global?conv1d_index????????with?tf.variable_scope("conv1d_"?+?str(conv1d_index)):????????????W?=?tf.get_variable('W',?(size,?input_tensor.get_shape().as_list()[-1],?dim),?dtype=tf.float32,?initializer=tf.random_uniform_initializer(minval=-scale,?maxval=scale))????????????if?bias:????????????????b?=?tf.get_variable('b',?[dim],?dtype?=?tf.float32,?initializer=tf.constant_initializer(0))????????????out?=?tf.nn.conv1d(input_tensor,?W,?stride=1,?padding='SAME')?+?(b?if?bias?else?0)????????????????if?not?bias:????????????????beta?=?tf.get_variable('beta',?dim,?dtype=tf.float32,?initializer=tf.constant_initializer(0))????????????????gamma?=?tf.get_variable('gamma',?dim,?dtype=tf.float32,?initializer=tf.constant_initializer(1))????????????????mean_running?=?tf.get_variable('mean',?dim,?dtype=tf.float32,?initializer=tf.constant_initializer(0))????????????????variance_running?=?tf.get_variable('variance',?dim,?dtype=tf.float32,?initializer=tf.constant_initializer(1))????????????????mean,?variance?=?tf.nn.moments(out,?axes=list(range(len(out.get_shape())?-?1)))????????????????????def?update_running_stat():????????????????????decay?=?0.99??????????????????????????????????????????update_op?=?[mean_running.assign(mean_running?*?decay?+?mean?*?(1?-?decay)),?variance_running.assign(variance_running?*?decay?+?variance?*?(1?-?decay))]??????????????????????????????????????????with?tf.control_dependencies(update_op):????????????????????????return?tf.identity(mean),?tf.identity(variance)??????????????????????????????????m,?v?=?tf.cond(tf.Variable(False,?trainable=False),?update_running_stat,lambda:?(mean_running,?variance_running))????????????????out?=?tf.nn.batch_normalization(out,?m,?v,?beta,?gamma,?1e-8)????????????????if?activation?==?'tanh':????????????????out?=?tf.nn.tanh(out)????????????elif?activation?==?'sigmoid':????????????????out?=?tf.nn.sigmoid(out)????????????????conv1d_index?+=?1????????????return?out????????????aconv1d_index?=?0????def?aconv1d_layer(input_tensor,?size,?rate,?activation,?scale,?bias):????????global?aconv1d_index????????with?tf.variable_scope('aconv1d_'?+?str(aconv1d_index)):????????????shape?=?input_tensor.get_shape().as_list()????????????????????????????????????W?=?tf.get_variable('W',?(1,?size,?shape[-1],?shape[-1]),?dtype=tf.float32,?initializer=tf.random_uniform_initializer(minval=-scale,?maxval=scale))????????????if?bias:????????????????b?=?tf.get_variable('b',?[shape[-1]],?dtype=tf.float32,?initializer=tf.constant_initializer(0))????????????out?=?tf.nn.atrous_conv2d(tf.expand_dims(input_tensor,?dim=1),?W,?rate?=?rate,?padding='SAME')????????????out?=?tf.squeeze(out,?[1])????????????????if?not?bias:????????????????beta?=?tf.get_variable('beta',?shape[-1],?dtype=tf.float32,?initializer=tf.constant_initializer(0))????????????????gamma?=?tf.get_variable('gamma',?shape[-1],?dtype=tf.float32,?initializer=tf.constant_initializer(1))????????????????mean_running?=?tf.get_variable('mean',?shape[-1],?dtype=tf.float32,?initializer=tf.constant_initializer(0))????????????????variance_running?=?tf.get_variable('variance',?shape[-1],?dtype=tf.float32,?initializer=tf.constant_initializer(1))????????????????mean,?variance?=?tf.nn.moments(out,?axes=list(range(len(out.get_shape())?-?1)))????????????????????def?update_running_stat():????????????????????decay?=?0.99????????????????????update_op?=?[mean_running.assign(mean_running?*?decay?+?mean?*?(1?-?decay)),?variance_running.assign(variance_running?*?decay?+?variance?*?(1?-?decay))]????????????????????with?tf.control_dependencies(update_op):????????????????????????return?tf.identity(mean),?tf.identity(variance)????????????????????m,?v?=?tf.cond(tf.Variable(False,?trainable=False),?update_running_stat,lambda:?(mean_running,?variance_running))????????????????out?=?tf.nn.batch_normalization(out,?m,?v,?beta,?gamma,?1e-8)????????????????if?activation?==?'tanh':????????????????out?=?tf.nn.tanh(out)????????????elif?activation?==?'sigmoid':????????????????out?=?tf.nn.sigmoid(out)????????????????aconv1d_index?+=?1????????????return?out????????def?speech_to_text_network(n_dim?=?128,?n_blocks?=?3):????????out?=?conv1d_layer(input_tensor=X,?size=1,?dim?=?n_dim,?activation='tanh',?scale=0.14,?bias=False)????????????def?residual_block(input_sensor,?size,?rate):????????????conv_filter?=?aconv1d_layer(input_tensor=input_sensor,?size=size,?rate=rate,?activation='tanh',?scale=0.03,?bias=False)????????????conv_gate?=?aconv1d_layer(input_tensor=input_sensor,?size=size,?rate=rate,?activation='sigmoid',?scale=0.03,?bias=False)????????????out?=?conv_filter?*?conv_gate????????????out?=?conv1d_layer(out,?size?=?1,?dim=n_dim,?activation='tanh',?scale=0.08,?bias=False)????????????return?out?+?input_sensor,?out????????????skip?=?0????????for?_?in?range(n_blocks):????????????for?r?in?[1,?2,?4,?8,?16]:????????????????out,?s?=?residual_block(out,?size?=?7,?rate?=?r)????????????????skip?+=?s????????????logit?=?conv1d_layer(skip,?size?=?1,?dim?=?skip.get_shape().as_list()[-1],?activation='tanh',?scale?=?0.08,?bias=False)??????????????????logit?=?conv1d_layer(logit,?size?=?1,?dim?=?words_size,?activation?=?None,?scale?=?0.04,?bias?=?True)????????????return?logit??????????class?MaxPropOptimizer(tf.train.Optimizer):????????def?__init__(self,?learning_rate=0.001,?beta2=0.999,?use_locking=False,?name="MaxProp"):????????????super(MaxPropOptimizer,?self).__init__(use_locking,?name)????????????self._lr?=?learning_rate????????????self._beta2?=?beta2????????????self._lr_t?=?None????????????self._beta2_t?=?None????????def?_prepare(self):????????????self._lr_t?=?tf.convert_to_tensor(self._lr,?name="learning_rate")????????????self._beta2_t?=?tf.convert_to_tensor(self._beta2,?name="beta2")????????def?_create_slots(self,?var_list):????????????for?v?in?var_list:????????????????self._zeros_slot(v,?"m",?self._name)????????def?_apply_dense(self,?grad,?var):????????????lr_t?=?tf.cast(self._lr_t,?var.dtype.base_dtype)????????????beta2_t?=?tf.cast(self._beta2_t,?var.dtype.base_dtype)????????????if?var.dtype.base_dtype?==?tf.float16:????????????????eps?=?1e-7????????????else:????????????????eps?=?1e-8????????????m?=?self.get_slot(var,?"m")????????????m_t?=?m.assign(tf.maximum(beta2_t?*?m?+?eps,?tf.abs(grad)))????????????g_t?=?grad?/?m_t????????????var_update?=?tf.assign_sub(var,?lr_t?*?g_t)????????????return?tf.group(*[var_update,?m_t])????????def?_apply_sparse(self,?grad,?var):????????????return?self._apply_dense(grad,?var)????????def?train_speech_to_text_network(wav_max_len):????????logit?=?speech_to_text_network()??????????????????indices?=?tf.where(tf.not_equal(tf.cast(Y,?tf.float32),?0.))????????target?=?tf.SparseTensor(indices=indices,?values=tf.gather_nd(Y,?indices)?-?1,?dense_shape=tf.cast(tf.shape(Y),?tf.int64))????????loss?=?tf.nn.ctc_loss(target,?logit,?sequence_len,?time_major=False)??????????????lr?=?tf.Variable(0.001,?dtype=tf.float32,?trainable=False)????????optimizer?=?MaxPropOptimizer(learning_rate=lr,?beta2=0.99)????????var_list?=?[t?for?t?in?tf.trainable_variables()]????????gradient?=?optimizer.compute_gradients(loss,?var_list=var_list)????????optimizer_op?=?optimizer.apply_gradients(gradient)????????????with?tf.Session()?as?sess:????????????sess.run(tf.global_variables_initializer())????????????????saver?=?tf.train.Saver(tf.global_variables())????????????????for?epoch?in?range(16):????????????????sess.run(tf.assign(lr,?0.001?*?(0.97?**?epoch)))????????????????????global?pointer????????????????pointer?=?0????????????????for?batch?in?range(n_batch):????????????????????batches_wavs,?batches_labels?=?get_next_batches(batch_size,?wav_max_len)????????????????????train_loss,?_?=?sess.run([loss,?optimizer_op],?feed_dict={X:?batches_wavs,?Y:?batches_labels})????????????????????print(epoch,?batch,?train_loss)????????????????if?epoch?%?1?==?0:?????????????????????saver.save(sess,?r'D:\\tensorflow\\Speech_Recognition\\speech.module',?global_step=epoch)????????????????????def?speech_to_text(wav_file):????????wav,?sr?=?librosa.load(wav_file,?mono=True)????????mfcc?=?np.transpose(np.expand_dims(librosa.feature.mfcc(wav,?sr),?axis=0),?[0,2,1])????????????logit?=?speech_to_text_network()????????????saver?=?tf.train.Saver()????????with?tf.Session()?as?sess:????????????saver.restore(sess,?tf.train.latest_checkpoint('.'))????????????decoded?=?tf.transpose(logit,?perm=[1,?0,?2])??????decoded,?_?=?tf.nn.ctc_beam_search_decoder(decoded,?sequence_len,?merge_repeated=False)??????decoded?=?sess.run(decoded,?feed_dict={X:?mfcc})????????????print?(decoded)??????predict?=?tf.sparse_to_dense(decoded[0].indices,decoded[0].dense_shape,decoded[0].values)?+?1????????????predict?=?sess.run(predict)??????print(predict)??????????if?__name__?==?"__main__":????????wav_files?=?get_wav_files()????????wav_files,?labels?=?get_wav_label()????????print(u"樣本數?：",?len(wav_files))????????????all_words?=?[]????????for?label?in?labels:??????????????????????all_words?+=?[word?for?word?in?label]????????????counter?=?Counter(all_words)????????count_pairs?=?sorted(counter.items(),?key=lambda?x:?-x[1])????????????words,?_?=?zip(*count_pairs)????????words_size?=?len(words)????????print(u"詞匯表大小：",?words_size)????????????word_num_map?=?dict(zip(words,?range(len(words))))??????????????????to_num?=?lambda?word:?word_num_map.get(word,?len(words))??????????????????labels_vector?=?[list(map(to_num,?label))?for?label?in?labels]????????????label_max_len?=?np.max([len(label)?for?label?in?labels_vector])????????print(u"最長句子的字數:"?+?str(label_max_len))????????????????????????parallel_read?=?False????????if?parallel_read:????????????wav_max_len?=?np.max(Parallel(n_jobs=7)(delayed(get_wav_length)(wav)?for?wav?in?wav_files))????????else:????????????wav_max_len?=?673????????print("最長的語音",?wav_max_len)????????????batch_size?=?8????????n_batch?=?len(wav_files)?//?batch_size????????????X?=?tf.placeholder(dtype=tf.float32,?shape=[batch_size,?None,?20])??????????????????sequence_len?=?tf.reduce_sum(tf.cast(tf.not_equal(tf.reduce_sum(X,?reduction_indices=2),?0.),?tf.int32),?reduction_indices=1)????????????Y?=?tf.placeholder(dtype=tf.int32,?shape=[batch_size,?None])????????????train_speech_to_text_network(wav_max_len)????
后續：從麥克風獲得語音輸入，使用上面的模型進行識別。
然后預測可以用下邊這個：
[python]? view plain copy
if?__name__?==?"__main__":????????????wav_files?=?get_wav_files()????????wav_files,?labels?=?get_wav_label()????????print(u"樣本數?：",?len(wav_files))????????????all_words?=?[]????????for?label?in?labels:??????????????????????all_words?+=?[word?for?word?in?label]????????????counter?=?Counter(all_words)????????count_pairs?=?sorted(counter.items(),?key=lambda?x:?-x[1])????????????words,?_?=?zip(*count_pairs)????????words_size?=?len(words)????????print(u"詞匯表大小：",?words_size)????????????word_num_map?=?dict(zip(words,?range(len(words))))??????????????????to_num?=?lambda?word:?word_num_map.get(word,?len(words))??????????????????labels_vector?=?[list(map(to_num,?label))?for?label?in?labels]????????????label_max_len?=?np.max([len(label)?for?label?in?labels_vector])????????print(u"最長句子的字數:"?+?str(label_max_len))????????????????????????parallel_read?=?False????????if?parallel_read:????????????wav_max_len?=?np.max(Parallel(n_jobs=7)(delayed(get_wav_length)(wav)?for?wav?in?wav_files))????????else:????????????wav_max_len?=?673????????print("最長的語音",?wav_max_len)????????????batch_size?=?1???????n_batch?=?len(wav_files)?//?batch_size??????????X?=?tf.placeholder(dtype=tf.float32,?shape=[batch_size,?None,?20])????????????????sequence_len?=?tf.reduce_sum(tf.cast(tf.not_equal(tf.reduce_sum(X,?reduction_indices=2),?0.),?tf.int32),?reduction_indices=1)????????????Y?=?tf.placeholder(dtype=tf.int32,?shape=[batch_size,?None])??????????????????????wav_file?=?"./D4_750.wav"??????print?(wav_file)??????speech_to_text(wav_file) ?
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