百度语音识别+百度ERNIE-Gram+pyaudio麦克风录音(NLP自然语言处理)

1.pyaudio安装及通过pyaudio实现录音

pip install pyaudio

在conda环境中安装没有报错

实现录音的代码如下:

# -*- coding: utf-8 -*-
from pyaudio import PyAudio, paInt16
import numpy as np
from datetime import datetime
import wave
class recoder:
  NUM_SAMPLES = 2000   # pyaudio内置缓冲大小
  SAMPLING_RATE = 16000  # 取样频率
  LEVEL = 500     # 声音保存的阈值
  COUNT_NUM = 20   # NUM_SAMPLES个取样之内出现COUNT_NUM个大于LEVEL的取样则记录声音
  SAVE_LENGTH = 8     # 声音记录的最小长度:SAVE_LENGTH * NUM_SAMPLES 个取样
  TIME_COUNT = 60   # 录音时间,单位s
  Voice_String = []
  def savewav(self,filename):
    wf = wave.open(filename, 'wb')
    wf.setnchannels(1)
    wf.setsampwidth(2)
    wf.setframerate(self.SAMPLING_RATE)
    wf.writeframes(np.array(self.Voice_String).tostring())
    # wf.writeframes(self.Voice_String.decode())
    wf.close()
  def recoder(self):
    pa = PyAudio()
    stream = pa.open(format=paInt16, channels=1, rate=self.SAMPLING_RATE, input=True,
      frames_per_buffer = self.NUM_SAMPLES)
    save_count = 0
    save_buffer = []
    time_count = self.TIME_COUNT
    while True:
      time_count -= 1
      # print time_count
      # 读入NUM_SAMPLES个取样
      string_audio_data = stream.read(self.NUM_SAMPLES)
      # 将读入的数据转换为数组
      audio_data = np.fromstring(string_audio_data, dtype=np.short)
      # 计算大于LEVEL的取样的个数
      large_sample_count = np.sum( audio_data > self.LEVEL )
      print(np.max(audio_data))
        # 如果个数大于COUNT_NUM,则至少保存SAVE_LENGTH个块
      if large_sample_count > self.COUNT_NUM:
        save_count = self.SAVE_LENGTH
      else:
        save_count -= 1
      if save_count < 0:
        save_count = 0
      if save_count > 0:
        #  将要保存的数据存放到save_buffer中
        # print save_count > 0 and time_count >0
        save_buffer.append(string_audio_data)
      else:
        # print save_buffer
        # 将save_buffer中的数据写入WAV文件,WAV文件的文件名是保存的时刻
        # print "debug"
        if len(save_buffer) > 0:
          self.Voice_String = save_buffer
          save_buffer = []
          print("Recode a piece of voice successfully!")
          return True
      if time_count==0:
        if len(save_buffer)>0:
          self.Voice_String = save_buffer
          save_buffer = []
          print("Recode a piece of voice successfully!")
          return True
        else:
          return False
if __name__ == "__main__":
  r = recoder()
  r.recoder()
  r.savewav("test.wav") # 储存录音文件

 

运行结果如下:

 

出现 successfully 即表示录音成功 。

2.百度语音识别

百度搜索:百度AI开放平台

百度语音识别+百度ERNIE-Gram+pyaudio麦克风录音(NLP自然语言处理)_第1张图片

 百度语音识别+百度ERNIE-Gram+pyaudio麦克风录音(NLP自然语言处理)_第2张图片

 点击 开放能力  短语音识别 立即使用  登陆后界面如下

百度语音识别+百度ERNIE-Gram+pyaudio麦克风录音(NLP自然语言处理)_第3张图片

 点击右下方的免费领取资源,把需要使用的都领取了,然后点击创建应用

创建界面如下

百度语音识别+百度ERNIE-Gram+pyaudio麦克风录音(NLP自然语言处理)_第4张图片

 随后下载SDK

百度语音识别+百度ERNIE-Gram+pyaudio麦克风录音(NLP自然语言处理)_第5张图片

 我是linux的系统,就下载如上图的sdk

在应用列表里面会看到api_key和secret_key,后面会用到

 

打开下载的speech-demo文件夹,打开/rest-api-asr/python/asr_json.py

百度语音识别+百度ERNIE-Gram+pyaudio麦克风录音(NLP自然语言处理)_第6张图片

将api和secret换成刚刚申请的

将需要识别的 文件这个地方的语音文件换成步骤1生成的test.wav的路径

 整段代码如下:

# coding=utf-8

import sys
import json
import base64
import time
import re
import csv

IS_PY3 = sys.version_info.major == 3

if IS_PY3:
    from urllib.request import urlopen
    from urllib.request import Request
    from urllib.error import URLError
    from urllib.parse import urlencode
    timer = time.perf_counter
else:
    from urllib2 import urlopen
    from urllib2 import Request
    from urllib2 import URLError
    from urllib import urlencode
    if sys.platform == "win32":
        timer = time.clock
    else:
        # On most other platforms the best timer is time.time()
        timer = time.time

API_KEY = 'vj0CwKGVpvcXLqwdwaNbD2Mw'
SECRET_KEY = 'VAagIiVdcujgGI9ZttZmTyXUO2Xfopq7'

# 需要识别的文件
AUDIO_FILE = '/home/xwt/桌面/sentences transformer/test.wav'  # 只支持 pcm/wav/amr 格式,极速版额外支持m4a 格式
# 文件格式
FORMAT = AUDIO_FILE[-3:]  # 文件后缀只支持 pcm/wav/amr 格式,极速版额外支持m4a 格式

CUID = '123456PYTHON'
# 采样率
RATE = 16000  # 固定值

# 普通版

DEV_PID = 1537  # 1537 表示识别普通话,使用输入法模型。根据文档填写PID,选择语言及识别模型
ASR_URL = 'http://vop.baidu.com/server_api'
SCOPE = 'audio_voice_assistant_get'  # 有此scope表示有asr能力,没有请在网页里勾选,非常旧的应用可能没有

#测试自训练平台需要打开以下信息, 自训练平台模型上线后,您会看见 第二步:“”获取专属模型参数pid:8001,modelid:1234”,按照这个信息获取 dev_pid=8001,lm_id=1234
# DEV_PID = 8001 ;   
# LM_ID = 1234 ;

# 极速版 打开注释的话请填写自己申请的appkey appSecret ,并在网页中开通极速版(开通后可能会收费)

# DEV_PID = 80001
# ASR_URL = 'http://vop.baidu.com/pro_api'
# SCOPE = 'brain_enhanced_asr'  # 有此scope表示有极速版能力,没有请在网页里开通极速版

# 忽略scope检查,非常旧的应用可能没有
# SCOPE = False

class DemoError(Exception):
    pass


"""  TOKEN start """

TOKEN_URL = 'http://aip.baidubce.com/oauth/2.0/token'


def fetch_token():
    params = {'grant_type': 'client_credentials',
              'client_id': API_KEY,
              'client_secret': SECRET_KEY}
    post_data = urlencode(params)
    if (IS_PY3):
        post_data = post_data.encode( 'utf-8')
    req = Request(TOKEN_URL, post_data)
    try:
        f = urlopen(req)
        result_str = f.read()
    except URLError as err:
        print('token http response http code : ' + str(err.code))
        result_str = err.read()
    if (IS_PY3):
        result_str =  result_str.decode()

    print(result_str)
    result = json.loads(result_str)
    print(result)
    if ('access_token' in result.keys() and 'scope' in result.keys()):
        print(SCOPE)
        if SCOPE and (not SCOPE in result['scope'].split(' ')):  # SCOPE = False 忽略检查
            raise DemoError('scope is not correct')
        print('SUCCESS WITH TOKEN: %s  EXPIRES IN SECONDS: %s' % (result['access_token'], result['expires_in']))
        return result['access_token']
    else:
        raise DemoError('MAYBE API_KEY or SECRET_KEY not correct: access_token or scope not found in token response')

"""  TOKEN end """

if __name__ == '__main__':
    token = fetch_token()

    speech_data = []
    with open(AUDIO_FILE, 'rb') as speech_file:
        speech_data = speech_file.read()

    length = len(speech_data)
    if length == 0:
        raise DemoError('file %s length read 0 bytes' % AUDIO_FILE)
    speech = base64.b64encode(speech_data)
    if (IS_PY3):
        speech = str(speech, 'utf-8')
    params = {'dev_pid': DEV_PID,
             #"lm_id" : LM_ID,    #测试自训练平台开启此项
              'format': FORMAT,
              'rate': RATE,
              'token': token,
              'cuid': CUID,
              'channel': 1,
              'speech': speech,
              'len': length
              }
    post_data = json.dumps(params, sort_keys=False)
    # print post_data
    req = Request(ASR_URL, post_data.encode('utf-8'))
    req.add_header('Content-Type', 'application/json')
    try:
        begin = timer()
        f = urlopen(req)
        result_str = f.read()
    except URLError as err:
        result_str = err.read()

    if (IS_PY3):
        result_str = str(result_str, 'utf-8')
    print(result_str)
    with open("result.txt", "w") as f:
        f.write(result_str)
with open("result.txt", "r") as of:
        content = of.read()
        re_str = r'[\u4e00-\u9fa5]+'
        resp = re.findall(re_str, content)
        result = ' '.join(resp)
        print(result)

代码最后一部分我进行了修改,能够输入识别的中文,源代码还会输出其他的一些符号,有需要的可以参考改一下。建议对着源代码参考一下。

百度语音识别+百度ERNIE-Gram+pyaudio麦克风录音(NLP自然语言处理)_第7张图片

 为了后续进行短句相似度匹配 在这个最后面又加了一段代码,意思是将识别结果保存到tsv文件中,方便后续操作。

with open("test.tsv", "w", newline='', encoding='utf-8') as d:
    tsv_w = csv.writer(d, delimiter='\t')
    tsv_w.writerow([result, '把桌子擦干净'])  # 单行写入

3.基于ERNIE-Gam进行语句相似度匹配

首先下载千言数据集lcqmc,下载连接如下

https://aistudio.baidu.com/aistudio/datasetdetail/78992

接下来需要安装环境cuda和paddle,这个不细讲(最好在conda中创建一个环境),网上很多教程,注意!!!cuda和paddle的版本一定要对应!!!不然会出现很多意想不到的错误!!!

进入pycharm 选择创建的环境

pip install --upgrade paddlenlp -i https://pypi.org/simple

创建data_pre.py文件

import time
import os
import numpy as np
import paddle
import paddle.nn.functional as F
from paddlenlp.datasets import load_dataset
from paddlenlp.datasets import MapDataset
import paddlenlp
import pandas as pd
from paddle.io import Dataset
from sklearn.model_selection import train_test_split

class MyDataset(Dataset):
    """
    构造自己的数据类,配合后面的Dataloader使用
    """
    def __init__(self, data, mode='train'):
        super(MyDataset, self).__init__()
        self.mode = mode
        self.data = self._load_data(data)

    # 改造数据集 将dataframe格式数据转换为[{},{}]形式数据
    def _load_data(self, data):
        data_set = []
        for index, row in data.iterrows():
            data_dict = {}
            data_dict['query'] = row['query']
            data_dict['title'] = row['title']
            if self.mode == 'train':
                data_dict['label'] = row['label']
            data_set.append(data_dict)
        return data_set

    def __getitem__(self, idx):
        return self.data[idx]

    def __len__(self):
        return len(self.data)

train = pd.read_csv('/home/xwt/桌面/sentences transformer/own/train.tsv',sep='\t', names=['query', 'title','label'])
x_train,x_dev = train_test_split(train,test_size=0.1, stratify=train['label'].iloc[:])


x_train.shape

train_ds = MapDataset(MyDataset(x_train))
dev_ds = MapDataset(MyDataset(x_dev))

#输出训练集的前 3 条样本
#for idx, example in enumerate(train_ds):
 #   if idx <= 3:
  #      print(example)

此处需要将下载的lcqmc文件下的train.tsv文件的路径修改到此处

 

创建data_predict.py文件

import data_pre
from data_pre import *
tokenizer = paddlenlp.transformers.ErnieGramTokenizer.from_pretrained('ernie-gram-zh')

# 将 1 条明文数据的 query、title 拼接起来,根据预训练模型的 tokenizer 将明文转换为 ID 数据
# 返回 input_ids 和 token_type_ids

def convert_example(example, tokenizer, max_seq_length=512, is_test=False):

    query, title = example["query"], example["title"]

    encoded_inputs = tokenizer(
        text=query, text_pair=title, max_seq_len=max_seq_length)

    input_ids = encoded_inputs["input_ids"]
    token_type_ids = encoded_inputs["token_type_ids"]

    if not is_test:
        label = np.array([example["label"]], dtype="int64")
        return input_ids, token_type_ids, label
    # 在预测或者评估阶段,不返回 label 字段
    else:
        return input_ids, token_type_ids

# 为了后续方便使用,我们使用python偏函数(partial)给 convert_example 赋予一些默认参数
from functools import partial

# 训练集和验证集的样本转换函数
trans_func = partial(
    convert_example,
    tokenizer=tokenizer,
    max_seq_length=512)

from paddlenlp.data import Stack, Pad, Tuple
# 我们的训练数据会返回 input_ids, token_type_ids, labels 3 个字段
# 因此针对这 3 个字段需要分别定义 3 个组 batch 操作
batchify_fn = lambda samples, fn=Tuple(
    Pad(axis=0, pad_val=tokenizer.pad_token_id),  # input_ids
    Pad(axis=0, pad_val=tokenizer.pad_token_type_id),  # token_type_ids
    Stack(dtype="int64")  # label
): [data for data in fn(samples)]

# 定义分布式 Sampler: 自动对训练数据进行切分,支持多卡并行训练
batch_sampler = paddle.io.DistributedBatchSampler(train_ds, batch_size=32, shuffle=True)

# 基于 train_ds 定义 train_data_loader
# 因为我们使用了分布式的 DistributedBatchSampler, train_data_loader 会自动对训练数据进行切分
train_data_loader = paddle.io.DataLoader(
        dataset=train_ds.map(trans_func),
        batch_sampler=batch_sampler,
        collate_fn=batchify_fn,
        return_list=True)

# 针对验证集数据加载,我们使用单卡进行评估,所以采用 paddle.io.BatchSampler 即可
# 定义 dev_data_loader
batch_sampler = paddle.io.BatchSampler(dev_ds, batch_size=32, shuffle=False)
dev_data_loader = paddle.io.DataLoader(
        dataset=dev_ds.map(trans_func),
        batch_sampler=batch_sampler,
        collate_fn=batchify_fn,
        return_list=True)

创建modle_build.py文件

import data_pre
import paddle.nn as nn
from data_pre import *
# 我们基于 ERNIE-Gram 模型结构搭建 Point-wise 语义匹配网络
# 所以此处先定义 ERNIE-Gram 的 pretrained_model
pretrained_model = paddlenlp.transformers.ErnieGramModel.from_pretrained('ernie-gram-zh')


# pretrained_model = paddlenlp.transformers.ErnieModel.from_pretrained('ernie-1.0')


class PointwiseMatching(nn.Layer):

    # 此处的 pretained_model 在本例中会被 ERNIE-Gram 预训练模型初始化
    def __init__(self, pretrained_model, dropout=None):
        super().__init__()
        self.ptm = pretrained_model
        self.dropout = nn.Dropout(dropout if dropout is not None else 0.1)

        # 语义匹配任务: 相似、不相似 2 分类任务
        self.classifier = nn.Linear(self.ptm.config["hidden_size"], 2)

    def forward(self,
                input_ids,
                token_type_ids=None,
                position_ids=None,
                attention_mask=None):
        # 此处的 Input_ids 由两条文本的 token ids 拼接而成
        # token_type_ids 表示两段文本的类型编码
        # 返回的 cls_embedding 就表示这两段文本经过模型的计算之后而得到的语义表示向量
        _, cls_embedding = self.ptm(input_ids, token_type_ids, position_ids,
                                    attention_mask)

        cls_embedding = self.dropout(cls_embedding)

        # 基于文本对的语义表示向量进行 2 分类任务
        logits = self.classifier(cls_embedding)
        probs = F.softmax(logits)

        return probs


# 定义 Point-wise 语义匹配网络
model = PointwiseMatching(pretrained_model)

创建modle_train.py文件

import data_pre
import data_predict
import modle_build
from paddlenlp.transformers import LinearDecayWithWarmup
from data_pre import *
from data_predict import *
from modle_build import *
epochs = 3
num_training_steps = len(train_data_loader) * epochs

# 定义 learning_rate_scheduler,负责在训练过程中对 lr 进行调度
lr_scheduler = LinearDecayWithWarmup(5E-5, num_training_steps, 0.0)

# Generate parameter names needed to perform weight decay.
# All bias and LayerNorm parameters are excluded.
decay_params = [
    p.name for n, p in model.named_parameters()
    if not any(nd in n for nd in ["bias", "norm"])
]

# 定义 Optimizer
optimizer = paddle.optimizer.AdamW(
    learning_rate=lr_scheduler,
    parameters=model.parameters(),
    weight_decay=0.0,
    apply_decay_param_fun=lambda x: x in decay_params)

# 采用交叉熵 损失函数
criterion = paddle.nn.loss.CrossEntropyLoss()

# 评估的时候采用准确率指标
metric = paddle.metric.Accuracy()

# 因为训练过程中同时要在验证集进行模型评估,因此我们先定义评估函数

@paddle.no_grad()
def evaluate(model, criterion, metric, data_loader, phase="dev"):
    model.eval()
    metric.reset()
    losses = []
    for batch in data_loader:
        input_ids, token_type_ids, labels = batch
        probs = model(input_ids=input_ids, token_type_ids=token_type_ids)
        loss = criterion(probs, labels)
        losses.append(loss.numpy())
        correct = metric.compute(probs, labels)
        metric.update(correct)
        accu = metric.accumulate()
    print("eval {} loss: {:.5}, accu: {:.5}".format(phase,
                                                    np.mean(losses), accu))
    model.train()
    metric.reset()


# 接下来,开始正式训练模型,训练时间较长,可注释掉这部分

global_step = 0
tic_train = time.time()

for epoch in range(1, epochs + 1):
    for step, batch in enumerate(train_data_loader, start=1):

        input_ids, token_type_ids, labels = batch
        probs = model(input_ids=input_ids, token_type_ids=token_type_ids)
        loss = criterion(probs, labels)
        correct = metric.compute(probs, labels)
        metric.update(correct)
        acc = metric.accumulate()

        global_step += 1

        # 每间隔 10 step 输出训练指标
        if global_step % 10 == 0:
            print(
                "global step %d, epoch: %d, batch: %d, loss: %.5f, accu: %.5f, speed: %.2f step/s"
                % (global_step, epoch, step, loss, acc,
                   10 / (time.time() - tic_train)))
            tic_train = time.time()
        loss.backward()
        optimizer.step()
        lr_scheduler.step()
        optimizer.clear_grad()

        # 每间隔 100 step 在验证集和测试集上进行评估
        if global_step % 100 == 0:
            evaluate(model, criterion, metric, dev_data_loader, "dev")

# 训练结束后,存储模型参数
save_dir = os.path.join("checkpoint", "model_%d" % global_step)
os.makedirs(save_dir)

save_param_path = os.path.join(save_dir, 'model_state.pdparams')
paddle.save(model.state_dict(), save_param_path)
tokenizer.save_pretrained(save_dir)

此处就开始训练数据集,训练时间比较长,可以根据实际情况将数据集的内容进行删减,选练结束后会生成一个文件夹

百度语音识别+百度ERNIE-Gram+pyaudio麦克风录音(NLP自然语言处理)_第8张图片

 每次训练生成的model_162似乎不是固定的,不用特别在意。

创建modle_predict.py文件

import data_pre
import data_predict
import modle_build
# import sys
# sys.path.append('/home/xwt/桌面/sentences transformer/speech-demo/rest-api-asr/python/asr_json.py')
# import asr_json
from data_pre import *
from data_predict import *
from functools import partial
from modle_build import *

# 定义预测函数
def predict(model, data_loader):
    batch_probs = []

    # 预测阶段打开 eval 模式,模型中的 dropout 等操作会关掉
    model.eval()

    with paddle.no_grad():
        for batch_data in data_loader:
            input_ids, token_type_ids = batch_data
            input_ids = paddle.to_tensor(input_ids)
            token_type_ids = paddle.to_tensor(token_type_ids)

            # 获取每个样本的预测概率: [batch_size, 2] 的矩阵
            batch_prob = model(
                input_ids=input_ids, token_type_ids=token_type_ids).numpy()

            batch_probs.append(batch_prob)
        batch_probs = np.concatenate(batch_probs, axis=0)

        return batch_probs

# 定义预测数据的 data_loader
# 预测数据的转换函数
# predict 数据没有 label, 因此 convert_exmaple 的 is_test 参数设为 True
trans_func = partial(
    convert_example,
    tokenizer=tokenizer,
    max_seq_length=512,
    is_test=True)

# 预测数据的组 batch 操作
# predict 数据只返回 input_ids 和 token_type_ids,因此只需要 2 个 Pad 对象作为 batchify_fn
batchify_fn = lambda samples, fn=Tuple(
    Pad(axis=0, pad_val=tokenizer.pad_token_id),  # input_ids
    Pad(axis=0, pad_val=tokenizer.pad_token_type_id),  # segment_ids
): [data for data in fn(samples)]

# 加载预测数据
test = pd.read_csv('/home/xwt/桌面/sentences transformer/speech-demo/rest-api-asr/python/test.tsv',sep='\t', names=['query', 'title'])
test_ds = MapDataset(MyDataset(test,mode='test'))

batch_sampler = paddle.io.BatchSampler(test_ds, batch_size=32, shuffle=False)

# 生成预测数据 data_loader
predict_data_loader = paddle.io.DataLoader(
        dataset=test_ds.map(trans_func),
        batch_sampler=batch_sampler,
        collate_fn=batchify_fn,
        return_list=True)

pretrained_model = paddlenlp.transformers.ErnieGramModel.from_pretrained('ernie-gram-zh')

model = PointwiseMatching(pretrained_model)

state_dict = paddle.load("/home/xwt/桌面/sentences transformer/checkpoint/model_162/model_state.pdparams")
model.set_dict(state_dict)

# 执行预测函数
y_probs = predict(model, predict_data_loader)

# 根据预测概率获取预测 label
y_preds = np.argmax(y_probs, axis=1)

# 将预测结果存储在lcqmc.tsv 中,用来后续提交
# 同时将预测结果输出到终端,便于大家直观感受模型预测效果

test = pd.read_csv('/home/xwt/桌面/sentences transformer/speech-demo/rest-api-asr/python/test.tsv', sep='\t', names=['query', 'title'])
test_ds = MapDataset(MyDataset(test,mode='test'))



with open("lcqmc.tsv", 'w', encoding="utf-8") as f:
    f.write("index\tprediction\n")
    for idx, y_pred in enumerate(y_preds):
        f.write("{}\t{}\n".format(idx, y_pred))
        text_pair = test_ds[idx]
        text_pair["label"] = y_pred
        print(text_pair)
        if y_pred == 1:
            print("擦桌子")
        else:
            print("请给出正确指令")

这里有两个需要修改的地方

第一个

 将预测数据更换为步骤2生成的test.tsv文件的路径

第二个

将paddle.load()里面的路径修改刚刚上一个py文件运行后生成的文件的路径

从上到下 依次运行每一个py文件

最后的输出结果如下

最后根据自己的需要进行修改即可!

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