Python在数据分析领域应用广泛吗？

本文共计1968个文字，预计阅读时间需要8分钟。

目录

一、基于PaddleSpeech的婴儿啼哭识别

1.项目背景

2.数据说明

二、PaddleSpeech环境准备

三、数据处理

1.数据解压

2.查看音频文件

3.音频文件长度处理

4.自定义数据集

四、模型训练

目录

• 一、基于PaddleSpeech的婴儿啼哭识别
• 1.项目背景
• 2.数据说明：
• 二、PaddleSpeech环境准备
• 三、数据预处理
• 1.数据解压缩
• 2.查看声音文件
• 3.音频文件长度处理
• 3.自定义数据集
• 四、模型训练
• 1.选取预训练模型
• 2.构建分类模型
• 3.finetune
• 五、模型训练
• 六、注意事项

一、基于PaddleSpeech的婴儿啼哭识别

1.项目背景

对婴儿来说，啼哭声是一种通讯的方式，一个非常有限的，但类似成年人进行交流的方式。它也是一种生物报警器，向外界传达着婴儿生理和心理的需求。基于啼哭声声波携带的信息，婴儿的身体状况才能被确定，疾病才能被检测出来。因此，有效辨识啼哭声，成功地将婴儿啼哭声“翻译”成“成人语言”，让我们能够读懂啼哭声的含义，有重大的实际意义。

2.数据说明：

• 1.训练数据集包含六类哭声，已人工添加噪声。

A：awake（苏醒）

B：diaper（换尿布）

C：hug（要抱抱）

D：hungry（饥饿）

E：sleepy（困乏）

F：uncomfortable（不舒服）

• 2.噪声数据来源Noisex-92标准数据库。

二、PaddleSpeech环境准备

# 环境准备：安装paddlespeech和paddleaudio !python -m pip install -q -U pip --user !pip install paddlespeech paddleaudio -U -q

!pip list|grep paddle

import warnings warnings.filterwarnings("ignore") import IPython import numpy as np import matplotlib.pyplot as plt import paddle %matplotlib inline

三、数据预处理

1.数据解压缩

# !unzip -qoa data/data41960/dddd.zip

2.查看声音文件

from paddleaudio import load data, sr = load(file='train/awake/awake_0.wav', mono=True, dtype='float32') # 单通道，float32音频样本点 print('wav shape: {}'.format(data.shape)) print('sample rate: {}'.format(sr)) # 展示音频波形 plt.figure() plt.plot(data) plt.show()

from paddleaudio import load data, sr = load(file='train/diaper/diaper_0.wav', mono=True, dtype='float32') # 单通道，float32音频样本点 print('wav shape: {}'.format(data.shape)) print('sample rate: {}'.format(sr)) # 展示音频波形 plt.figure() plt.plot(data) plt.show()

!paddlespeech cls --input train/awake/awake_0.wav

!paddlespeech help

3.音频文件长度处理

# 查音频长度 import contextlib import wave def get_sound_len(file_path): with contextlib.closing(wave.open(file_path, 'r')) as f: frames = f.getnframes() rate = f.getframerate() wav_length = frames / float(rate) return wav_length

# 编译wav文件 import glob sound_files=glob.glob('train/*/*.wav') print(sound_files[0]) print(len(sound_files))

# 统计最长、最短音频 sounds_len=[] for sound in sound_files: sounds_len.append(get_sound_len(sound)) print("音频最大长度：",max(sounds_len),"秒") print("音频最小长度：",min(sounds_len),"秒")

!cp train/hungry/hungry_0.wav ~/

!pip install pydub -q

# 音频信息查看 import math import soundfile as sf import numpy as np import librosa data, samplerate = sf.read('hungry_0.wav') channels = len(data.shape) length_s = len(data)/float(samplerate) format_rate=16000 print(f"channels: {channels}") print(f"length_s: {length_s}") print(f"samplerate: {samplerate}")

# 统一到34s from pydub import AudioSegment audio = AudioSegment.from_wav('hungry_0.wav') print(str(audio.duration_seconds)) i = 1 padded = audio while padded.duration_seconds * 1000 < 34000: padded = audio * i i = i + 1 padded[0:34000].set_frame_rate(16000).export('padded-file.wav', format='wav')

import math import soundfile as sf import numpy as np import librosa data, samplerate = sf.read('padded-file.wav') channels = len(data.shape) length_s = len(data)/float(samplerate) format_rate=16000 print(f"channels: {channels}") print(f"length_s: {length_s}") print(f"samplerate: {samplerate}")

# 定义函数，如未达到最大长度，则重复填充，最终从超过34s的音频中截取 from pydub import AudioSegment def convert_sound_len(filename): audio = AudioSegment.from_wav(filename) i = 1 padded = audio*i while padded.duration_seconds * 1000 < 34000: i = i + 1 padded = audio * i padded[0:34000].set_frame_rate(16000).export(filename, format='wav')

# 统一所有音频到定长 for sound in sound_files: convert_sound_len(sound)

3.自定义数据集

import os from paddlespeech.audio.datasets.dataset import AudioClassificationDataset class CustomDataset(AudioClassificationDataset): # List all the class labels label_list = [ 'awake', 'diaper', 'hug', 'hungry', 'sleepy', 'uncomfortable' ] train_data_dir='./train/' def __init__(self, **kwargs): files, labels = self._get_data() super(CustomDataset, self).__init__( files=files, labels=labels, feat_type='raw', **kwargs) # 返回音频文件、label值 def _get_data(self): ''' This method offer information of wave files and labels. ''' files = [] labels = [] for i in range(len(self.label_list)): single_class_path=os.path.join(self.train_data_dir, self.label_list[i]) for sound in os.listdir(single_class_path): # print(sound) if 'wav' in sound: sound=os.path.join(single_class_path, sound) files.append(sound) labels.append(i) return files, labels

# 定义dataloader import paddle from paddlespeech.audio.features import LogMelSpectrogram # Feature config should be align with pretrained model sample_rate = 16000 feat_conf = { 'sr': sample_rate, 'n_fft': 1024, 'hop_length': 320, 'window': 'hann', 'win_length': 1024, 'f_min': 50.0, 'f_max': 14000.0, 'n_mels': 64, } train_ds = CustomDataset(sample_rate=sample_rate) feature_extractor = LogMelSpectrogram(**feat_conf) train_sampler = paddle.io.DistributedBatchSampler( train_ds, batch_size=64, shuffle=True, drop_last=False) train_loader = paddle.io.DataLoader( train_ds, batch_sampler=train_sampler, return_list=True, use_buffer_reader=True)

四、模型训练

1.选取预训练模型

选取cnn14作为 backbone，用于提取音频的特征：

from paddlespeech.cls.models import cnn14 backbone = cnn14(pretrained=True, extract_embedding=True)

2.构建分类模型

SoundClassifer接收cnn14作为backbone模型，并创建下游的分类网络：

import paddle.nn as nn class SoundClassifier(nn.Layer): def __init__(self, backbone, num_class, dropout=0.1): super().__init__() self.backbone = backbone self.dropout = nn.Dropout(dropout) self.fc = nn.Linear(self.backbone.emb_size, num_class) def forward(self, x): x = x.unsqueeze(1) x = self.backbone(x) x = self.dropout(x) logits = self.fc(x) return logits model = SoundClassifier(backbone, num_class=len(train_ds.label_list))

3.finetune

# 定义优化器和 Loss optimizer = paddle.optimizer.Adam(learning_rate=1e-4, parameters=model.parameters()) criterion = paddle.nn.loss.CrossEntropyLoss()

from paddleaudio.utils import logger epochs = 20 steps_per_epoch = len(train_loader) log_freq = 10 eval_freq = 10 for epoch in range(1, epochs + 1): model.train() avg_loss = 0 num_corrects = 0 num_samples = 0 for batch_idx, batch in enumerate(train_loader): waveforms, labels = batch feats = feature_extractor(waveforms) feats = paddle.transpose(feats, [0, 2, 1]) # [B, N, T] -> [B, T, N] logits = model(feats) loss = criterion(logits, labels) loss.backward() optimizer.step() if isinstance(optimizer._learning_rate, paddle.optimizer.lr.LRScheduler): optimizer._learning_rate.step() optimizer.clear_grad() # Calculate loss avg_loss += loss.numpy()[0] # Calculate metrics preds = paddle.argmax(logits, axis=1) num_corrects += (preds == labels).numpy().sum() num_samples += feats.shape[0] if (batch_idx + 1) % log_freq == 0: lr = optimizer.get_lr() avg_loss /= log_freq avg_acc = num_corrects / num_samples print_msg = 'Epoch={}/{}, Step={}/{}'.format( epoch, epochs, batch_idx + 1, steps_per_epoch) print_msg += ' loss={:.4f}'.format(avg_loss) print_msg += ' acc={:.4f}'.format(avg_acc) print_msg += ' lr={:.6f}'.format(lr) logger.train(print_msg) avg_loss = 0 num_corrects = 0 num_samples = 0

[2022-08-24 02:20:49,381] [ TRAIN] - Epoch=17/20, Step=10/15 loss=1.3319 acc=0.4875 lr=0.000100
[2022-08-24 02:21:08,107] [ TRAIN] - Epoch=18/20, Step=10/15 loss=1.3222 acc=0.4719 lr=0.000100
[2022-08-24 02:21:08,107] [ TRAIN] - Epoch=18/20, Step=10/15 loss=1.3222 acc=0.4719 lr=0.000100
[2022-08-24 02:21:26,884] [ TRAIN] - Epoch=19/20, Step=10/15 loss=1.2539 acc=0.5125 lr=0.000100
[2022-08-24 02:21:26,884] [ TRAIN] - Epoch=19/20, Step=10/15 loss=1.2539 acc=0.5125 lr=0.000100
[2022-08-24 02:21:45,579] [ TRAIN] - Epoch=20/20, Step=10/15 loss=1.2021 acc=0.5281 lr=0.000100
[2022-08-24 02:21:45,579] [ TRAIN] - Epoch=20/20, Step=10/15 loss=1.2021 acc=0.5281 lr=0.000100

五、模型训练

top_k = 3 wav_file = 'test/test_0.wav' n_fft = 1024 win_length = 1024 hop_length = 320 f_min=50.0 f_max=16000.0 waveform, sr = load(wav_file, sr=sr) feature_extractor = LogMelSpectrogram( sr=sr, n_fft=n_fft, hop_length=hop_length, win_length=win_length, window='hann', f_min=f_min, f_max=f_max, n_mels=64) feats = feature_extractor(paddle.to_tensor(paddle.to_tensor(waveform).unsqueeze(0))) feats = paddle.transpose(feats, [0, 2, 1]) # [B, N, T] -> [B, T, N] logits = model(feats) probs = nn.functional.softmax(logits, axis=1).numpy() sorted_indices = probs[0].argsort() msg = f'[{wav_file}]\n' for idx in sorted_indices[-1:-top_k-1:-1]: msg += f'{train_ds.label_list[idx]}: {probs[0][idx]:.5f}\n' print(msg)

[test/test_0.wav]
diaper: 0.50155
sleepy: 0.41397
hug: 0.05912

六、注意事项

• 1.自定义数据集，格式可参考文档；
• 2.统一音频尺寸（例如音频长度、采样频率）

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