李宏毅-机器学习hw4-self-attention结构-辨别600个speaker的身份

一、慢慢分析+学习pytorch中的各个模块的参数含义、使用方法、功能：

1.encoder编码器中的nhead参数：

self.encoder_layer = nn.TransformerEncoderLayer( d_model=d_model, dim_feedforward=256, nhead=2)

所以说，这个nhead的意思，就是有window窗口的大小，也就是一个b由几个a得到

2.tensor.permute改变维度的用法示例：

#尝试使用permute函数进行测试:可以通过tensor张量直接调用
import torch
import numpy as np
x = np.array([[[1,1,1],[2,2,2]],[[3,3,3],[4,4,4]]])
y = torch.tensor(x)
#y.shape
z=y.permute(2,1,0)
z.shape
print(z) #permute之后变成了3*2*2的维度
print(y) #本来是一个2*2*3从外到内的维度

3.tensor.mean求均值：从1个向量 到 1个数值:

4.python中字典（映射）的使用：

二、model的neural network设计部分：

import torch
import torch.nn as nn
import torch.nn.functional as Fclass Classifier(nn.Module):def __init__(self, d_model=80, n_spks=600, dropout=0.1):super().__init__()# Project the dimension of features from that of input into d_model.self.prenet = nn.Linear(40, d_model) #通过一个线性的输入层，从40个维度，变成d_model个#展示不需要使用这个conformer进行实验# TODO:#   Change Transformer to Conformer. #   https://arxiv.org/abs/2005.08100#这里是不需要自己设计 self-attention层的，因为transformer的encoder层用到self-attention层self.encoder_layer = nn.TransformerEncoderLayer( d_model=d_model, dim_feedforward=256, nhead=2 #输入维度是上面的d_model,输出维度是256，这2个nhead是啥？一个b由几个a得到)#下面这个暂时用不到# self.encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=2)# Project the the dimension of features from d_model into speaker nums.#predict_layerself.pred_layer = nn.Sequential( #这里其实就相当于是一个线性输出层了,最终输出的是一个n_soks维度600的向量nn.Linear(d_model, d_model),nn.ReLU(),nn.Linear(d_model, n_spks),)def forward(self, mels):"""args:mels: (batch size, length, 40) #我来试图解释一下这个东西,反正就是一段声音信号处理后得到的3维tensor，最里面那一维是40return:out: (batch size, n_spks) #最后只要输出每个batch中的行数 + 每一行中的n_spks的数值"""# out: (batch size, length, d_model) #原来out设置的3个维度的数据分别是batchsize , out = self.prenet(mels) #通过一个prenet层之后，最里面的那一维空间 就变成了一个d_model维度# out: (length, batch size, d_model)out = out.permute(1, 0, 2) #利用permute将0维和1维进行交换# The encoder layer expect features in the shape of (length, batch size, d_model).out = self.encoder_layer(out)# out: (batch size, length, d_model)out = out.transpose(0, 1) #重新得到原来的维度，这次用transpose和上一次用permute没有区别# mean poolingstats = out.mean(dim=1) #对维度1（第二个维度）计算均值，也就是将整个向量空间-->转成1个数值#得到的是batch,d_model (len就是一行的数据，从这一行中取均值,就是所谓的均值池化)# out: (batch, n_spks)out = self.pred_layer(stats) #这里得到n_spks还不是one-hot vecreturn out

三、warming up 的设计过程：

import mathimport torch
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR#这部分的代码感觉有一点诡异，好像是设计了一个learning rate的warmup过程，算了，之后再回来阅读好了def get_cosine_schedule_with_warmup(optimizer: Optimizer,num_warmup_steps: int,num_training_steps: int,num_cycles: float = 0.5,last_epoch: int = -1,
):"""Create a schedule with a learning rate that decreases following the values of the cosine function between theinitial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and theinitial lr set in the optimizer.Args:optimizer (:class:`~torch.optim.Optimizer`):The optimizer for which to schedule the learning rate.num_warmup_steps (:obj:`int`):The number of steps for the warmup phase.num_training_steps (:obj:`int`):The total number of training steps.num_cycles (:obj:`float`, `optional`, defaults to 0.5):The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0following a half-cosine).last_epoch (:obj:`int`, `optional`, defaults to -1):The index of the last epoch when resuming training.Return::obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule."""def lr_lambda(current_step):# Warmupif current_step < num_warmup_steps:return float(current_step) / float(max(1, num_warmup_steps))# decadenceprogress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)))return LambdaLR(optimizer, lr_lambda, last_epoch)

四、train中每个batch进行的处理:

import torch#这里面其实就是原来train部分的代码处理一个batch的操作def model_fn(batch, model, criterion, device): #这个函数的参数是batch数据，model，loss_func,设备"""Forward a batch through the model."""mels, labels = batch #获取mels参数 和 labels参数mels = mels.to(device)labels = labels.to(device)outs = model(mels) #得到的输出结果loss = criterion(outs, labels) #通过和labels进行比较得到loss# Get the speaker id with highest probability.preds = outs.argmax(1) #按照列的方向 计算出最大的索引位置# Compute accuracy.accuracy = torch.mean((preds == labels).float()) #通过将preds和labels进行比较得到acc的数值return loss, accuracy

五、validation的处理函数:

from tqdm import tqdm
import torchdef valid(dataloader, model, criterion, device):  #感觉就是整个validationset中的数据都进行了操作"""Validate on validation set."""model.eval() #开启evaluation模式running_loss = 0.0running_accuracy = 0.0pbar = tqdm(total=len(dataloader.dataset), ncols=0, desc="Valid", unit=" uttr") #创建进度条，实现可视化process_barfor i, batch in enumerate(dataloader): #下标i，batch数据存到batch中with torch.no_grad(): #先说明不会使用SGDloss, accuracy = model_fn(batch, model, criterion, device) #调用上面定义的batch处理函数得到loss 和 accrunning_loss += loss.item()running_accuracy += accuracy.item()pbar.update(dataloader.batch_size) #这些处理进度条的内容可以暂时不用管 pbar.set_postfix(loss=f"{running_loss / (i+1):.2f}",accuracy=f"{running_accuracy / (i+1):.2f}",)pbar.close()model.train()return running_accuracy / len(dataloader) #返回正确率

六、train的main调用：

from tqdm import tqdmimport torch
import torch.nn as nn
from torch.optim import AdamW
from torch.utils.data import DataLoader, random_splitdef parse_args(): #定义一个给config赋值的函数"""arguments"""config = {"data_dir": "./Dataset","save_path": "model.ckpt","batch_size": 32,"n_workers": 1, #这个参数太大的时候，我的这个会error"valid_steps": 2000,"warmup_steps": 1000,"save_steps": 10000,"total_steps": 70000,}return configdef main( #可以直接用上面定义那些参数作为这个main里面的参数data_dir,save_path,batch_size,n_workers,valid_steps,warmup_steps,total_steps,save_steps,
):"""Main function."""device = torch.device("cuda" if torch.cuda.is_available() else "cpu")print(f"[Info]: Use {device} now!")train_loader, valid_loader, speaker_num = get_dataloader(data_dir, batch_size, n_workers) #获取所需的data,调用get_dataloader函数train_iterator = iter(train_loader) #定义一个train_data的迭代器print(f"[Info]: Finish loading data!",flush = True)model = Classifier(n_spks=speaker_num).to(device) #构造一个model的实例criterion = nn.CrossEntropyLoss() #分别构造loss_func 和 optimizer的实例optimizer = AdamW(model.parameters(), lr=1e-3)scheduler = get_cosine_schedule_with_warmup(optimizer, warmup_steps, total_steps) #构造warmup的实例print(f"[Info]: Finish creating model!",flush = True)best_accuracy = -1.0best_state_dict = Nonepbar = tqdm(total=valid_steps, ncols=0, desc="Train", unit=" step") #process_bar相关的东西，不用管它for step in range(total_steps): #一共需要的步数进行for循环# Get datatry:batch = next(train_iterator) #从train_data中获取到下一个batch的数据except StopIteration:train_iterator = iter(train_loader)batch = next(train_iterator)loss, accuracy = model_fn(batch, model, criterion, device) #传递对应的数据、模型参数，得到这个batch的loss和accbatch_loss = loss.item()batch_accuracy = accuracy.item()# Updata modelloss.backward()optimizer.step()scheduler.step()optimizer.zero_grad() #更新进行Gradient descend 更新模型，并且将grad清空# Logpbar.update() #process_bar的东西先不管pbar.set_postfix(loss=f"{batch_loss:.2f}",accuracy=f"{batch_accuracy:.2f}",step=step + 1,)# Do validationif (step + 1) % valid_steps == 0:pbar.close()valid_accuracy = valid(valid_loader, model, criterion, device) #调用valid函数计算这一次validation的正确率# keep the best modelif valid_accuracy > best_accuracy: #总是保持最好的valid_accbest_accuracy = valid_accuracybest_state_dict = model.state_dict()pbar = tqdm(total=valid_steps, ncols=0, desc="Train", unit=" step")# Save the best model so far.if (step + 1) % save_steps == 0 and best_state_dict is not None:torch.save(best_state_dict, save_path) #保存最好的model参数pbar.write(f"Step {step + 1}, best model saved. (accuracy={best_accuracy:.4f})")pbar.close()if __name__ == "__main__": #调用这个main函数main(**parse_args())

七、inference部分的test内容：

import os
import json
import torch
from pathlib import Path
from torch.utils.data import Datasetclass InferenceDataset(Dataset):def __init__(self, data_dir):testdata_path = Path(data_dir) / "testdata.json"metadata = json.load(testdata_path.open())self.data_dir = data_dirself.data = metadata["utterances"]def __len__(self):return len(self.data)def __getitem__(self, index):utterance = self.data[index]feat_path = utterance["feature_path"]mel = torch.load(os.path.join(self.data_dir, feat_path))return feat_path, meldef inference_collate_batch(batch):"""Collate a batch of data."""feat_paths, mels = zip(*batch)return feat_paths, torch.stack(mels)

import json
import csv
from pathlib import Path
from tqdm.notebook import tqdmimport torch
from torch.utils.data import DataLoaderdef parse_args():"""arguments"""config = {"data_dir": "./Dataset","model_path": "./model.ckpt","output_path": "./output.csv",}return configdef main(data_dir,model_path,output_path,
):"""Main function."""device = torch.device("cuda" if torch.cuda.is_available() else "cpu")print(f"[Info]: Use {device} now!")mapping_path = Path(data_dir) / "mapping.json"mapping = json.load(mapping_path.open())dataset = InferenceDataset(data_dir)dataloader = DataLoader(dataset,batch_size=1,shuffle=False,drop_last=False,num_workers=8,collate_fn=inference_collate_batch,)print(f"[Info]: Finish loading data!",flush = True)speaker_num = len(mapping["id2speaker"])model = Classifier(n_spks=speaker_num).to(device)model.load_state_dict(torch.load(model_path))model.eval()print(f"[Info]: Finish creating model!",flush = True)results = [["Id", "Category"]]for feat_paths, mels in tqdm(dataloader):with torch.no_grad():mels = mels.to(device)outs = model(mels)  #调用model计算得到outspreds = outs.argmax(1).cpu().numpy() #对outs进行argmax,得到的索引存储到preds中for feat_path, pred in zip(feat_paths, preds):results.append([feat_path, mapping["id2speaker"][str(pred)]]) #将每一次的结果存放的到results中with open(output_path, 'w', newline='') as csvfile:writer = csv.writer(csvfile)writer.writerows(results)if __name__ == "__main__":main(**parse_args())

inference部分的代码暂时就看看好了，这个2022版本的数据在github上404了。。。

七、Dataset的处理过程：

import os
import json
import torch
import random
from pathlib import Path
from torch.utils.data import Dataset
from torch.nn.utils.rnn import pad_sequenceclass myDataset(Dataset):def __init__(self, data_dir, segment_len=128):self.data_dir = data_dirself.segment_len = segment_len# Load the mapping from speaker neme to their corresponding id. mapping_path = Path(data_dir) / "mapping.json"mapping = json.load(mapping_path.open()) #将这个json文件load到变量mapping中self.speaker2id = mapping["speaker2id"] #其实speaker2id这个变量就是mapping里面的内容#其实也就是原来数据集中的"id00464"变成我们这里的600个人的数据集的0-599的id# Load metadata of training data.metadata_path = Path(data_dir) / "metadata.json"metadata = json.load(open(metadata_path))["speakers"]#和上面类似的操作，这里的metadata就是打开那个json文件中的内容#我觉得按照他上课的说法，这里的n_mels的意思就是每个特征音频长度取出40就好了,?对吗#然后，这个json文件里面的内容就是不同speakerid所发声的音频文件的路径和mel_len# Get the total number of speaker.self.speaker_num = len(metadata.keys())self.data = [] #data就是这个class中的数据for speaker in metadata.keys(): #逐个遍历每个speakerfor utterances in metadata[speaker]: #遍历每个speaker的每一段录音self.data.append([utterances["feature_path"], self.speaker2id[speaker]])#将每一段录音按照 (路径，新id)存入data变量中def __len__(self):return len(self.data) #返回总共的data数量def __getitem__(self, index):feat_path, speaker = self.data[index] #从下标位置获取到该段录音的路径 和 speakerid# Load preprocessed mel-spectrogram.mel = torch.load(os.path.join(self.data_dir, feat_path)) #从路径中获取到该mel录音文件# Segmemt mel-spectrogram into "segment_len" frames.if len(mel) > self.segment_len: #如果大于128这个seg , 一些处理....# Randomly get the starting point of the segment.start = random.randint(0, len(mel) - self.segment_len)# Get a segment with "segment_len" frames.mel = torch.FloatTensor(mel[start:start+self.segment_len])else:mel = torch.FloatTensor(mel)# Turn the speaker id into long for computing loss later.speaker = torch.FloatTensor([speaker]).long() #将speakerid转换为long类型 return mel, speaker #返回这个录音mel文件和对应的speakeriddef get_speaker_number(self):return self.speaker_num

这里附带我下载的文件资源路径:

ML2022Spring-hw4 | Kaggle

下面dropbox的链接是可以使用的

!wget https://www.dropbox.com/s/vw324newiku0sz0/Dataset.tar.gz.aa?d1=0
!wget https://www.dropbox.com/s/vw324newiku0sz0/Dataset.tar.gz.aa?d1=0 
!wget https://www.dropbox.com/s/z840g69e71nkayo/Dataset.tar.gz.ab?d1=0 
!wget https://www.dropbox.com/s/h1081e1ggonio81/Dataset.tar.gz.ac?d1=0 
!wget https://www.dropbox.com/s/fh3zd8ow668c4th/Dataset.tar.gz.ad?d1=0 
!wget https://www.dropbox.com/s/ydzygoy2pv6gw9d/Dataset.tar.gz.ae?d1=0
!cat Dataset.tar.gz.*  |  tar zxvf  -

这样才能下载到你需要的数据

怎么说呢？最后的最后，还是这个dropbox中下载的内容不全，少了一些文件

有一个解决的方法是，直接在kaggle上面下载那个5.2GB的压缩包，不过解压之后可能有70GB,文件似乎太大了，而且下载之后，只要全部解压导入到Dataset文件夹就可以运行了

方法三：尝试一下那个GoogleDrive上面的文件 :

失败了，算了还是自己老老实实下载然后上传吧

!gdown --id '1CtHZhJ-mTpNsO-MqvAPIi4Yrt3oSBXYV' --output Dataset.zip
!gdown --id '14hmoMgB1fe6v50biIceKyndyeYABGrRq' --output Dataset.zip 
!gdown --id '1e9x-Pj13n7-9tK9LS_WjiMo21ru4UBH9' --output Dataset.zip 
!gdown --id '10TC0g46bcAz_jkiM165zNmwttT4RiRgY' --output Dataset.zip 
!gdown --id '1MUGBvG_Jjq00C2JYHuyV3B01vaf1kWIm' --output Dataset.zip 
!gdown --id '18M91P5DHwILNy01ssZ57AiPOR0OwutOM' --output Dataset.zip 
!unzip Dataset.zip