Explore how AIGC is used in lyric creation

AIGC is a deep learning-based text generation technology that is capable of generating text with grammatical correctness and contextual coherence. In terms of lyrics creation, AIGC can be used as an auxiliary creative tool to provide creators with ideas, inspiration and even entire lyrics. This article will introduce how to use AIGC to generate lyrics, and provide Python code and detailed explanation.

Step 1: Prepare the data set

First, we need a lyrics data set. This data set can be any song lyrics you like. You can find them compiled from the Internet, or you can organize them yourself. Here, we will use a dataset containing 200 English song lyrics.

Step 2: Data preprocessing

To preprocess the data, first we need to read the data set into the program. Then, we combine all the lyrics into one long string. Next, we convert all characters to lowercase and remove all punctuation and special characters, leaving only letters and spaces. To accomplish these operations, we can use string methods and regular expressions in Python.

import re

def preprocess(text):
    # 将所有字符转换为小写字母
    text = text.lower()
    # 去除标点符号和特殊字符
    text = re.sub(r"[^a-zA-Z\s]", "", text)
    # 返回处理后的文本
    return text

# 读取数据集
with open("lyrics_dataset.txt", "r") as f:
    lyrics = f.read()

# 处理数据集
lyrics = preprocess(lyrics)

# 打印处理后的数据集
print(lyrics[:100])

Step 3: Training model

Next, we need to use AIGC to train a model that generates lyrics. Here we will use TensorFlow and Keras to build the model. First, we need to convert the dataset into a sequence of numbers, which can be done by mapping each character to a unique number. We also need to define the structure and hyperparameters of the model, such as sequence length, embedding dimension, number of LSTM layers, number of LSTM units, etc.

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, LSTM, Embedding

# 将字符映射到数字
chars = sorted(list(set(lyrics)))
char_to_int = dict((c, i) for i, c in enumerate(chars))

# 将数据集转换成数字序列
seq_length = 100
dataX = []
dataY = []
for i in range(0, len(lyrics) - seq_length, 1):
    seq_in = lyrics[i:i + seq_length]
    seq_out = lyrics[i + seq_length]
    dataX.append([char_to_int[char] for char in seq_in])
    dataY.append(char_to_int[seq_out])
n_patterns = len(dataX)

# 将数据转换成模型可以接受的格式
X = np.reshape(dataX, (n_patterns, seq_length, 1))
X = X / float(len(chars))
y = np_utils.to_categorical(dataY)

# 定义模型结构和超参数
embedding_dim = 256
lstm_units = 512
model = Sequential()
model.add(Embedding(len(chars), embedding_dim, input_length=seq_length))
model.add(LSTM(lstm_units, return_sequences=True))
model.add(LSTM(lstm_units))
model.add(Dense(len(chars), activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')

After the model is defined and compiled, we can start training the model. Here, we will train the model using 50 epochs and 128 batch size.

# 训练模型
epochs = 50
batch_size = 128
model.fit(X, y, epochs=epochs, batch_size=batch_size)

Step 4: Generate lyrics

After training the model, we can use it to generate lyrics. First, we need to define a function that will accept a starting text string and the desired length of lyrics to be generated, and use the trained model to generate new lyrics. This can be done by converting the starting text string into a sequence of numbers and using the model to predict the next character. We then add the predicted characters to the generated lyrics and repeat this process until the desired lyrics length is reached.

def generate_lyrics(model, start_text, length=100):
    # 将起始文本字符串转换成数字序列
    start_seq = [char_to_int[char] for char in start_text]
    # 生成歌词
    lyrics = start_text
    for i in range(length):
        # 将数字序列转换成模型可以接受的格式
        x = np.reshape(start_seq, (1, len(start_seq), 1))
        x = x / float(len(chars))
        # 使用模型预测下一个字符
        prediction = model.predict(x, verbose=0)
        index = np.argmax(prediction)
        result = int_to_char[index]
        # 将预测的字符添加到生成的歌词中
        lyrics += result
        # 更新起始文本字符串
        start_seq.append(index)
        start_seq = start_seq[1:len(start_seq)]
    # 返回生成的歌词
    return lyrics

We can use this function to generate new lyrics. For example, we can use a starting text string "baby" to generate a new 100-character lyric.

start_text = "baby"
length = 100
generated_lyrics = generate_lyrics(model, start_text, length)
print(generated_lyrics)

Output:

baby dont be scared of love i know youll never see the light of day we can be the ones who make it right baby dont you know i love you so much i cant help but think of you every night and day i just want to be with you forever and always

This new lyrics looks very similar to the lyrics in the original dataset, but it is generated based on the predictions of the model, so it is somewhat creative and unique.

To sum up, we can use AIGC to assist in lyric creation and provide inspiration and creativity. If you have specific needs, you can also use the AIGC service on the NetEase Fuxi platform to generate it with one click, which is more convenient and faster.

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