https://github.com/nishant2018/lyrics-generator-rnn-
A lyrics generator is a fascinating application of Recurrent Neural Networks (RNNs), where the model learns patterns in sequences of text (lyrics) and generates new, coherent sequences.
https://github.com/nishant2018/lyrics-generator-rnn-
audio-processing deep-learning lyrics-generator rnn
Last synced: 8 months ago
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A lyrics generator is a fascinating application of Recurrent Neural Networks (RNNs), where the model learns patterns in sequences of text (lyrics) and generates new, coherent sequences.
- Host: GitHub
- URL: https://github.com/nishant2018/lyrics-generator-rnn-
- Owner: Nishant2018
- Created: 2024-06-10T08:25:03.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-06-10T10:28:55.000Z (over 1 year ago)
- Last Synced: 2025-01-09T07:50:27.969Z (9 months ago)
- Topics: audio-processing, deep-learning, lyrics-generator, rnn
- Language: Jupyter Notebook
- Homepage: https://www.activestate.com/blog/how-to-build-a-lyrics-generator-with-python-recurrent-neural-networks/
- Size: 2.1 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
## Lyrics Generator using Recurrent Neural Network (RNN)
### Introduction
A lyrics generator is a fascinating application of Recurrent Neural Networks (RNNs), where the model learns patterns in sequences of text (lyrics) and generates new, coherent sequences. By training an RNN on a large corpus of song lyrics, the network can generate new lyrics that mimic the style and structure of the training data.
### Why Use RNN for Lyrics Generation?
- **Sequence Modeling**: RNNs are well-suited for modeling sequences and maintaining context across long sequences, making them ideal for text generation.
- **Memory**: RNNs can remember previous inputs, which is crucial for generating coherent lyrics.
- **Flexibility**: RNNs can be trained on lyrics from various genres and artists to create diverse styles.
### How RNN Works for Lyrics Generation
1. **Data Preparation**: The lyrics text is preprocessed and encoded into numerical format, suitable for training the RNN.
2. **Model Architecture**: An RNN model (often with LSTM or GRU units) is designed to learn the patterns in the lyrics.
3. **Training**: The model is trained on the lyrics dataset to minimize the difference between the predicted and actual next characters/words.
4. **Generation**: The trained model generates new lyrics by predicting the next character/word in the sequence, given a starting seed text.
### Example Code
Here is an example of how to build and train an RNN for lyrics generation using Python's `Keras` library:
```python
import numpy as np
import keras
from keras.models import Sequential
from keras.layers import LSTM, Dense
from keras.utils import to_categorical
# Load and preprocess the data
with open('lyrics.txt', 'r') as file:
text = file.read()
# Create a mapping of unique characters to integers
chars = sorted(list(set(text)))
char_to_int = {c: i for i, c in enumerate(chars)}
int_to_char = {i: c for i, c in enumerate(chars)}
# Prepare the dataset of input to output pairs encoded as integers
seq_length = 100
dataX = []
dataY = []
for i in range(0, len(text) - seq_length, 1):
seq_in = text[i:i + seq_length]
seq_out = text[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)
n_vocab = len(chars)
# Reshape and normalize the input
X = np.reshape(dataX, (n_patterns, seq_length, 1))
X = X / float(n_vocab)
# One-hot encode the output variable
y = to_categorical(dataY)
# Define the RNN model
model = Sequential()
model.add(LSTM(256, input_shape=(X.shape[1], X.shape[2]), return_sequences=True))
model.add(LSTM(256))
model.add(Dense(y.shape[1], activation='softmax'))
# Compile the model
model.compile(loss='categorical_crossentropy', optimizer='adam')
# Fit the model
model.fit(X, y, epochs=20, batch_size=128)
# Function to generate lyrics
def generate_lyrics(seed_text, length=100):
pattern = [char_to_int[char] for char in seed_text.lower()]
for _ in range(length):
x = np.reshape(pattern, (1, len(pattern), 1))
x = x / float(n_vocab)
prediction = model.predict(x, verbose=0)
index = np.argmax(prediction)
result = int_to_char[index]
seed_text += result
pattern.append(index)
pattern = pattern[1:len(pattern)]
return seed_text
# Generate lyrics using a seed text
seed_text = "I want to hold your hand"
generated_lyrics = generate_lyrics(seed_text, length=200)
print(generated_lyrics)
```
### Conclusion
Using RNNs for lyrics generation is an exciting and creative application of machine learning. By training on a large corpus of lyrics, RNNs can generate new lyrics that reflect the style and structure of the training data. This approach showcases the power of RNNs in sequence modeling and text generation.
### Considerations
- **Data Quality**: The quality of the generated lyrics heavily depends on the quality and size of the training dataset.
- **Training Time**: Training RNNs, especially with large datasets and complex models, can be time-consuming and computationally expensive.
- **Fine-Tuning**: Hyperparameters like the number of LSTM units, batch size, and learning rate can significantly impact the model's performance and need careful tuning.