Welcome to the creative world of Emo music lyric generation! In this blog post, we’ll explore how to leverage a fine-tuned version of GPT-Neo-125M to craft lyrics that resonate with the Emo genre. If you’re a musician, programmer, or simply an Emo aficionado, this guide will help you step into a realm of lyrical expression.

Model Description

This Emo Bot is a specialized model designed to generate music lyrics inspired by Emo culture. It draws on a dataset of 2381 songs from 15 influential bands from the early 2000s, ensuring authentic Emo vibes while crafting new lyrical compositions.

Training the Emo Bot

The training process for this Emo Bot involves the use of the Hugging Face library, specifically the Trainer Class. Below are the key training parameters:

• Learning Rate: 2e-4
• Epochs: 40
• Colab for Finetuning: Finetuning Link
• Colab for Testing: Testing Link

How to Use the Emo Bot

Using the Emo Bot for generating lyrics is straightforward. Here’s a step-by-step guide:

from transformers import AutoTokenizer, AutoModelForCausalLM
import re

if torch.cuda.is_available():
    device = torch.device('cuda')
else:
    device = torch.device('cpu')

print(device)

tokenizer = AutoTokenizer.from_pretrained('HeyLucasLeaogpt-neo-small-emo-lyrics')
model = AutoModelForCausalLM.from_pretrained('HeyLucasLeaogpt-neo-small-emo-lyrics')
model.to(device)

generated = tokenizer('I miss you', return_tensors='pt').input_ids.to(device)

# Generating texts
sample_outputs = model.generate(generated,
                                 do_sample=True,
                                 top_k=10,
                                 max_length=200,
                                 top_p=0.95,
                                 temperature=2.0,
                                 num_return_sequences=3)

# Decoding and printing sequences
for i, sample_output in enumerate(sample_outputs):
    texto = tokenizer.decode(sample_output.tolist())
    regex_padding = re.sub('pad', '', texto)
    regex_barra = re.sub('[+]', '', regex_padding)
    espaço = re.sub('[ +]', ' ', regex_barra)
    resultado = re.sub('[\n](2,)', '\n', espaço)
    print('Texto {}: {}'.format(i + 1, resultado + '\n'))

Understanding the Code: An Analogy

Imagine you’re a chef in a kitchen filled with ingredients, where each ingredient represents a portion of the code above. To create the perfect dish (or in this case, Emo lyrics), you need the right tools and methods:

• Ingredients: Here, the ingredients are the libraries you import (like transformers and re).
• Preparation: You check if your kitchen (device) is equipped with the right appliances (CUDA or CPU). This is akin to establishing the computational resources available to you.
• Gathering Tools: The tokenizer and model represent your kitchen tools. The tokenizer prepares the input (lyrics prompt) while the model cooks up the output (generated lyrics).
• Cooking: The model generates samples of lyrics based on the prompt you provided, much like how you’d adjust seasoning in the kitchen until it’s just right.
• Serving: Finally, the cleaned-up lyrics are printed out for your enjoyment, showcasing the final product of your creative culinary efforts.

Troubleshooting Tips

If you encounter any issues while using the Emo Bot, consider these troubleshooting steps:

• Ensure you have the necessary libraries installed, especially Hugging Face Transformers.
• Double-check your internet connection if you’re using Google Colab, as occasional connectivity issues can disrupt the model’s downloading process.
• If you encounter runtime errors, confirm that your runtime environment supports CUDA (for GPU usage) if you plan to utilize it.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

Coding up an Emo Bot serves as a creative fusion of technology and art—a wonderful example of how AI can contribute to music and culture. As you delve into the world of lyric generation, we encourage you to experiment with prompts, adjust parameters, and really find your unique voice in Emo music!

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.