Datasets:

alvanalrakib
/

MuseCraft-Music

	---
	license: mit
	language:
	- en
	pretty_name: a
	---
	# 🎵 MuseCraft-Music: Million Lyrics Chat Dataset

	<div align="center">

	![MuseCraft Logo](https://img.shields.io/badge/🎵_MuseCraft-Music_AI-purple?style=for-the-badge)

	[![🤗 Hugging Face](https://img.shields.io/badge/🤗_Hugging_Face-Dataset-orange?style=flat-square)](https://huggingface.co/datasets/alvanalrakib/MuseCraft-Music)
	[![📊 Size](https://img.shields.io/badge/Size-1.5GB-blue?style=flat-square)](https://huggingface.co/datasets/alvanalrakib/MuseCraft-Music)
	[![💬 Conversations](https://img.shields.io/badge/Conversations-992K+-green?style=flat-square)](https://huggingface.co/datasets/alvanalrakib/MuseCraft-Music)
	[![⚖️ License](https://img.shields.io/badge/License-MIT-yellow?style=flat-square)](LICENSE)

	Train AI models to generate emotionally-rich, contextually-aware lyrics with 992K+ conversation pairs

	[🚀 Quick Start](#-quick-start) • [📊 Dataset Info](#-dataset-overview) • [💻 Usage](#-usage-examples) • [🏷️ Citation](#-citation)

	</div>

	---

	## 🎯 What is MuseCraft-Music?

	MuseCraft-Music is a comprehensive dataset containing 992,246 conversation pairs designed specifically for training AI models to generate high-quality, emotionally-aware lyrics. Perfect for fine-tuning language models like LLaMA, GPT, and other text generation systems.

	<details>
	<summary>🌟 <strong>Key Highlights</strong></summary>

	- 🎭 6 different prompt styles for diverse training scenarios
	- 😊 8 emotion categories for emotion-aware generation
	- 🎸 20+ music genres including Metal, Pop, Rock, Hip-Hop
	- 💬 Chat-optimized format ready for modern AI training
	- 🌍 English lyrics from 50,000+ diverse artists

	</details>

	---

	## 📊 Dataset Overview

	\| Metric \| Value \|
	\|--------\|-------\|
	\| Total Conversations \| 992,246 \|
	\| File Size \| 1.5 GB \|
	\| Format \| JSONL \|
	\| Language \| English \|
	\| Unique Artists \| 50,000+ \|

	### 📈 Source Distribution

	```
	🎼 Large Music Lyrics ████████████████████████████ 498,280 (50.2%)
	🎵 Genius Lyrics ███████████████████████████ 477,844 (48.2%)
	😊 Emotional Lyrics ██ 16,122 (1.6%)
	```

	### 🎭 Prompt Styles

	<details>
	<summary>📝 <strong>Instruction-Based</strong> (204,242 conversations)</summary>

	Theme and topic-based prompts
	```
	"Compose lyrics that capture the essence of self-discovery."
	```
	</details>

	<details>
	<summary>🎸 <strong>Genre-Specific</strong> (204,302 conversations)</summary>

	Genre-targeted prompts
	```
	"Write Metal lyrics that would fit perfectly in the genre."
	```
	</details>

	<details>
	<summary>🌙 <strong>Mood-Based</strong> (204,461 conversations)</summary>

	Atmosphere and mood prompts
	```
	"Write lyrics that convey a powerful atmosphere."
	```
	</details>

	<details>
	<summary>✨ <strong>General Creative</strong> (203,954 conversations)</summary>

	Open-ended creative prompts
	```
	"Create original song lyrics with emotional depth."
	```
	</details>

	<details>
	<summary>😊 <strong>Emotion-Focused</strong> (95,673 conversations)</summary>

	8 Emotion Categories:
	- 😢 Sadness • 😊 Joy • 😠 Anger • ❤️ Love
	- 😨 Fear • 😲 Surprise • 🙏 Thankfulness • 😐 Neutral
	</details>

	<details>
	<summary>🎤 <strong>Artist-Style</strong> (79,614 conversations)</summary>

	Artist-inspired style prompts
	```
	"Write lyrics in the style of [Artist] with emotional depth."
	```
	</details>

	---

	## 🚀 Quick Start

	### Installation

	```bash
	pip install datasets transformers
	```

	### Load Dataset

	```python
	from datasets import load_dataset

	# Load from Hugging Face Hub
	dataset = load_dataset("alvanalrakib/MuseCraft-Music")

	# Preview first conversation
	print(dataset['train'][0])
	```

	### Dataset Structure

	```json
	{
	"messages": [
	{
	"role": "user",
	"content": "Write Metal lyrics that would fit perfectly in the genre."
	},
	{
	"role": "assistant",
	"content": "On the edge of time / We rise when worlds collide..."
	}
	],
	"metadata": {
	"source": "large_lyrics",
	"genre": "Metal",
	"style": "genre_specific"
	}
	}
	```

	---

	## 💻 Usage Examples

	<details>
	<summary>🐍 <strong>Basic Data Loading</strong></summary>

	```python
	import json
	from datasets import load_dataset

	# Load dataset
	dataset = load_dataset("alvanalrakib/MuseCraft-Music")

	# Access conversations
	for example in dataset['train']:
	user_prompt = example['messages'][0]['content']
	lyrics = example['messages'][1]['content']
	genre = example['metadata'].get('genre', 'Unknown')

	print(f"Genre: {genre}")
	print(f"Prompt: {user_prompt}")
	print(f"Lyrics: {lyrics[:100]}...")
	break
	```
	</details>

	<details>
	<summary>🤖 <strong>Transformers Training</strong></summary>

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments
	from datasets import load_dataset

	# Load model and tokenizer
	model_name = "microsoft/DialoGPT-medium"
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModelForCausalLM.from_pretrained(model_name)

	# Load and format dataset
	dataset = load_dataset("alvanalrakib/MuseCraft-Music")

	def format_conversation(example):
	messages = example['messages']
	text = f"User: {messages[0]['content']}\nAssistant: {messages[1]['content']}"
	return {"text": text}

	# Format dataset
	formatted_dataset = dataset.map(format_conversation)
	```
	</details>

	<details>
	<summary>🦙 <strong>Unsloth Fine-tuning (Recommended)</strong></summary>

	```python
	from unsloth import FastLanguageModel
	from datasets import load_dataset

	# Load model with Unsloth
	model, tokenizer = FastLanguageModel.from_pretrained(
	model_name="unsloth/llama-3.1-8b-bnb-4bit",
	max_seq_length=2048,
	dtype=None,
	load_in_4bit=True,
	)

	# Load dataset
	dataset = load_dataset("alvanalrakib/MuseCraft-Music")

	# Apply LoRA for efficient training
	model = FastLanguageModel.get_peft_model(
	model,
	r=16,
	target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
	lora_alpha=16,
	lora_dropout=0,
	bias="none"
	)

	# Training configuration
	training_args = {
	"per_device_train_batch_size": 2,
	"gradient_accumulation_steps": 4,
	"learning_rate": 2e-4,
	"max_steps": 1000,
	"fp16": True,
	"optim": "adamw_8bit"
	}
	```
	</details>

	<details>
	<summary>🎯 <strong>Filter by Genre/Emotion</strong></summary>

	```python
	# Filter by genre
	metal_lyrics = dataset['train'].filter(
	lambda x: x['metadata'].get('genre') == 'Metal'
	)

	# Filter by emotion
	sad_lyrics = dataset['train'].filter(
	lambda x: x['metadata'].get('emotion') == 'sadness'
	)

	# Filter by style
	instruction_based = dataset['train'].filter(
	lambda x: x['metadata'].get('style') == 'instruction_based'
	)
	```
	</details>

	---

	## 🎯 Training Recommendations

	### ✅ Optimal Use Cases

	- 🦙 LLaMA 3.1 Fine-tuning with Unsloth for efficient training
	- 💬 Conversational AI for creative lyrics generation
	- 😊 Emotion-aware models for targeted expression
	- 🎭 Multi-genre systems for diverse musical styles

	### ⚙️ Recommended Parameters

	```python
	training_config = {
	"batch_size": 2,
	"gradient_accumulation": 4,
	"learning_rate": 2e-4,
	"max_steps": 1000,
	"sequence_length": 2048,
	"optimizer": "adamw_8bit"
	}
	```

	### 🎨 Expected Capabilities

	After training, your model will be able to:

	- ✅ Generate lyrics in specific emotional tones
	- ✅ Adapt to different music genres (Metal, Pop, Rock, etc.)
	- ✅ Create artist-inspired content
	- ✅ Respond to creative prompts contextually
	- ✅ Maintain quality across diverse prompt styles

	---

	## 🙏 Credits & Acknowledgments

	This dataset combines three excellent sources:

	\| Source \| Contribution \| Creator \|
	\|--------\|-------------\|---------\|
	\| [🎼 Music Lyrics 500K](https://huggingface.co/datasets/D3STRON/music_lyrics_500k) \| 498,280 (50.2%) \| D3STRON \|
	\| [🎵 Genius Lyrics](https://huggingface.co/datasets/brunokreiner/genius-lyrics) \| 477,844 (48.2%) \| Bruno Kreiner \|
	\| [😊 Lyrics Emotion](https://huggingface.co/datasets/ernestchu/lyrics-emotion-classification) \| 16,122 (1.6%) \| Ernest Chu \|

	Special thanks to:
	- The original dataset creators for their excellent work
	- Hugging Face for hosting and infrastructure
	- The AI community for fostering open-source development

	---

	## 📄 License

	Released under MIT License. Please respect the terms of the original source datasets.

	---

	## 🏷️ Citation

	```bibtex
	@dataset{musecraft_music_2025,
	title={MuseCraft-Music: Million Lyrics Chat Dataset},
	author={Alvan Alrakib},
	year={2025},
	publisher={Hugging Face},
	url={https://huggingface.co/datasets/alvanalrakib/MuseCraft-Music}
	}
	```

	---

	<div align="center">

	🎵 Ready to create amazing lyrics AI? Let's build something musical! 🎵

	[![🤗 View on Hugging Face](https://img.shields.io/badge/🤗_View_on-Hugging_Face-orange?style=for-the-badge)](https://huggingface.co/datasets/alvanalrakib/MuseCraft-Music)

	Built with ❤️ for the AI Music Community

	</div>