Tutorial
Post #8
Building Your Own AI Lyric Generator: Tools, Techniques, and Best Practices
Practical guide to fine-tuning LLMs, leveraging open-source models on Hugging Face, and implementing music-conditioned generation with real code examples and deployment strategies.
JewelMusic Dev Team
January 8, 2025
18 min read
Training Paradigms: From Scratch vs Fine-Tuning
Training from Scratch ❌
- • Requires massive datasets (100GB+)
- • Expensive compute (weeks on GPUs)
- • Complex architecture design
- • Often inferior results
Fine-Tuning Pre-trained ✓
- • Works with small datasets (MB)
- • Hours on consumer GPUs
- • Leverages existing knowledge
- • State-of-the-art results
💡 Recommendation:
Always start with fine-tuning a pre-trained model like GPT-2, Llama 2, or Flan-T5. These models already understand language fundamentals and just need to learn your specific style.
Step-by-Step Implementation Guide
1
Environment Setup
Install required dependencies for training and inference:
requirements.txt
1# Core dependencies
2pip install transformers datasets accelerate
3pip install torch torchvision torchaudio
4
5# For music feature extraction
6pip install librosa pretty_midi
7
8# Hugging Face CLI for model upload
9pip install huggingface_hub2
Data Preparation
Structure your lyric dataset for fine-tuning:
data_preparation.py
1import pandas as pd
2from datasets import Dataset
3
4# Load your lyrics data
5lyrics_df = pd.read_csv('lyrics.csv')
6
7# Format for training
8def format_lyrics(example):
9 return {
10 'text': f"<genre>{example['genre']}</genre> "
11 f"<mood>{example['mood']}</mood>\n"
12 f"{example['lyrics']}"
13 }
14
15# Create HF Dataset
16dataset = Dataset.from_pandas(lyrics_df)
17dataset = dataset.map(format_lyrics)Tip: Include metadata tags like genre, mood, or artist to enable conditional generation.
3
Fine-Tuning Script
Fine-tune a pre-trained model on your lyrics:
fine_tuning.py
1from transformers import (
2 AutoTokenizer,
3 AutoModelForCausalLM,
4 TrainingArguments,
5 Trainer,
6 DataCollatorForLanguageModeling
7)
8
9# Load pre-trained model
10model_name = "gpt2" # or "meta-llama/Llama-2-7b"
11tokenizer = AutoTokenizer.from_pretrained(model_name)
12model = AutoModelForCausalLM.from_pretrained(model_name)
13
14# Add padding token
15tokenizer.pad_token = tokenizer.eos_token
16
17# Tokenize dataset
18def tokenize_function(examples):
19 return tokenizer(
20 examples["text"],
21 truncation=True,
22 padding="max_length",
23 max_length=512
24 )
25
26tokenized_dataset = dataset.map(tokenize_function, batched=True)
27
28# Training arguments
29training_args = TrainingArguments(
30 output_dir="./lyric-generator",
31 overwrite_output_dir=True,
32 num_train_epochs=3,
33 per_device_train_batch_size=4,
34 save_steps=500,
35 save_total_limit=2,
36 prediction_loss_only=True,
37 logging_steps=100,
38 warmup_steps=500,
39 learning_rate=5e-5,
40 fp16=True, # Mixed precision training
41)
42
43# Create trainer
44trainer = Trainer(
45 model=model,
46 args=training_args,
47 data_collator=DataCollatorForLanguageModeling(
48 tokenizer=tokenizer,
49 mlm=False,
50 ),
51 train_dataset=tokenized_dataset,
52)
53
54# Start training
55trainer.train()4
Music-Conditioned Generation
Extract musical features and condition generation:
music_conditioning.py
1import librosa
2import numpy as np
3
4def extract_music_features(audio_path):
5 """Extract musical features for conditioning"""
6 y, sr = librosa.load(audio_path)
7
8 # Tempo and beat tracking
9 tempo, beats = librosa.beat.beat_track(y=y, sr=sr)
10
11 # Chroma features (pitch content)
12 chroma = librosa.feature.chroma_stft(y=y, sr=sr)
13
14 # Spectral features (timbre)
15 spectral_centroid = librosa.feature.spectral_centroid(y=y, sr=sr)
16
17 # Structure (verse/chorus detection)
18 boundaries = librosa.segment.agglomerative(
19 librosa.feature.mfcc(y=y, sr=sr).T,
20 k=5
21 )
22
23 return {
24 'tempo': tempo,
25 'key': np.argmax(np.mean(chroma, axis=1)),
26 'energy': np.mean(spectral_centroid),
27 'structure': boundaries
28 }
29
30def generate_conditioned_lyrics(model, tokenizer, music_features):
31 """Generate lyrics conditioned on music"""
32 # Create prompt with musical context
33 prompt = f"""<tempo>{music_features['tempo']:.0f}</tempo>
34<key>{music_features['key']}</key>
35<energy>{music_features['energy']:.2f}</energy>
36[Verse 1]"""
37
38 # Tokenize and generate
39 inputs = tokenizer(prompt, return_tensors="pt")
40
41 with torch.no_grad():
42 outputs = model.generate(
43 **inputs,
44 max_length=256,
45 temperature=0.8,
46 do_sample=True,
47 top_p=0.9,
48 repetition_penalty=1.2
49 )
50
51 return tokenizer.decode(outputs[0], skip_special_tokens=True)5
Syllable-Constrained Decoding
Implement LYRA-style constraint-based generation:
constrained_generation.py
1import pyphen
2
3class ConstrainedLyricGenerator:
4 def __init__(self, model, tokenizer):
5 self.model = model
6 self.tokenizer = tokenizer
7 self.dic = pyphen.Pyphen(lang='en')
8
9 def count_syllables(self, text):
10 """Count syllables in text"""
11 words = text.split()
12 total = 0
13 for word in words:
14 syllables = self.dic.inserted(word).split('-')
15 total += len(syllables)
16 return total
17
18 def generate_with_constraints(self,
19 melody_notes,
20 prompt=""):
21 """Generate lyrics matching melody structure"""
22 lyrics = []
23
24 for phrase_notes in melody_notes:
25 target_syllables = len(phrase_notes)
26
27 # Generate until syllable count matches
28 attempts = 0
29 while attempts < 10:
30 candidate = self._generate_line(prompt)
31
32 if self.count_syllables(candidate) == target_syllables:
33 lyrics.append(candidate)
34 prompt = " ".join(lyrics[-2:]) # Context
35 break
36
37 attempts += 1
38
39 if attempts == 10:
40 # Fallback: truncate/pad to match
41 lyrics.append(self._adjust_syllables(
42 candidate, target_syllables
43 ))
44
45 return "\n".join(lyrics)Available Open-Source Models
Best Practices for Production
Version Control & Experimentation
- • Use MLflow or Weights & Biases for experiment tracking
- • Version datasets alongside model checkpoints
- • Document hyperparameters and training configs
Deployment Strategies
- • Use ONNX or TorchScript for production inference
- • Implement caching for common prompts
- • Deploy with FastAPI + Docker for scalability
Quality Control
- • Implement profanity and toxicity filters
- • Add plagiarism detection against training data
- • Human-in-the-loop validation for critical use cases
Quick Start Template
Complete Starter Project
Clone our complete starter template with pre-configured training scripts, inference API, and web interface:
Quick Start Commands
1# Clone the starter template
2git clone https://github.com/jewelmusic/lyric-generator-starter
3
4# Install dependencies
5cd lyric-generator-starter
6pip install -r requirements.txt
7
8# Download pre-trained model
9python download_model.py
10
11# Start training on your data
12python train.py --data_path ./data/lyrics.csv
13
14# Launch inference API
15python app.pyAdvanced Techniques
LoRA Fine-Tuning
Use Low-Rank Adaptation for efficient fine-tuning of large models with minimal memory:
1pip install peft # Parameter-Efficient Fine-TuningMulti-Modal Fusion
Combine audio embeddings with text generation using cross-attention layers for tighter music-lyric coupling.
Reinforcement Learning
Use RLHF (Reinforcement Learning from Human Feedback) to align outputs with musical preferences.
Resources & Community
Start Building Today
With the tools and techniques covered in this guide, you're ready to build your own AI lyric generator. Whether you're creating a commercial product or experimenting with creative AI, the combination of pre-trained models, fine-tuning, and music-aware constraints provides a powerful foundation.