---
library_name: transformers
license: apache-2.0
base_model: t5-base
tags:
- text2text-generation
- music
- spotify
- audio-features
- t5
language:
- en
datasets:
- custom
metrics:
- mae
- mse
- correlation
---

# T5 Spotify Features Generator

A fine-tuned T5-base model that generates Spotify audio features from natural language music descriptions.

## Model Details

### Model Description

This model converts natural language descriptions of music preferences into Spotify audio feature values. For example, "energetic dance music for a party" becomes `"danceability": 0.9, "energy": 0.9, "valence": 0.9`.

- **Developed by:** afsagag
- **Model type:** Text-to-Text Generation (T5)
- **Language(s):** English
- **License:** Apache-2.0
- **Finetuned from model:** [t5-base](https://huggingface.co/t5-base)

### Model Sources

- **Repository:** https://huggingface.co/afsagag/t5-spotify-features-generator

## Uses

### Direct Use

Generate Spotify audio features from music descriptions for:
- Music recommendation systems
- Playlist generation
- Music discovery applications
- Audio feature prediction research

```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch

# Load model and tokenizer
model = T5ForConditionalGeneration.from_pretrained("afsagag/t5-spotify-features-generator")
tokenizer = T5Tokenizer.from_pretrained("afsagag/t5-spotify-features-generator")

def generate_spotify_features(prompt, model, tokenizer):
    input_text = f"prompt: {prompt}"
    input_ids = tokenizer(input_text, return_tensors="pt", max_length=256, truncation=True).input_ids
    
    with torch.no_grad():
        outputs = model.generate(
            input_ids,
            max_length=256,
            num_beams=4,
            early_stopping=True,
            do_sample=False,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=tokenizer.eos_token_id
        )
    
    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return result

# Example usage
prompt = "I need energetic dance music for a party"
features = generate_spotify_features(prompt, model, tokenizer)
print(features)  # Output: "danceability": 0.9, "energy": 0.9, "valence": 0.9
```

### Out-of-Scope Use

- Generating actual audio or music files
- Non-English music descriptions (model trained on English only)
- Precise music recommendation without human oversight
- Applications requiring guaranteed JSON format output

## Bias, Risks, and Limitations

- **Training Data Bias:** Reflects patterns in the training dataset, may not represent all musical styles or cultural contexts
- **JSON Format Issues:** May occasionally generate incomplete JSON objects
- **Subjective Features:** Audio features like "valence" and "energy" are subjective and may not align with all listeners' perceptions
- **Western Music Bias:** Training focused on Western musical concepts and terminology

### Recommendations

- Validate generated features against expected ranges
- Use as a starting point rather than definitive feature values
- Consider cultural and stylistic diversity when applying to diverse music catalogs
- Implement post-processing to ensure valid JSON output if required

## Training Details

### Training Data

Custom dataset of 4,206 examples pairing natural language music descriptions with Spotify audio features:
- **Training set:** 3,364 examples
- **Validation set:** 421 examples  
- **Test set:** 421 examples

### Training Procedure

#### Training Hyperparameters

- **Training epochs:** 5
- **Learning rate:** 2e-4
- **Batch size:** 32 (train), 16 (eval)
- **Gradient accumulation steps:** 2
- **LR scheduler:** Cosine with 5% warmup
- **Max sequence length:** 256 tokens
- **Training regime:** bf16 mixed precision

#### Speeds, Sizes, Times

- **Training time:** ~58 minutes
- **Final training loss:** 0.5579
- **Model size:** ~892MB

## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data

Same distribution as training data: natural language music descriptions paired with Spotify audio features.

#### Metrics

- Mean Absolute Error (MAE) between predicted and actual feature values
- Mean Squared Error (MSE) for regression accuracy
- Pearson correlation coefficients for individual features
- Valid JSON ratio for output format correctness

### Results

The model demonstrates strong semantic understanding of musical concepts:

| Prompt | Generated Features |
|--------|-------------------|
| "I need energetic dance music for a party" | `"danceability": 0.9, "energy": 0.9, "valence": 0.9` |
| "Play calm acoustic songs for studying" | `"acousticness": 0.8, "energy": 0.2, "valence": 0.2` |
| "Upbeat music for working out" | `"danceability": 0.7, "energy": 0.8, "valence": 0.7` |
| "Relaxing instrumental background music" | `"acousticness": 0.3, "energy": 0.2, "instrumentalness": 0.8, "valence": 0.2` |
| "Happy pop music for driving" | `"danceability": 0.8, "energy": 0.8, "valence": 0.8` |

## Technical Specifications

### Model Architecture and Objective

- **Base Architecture:** T5 (Text-To-Text Transfer Transformer)
- **Model Size:** t5-base (220M parameters)
- **Objective:** Sequence-to-sequence generation of audio features from text descriptions
- **Input Format:** `"prompt: {natural_language_description}"`
- **Output Format:** JSON-style audio feature values

### Compute Infrastructure

#### Hardware

- GPU with CUDA support
- Mixed precision training (bf16)

#### Software

- PyTorch with CUDA
- Transformers library
- Datasets library for data processing

## Spotify Audio Features Reference

The model generates these Spotify audio features:

- **danceability** (0.0-1.0): How suitable a track is for dancing
- **energy** (0.0-1.0): Perceptual measure of intensity and power  
- **valence** (0.0-1.0): Musical positivity (happy vs sad)
- **acousticness** (0.0-1.0): Confidence measure of acoustic nature
- **instrumentalness** (0.0-1.0): Predicts absence of vocals
- **speechiness** (0.0-1.0): Presence of spoken words
- **liveness** (0.0-1.0): Presence of live audience
- **loudness** (dB): Overall loudness, typically -60 to 0 dB
- **tempo** (BPM): Estimated beats per minute
- **duration_ms**: Track duration in milliseconds
- **key** (0-11): Musical key (C=0, C♯/D♭=1, etc.)
- **mode** (0-1): Modality (0=minor, 1=major)
- **time_signature** (3-7): Time signature
- **popularity** (0-100): Spotify popularity score

## Citation

```bibtex
@misc{t5-spotify-features-generator,
  author = {afsagag},
  title = {T5 Spotify Features Generator: Fine-tuned T5 for Music Feature Prediction from Natural Language},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/afsagag/t5-spotify-features-generator}}
}
```

## Model Card Authors

afsagag

## Model Card Contact

Contact through Hugging Face profile: [@afsagag](https://huggingface.co/afsagag)