afsagag/t5-spotify-features

T5-Base Fine-tuned for Spotify Features Prediction

T5-Base fine-tuned to convert natural language prompts into Spotify audio feature JSON

Model Details

• Base Model: t5-base
• Model Type: Text-to-JSON generation
• Language: English
• Task: Convert natural language music preferences into Spotify audio feature JSON objects
• Fine-tuning Dataset: Custom dataset of prompts to Spotify audio features

Training Configuration

• Epochs: 7
• Learning Rate: 3e-4
• Batch Size: 8 (per device)
• Gradient Accumulation Steps: 4
• Scheduler: Cosine with warmup
• Optimizer: AdamW
• Max Length: 256 tokens
• Precision: bfloat16

Usage

from transformers import T5ForConditionalGeneration, T5Tokenizer
import json

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

# Example usage
prompt = "I want energetic dance music with high energy and danceability"
input_text = f"prompt: {prompt}"

# Tokenize and generate
input_ids = tokenizer(input_text, return_tensors="pt", max_length=256, truncation=True).input_ids
outputs = model.generate(
    input_ids, 
    max_length=256, 
    num_beams=4, 
    early_stopping=True,
    do_sample=False
)

# Decode result
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)

# Parse JSON output
try:
    spotify_features = json.loads(result)
    print("Generated Spotify Features:", spotify_features)
except json.JSONDecodeError:
    print("Generated text is not valid JSON")

Expected Output Format

The model generates JSON objects with Spotify audio features:

{
  "danceability": 0.85,
  "energy": 0.90,
  "valence": 0.75,
  "acousticness": 0.15,
  "instrumentalness": 0.05,
  "speechiness": 0.08,
}

Metrics

• Per-set Mean Absolute Error: Measures average prediction accuracy across feature sets
• Per-set Root Mean Squared Error: Measures prediction variance
• Per-feature Correlation: Pearson correlation for individual audio features

Model Files

• config.json: Model configuration
• pytorch_model.bin: Model weights
• tokenizer.json: Tokenizer vocabulary
• tokenizer_config.json: Tokenizer configuration
• special_tokens_map.json: Special token mappings

Limitations

• Model may occasionally generate invalid JSON that requires post-processing
• Trained on specific prompt formats starting with "prompt: "
• Performance depends on similarity to training data distribution
• May not generalize well to very abstract or unusual music descriptions

Training Data

The model was trained on a custom dataset pairing natural language music descriptions with corresponding Spotify audio feature values.

Ethical Considerations

This model generates music preference predictions and should not be used as the sole basis for music recommendation systems without human oversight.