File size: 1,830 Bytes
9d74db1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
"""
Example script for using the T5 Spotify Features model
"""
from transformers import T5ForConditionalGeneration, T5Tokenizer
import json
def predict_spotify_features(prompt_text, model_name="afsagag/t5-spotify-features"):
"""
Generate Spotify audio features from a text prompt
Args:
prompt_text (str): Natural language description of music preferences
model_name (str): Hugging Face model name
Returns:
dict: Spotify audio features or None if JSON parsing fails
"""
# Load model and tokenizer
model = T5ForConditionalGeneration.from_pretrained(model_name)
tokenizer = T5Tokenizer.from_pretrained(model_name)
# Format input
input_text = f"prompt: {prompt_text}"
# 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 and clean result
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
cleaned_result = result.replace("ll", "null").replace("nu", "null")
try:
return json.loads(cleaned_result)
except json.JSONDecodeError:
print(f"Failed to parse JSON: {cleaned_result}")
return None
if __name__ == "__main__":
# Example prompts
test_prompts = [
"I want energetic dance music",
"Play some calm acoustic songs",
"Upbeat pop music for working out",
"Sad slow songs for rainy days"
]
for prompt in test_prompts:
print(f"\nPrompt: {prompt}")
features = predict_spotify_features(prompt)
if features:
print(f"Features: {json.dumps(features, indent=2)}")
|