Spaces:
Sleeping
Sleeping
File size: 5,912 Bytes
f6feac1 fc7b4a9 97eaafb fc7b4a9 97eaafb fc7b4a9 97eaafb fc7b4a9 f6feac1 fc7b4a9 253a78c fc7b4a9 f6feac1 fc7b4a9 f6feac1 fc7b4a9 f6feac1 fc7b4a9 97eaafb |
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 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 |
import os
import numpy as np
from datetime import datetime
from src.musiclime.explainer import MusicLIMEExplainer
from src.musiclime.wrapper import MusicLIMEPredictor, AudioOnlyPredictor
def musiclime_multimodal(audio_data, lyrics_text):
"""
Generate multimodal MusicLIME explanations for audio and lyrics.
Parameters
----------
audio_data : array-like
Audio waveform data from librosa.load or similar
lyrics_text : str
String containing song lyrics
Returns
-------
dict
Structured explanation results containing prediction info, feature explanations,
and processing metadata
"""
start_time = datetime.now()
# Get number of samples from environment variable, default to 1000
num_samples = int(os.getenv("MUSICLIME_NUM_SAMPLES", "1000"))
num_features = int(os.getenv("MUSICLIME_NUM_FEATURES", "10"))
print(f"[MusicLIME] Using num_samples={num_samples}, num_features={num_features}")
# Create musiclime instances
explainer = MusicLIMEExplainer()
predictor = MusicLIMEPredictor()
# Then generate explanations
explanation = explainer.explain_instance(
audio=audio_data,
lyrics=lyrics_text,
predict_fn=predictor,
num_samples=num_samples,
labels=(1,),
)
# Get prediction info
original_prediction = explanation.predictions[0]
predicted_class = np.argmax(original_prediction)
confidence = float(np.max(original_prediction))
# Get top features (I also made this configurable to prevent rebuilding)
top_features = explanation.get_explanation(label=1, num_features=num_features)
# Calculate runtime
end_time = datetime.now()
runtime_seconds = (end_time - start_time).total_seconds()
return {
"prediction": {
"class": int(predicted_class),
"class_name": "Human-Composed" if predicted_class == 1 else "AI-Generated",
"confidence": confidence,
"probabilities": original_prediction.tolist(),
},
"explanations": [
{
"rank": i + 1,
"modality": item["type"],
"feature_text": item["feature"],
"weight": float(item["weight"]),
"importance": abs(float(item["weight"])),
}
for i, item in enumerate(top_features)
],
"summary": {
"total_features_analyzed": len(top_features),
"audio_features_count": len(
[f for f in top_features if f["type"] == "audio"]
),
"lyrics_features_count": len(
[f for f in top_features if f["type"] == "lyrics"]
),
"runtime_seconds": runtime_seconds,
"samples_generated": num_samples,
"timestamp": start_time.isoformat(),
},
}
def musiclime_unimodal(audio_data, modality="audio"):
"""
Generate unimodal MusicLIME explanations for single modality.
Parameters
----------
audio_data : array-like
Audio waveform data from librosa.load or similar
modality : str, default='audio'
Explanation modality, currently only supports 'audio'
Returns
-------
dict
Structured explanation results containing prediction info, audio-only feature
explanations, and processing metadata
Raises
------
ValueError
If modality is not 'audio' (lyrics is not yet implemented)
"""
if modality != "audio":
raise ValueError(
"Currently only 'audio' modality is supported for unimodal explanations"
)
start_time = datetime.now()
# Get number of samples from environment variable, default to 1000
num_samples = int(os.getenv("MUSICLIME_NUM_SAMPLES", "1000"))
num_features = int(os.getenv("MUSICLIME_NUM_FEATURES", "10"))
print(
f"[MusicLIME] Using num_samples={num_samples}, num_features={num_features} (audio-only mode)"
)
# Create musiclime instances
explainer = MusicLIMEExplainer(random_state=42)
predictor = AudioOnlyPredictor()
# Use empty lyrics for audio-only since they're ignored anyways
dummy_lyrics = ""
# Generate explanation
explanation = explainer.explain_instance(
audio=audio_data,
lyrics=dummy_lyrics,
predict_fn=predictor,
num_samples=num_samples,
labels=(1,),
modality=modality,
)
# Get prediction info
original_prediction = explanation.predictions[0]
predicted_class = np.argmax(original_prediction)
confidence = float(np.max(original_prediction))
# Get top features
top_features = explanation.get_explanation(label=1, num_features=num_features)
# Calculate runtime
end_time = datetime.now()
runtime_seconds = (end_time - start_time).total_seconds()
return {
"prediction": {
"class": int(predicted_class),
"class_name": "Human-Composed" if predicted_class == 1 else "AI-Generated",
"confidence": confidence,
"probabilities": original_prediction.tolist(),
},
"explanations": [
{
"rank": i + 1,
"modality": item["type"], # "audio" for all features
"feature_text": item["feature"],
"weight": float(item["weight"]),
"importance": abs(float(item["weight"])),
}
for i, item in enumerate(top_features)
],
"summary": {
"total_features_analyzed": len(top_features),
"audio_features_count": len(top_features), # All features are audio
"lyrics_features_count": 0, # No lyrics features
"runtime_seconds": runtime_seconds,
"samples_generated": num_samples,
"timestamp": start_time.isoformat(),
},
}
|