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Music EQ (Emotional Intelligence) Adapter for TouchGrass.
Detects frustration and adapts responses for music learning context.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Dict, Tuple, List
class MusicEQAdapter(nn.Module):
"""
Frustration detection adapted for music learning context.
Music learners get frustrated differently than general users:
- Finger pain/difficulty ("my fingers hurt", "I can't get this chord")
- Rhythm frustration ("I keep losing the beat")
- Progress frustration ("I've been practicing for weeks and still...")
- Theory overwhelm ("this is too complicated")
When frustration detected:
- Simplify explanations automatically
- Suggest easier alternatives ("try the open G chord instead of barre")
- Add encouragement naturally
- Break things into smaller steps
- Remind them learning music takes time
4-emotion classification for music context:
frustrated, confused, excited, confident
(simpler than general 8-emotion — music context needs fewer)
"""
# Emotion labels
EMOTIONS = ["frustrated", "confused", "excited", "confident"]
# Frustration triggers (keywords/phrases)
FRUSTRATION_TRIGGERS = [
"can't", "cannot", "impossible", "too hard", "difficult",
"fingers hurt", "pain", "hurt", "struggling", "stuck",
"weeks", "months", "still can't", "giving up", "quit",
"confused", "don't understand", "too complicated",
"lost", "overwhelmed", "frustrated", "annoyed",
"beat", "rhythm", "timing", "off beat",
"barre", "stretch", "impossible chord",
]
# Encouragement templates for frustrated learners
ENCOURAGEMENT_TEMPLATES = {
"frustrated": [
"I understand this is challenging — learning {instrument} takes time and patience.",
"Many students struggle with this at first. Let's break it down into smaller steps.",
"Frustration is normal when learning something new. You're making progress, even if it doesn't feel like it.",
"Every musician has been where you are. Keep going — it gets easier!",
],
"confused": [
"Let me explain that in a different way.",
"I see this is confusing. Here's a simpler approach...",
"Music theory can be overwhelming. Let's focus on one piece at a time.",
"That's a great question! Let me break it down step by step.",
],
"excited": [
"I'm glad you're excited! That enthusiasm will help you learn faster.",
"Your excitement is contagious! Let's keep that momentum going.",
"That's the spirit! Music is a wonderful journey.",
],
"confident": [
"Great confidence! You're on the right track.",
"Your progress shows you're getting the hang of this.",
"Keep that confidence — it's key to musical growth.",
],
}
# Simplification strategies by emotion
SIMPLIFICATION_STRATEGIES = {
"frustrated": [
"suggest_open_chord_alternative",
"reduce_tempo",
"break_into_parts",
"use_easier_tuning",
"skip_complex_theory",
],
"confused": [
"use_analogy",
"show_visual_example",
"step_by_step",
"check_prerequisites",
],
"excited": [
"add_challenge",
"introduce_next_concept",
"suggest_creative_exercise",
],
"confident": [
"maintain_pace",
"introduce_advanced_topics",
"suggest_performance_opportunities",
],
}
def __init__(self, d_model: int, eq_hidden: int = 32):
"""
Initialize MusicEQAdapter.
Args:
d_model: Hidden dimension from base model
eq_hidden: Hidden dimension for EQ layers (small, lightweight)
"""
super().__init__()
self.d_model = d_model
self.eq_hidden = eq_hidden
# Frustration detector (binary: frustrated or not)
self.frustration_detector = nn.Sequential(
nn.Linear(d_model, eq_hidden),
nn.ReLU(),
nn.Dropout(0.1),
nn.Linear(eq_hidden, 1),
nn.Sigmoid()
)
# 4-emotion classifier for music context
self.emotion_classifier = nn.Sequential(
nn.Linear(d_model, eq_hidden),
nn.ReLU(),
nn.Dropout(0.1),
nn.Linear(eq_hidden, 4),
)
# Simplification gate: modulates response complexity
# Takes: frustration_score + 4 emotion probs = 5 inputs
self.simplify_gate = nn.Sequential(
nn.Linear(5, eq_hidden),
nn.ReLU(),
nn.Linear(eq_hidden, d_model),
nn.Sigmoid() # Output 0-1 per dimension
)
# EQ loss weight (for training)
self.eq_loss_weight = 0.1
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
) -> Dict[str, torch.Tensor]:
"""
Forward pass through MusicEQAdapter.
Args:
hidden_states: Base model hidden states [batch, seq_len, d_model]
attention_mask: Attention mask [batch, seq_len]
Returns:
Dictionary with emotion predictions and simplification gate
"""
batch_size, seq_len, d_model = hidden_states.shape
# Pool hidden states (weighted by attention mask if provided)
if attention_mask is not None:
# Mask-based pooling
mask_expanded = attention_mask.unsqueeze(-1).float()
pooled = (hidden_states * mask_expanded).sum(dim=1) / mask_expanded.sum(dim=1)
else:
pooled = hidden_states.mean(dim=1) # [batch, d_model]
# Detect frustration (0-1 score)
frustration_score = self.frustration_detector(pooled) # [batch, 1]
# Classify emotion (4 classes)
emotion_logits = self.emotion_classifier(pooled) # [batch, 4]
emotion_probs = F.softmax(emotion_logits, dim=-1)
# Compute simplification gate input
simplify_input = torch.cat([frustration_score, emotion_probs], dim=1) # [batch, 5]
# Generate simplification gate (per-dimension modulation)
simplify_gate = self.simplify_gate(simplify_input) # [batch, d_model]
outputs = {
"frustration_score": frustration_score,
"emotion_logits": emotion_logits,
"emotion_probs": emotion_probs,
"simplify_gate": simplify_gate,
}
return outputs
def detect_frustration(
self,
text: str,
threshold: float = 0.5,
) -> Tuple[bool, float, str]:
"""
Detect frustration in user text (rule-based fallback).
Args:
text: User input text
threshold: Frustration score threshold
Returns:
(is_frustrated, score, detected_emotion)
"""
text_lower = text.lower()
# Count frustration triggers
trigger_count = sum(1 for trigger in self.FRUSTRATION_TRIGGERS if trigger in text_lower)
# Simple scoring
score = min(1.0, trigger_count / 5.0) # Normalize to 0-1
is_frustrated = score >= threshold
# Determine emotion (simplified rule-based)
if "confused" in text_lower or "don't understand" in text_lower:
emotion = "confused"
elif "excited" in text_lower or "love" in text_lower or "awesome" in text_lower:
emotion = "excited"
elif "got it" in text_lower or "understand" in text_lower or "easy" in text_lower:
emotion = "confident"
else:
emotion = "frustrated" if is_frustrated else "neutral"
return is_frustrated, score, emotion
def get_encouragement(
self,
emotion: str,
instrument: Optional[str] = None,
context: Optional[str] = None,
) -> str:
"""
Generate encouragement message based on detected emotion.
Args:
emotion: Detected emotion (frustrated, confused, excited, confident)
instrument: Optional instrument context
context: Optional specific context (chord, theory, etc)
Returns:
Encouragement string
"""
import random
if emotion not in self.ENCOURAGEMENT_TEMPLATES:
emotion = "frustrated" # Default
templates = self.ENCOURAGEMENT_TEMPLATES[emotion]
template = random.choice(templates)
# Fill in instrument placeholder if present
if "{instrument}" in template and instrument:
return template.format(instrument=instrument)
else:
return template
def get_simplification_strategy(
self,
emotion: str,
instrument: Optional[str] = None,
user_level: str = "beginner",
) -> List[str]:
"""
Get list of simplification strategies to apply.
Args:
emotion: Detected emotion
instrument: Optional instrument context
user_level: User skill level
Returns:
List of strategy names
"""
strategies = self.SIMPLIFICATION_STRATEGIES.get(emotion, [])
# Add level-specific strategies
if user_level == "beginner":
strategies.append("use_basic_terminology")
strategies.append("avoid_music_jargon")
return strategies
def apply_simplification(
self,
response_text: str,
strategies: List[str],
emotion: str,
) -> str:
"""
Apply simplification strategies to response text.
Args:
response_text: Original response
strategies: List of strategies to apply
emotion: Detected emotion
Returns:
Simplified response
"""
simplified = response_text
for strategy in strategies:
if strategy == "suggest_open_chord_alternative":
# Replace barre chords with open alternatives
simplified = self._replace_barre_with_open(simplified)
elif strategy == "reduce_tempo":
# Add tempo suggestion
if "BPM" in simplified or "tempo" in simplified:
simplified += "\n\nTip: Try practicing this at a slower tempo (60-80 BPM) and gradually increase."
elif strategy == "break_into_parts":
# Add step-by-step suggestion
simplified = "Let's break this down:\n\n" + simplified
elif strategy == "skip_complex_theory":
# Simplify theory explanations
simplified = self._simplify_theory(simplified)
elif strategy == "use_analogy":
# Add analogies
simplified = self._add_analogy(simplified)
elif strategy == "step_by_step":
# Add numbered steps
simplified = self._add_numbered_steps(simplified)
# Prepend encouragement if frustrated
if emotion == "frustrated":
encouragement = self.get_encouragation("frustrated")
simplified = encouragement + "\n\n" + simplified
return simplified
def _replace_barre_with_open(self, text: str) -> str:
"""Replace barre chord suggestions with open alternatives."""
replacements = {
"F major": "F major (try Fmaj7 or F/C if barre is hard)",
"B minor": "B minor (try Bm7 or alternative fingering)",
"barre": "barre (you can also try a partial barre or capo)",
}
for original, replacement in replacements.items():
text = text.replace(original, replacement)
return text
def _simplify_theory(self, text: str) -> str:
"""Simplify music theory explanations."""
# Replace complex terms with simpler explanations
simplifications = {
"diatonic": "within the key",
"chromatic": "all 12 notes",
"modulation": "changing key",
"cadence": "ending chord progression",
"arpeggio": "playing chord notes one at a time",
}
for complex_term, simple_term in simplifications.items():
text = text.replace(complex_term, simple_term)
return text
def _add_analogy(self, text: str) -> str:
"""Add musical analogies to explanation."""
analogy = "\n\nThink of it like this: music is a language — you learn the alphabet (notes), then words (chords), then sentences (progressions)."
return text + analogy
def _add_numbered_steps(self, text: str) -> str:
"""Convert paragraph to numbered steps."""
# Simple implementation: add numbered list if not already
if "1." not in text and "Step" not in text:
lines = text.split("\n")
new_lines = []
step_num = 1
for line in lines:
if line.strip() and not line.strip().startswith(("##", "**", "-", "*")):
new_lines.append(f"{step_num}. {line}")
step_num += 1
else:
new_lines.append(line)
return "\n".join(new_lines)
return text
def compute_eq_loss(
self,
outputs: Dict[str, torch.Tensor],
emotion_labels: torch.Tensor,
frustration_labels: torch.Tensor,
) -> torch.Tensor:
"""
Compute EQ training loss.
Args:
outputs: Forward pass outputs
emotion_labels: Ground truth emotion labels [batch]
frustration_labels: Ground truth frustration labels [batch]
Returns:
EQ loss
"""
# Emotion classification loss
emotion_logits = outputs["emotion_logits"]
emotion_loss = F.cross_entropy(emotion_logits, emotion_labels)
# Frustration detection loss (binary cross-entropy)
frustration_score = outputs["frustration_score"].squeeze()
frustration_loss = F.binary_cross_entropy(frustration_score, frustration_labels.float())
# Combined EQ loss
eq_loss = emotion_loss + frustration_loss
return eq_loss * self.eq_loss_weight
def test_eq_adapter():
"""Test the MusicEQAdapter."""
import torch
# Create adapter
d_model = 4096
adapter = MusicEQAdapter(d_model=d_model, eq_hidden=32)
# Test input
batch_size = 2
seq_len = 20
hidden_states = torch.randn(batch_size, seq_len, d_model)
attention_mask = torch.ones(batch_size, seq_len)
# Forward pass
outputs = adapter.forward(hidden_states, attention_mask)
print("Music EQ Adapter outputs:")
for key, value in outputs.items():
if isinstance(value, torch.Tensor):
print(f" {key}: {value.shape}")
else:
print(f" {key}: {value}")
# Test frustration detection
print("\nFrustration detection (rule-based):")
test_texts = [
"I've been trying this chord for an hour and I still can't get it",
"This is so confusing, I don't understand music theory",
"I'm so excited to learn guitar!",
"I think I'm getting the hang of this",
]
for text in test_texts:
is_frustrated, score, emotion = adapter.detect_frustration(text)
print(f" '{text[:50]}...' -> frustrated={is_frustrated}, score={score:.2f}, emotion={emotion}")
# Test encouragement generation
print("\nEncouragement messages:")
for emotion in ["frustrated", "confused", "excited", "confident"]:
msg = adapter.get_encouragement(emotion, instrument="guitar")
print(f" {emotion}: {msg[:80]}...")
# Test simplification
print("\nSimplification example:")
original = "To play an F major barre chord, place your index finger across all six strings at the first fret..."
strategies = ["suggest_open_chord_alternative", "break_into_parts"]
simplified = adapter.apply_simplification(original, strategies, "frustrated")
print(f" Original: {original[:60]}...")
print(f" Simplified: {simplified[:80]}...")
# Test loss computation
print("\nEQ loss computation:")
emotion_labels = torch.tensor([0, 2]) # frustrated, excited
frustration_labels = torch.tensor([1.0, 0.0]) # first frustrated, second not
eq_loss = adapter.compute_eq_loss(outputs, emotion_labels, frustration_labels)
print(f" EQ loss: {eq_loss.item():.4f}")
print("\nMusic EQ Adapter test complete!")
if __name__ == "__main__":
test_eq_adapter() |