import os
import io
import gradio as gr
import torch
import numpy as np
import re
import pronouncing # Add this to requirements.txt for syllable counting
import functools # Add this for lru_cache functionality
from transformers import (
AutoModelForAudioClassification,
AutoFeatureExtractor,
AutoTokenizer,
pipeline,
AutoModelForCausalLM,
BitsAndBytesConfig
)
from huggingface_hub import login
from utils import (
load_audio,
extract_audio_duration,
extract_mfcc_features,
format_genre_results,
ensure_cuda_availability
)
from emotionanalysis import MusicAnalyzer
import librosa
# Login to Hugging Face Hub if token is provided
if "HF_TOKEN" in os.environ:
login(token=os.environ["HF_TOKEN"])
# Constants
GENRE_MODEL_NAME = "dima806/music_genres_classification"
MUSIC_DETECTION_MODEL = "MIT/ast-finetuned-audioset-10-10-0.4593"
LLM_MODEL_NAME = "Qwen/Qwen3-32B"
SAMPLE_RATE = 22050 # Standard sample rate for audio processing
# Check CUDA availability (for informational purposes)
CUDA_AVAILABLE = ensure_cuda_availability()
# Create music detection pipeline
print(f"Loading music detection model: {MUSIC_DETECTION_MODEL}")
try:
music_detector = pipeline(
"audio-classification",
model=MUSIC_DETECTION_MODEL,
device=0 if CUDA_AVAILABLE else -1
)
print("Successfully loaded music detection pipeline")
except Exception as e:
print(f"Error creating music detection pipeline: {str(e)}")
# Fallback to manual loading
try:
music_processor = AutoFeatureExtractor.from_pretrained(MUSIC_DETECTION_MODEL)
music_model = AutoModelForAudioClassification.from_pretrained(MUSIC_DETECTION_MODEL)
print("Successfully loaded music detection model and feature extractor")
except Exception as e2:
print(f"Error loading music detection model components: {str(e2)}")
raise RuntimeError(f"Could not load music detection model: {str(e2)}")
# Create genre classification pipeline
print(f"Loading audio classification model: {GENRE_MODEL_NAME}")
try:
genre_classifier = pipeline(
"audio-classification",
model=GENRE_MODEL_NAME,
device=0 if CUDA_AVAILABLE else -1
)
print("Successfully loaded audio classification pipeline")
except Exception as e:
print(f"Error creating pipeline: {str(e)}")
# Fallback to manual loading
try:
genre_processor = AutoFeatureExtractor.from_pretrained(GENRE_MODEL_NAME)
genre_model = AutoModelForAudioClassification.from_pretrained(GENRE_MODEL_NAME)
print("Successfully loaded audio classification model and feature extractor")
except Exception as e2:
print(f"Error loading model components: {str(e2)}")
raise RuntimeError(f"Could not load genre classification model: {str(e2)}")
# Load LLM with appropriate quantization for T4 GPU
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16,
)
llm_tokenizer = AutoTokenizer.from_pretrained(LLM_MODEL_NAME)
llm_model = AutoModelForCausalLM.from_pretrained(
LLM_MODEL_NAME,
device_map="auto",
quantization_config=bnb_config,
torch_dtype=torch.float16,
)
# Create LLM pipeline
llm_pipeline = pipeline(
"text-generation",
model=llm_model,
tokenizer=llm_tokenizer,
max_new_tokens=512,
)
# Initialize music emotion analyzer
music_analyzer = MusicAnalyzer()
# New global function moved outside of verify_flexible_syllable_counts
@functools.lru_cache(maxsize=512)
def cached_phones_for_word(word):
"""Get word pronunciations with caching for better performance."""
return pronouncing.phones_for_word(word)
@functools.lru_cache(maxsize=512)
def count_syllables_for_word(word):
"""Count syllables in a single word with caching for performance."""
# Try using pronouncing library first
pronunciations = cached_phones_for_word(word.lower())
if pronunciations:
return pronouncing.syllable_count(pronunciations[0])
# Fallback method for words not in the pronouncing dictionary
vowels = "aeiouy"
word = word.lower()
count = 0
prev_is_vowel = False
for char in word:
is_vowel = char in vowels
if is_vowel and not prev_is_vowel:
count += 1
prev_is_vowel = is_vowel
# Handle special cases
if word.endswith('e') and not word.endswith('le'):
count -= 1
if word.endswith('le') and len(word) > 2 and word[-3] not in vowels:
count += 1
if count == 0:
count = 1
return count
@functools.lru_cache(maxsize=512)
def get_word_stress(word):
"""Get the stress pattern for a word with improved fallback handling."""
pronunciations = cached_phones_for_word(word.lower())
if pronunciations:
return pronouncing.stresses(pronunciations[0])
# Enhanced fallback for words not in the dictionary
syllables = count_syllables_for_word(word)
# Common English stress patterns by word length
if syllables == 1:
return "1" # Single syllable words are stressed
elif syllables == 2:
# Most 2-syllable nouns and adjectives stress first syllable
# Common endings that indicate second-syllable stress
second_syllable_stress = ["ing", "er", "or", "ize", "ise", "ate", "ect", "end", "ure"]
if any(word.endswith(ending) for ending in second_syllable_stress):
return "01"
else:
return "10" # Default for 2-syllable words
elif syllables == 3:
# Common endings for specific stress patterns in 3-syllable words
if any(word.endswith(ending) for ending in ["ity", "ety", "ify", "ogy", "graphy"]):
return "100" # First syllable stress
elif any(word.endswith(ending) for ending in ["ation", "ious", "itis"]):
return "010" # Middle syllable stress
else:
return "100" # Default for 3-syllable words
else:
# For longer words, use common English patterns
return "1" + "0" * (syllables - 1)
# New function: Count syllables in text
def count_syllables(text):
"""Count syllables in a given text using the pronouncing library."""
words = re.findall(r'\b[a-zA-Z]+\b', text.lower())
syllable_count = 0
for word in words:
syllable_count += count_syllables_for_word(word)
return syllable_count
def extract_audio_features(audio_file):
"""Extract audio features from an audio file."""
try:
# Load the audio file using utility function
y, sr = load_audio(audio_file, SAMPLE_RATE)
if y is None or sr is None:
raise ValueError("Failed to load audio data")
# Get audio duration in seconds
duration = extract_audio_duration(y, sr)
# Extract MFCCs for genre classification (may not be needed with the pipeline)
mfccs_mean = extract_mfcc_features(y, sr, n_mfcc=20)
return {
"features": mfccs_mean,
"duration": duration,
"waveform": y,
"sample_rate": sr,
"path": audio_file # Keep path for the pipeline
}
except Exception as e:
print(f"Error extracting audio features: {str(e)}")
raise ValueError(f"Failed to extract audio features: {str(e)}")
def classify_genre(audio_data):
"""Classify the genre of the audio using the loaded model."""
try:
# First attempt: Try using the pipeline if available
if 'genre_classifier' in globals():
results = genre_classifier(audio_data["path"])
# Transform pipeline results to our expected format
top_genres = [(result["label"], result["score"]) for result in results[:3]]
return top_genres
# Second attempt: Use manually loaded model components
elif 'genre_processor' in globals() and 'genre_model' in globals():
# Process audio input with feature extractor
inputs = genre_processor(
audio_data["waveform"],
sampling_rate=audio_data["sample_rate"],
return_tensors="pt"
)
with torch.no_grad():
outputs = genre_model(**inputs)
predictions = outputs.logits.softmax(dim=-1)
# Get the top 3 genres
values, indices = torch.topk(predictions, 3)
# Map indices to genre labels
genre_labels = genre_model.config.id2label
top_genres = []
for i, (value, index) in enumerate(zip(values[0], indices[0])):
genre = genre_labels[index.item()]
confidence = value.item()
top_genres.append((genre, confidence))
return top_genres
else:
raise ValueError("No genre classification model available")
except Exception as e:
print(f"Error in genre classification: {str(e)}")
# Fallback: return a default genre if everything fails
return [("rock", 1.0)]
def detect_music(audio_data):
"""Detect if the audio is music using the MIT AST model."""
try:
# First attempt: Try using the pipeline if available
if 'music_detector' in globals():
results = music_detector(audio_data["path"])
# Look for music-related classes in the results
music_confidence = 0.0
for result in results:
label = result["label"].lower()
if any(music_term in label for music_term in ["music", "song", "singing", "instrument"]):
music_confidence = max(music_confidence, result["score"])
return music_confidence >= 0.2, results
# Second attempt: Use manually loaded model components
elif 'music_processor' in globals() and 'music_model' in globals():
# Process audio input with feature extractor
inputs = music_processor(
audio_data["waveform"],
sampling_rate=audio_data["sample_rate"],
return_tensors="pt"
)
with torch.no_grad():
outputs = music_model(**inputs)
predictions = outputs.logits.softmax(dim=-1)
# Get the top predictions
values, indices = torch.topk(predictions, 5)
# Map indices to labels
labels = music_model.config.id2label
# Check for music-related classes
music_confidence = 0.0
results = []
for i, (value, index) in enumerate(zip(values[0], indices[0])):
label = labels[index.item()].lower()
score = value.item()
results.append({"label": label, "score": score})
if any(music_term in label for music_term in ["music", "song", "singing", "instrument"]):
music_confidence = max(music_confidence, score)
return music_confidence >= 0.2, results
else:
raise ValueError("No music detection model available")
except Exception as e:
print(f"Error in music detection: {str(e)}")
return False, []
def detect_beats(y, sr):
"""Enhanced beat detection with adaptive threshold analysis, improved time signature detection and scientific confidence metrics."""
# STEP 1: Improved pre-processing with robustness for quiet sections
# Apply a small floor to avoid division-by-zero issues
y = np.clip(y, 1e-10, None) # Prevent extreme quiet sections from causing NaN
# Separate harmonic and percussive components
y_harmonic, y_percussive = librosa.effects.hpss(y)
# Generate multiple onset envelopes with smoothing for stability
onset_env_full = librosa.onset.onset_strength(y=y, sr=sr)
onset_env_perc = librosa.onset.onset_strength(y=y_percussive, sr=sr)
# Apply small smoothing to handle quiet sections
onset_env_full = np.maximum(onset_env_full, 1e-6) # Minimum threshold to avoid NaN
onset_env_perc = np.maximum(onset_env_perc, 1e-6)
# Create weighted combination
combined_onset = onset_env_full * 0.3 + onset_env_perc * 0.7
# STEP 2: Multi-strategy tempo and beat detection with confidence tracking
tempo_candidates = []
beat_candidates = []
consistency_metrics = []
# Strategy 1: Standard detection
tempo1, beats1 = librosa.beat.beat_track(
onset_envelope=combined_onset,
sr=sr,
tightness=100 # More sensitive tracking
)
tempo_candidates.append(tempo1)
beat_candidates.append(beats1)
# Calculate autocorrelation-based confidence for this tempo
ac = librosa.autocorrelate(combined_onset)
estimated_period = int(sr * 60.0 / (tempo1 * librosa.get_duration(y=y, sr=sr) / len(combined_onset)))
if estimated_period < len(ac) and estimated_period > 0:
# Measure peak height relative to surroundings
local_ac = ac[max(0, estimated_period-5):min(len(ac), estimated_period+6)]
if np.max(local_ac) > 0:
tempo1_confidence = ac[estimated_period] / np.max(local_ac)
else:
tempo1_confidence = 0.5
else:
tempo1_confidence = 0.5
consistency_metrics.append(tempo1_confidence)
# Strategy 2: Try with different tempo range for complex signatures
tempo2, beats2 = librosa.beat.beat_track(
onset_envelope=combined_onset,
sr=sr,
tightness=100,
start_bpm=60 # Lower starting BPM helps find different time signatures
)
tempo_candidates.append(tempo2)
beat_candidates.append(beats2)
# Calculate confidence for the second tempo estimate
estimated_period2 = int(sr * 60.0 / (tempo2 * librosa.get_duration(y=y, sr=sr) / len(combined_onset)))
if estimated_period2 < len(ac) and estimated_period2 > 0:
local_ac2 = ac[max(0, estimated_period2-5):min(len(ac), estimated_period2+6)]
if np.max(local_ac2) > 0:
tempo2_confidence = ac[estimated_period2] / np.max(local_ac2)
else:
tempo2_confidence = 0.5
else:
tempo2_confidence = 0.5
consistency_metrics.append(tempo2_confidence)
# Strategy 3: Use dynamic programming for beat tracking
try:
tempo3, beats3 = librosa.beat.beat_track(
onset_envelope=combined_onset,
sr=sr,
tightness=300, # Higher tightness for more structured detection
trim=False
)
tempo_candidates.append(tempo3)
beat_candidates.append(beats3)
# Calculate DP-based confidence
if len(beats3) > 1:
beat_times3 = librosa.frames_to_time(beats3, sr=sr)
intervals3 = np.diff(beat_times3)
tempo3_consistency = 1.0 / (1.0 + np.std(intervals3)/np.mean(intervals3)) if np.mean(intervals3) > 0 else 0.5
else:
tempo3_consistency = 0.5
consistency_metrics.append(tempo3_consistency)
except Exception:
# Skip if this approach fails
pass
# Select the best strategy based on improved consistency measurement
beat_consistency = []
for i, beats in enumerate(beat_candidates):
if len(beats) <= 1:
beat_consistency.append(0)
continue
times = librosa.frames_to_time(beats, sr=sr)
intervals = np.diff(times)
# Comprehensive consistency metrics with better statistical justification
if np.mean(intervals) > 0:
# Combine coefficient of variation with autocorrelation confidence
cv = np.std(intervals)/np.mean(intervals) # Lower is better
# Add adjustments for beat count reasonability
duration = librosa.get_duration(y=y, sr=sr)
expected_beats = duration * tempo_candidates[i] / 60
beats_ratio = min(len(beats) / expected_beats, expected_beats / len(beats)) if expected_beats > 0 else 0.5
# Combine metrics with scientific weighting
consistency = (0.7 * (1.0 / (1.0 + cv))) + (0.3 * consistency_metrics[i]) + (0.2 * beats_ratio)
beat_consistency.append(consistency)
else:
beat_consistency.append(0)
# Select best model with scientific confidence calculation
if beat_consistency:
best_idx = np.argmax(beat_consistency)
best_confidence = beat_consistency[best_idx] * 100 # Convert to percentage
else:
best_idx = 0
best_confidence = 50.0 # Default 50% confidence if no good metrics
tempo = tempo_candidates[best_idx]
beat_frames = beat_candidates[best_idx]
# Calculate beat entropy - scientific measure of beat pattern predictability
beat_entropy = 0.0
if len(beat_frames) > 2:
times = librosa.frames_to_time(beat_frames, sr=sr)
intervals = np.diff(times)
# Quantize intervals to detect patterns
if len(intervals) > 0 and np.std(intervals) > 0:
quantized = np.round(intervals / np.min(intervals))
# Count frequencies of each interval type
unique, counts = np.unique(quantized, return_counts=True)
probs = counts / np.sum(counts)
# Calculate Shannon entropy
beat_entropy = -np.sum(probs * np.log2(probs))
# STEP 3: Improved beat strength extraction
beat_times = librosa.frames_to_time(beat_frames, sr=sr)
# Vectorized extraction of beat strengths with improved error handling
beat_strengths = []
if len(beat_frames) > 0:
# Filter out beat frames that exceed the onset envelope length
valid_frames = [frame for frame in beat_frames if frame < len(combined_onset)]
if valid_frames:
# Vectorized extraction with normalization for consistency
raw_strengths = combined_onset[valid_frames]
# Normalize strengths to [0,1] for scientific consistency
if np.max(raw_strengths) > 0:
normalized_strengths = raw_strengths / np.max(raw_strengths)
else:
normalized_strengths = np.ones_like(raw_strengths)
beat_strengths = normalized_strengths.tolist()
# Handle remaining beats with interpolation instead of constant values
if len(beat_times) > len(beat_strengths):
missing_count = len(beat_times) - len(beat_strengths)
# Use linear interpolation for more scientific approach
if beat_strengths:
last_strength = beat_strengths[-1]
decay_factor = 0.9 # Gradual decay for trailing beats
beat_strengths.extend([last_strength * (decay_factor ** (i+1))
for i in range(missing_count)])
else:
beat_strengths = [1.0] * len(beat_times)
else:
beat_strengths = [1.0] * len(beat_times)
else:
beat_strengths = [1.0] * len(beat_times)
# STEP 4: Calculate intervals between beats
intervals = np.diff(beat_times).tolist() if len(beat_times) > 1 else []
# STEP 5: Improved time signature detection with scientific confidence
# Start with default assumption
time_signature = 4
time_sig_confidence = 70.0 # Default confidence
if len(beat_strengths) > 8:
# Use autocorrelation to find periodicity in beat strengths
if len(beat_strengths) > 4:
# Normalize beat strengths for better pattern detection
norm_strengths = np.array(beat_strengths)
if np.max(norm_strengths) > 0:
norm_strengths = norm_strengths / np.max(norm_strengths)
# Compute autocorrelation to find periodic patterns (N)
ac = librosa.autocorrelate(norm_strengths, max_size=len(norm_strengths)//2)
# Find peaks in autocorrelation (indicates periodicity)
if len(ac) > 3: # Need enough data for peak picking
# Find peaks after lag 0
peaks = librosa.util.peak_pick(ac[1:], pre_max=1, post_max=1, pre_avg=1, post_avg=1, delta=0.1, wait=1)
peaks = peaks + 1 # Adjust for the removed lag 0
if len(peaks) > 0:
# Get the first significant peak position (cycle length N)
peak_idx = peaks[0]
N = peak_idx
# Calculate confidence based on peak prominence
if peak_idx < len(ac):
peak_height = ac[peak_idx]
local_prominence = peak_height / np.mean(ac[max(0, peak_idx-2):min(len(ac), peak_idx+3)])
time_sig_confidence = min(95, 60 + 35 * local_prominence) # Scale between 60-95%
# Map common cycle lengths to time signatures with improved musical theory
if N == 2:
time_signature = 2 # Clear binary meter (2/4, 2/2, etc.)
time_sig_confidence += 5 # Boost for simple meter
elif N == 3:
time_signature = 3 # Clear triple meter (3/4, 3/8, etc.)
time_sig_confidence += 5 # Boost for simple meter
elif 4 <= N <= 5:
time_signature = N # Direct mapping for common cases (4/4 or 5/4)
elif N == 6:
# Could be 6/8 (compound duple) or 3/4 with subdivisions
# Further analyze to distinguish
group_3_count = 0
for i in range(0, len(beat_strengths) - 6, 3):
if i + 2 < len(beat_strengths):
if beat_strengths[i] > beat_strengths[i+1] and beat_strengths[i] > beat_strengths[i+2]:
group_3_count += 1
group_2_count = 0
for i in range(0, len(beat_strengths) - 4, 2):
if i + 1 < len(beat_strengths):
if beat_strengths[i] > beat_strengths[i+1]:
group_2_count += 1
# Determine if it's grouped in 2s or 3s
time_signature = 3 if group_3_count > group_2_count else 6
elif N == 8:
time_signature = 4 # 4/4 with embellishments
elif N == 5 or N == 7:
time_signature = N # Odd time signatures like 5/4 or 7/8
# STEP 6: Enhanced phrase detection with adaptive thresholds and scientific justification
phrases = []
current_phrase = []
if len(beat_times) > 0:
# Calculate adaptive thresholds using percentiles instead of fixed ratios
if len(beat_strengths) > 4:
# Define thresholds based on distribution rather than fixed values
strong_threshold = np.percentile(beat_strengths, 75) # Top 25% are "strong" beats
# For gaps, calculate significant deviation using z-scores if we have intervals
if intervals:
mean_interval = np.mean(intervals)
std_interval = np.std(intervals)
# A significant gap is > 1.5 standard deviations above mean (95th percentile)
significant_gap = mean_interval + (1.5 * std_interval) if std_interval > 0 else mean_interval * 1.3
else:
significant_gap = 0
else:
# Fallback for limited data
strong_threshold = np.max(beat_strengths) * 0.8 if beat_strengths else 1.0
significant_gap = 0
# Identify phrase boundaries with improved musical heuristics
for i in range(len(beat_times)):
current_phrase.append(i)
# Check for phrase boundary conditions
if i < len(beat_times) - 1:
# Strong beat coming up (using adaptive threshold)
is_stronger_next = False
if i < len(beat_strengths) - 1:
is_stronger_next = beat_strengths[i+1] > strong_threshold and beat_strengths[i+1] > beat_strengths[i] * 1.1
# Significant gap (using adaptive threshold)
is_longer_gap = False
if i < len(beat_times) - 1 and intervals and i < len(intervals):
is_longer_gap = intervals[i] > significant_gap
# Measure boundary based on time signature
is_measure_boundary = (i + 1) % time_signature == 0 and i > 0
# Check for significant dip in onset strength (phrase boundary often has reduced energy)
is_energy_dip = False
if i < len(beat_strengths) - 1:
onset_ratio = beat_strengths[i+1] / max(beat_strengths[i], 0.001)
is_energy_dip = onset_ratio < 0.6
# Combined decision for phrase boundary with scientific weighting
phrase_boundary_score = (
(1.5 if is_stronger_next else 0) +
(2.0 if is_longer_gap else 0) +
(1.0 if is_measure_boundary else 0) +
(0.5 if is_energy_dip else 0)
)
if (phrase_boundary_score >= 1.5 and len(current_phrase) >= 2) or \
(is_measure_boundary and len(current_phrase) >= time_signature):
phrases.append(current_phrase)
current_phrase = []
# Add the last phrase if not empty
if current_phrase and len(current_phrase) >= 2:
phrases.append(current_phrase)
# Ensure we have at least one phrase
if not phrases and len(beat_times) >= 2:
# Default to grouping by measures based on detected time signature
for i in range(0, len(beat_times), time_signature):
end = min(i + time_signature, len(beat_times))
if end - i >= 2: # Ensure at least 2 beats per phrase
phrases.append(list(range(i, end)))
# Calculate beat periodicity (average time between beats)
beat_periodicity = np.mean(intervals) if intervals else (60 / tempo)
# Return enhanced results with scientific confidence metrics
return {
"tempo": tempo,
"tempo_confidence": best_confidence, # New scientific confidence metric
"time_signature": time_signature,
"time_sig_confidence": time_sig_confidence, # New scientific confidence metric
"beat_frames": beat_frames,
"beat_times": beat_times,
"beat_count": len(beat_times),
"beat_strengths": beat_strengths,
"intervals": intervals,
"phrases": phrases,
"beat_periodicity": beat_periodicity,
"beat_entropy": beat_entropy # New scientific measure of rhythm complexity
}
def detect_beats_and_subbeats(y, sr, subdivision=4):
"""
Detect main beats and interpolate subbeats between consecutive beats.
Parameters:
y: Audio time series
sr: Sample rate
subdivision: Number of subdivisions between beats (default: 4 for quarter beats)
Returns:
Dictionary containing beat times, subbeat times, and tempo information
"""
# Detect main beats using librosa
try:
tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr)
beat_times = librosa.frames_to_time(beat_frames, sr=sr)
# Convert numpy values to native Python types
if isinstance(tempo, np.ndarray) or isinstance(tempo, np.number):
tempo = float(tempo)
# Convert beat_times to a list of floats
if isinstance(beat_times, np.ndarray):
beat_times = [float(t) for t in beat_times]
except Exception as e:
print(f"Error in beat detection: {e}")
# Default fallbacks
tempo = 120.0
beat_times = []
# Create subbeats by interpolating between main beats
subbeat_times = []
# Early return if no beats detected
if not beat_times or len(beat_times) < 2:
return {
"tempo": float(tempo) if tempo is not None else 120.0,
"beat_times": beat_times,
"subbeat_times": []
}
for i in range(len(beat_times) - 1):
# Get current and next beat time
try:
current_beat = float(beat_times[i])
next_beat = float(beat_times[i + 1])
except (IndexError, ValueError, TypeError):
continue
# Calculate time interval between beats
interval = (next_beat - current_beat) / subdivision
# Add the main beat
subbeat_times.append({
"time": float(current_beat),
"type": "main",
"strength": 1.0,
"beat_index": i
})
# Add subbeats
for j in range(1, subdivision):
subbeat_time = current_beat + j * interval
# Calculate strength based on position
# For 4/4 time, beat 3 is stronger than beats 2 and 4
if j == subdivision // 2 and subdivision == 4:
strength = 0.8 # Stronger subbeat (e.g., beat 3 in 4/4)
else:
strength = 0.5 # Weaker subbeat
subbeat_times.append({
"time": float(subbeat_time),
"type": "sub",
"strength": float(strength),
"beat_index": i,
"subbeat_index": j
})
# Add the last main beat
if beat_times:
try:
subbeat_times.append({
"time": float(beat_times[-1]),
"type": "main",
"strength": 1.0,
"beat_index": len(beat_times) - 1
})
except (ValueError, TypeError):
# Skip if conversion fails
pass
return {
"tempo": float(tempo) if tempo is not None else 120.0,
"beat_times": beat_times,
"subbeat_times": subbeat_times
}
def map_beats_to_seconds(subbeat_times, duration, fps=1.0):
"""
Map beats and subbeats to second-level intervals.
Parameters:
subbeat_times: List of dictionaries containing beat and subbeat information
duration: Total duration of the audio in seconds
fps: Frames per second (default: 1.0 for one-second intervals)
Returns:
List of dictionaries, each containing beats within a time window
"""
# Safety check for input parameters
if not isinstance(subbeat_times, list):
print("Warning: subbeat_times is not a list")
subbeat_times = []
try:
duration = float(duration)
except (ValueError, TypeError):
print("Warning: duration is not convertible to float, defaulting to 30")
duration = 30.0
# Calculate number of time windows
num_windows = int(duration * fps) + 1
# Initialize time windows
time_windows = []
for i in range(num_windows):
# Calculate window boundaries
start_time = i / fps
end_time = (i + 1) / fps
# Find beats and subbeats within this window
window_beats = []
for beat in subbeat_times:
# Safety check for beat object
if not isinstance(beat, dict):
continue
# Safely access beat time
try:
beat_time = float(beat.get("time", 0))
except (ValueError, TypeError):
continue
if start_time <= beat_time < end_time:
# Safely extract beat properties with defaults
beat_type = beat.get("type", "sub")
if not isinstance(beat_type, str):
beat_type = "sub"
# Safely handle strength
try:
strength = float(beat.get("strength", 0.5))
except (ValueError, TypeError):
strength = 0.5
# Add beat to this window
window_beats.append({
"time": beat_time,
"type": beat_type,
"strength": strength,
"relative_pos": (beat_time - start_time) / (1/fps) # Position within window (0-1)
})
# Add window to list
time_windows.append({
"second": i,
"start": start_time,
"end": end_time,
"beats": window_beats
})
return time_windows
def create_second_level_templates(sec_map, tempo, genre=None):
"""
Create syllable templates for each second-level window.
Parameters:
sec_map: List of second-level time windows with beat information
tempo: Tempo in BPM
genre: Optional genre for genre-specific adjustments
Returns:
List of template strings, one for each second
"""
# Helper function to map tempo to base syllable count
def tempo_to_syllable_base(tempo):
"""Continuous function mapping tempo to syllable base count"""
# Sigmoid-like function that smoothly transitions between syllable counts
if tempo > 180:
return 1.0
elif tempo > 140:
return 1.0 + (180 - tempo) * 0.02 # Gradual increase 1.0 → 1.8
elif tempo > 100:
return 1.8 + (140 - tempo) * 0.01 # Gradual increase 1.8 → 2.2
elif tempo > 70:
return 2.2 + (100 - tempo) * 0.02 # Gradual increase 2.2 → 2.8
else:
return 2.8 + max(0, (70 - tempo) * 0.04) # Continue increasing for very slow tempos
# Calculate base syllable count from tempo
base_syllables = tempo_to_syllable_base(tempo)
# Apply genre-specific adjustments
genre_factor = 1.0
if genre:
genre_lower = genre.lower()
if any(term in genre_lower for term in ["rap", "hip hop", "hip-hop"]):
genre_factor = 1.4 # Much higher syllable density for rap
elif any(term in genre_lower for term in ["folk", "country", "ballad"]):
genre_factor = 0.8 # Lower density for folk styles
# Create templates for each second
templates = []
for window in sec_map:
beats = window["beats"]
# If no beats in this second, create a default template
if not beats:
templates.append("w(0.5):1")
continue
# Create beat patterns for this second
beat_patterns = []
for beat in beats:
# Ensure we're dealing with a dictionary and that it has a "strength" key
if not isinstance(beat, dict):
continue # Skip this beat if it's not a dictionary
# Safely get beat type and strength
if "type" not in beat or not isinstance(beat["type"], str):
beat_type = "w" # Default to weak if type is missing or not a string
else:
beat_type = "S" if beat["type"] == "main" else "m" if beat.get("strength", 0) >= 0.7 else "w"
# Safely get strength value with fallback
try:
strength = float(beat.get("strength", 0.5))
except (ValueError, TypeError):
strength = 0.5 # Default if conversion fails
# Adjust syllable count based on beat type and strength
if beat_type == "S":
syllable_factor = 1.2 # More syllables for strong beats
elif beat_type == "m":
syllable_factor = 1.0 # Normal for medium beats
else:
syllable_factor = 0.8 # Fewer for weak beats
# Calculate final syllable count
syllable_count = base_syllables * syllable_factor * genre_factor
# Round to half-syllable precision
syllable_count = round(syllable_count * 2) / 2
# Ensure reasonable limits
syllable_count = max(0.5, min(4, syllable_count))
# Format with embedded strength value
strength_pct = round(strength * 100) / 100
beat_patterns.append(f"{beat_type}({strength_pct}):{syllable_count}")
# Join patterns with dashes - ensure we have at least one pattern
if not beat_patterns:
templates.append("w(0.5):1") # Default if no valid patterns were created
else:
second_template = "-".join(beat_patterns)
templates.append(second_template)
return templates
def detect_sections(y, sr):
"""
Detect musical segments without classifying them by type (verse, chorus, etc.).
Parameters:
y: Audio time series
sr: Sample rate
Returns:
A list of section dictionaries with start time, end time, and duration
"""
# Step 1: Extract rich feature set for comprehensive analysis
# ----------------------------------------------------------------------
hop_length = 512 # Common hop length for feature extraction
# Spectral features
S = np.abs(librosa.stft(y, hop_length=hop_length))
contrast = librosa.feature.spectral_contrast(S=S, sr=sr)
# Harmonic features with CQT-based chroma (better for harmonic analysis)
chroma = librosa.feature.chroma_cqt(y=y, sr=sr, hop_length=hop_length)
# Timbral features
mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13, hop_length=hop_length)
# Energy features
rms = librosa.feature.rms(y=y, hop_length=hop_length)
# Harmonic-percussive source separation for better rhythm analysis
y_harmonic, y_percussive = librosa.effects.hpss(y)
# Step 2: Adaptive determination of segment count based on song complexity
# ----------------------------------------------------------------------
duration = librosa.get_duration(y=y, sr=sr)
# Feature preparation for adaptive segmentation
# Stack features with proper normalization (addressing the scale issue)
feature_stack = np.vstack([
librosa.util.normalize(contrast),
librosa.util.normalize(chroma),
librosa.util.normalize(mfcc),
librosa.util.normalize(rms)
])
# Transpose to get time as first dimension
feature_matrix = feature_stack.T
# Step 3: Feature fusion using dimensionality reduction
# ----------------------------------------------------------------------
from sklearn.decomposition import PCA
# Handle very short audio files
n_components = min(8, feature_matrix.shape[0], feature_matrix.shape[1])
if feature_matrix.shape[0] > n_components and feature_matrix.shape[1] > 0:
try:
pca = PCA(n_components=n_components)
reduced_features = pca.fit_transform(feature_matrix)
except Exception as e:
print(f"PCA failed, falling back to original features: {e}")
# Fallback to simpler approach if PCA fails
reduced_features = feature_matrix
else:
# Not enough data for PCA
reduced_features = feature_matrix
# Step 4: Adaptive determination of optimal segment count
# ----------------------------------------------------------------------
# Initialize range of segment counts to try
min_segments = max(2, int(duration / 60)) # At least 2 segments, roughly 1 per minute
max_segments = min(10, int(duration / 20)) # At most 10 segments, roughly 1 per 20 seconds
# Ensure reasonable bounds
min_segments = max(2, min(min_segments, 4))
max_segments = max(min_segments + 1, min(max_segments, 8))
# Try different segment counts and evaluate with silhouette score
best_segments = min_segments
best_score = -1
from sklearn.metrics import silhouette_score
from sklearn.cluster import AgglomerativeClustering
# Only do this analysis if we have enough data
if reduced_features.shape[0] > max_segments:
for n_segments in range(min_segments, max_segments + 1):
try:
# Perform agglomerative clustering
clustering = AgglomerativeClustering(n_clusters=n_segments)
labels = clustering.fit_predict(reduced_features)
# Calculate silhouette score if we have enough samples
if len(np.unique(labels)) > 1 and len(labels) > n_segments + 1:
score = silhouette_score(reduced_features, labels)
if score > best_score:
best_score = score
best_segments = n_segments
except Exception as e:
print(f"Clustering with {n_segments} segments failed: {e}")
continue
# Use the optimal segment count for final segmentation
n_segments = best_segments
# Step 5: Final segmentation using the optimal segment count
# ----------------------------------------------------------------------
# Method 1: Use agglomerative clustering on the reduced features
try:
clustering = AgglomerativeClustering(n_clusters=n_segments)
labels = clustering.fit_predict(reduced_features)
# Convert cluster labels to boundaries by finding where labels change
boundaries = [0] # Start with the beginning
for i in range(1, len(labels)):
if labels[i] != labels[i-1]:
boundaries.append(i)
boundaries.append(len(labels)) # Add the end
# Convert to frames
bounds_frames = np.array(boundaries)
except Exception as e:
print(f"Final clustering failed: {e}")
# Fallback to librosa's agglomerative clustering on original features
bounds_frames = librosa.segment.agglomerative(feature_stack, n_segments)
# Step 6: Convert boundaries to time and create sections
# ----------------------------------------------------------------------
bounds_times = librosa.frames_to_time(bounds_frames, sr=sr, hop_length=hop_length)
# Create sections from the boundaries
sections = []
for i in range(len(bounds_times) - 1):
start = bounds_times[i]
end = bounds_times[i+1]
duration = end - start
# Skip extremely short sections
if duration < 4 and i > 0 and i < len(bounds_times) - 2:
continue
# Add section to the list (without classifying as verse/chorus/etc)
sections.append({
"type": "segment", # Generic type instead of verse/chorus/etc
"start": start,
"end": end,
"duration": duration
})
# Filter out any remaining extremely short sections
sections = [s for s in sections if s["duration"] >= 5]
return sections
def create_flexible_syllable_templates(beats_info, genre=None, phrase_mode='default'):
"""
Create enhanced syllable templates based on beat patterns with improved musical intelligence.
Parameters:
beats_info: Dictionary containing beat analysis data
genre: Optional genre to influence template creation
phrase_mode: 'default' uses provided phrases, 'auto' forces recalculation
Returns:
String of syllable templates with embedded strength values and flexible timing
"""
import numpy as np
from sklearn.cluster import KMeans
# Convert any numpy values to native Python types for safety - directly handle conversions
# Process the dictionary to convert numpy values to Python native types
if isinstance(beats_info, dict):
processed_beats_info = {}
for k, v in beats_info.items():
if isinstance(v, np.ndarray):
if v.size == 1:
processed_beats_info[k] = float(v.item())
else:
processed_beats_info[k] = [float(x) if isinstance(x, np.number) else x for x in v]
elif isinstance(v, np.number):
processed_beats_info[k] = float(v)
elif isinstance(v, list):
processed_beats_info[k] = [float(x) if isinstance(x, np.number) else x for x in v]
else:
processed_beats_info[k] = v
beats_info = processed_beats_info
# Extract basic beat information
beat_times = beats_info.get("beat_times", [])
beat_strengths = beats_info.get("beat_strengths", [1.0] * len(beat_times))
tempo = beats_info.get("tempo", 120)
time_signature = beats_info.get("time_signature", 4)
# Early return for insufficient data
if len(beat_times) < 2:
return "S(1.0):1-w(0.5):1|S(1.0):1-w(0.5):1" # Default fallback pattern
# Step 1: Improved adaptive thresholding using k-means clustering
# ----------------------------------------------------------------------
if len(beat_strengths) >= 6: # Need enough data points for clustering
# Reshape for k-means
X = np.array(beat_strengths).reshape(-1, 1)
# Use k-means with 3 clusters for Strong, Medium, Weak classification
kmeans = KMeans(n_clusters=3, random_state=0, n_init=10).fit(X)
# Find the centroid values and sort them
centroids = sorted([float(c[0]) for c in kmeans.cluster_centers_])
# Map to thresholds (using the midpoints between centroids)
if len(centroids) >= 3:
medium_threshold = (centroids[0] + centroids[1]) / 2
strong_threshold = (centroids[1] + centroids[2]) / 2
else:
# Fallback if clustering doesn't work well
medium_threshold = np.percentile(beat_strengths, 33)
strong_threshold = np.percentile(beat_strengths, 66)
else:
# For limited data, use percentile-based approach
medium_threshold = np.percentile(beat_strengths, 33)
strong_threshold = np.percentile(beat_strengths, 66)
# Step 2: Create or refine phrases based on mode
# ----------------------------------------------------------------------
phrases = beats_info.get("phrases", [])
if phrase_mode == 'auto' or not phrases:
# Create phrases based on time signature and beat strengths
phrases = []
current_phrase = []
for i in range(len(beat_times)):
current_phrase.append(i)
# Check for natural phrase endings
if (i + 1) % time_signature == 0 or i == len(beat_times) - 1:
if len(current_phrase) >= 2: # Ensure minimum phrase length
phrases.append(current_phrase)
current_phrase = []
# Add any remaining beats
if current_phrase and len(current_phrase) >= 2:
phrases.append(current_phrase)
# Step 3: Improved continuous tempo-to-syllable mapping function
# ----------------------------------------------------------------------
def tempo_to_syllable_base(tempo):
"""Continuous function mapping tempo to syllable base count with scientific curve"""
# Sigmoid-like function with more scientific parameters
# Using logistic function: L/(1+e^(-k(x-x0))) to create smooth transitions
if tempo < 40: # Very slow tempos
return 3.5 # Maximum syllables for extremely slow tempos
elif tempo > 200: # Very fast tempos
return 0.8 # Minimum syllables for extremely fast tempos
else:
# Scientific logistic function for middle range (40-200 BPM)
L = 3.5 # Upper limit
k = 0.04 # Steepness of curve
x0 = 120 # Midpoint (inflection point at normal tempo)
return L / (1 + np.exp(k * (tempo - x0)))
# Step 4: Generate enhanced templates with flexible timing
# ----------------------------------------------------------------------
syllable_templates = []
for phrase in phrases:
# Skip empty phrases
if not phrase:
continue
# Extract beat strengths for this phrase
phrase_strengths = [beat_strengths[i] for i in phrase if i < len(beat_strengths)]
if not phrase_strengths:
phrase_strengths = [1.0] * len(phrase)
# Apply improved adaptive thresholding for stress pattern detection
stress_pattern = []
for i, strength in enumerate(phrase_strengths):
# Consider both strength and metrical position with improved weighting
metrical_position = i % time_signature
# Apply improved position boosting based on musical theory
# In common time signatures, first beat gets strong emphasis,
# third beat gets moderate emphasis (in 4/4)
if metrical_position == 0: # Downbeat (first beat)
position_boost = 0.18 # Stronger boost for downbeats
elif time_signature == 4 and metrical_position == 2: # Third beat in 4/4
position_boost = 0.1 # Moderate boost for third beat
elif time_signature == 3 and metrical_position == 1: # Second beat in 3/4
position_boost = 0.05 # Slight boost for second beat in 3/4
else:
position_boost = 0 # No boost for other beats
effective_strength = strength + position_boost
if effective_strength >= strong_threshold:
stress_pattern.append(("S", effective_strength)) # Strong beat with strength
elif effective_strength >= medium_threshold:
stress_pattern.append(("m", effective_strength)) # Medium beat with strength
else:
stress_pattern.append(("w", effective_strength)) # Weak beat with strength
# Step 5: Calculate syllable counts using improved continuous function
# ----------------------------------------------------------------------
detailed_template = []
for i, (stress_type, strength) in enumerate(stress_pattern):
# Get base syllable count from tempo with more nuanced mapping
base_syllables = tempo_to_syllable_base(tempo)
# Adjust based on both stress type AND metrical position
metrical_position = i % time_signature
position_factor = 1.2 if metrical_position == 0 else 1.0
# More nuanced adjustment based on stress type
if stress_type == "S":
syllable_factor = 1.2 * position_factor # Emphasize strong beats more
elif stress_type == "m":
syllable_factor = 1.0 * position_factor # Medium beats
else:
syllable_factor = 0.8 # Weak beats
# Apply improved genre-specific adjustments with more granular factors
genre_factor = 1.0
if genre:
genre = genre.lower()
if "rap" in genre or "hip" in genre:
genre_factor = 1.5 # Significantly higher syllable density for rap
elif "folk" in genre or "country" in genre or "ballad" in genre:
genre_factor = 0.7 # Lower density for folk styles
elif "metal" in genre or "rock" in genre:
genre_factor = 1.1 # Slightly higher density for rock/metal
elif "jazz" in genre:
genre_factor = 1.2 # Higher density for jazz (complex rhythms)
elif "classical" in genre:
genre_factor = 0.9 # More moderate for classical
# Calculate adjusted syllable count with scientific weighting
raw_count = base_syllables * syllable_factor * genre_factor
# Use more precise rounding that preserves subtle differences
# Round to quarters rather than halves for more precision
rounded_count = round(raw_count * 4) / 4
# Limit to reasonable range (0.5 to 4) with improved bounds
syllable_count = max(0.5, min(4, rounded_count))
# Format with embedded strength value for reversibility
# Convert strength to 2-decimal precision percentage
strength_pct = round(strength * 100) / 100
detailed_template.append(f"{stress_type}({strength_pct}):{syllable_count}")
# Join beat templates for this phrase
phrase_template = "-".join(detailed_template)
syllable_templates.append(phrase_template)
# Step 6: Ensure valid output with improved defaults
# ----------------------------------------------------------------------
if not syllable_templates:
# Create sensible defaults based on time signature that reflect musical theory
if time_signature == 3: # 3/4 time - waltz pattern
syllable_templates = ["S(0.95):2-w(0.4):1-w(0.35):1"] # 3/4 default
elif time_signature == 2: # 2/4 time - march pattern
syllable_templates = ["S(0.95):1.5-w(0.4):1"] # 2/4 default
else: # 4/4 time - common time
syllable_templates = ["S(0.95):2-w(0.4):1-m(0.7):1.5-w(0.35):1"] # 4/4 default
# Join all phrase templates with the original separator for compatibility
return "|".join(syllable_templates)
def format_syllable_templates_for_prompt(syllable_templates, arrow="→", line_wrap=10,
structured_output=False, beat_types=None):
"""
Convert technical syllable templates into clear, human-readable instructions with
enhanced flexibility and customization options.
Parameters:
syllable_templates: String or list of templates
arrow: Symbol to use between beats (default: "→")
line_wrap: Number of beats before automatic line wrapping (0 = no wrapping)
structured_output: If True, return structured data instead of text
beat_types: Custom mapping for beat types (default: None, uses standard mapping)
Returns:
Human-readable instructions or structured data depending on parameters
"""
if not syllable_templates:
return {} if structured_output else ""
# Define standard beat type mapping (extensible)
default_beat_types = {
"S": {"name": "STRONG", "description": "stressed syllable"},
"m": {"name": "medium", "description": "medium-stressed syllable"},
"w": {"name": "weak", "description": "unstressed syllable"},
"X": {"name": "EXTRA", "description": "extra strong syllable"},
"L": {"name": "legato", "description": "connected/tied syllable"}
}
# Use custom mapping if provided, otherwise use default
beat_types = beat_types or default_beat_types
# Initialize structured output if requested
structured_data = {"lines": [], "explanations": []} if structured_output else None
# Improved format detection - more robust than just checking for "|"
is_enhanced_format = False
# Check if it's a string with enhanced format patterns
if isinstance(syllable_templates, str):
# Look for enhanced format patterns - check for beat type indicators
if any(bt + "(" in syllable_templates or bt + ":" in syllable_templates or bt + "[" in syllable_templates
for bt in beat_types.keys()):
is_enhanced_format = True
# Secondary check for the "|" delimiter between phrases
elif "|" in syllable_templates:
is_enhanced_format = True
# Initialize the output with a brief explanatory header
output = []
if is_enhanced_format:
# Split into individual phrase templates
phrases = syllable_templates.split("|") if "|" in syllable_templates else [syllable_templates]
# Process each phrase into human-readable instructions
for i, phrase in enumerate(phrases):
# Check for special annotations
has_swing = "(swing)" in phrase
if has_swing:
phrase = phrase.replace("(swing)", "") # Remove annotation for processing
beats = phrase.split("-")
beat_instructions = []
# Process each beat in the phrase
for j, beat in enumerate(beats):
# Extract beat type and information
beat_info = {"original": beat, "type": None, "count": None, "strength": None}
# Handle enhanced format with embedded strength values: S(0.95):2
if "(" in beat and ")" in beat and ":" in beat:
parts = beat.split(":")
beat_type = parts[0].split("(")[0] # Extract beat type
strength = parts[0].split("(")[1].rstrip(")") # Extract strength value
count = parts[1] # Extract syllable count
beat_info["type"] = beat_type
beat_info["count"] = count
beat_info["strength"] = strength
# Handle simpler format: S2, m1, w1
elif any(beat.startswith(bt) for bt in beat_types.keys()) and len(beat) > 1:
beat_type = beat[0]
count = beat[1:]
beat_info["type"] = beat_type
beat_info["count"] = count
# Fallback for any other format
else:
beat_instructions.append(beat)
continue
# Format the beat instruction based on type
if beat_info["type"] in beat_types:
type_name = beat_types[beat_info["type"]]["name"]
if beat_info["strength"]:
beat_instructions.append(f"{type_name}({beat_info['count']}) [{beat_info['strength']}]")
else:
beat_instructions.append(f"{type_name}({beat_info['count']})")
else:
# Unknown beat type, use as-is
beat_instructions.append(beat)
# Handle line wrapping for readability
if line_wrap > 0 and len(beat_instructions) > line_wrap:
wrapped_instructions = []
for k in range(0, len(beat_instructions), line_wrap):
section = beat_instructions[k:k+line_wrap]
wrapped_instructions.append(f"{arrow} ".join(section))
line_desc = f"\n {arrow} ".join(wrapped_instructions)
else:
line_desc = f" {arrow} ".join(beat_instructions)
# Add swing notation if present
if has_swing:
line_desc += " [with swing feel]"
# Add to output
line_output = f"Line {i+1}: {line_desc}"
output.append(line_output)
if structured_output:
structured_data["lines"].append({
"line_number": i+1,
"beats": [{"original": beats[j],
"type": beat_info.get("type"),
"count": beat_info.get("count"),
"strength": beat_info.get("strength")}
for j, beat_info in enumerate([b for b in beats if isinstance(b, dict)])],
"has_swing": has_swing
})
# Add explanation of notation after the lines
explanation = [
"\n📝 UNDERSTANDING THE NOTATION:"
]
# Add descriptions for each beat type that was actually used
used_beat_types = set()
for phrase in phrases:
for beat in phrase.split("-"):
for bt in beat_types.keys():
if beat.startswith(bt):
used_beat_types.add(bt)
for bt in used_beat_types:
if bt in beat_types:
name = beat_types[bt]["name"]
desc = beat_types[bt]["description"]
explanation.append(f"- {name}(n): Place a {desc} here, plus (n-1) unstressed syllables")
explanation.extend([
f"- {arrow}: Indicates flow from one beat to the next",
"- [0.xx]: Beat strength value (higher = more emphasis needed)"
])
output.extend(explanation)
if structured_output:
structured_data["explanations"] = explanation
# Add examples for half-syllable values if they appear in the templates
has_half_syllables = any((".5" in beat) for phrase in phrases for beat in phrase.split("-"))
if has_half_syllables:
half_syllable_examples = [
"\n🎵 HALF-SYLLABLE EXAMPLES:",
"- STRONG(1.5): One stressed syllable followed by an unstressed half-syllable",
" Example: \"LOVE you\" where \"LOVE\" is stressed and \"you\" is quick",
"- medium(2.5): One medium syllable plus one-and-a-half unstressed syllables",
" Example: \"Wait for the\" where \"Wait\" is medium-stressed and \"for the\" is quick"
]
output.extend(half_syllable_examples)
if structured_output:
structured_data["half_syllable_examples"] = half_syllable_examples
# Add swing explanation if needed
if any("swing" in phrase for phrase in phrases):
swing_guide = [
"\n🎶 SWING RHYTHM GUIDE:",
"- In swing, syllables should be unevenly timed (long-short pattern)",
"- Example: \"SUM-mer TIME\" in swing feels like \"SUM...mer-TIME\" with delay"
]
output.extend(swing_guide)
if structured_output:
structured_data["swing_guide"] = swing_guide
# Handle the original format or segment dictionaries
else:
formatted_lines = []
if isinstance(syllable_templates, list):
for i, template in enumerate(syllable_templates):
if isinstance(template, dict) and "syllable_template" in template:
line = f"Line {i+1}: {template['syllable_template']} syllables"
formatted_lines.append(line)
if structured_output:
structured_data["lines"].append({
"line_number": i+1,
"syllable_count": template["syllable_template"]
})
elif isinstance(template, str):
line = f"Line {i+1}: {template} syllables"
formatted_lines.append(line)
if structured_output:
structured_data["lines"].append({
"line_number": i+1,
"syllable_count": template
})
output = formatted_lines
else:
output = [str(syllable_templates)]
if structured_output:
structured_data["raw_content"] = str(syllable_templates)
# Add general application advice
application_tips = [
"\n💡 APPLICATION TIPS:",
"1. Strong beats need naturally stressed syllables (like the START of \"RE-mem-ber\")",
"2. Place important words on strong beats for natural emphasis",
"3. Vowel sounds work best for sustained or emphasized syllables",
"4. Keep consonant clusters (like \"str\" or \"thr\") on weak beats"
]
output.extend(application_tips)
if structured_output:
structured_data["application_tips"] = application_tips
return structured_data
return "\n".join(output)
def verify_flexible_syllable_counts(lyrics, templates, second_level_templates=None):
"""
Enhanced verification of syllable counts and stress patterns with precise alignment analysis
for both phrase-level and second-level templates.
"""
import re
import pronouncing
import numpy as np
import functools
from itertools import chain
print(f"DEBUG: In verify_flexible_syllable_counts, type of lyrics={type(lyrics)}")
print(f"DEBUG: Type of templates={type(templates)}")
# Ensure lyrics is a string
if not isinstance(lyrics, str):
print(f"DEBUG: lyrics is not a string, it's {type(lyrics)}")
# Convert to string if possible
try:
lyrics = str(lyrics)
except Exception as e:
print(f"DEBUG: Cannot convert lyrics to string: {str(e)}")
return "Error: Cannot process non-string lyrics"
# Ensure templates is a list
if not isinstance(templates, list):
print(f"DEBUG: templates is not a list, it's {type(templates)}")
# If it's not a list, create a single-item list
if templates is not None:
templates = [templates]
else:
templates = []
# Split lyrics into lines
lines = [line.strip() for line in lyrics.split("\n") if line.strip()]
# Initialize tracking variables
verification_notes = []
detailed_analysis = []
stress_misalignments = []
total_mismatch_count = 0
# Process each lyric line against its template
for i, line in enumerate(lines):
if i >= len(templates):
break
template = templates[i]
print(f"DEBUG: Processing template {i+1}, type={type(template)}")
# Extract the template string from different possible formats
template_str = None
if isinstance(template, dict) and "syllable_template" in template:
template_str = template["syllable_template"]
elif isinstance(template, str):
template_str = template
else:
print(f"DEBUG: Skipping template {i+1}, not a string or dict with syllable_template")
continue
if not isinstance(template_str, str):
print(f"DEBUG: template_str is not a string, it's {type(template_str)}")
continue
# Handle multiple phrases in template - process ALL phrases, not just the first
template_phrases = [template_str]
if "|" in template_str:
template_phrases = template_str.split("|")
# Check against all phrases and find the best match
best_match_diff = float('inf')
best_match_phrase = None
best_phrase_beats = None
actual_count = count_syllables(line)
for phrase_idx, phrase in enumerate(template_phrases):
# Extract beat patterns and expected syllable counts from template
beats_info = []
total_expected = 0
# Enhanced template parsing
if "-" in phrase:
beat_templates = phrase.split("-")
# Parse each beat template
for beat in beat_templates:
beat_info = {"original": beat, "type": None, "count": 1, "strength": None}
# Handle templates with embedded strength values: S(0.95):2
if "(" in beat and ")" in beat and ":" in beat:
parts = beat.split(":")
beat_type = parts[0].split("(")[0]
try:
strength = float(parts[0].split("(")[1].rstrip(")"))
except ValueError:
strength = 1.0
# Handle potential float syllable counts
try:
count = float(parts[1])
# Convert to int if it's a whole number
if count == int(count):
count = int(count)
except ValueError:
count = 1
beat_info.update({
"type": beat_type,
"count": count,
"strength": strength
})
# Handle simple format: S2, m1, w1
elif any(beat.startswith(x) for x in ["S", "m", "w", "X", "L"]):
beat_type = beat[0]
# Extract count, supporting float values
try:
count_str = beat[1:]
count = float(count_str)
if count == int(count):
count = int(count)
except ValueError:
count = 1
beat_info.update({
"type": beat_type,
"count": count
})
# Legacy format - just numbers
else:
try:
count = float(beat)
if count == int(count):
count = int(count)
beat_info["count"] = count
except ValueError:
pass
beats_info.append(beat_info)
total_expected += beat_info["count"]
# Compare this phrase to actual syllable count
phrase_diff = abs(actual_count - total_expected)
# Adaptive threshold based on expected syllables
expected_ratio = 0.15 if total_expected > 10 else 0.25
phrase_threshold = max(1, round(total_expected * expected_ratio))
# If this is the best match so far, store it
if phrase_diff < best_match_diff:
best_match_diff = phrase_diff
best_match_phrase = phrase
best_phrase_beats = beats_info
# For very simple templates without "-"
else:
try:
total_expected = float(phrase)
phrase_diff = abs(actual_count - total_expected)
if phrase_diff < best_match_diff:
best_match_diff = phrase_diff
best_match_phrase = phrase
best_phrase_beats = [{"count": total_expected}]
except ValueError:
pass
# If we found a reasonable match, proceed with analysis
if best_match_phrase and best_phrase_beats:
total_expected = sum(beat["count"] for beat in best_phrase_beats)
# Calculate adaptive threshold based on expected syllables
expected_ratio = 0.15 if total_expected > 10 else 0.25
threshold = max(1, round(total_expected * expected_ratio))
# Check if total syllable count is significantly off
if total_expected > 0 and best_match_diff > threshold:
verification_notes.append(f"Line {i+1}: Expected {total_expected} syllables, got {actual_count}")
total_mismatch_count += 1
# Extract words and perform detailed alignment analysis
words = re.findall(r'\b[a-zA-Z]+\b', line.lower())
# Get syllable count and stress for each word
word_analysis = []
cumulative_syllables = 0
for word in words:
syllable_count = count_syllables_for_word(word)
# Get stress pattern
stress_pattern = get_word_stress(word)
word_analysis.append({
"word": word,
"syllables": syllable_count,
"stress_pattern": stress_pattern,
"position": cumulative_syllables
})
cumulative_syllables += syllable_count
# Analyze alignment with beats - only if there are beat types
if best_phrase_beats and any(b.get("type") == "S" for b in best_phrase_beats if "type" in b):
# Identify positions where strong syllables should fall
strong_positions = []
current_pos = 0
for beat in best_phrase_beats:
if beat.get("type") == "S":
strong_positions.append(current_pos)
current_pos += beat.get("count", 1)
# Check if strong syllables align with strong beats
alignment_issues = []
for pos in strong_positions:
# Find which word contains this position
misaligned_word = None
for word_info in word_analysis:
word_start = word_info["position"]
word_end = word_start + word_info["syllables"]
if word_start <= pos < word_end:
# Check if a stressed syllable falls on this position
syllable_in_word = pos - word_start
# Get stress pattern for this word
stress = word_info["stress_pattern"]
# If we have stress information and this syllable isn't stressed
if stress and syllable_in_word < len(stress) and stress[syllable_in_word] != '1':
misaligned_word = word_info["word"]
alignment_issues.append(f"'{word_info['word']}' (unstressed syllable on strong beat)")
stress_misalignments.append({
"line": i+1,
"word": word_info["word"],
"position": pos,
"suggestion": get_stress_aligned_alternatives(word_info["word"], syllable_in_word)
})
break
if alignment_issues:
verification_notes.append(f" → Stress misalignments: {', '.join(alignment_issues)}")
# Generate a visual alignment map for better understanding
alignment_map = generate_alignment_visualization(line, best_phrase_beats, word_analysis)
if alignment_map:
detailed_analysis.append(f"Line {i+1} Alignment Analysis:\n{alignment_map}")
else:
# If no matching template was found
verification_notes.append(f"Line {i+1}: Unable to find matching template pattern")
# Add second-level verification if templates are provided
if second_level_templates:
verification_notes.append("\n=== SECOND-LEVEL VERIFICATION ===\n")
# Check each second against corresponding line
for i, template in enumerate(second_level_templates):
if i >= len(lines):
break
line = lines[i]
# Skip section headers
if line.startswith('[') and ']' in line:
continue
actual_count = count_syllables(line)
# Parse template to get expected syllable count
total_expected = 0
beat_patterns = []
# Handle templates with beat patterns like "S(0.95):2-w(0.4):1"
if isinstance(template, str) and "-" in template:
for beat in template.split("-"):
if ":" in beat:
try:
count_part = beat.split(":")[1]
count = float(count_part)
total_expected += count
# Extract beat type for alignment check
beat_type = beat.split("(")[0] if "(" in beat else beat[0]
beat_patterns.append((beat_type, count))
except (IndexError, ValueError):
pass
# Compare actual vs expected count
if total_expected > 0:
# Calculate adaptive threshold based on expected syllables
expected_ratio = 0.2 # More strict at second level
threshold = max(0.5, round(total_expected * expected_ratio))
difference = abs(actual_count - total_expected)
if difference > threshold:
verification_notes.append(f"Second {i+1}: Expected {total_expected} syllables, got {actual_count}")
total_mismatch_count += 1
# Check for stress misalignment in this second
words = re.findall(r'\b[a-zA-Z]+\b', line.lower())
word_analysis = []
cumulative_syllables = 0
for word in words:
syllable_count = count_syllables_for_word(word)
stress_pattern = get_word_stress(word)
word_analysis.append({
"word": word,
"syllables": syllable_count,
"stress_pattern": stress_pattern,
"position": cumulative_syllables
})
cumulative_syllables += syllable_count
# Check if stressed syllables align with strong beats
if beat_patterns:
strong_positions = []
current_pos = 0
for beat_type, count in beat_patterns:
if beat_type == "S":
strong_positions.append(current_pos)
current_pos += count
# Look for misalignments
for pos in strong_positions:
for word_info in word_analysis:
word_start = word_info["position"]
word_end = word_start + word_info["syllables"]
if word_start <= pos < word_end:
# Check if a stressed syllable falls on this position
syllable_in_word = int(pos - word_start)
stress = word_info["stress_pattern"]
if stress and syllable_in_word < len(stress) and stress[syllable_in_word] != '1':
verification_notes.append(f" → In second {i+1}, '{word_info['word']}' has unstressed syllable on strong beat")
break
# Only add detailed analysis if we have rhythm mismatches
if verification_notes:
lyrics += "\n\n[Note: Potential rhythm mismatches detected in these lines:]\n"
lyrics += "\n".join(verification_notes)
if detailed_analysis:
lyrics += "\n\n[Detailed Alignment Analysis:]\n"
lyrics += "\n\n".join(detailed_analysis)
lyrics += "\n\n[How to fix rhythm mismatches:]\n"
lyrics += "1. Make sure stressed syllables (like 'LO' in 'LOV-er') fall on STRONG beats\n"
lyrics += "2. Adjust syllable counts to match the template (add/remove words or use different words)\n"
lyrics += "3. Try using words where natural stress aligns with musical rhythm\n"
# Add specific word substitution suggestions if we found stress misalignments
if stress_misalignments:
lyrics += "\n[Specific word replacement suggestions:]\n"
for issue in stress_misalignments[:5]: # Limit to first 5 issues
if issue["suggestion"]:
lyrics += f"Line {issue['line']}: Consider replacing '{issue['word']}' with: {issue['suggestion']}\n"
return lyrics
def generate_alignment_visualization(line, beats_info, word_analysis):
"""Generate a visual representation of syllable alignment with beats."""
if not beats_info or not word_analysis:
return None
# Create a syllable breakdown with stress information
syllable_breakdown = []
syllable_stresses = []
for word_info in word_analysis:
word = word_info["word"]
syllables = word_info["syllables"]
stress = word_info["stress_pattern"] or ""
# Extend stress pattern if needed
while len(stress) < syllables:
stress += "0"
# Get syllable breakdown
parts = naive_syllable_split(word, syllables)
for i, part in enumerate(parts):
syllable_breakdown.append(part)
if i < len(stress):
syllable_stresses.append(stress[i])
else:
syllable_stresses.append("0")
# Create beat pattern
beat_types = []
current_pos = 0
for beat in beats_info:
beat_type = beat.get("type", "-")
count = beat.get("count", 1)
# Handle whole numbers and half syllables
if isinstance(count, int):
beat_types.extend([beat_type] * count)
else:
# For half syllables, round up and use markers
whole_part = int(count)
frac_part = count - whole_part
if whole_part > 0:
beat_types.extend([beat_type] * whole_part)
if frac_part > 0:
beat_types.append(f"{beat_type}½")
# Ensure we have enough beat types
while len(beat_types) < len(syllable_breakdown):
beat_types.append("-")
# Trim beat types if too many
beat_types = beat_types[:len(syllable_breakdown)]
# Generate the visualization with highlighted misalignments
result = []
# First line: syllable breakdown with stress indicators
syllable_display = []
for i, syllable in enumerate(syllable_breakdown):
if i < len(syllable_stresses) and syllable_stresses[i] == "1":
syllable_display.append(syllable.upper()) # Uppercase for stressed syllables
else:
syllable_display.append(syllable.lower()) # Lowercase for unstressed
result.append(" - ".join(syllable_display))
# Second line: beat indicators with highlighting for misalignments
beat_indicators = []
for i, (syllable, beat_type) in enumerate(zip(syllable_stresses, beat_types)):
if beat_type == "S" or beat_type.startswith("S"):
if syllable == "1":
beat_indicators.append("↑") # Aligned strong beat
else:
beat_indicators.append("❌") # Misaligned strong beat
elif beat_type == "m" or beat_type.startswith("m"):
beat_indicators.append("•") # Medium beat
elif beat_type == "w" or beat_type.startswith("w"):
beat_indicators.append("·") # Weak beat
else:
beat_indicators.append(" ")
result.append(" ".join(beat_indicators))
# Third line: beat types
result.append(" - ".join(beat_types))
return "\n".join(result)
@functools.lru_cache(maxsize=256)
def naive_syllable_split(word, syllable_count):
"""Naively split a word into the specified number of syllables, with caching for performance."""
if syllable_count <= 1:
return [word]
# Common syllable break patterns
vowels = "aeiouy"
consonants = "bcdfghjklmnpqrstvwxz"
# Find potential split points
splits = []
for i in range(1, len(word) - 1):
if word[i] in consonants and word[i-1] in vowels:
splits.append(i)
elif word[i] in vowels and word[i-1] in consonants and word[i+1] in consonants:
splits.append(i+1)
# Ensure we have enough split points
while len(splits) < syllable_count - 1:
for i in range(1, len(word)):
if i not in splits:
splits.append(i)
break
# Sort and limit
splits.sort()
splits = splits[:syllable_count - 1]
# Split the word
result = []
prev = 0
for pos in splits:
result.append(word[prev:pos])
prev = pos
result.append(word[prev:])
return result
def get_stress_aligned_alternatives(word, position_to_stress):
"""Suggest alternative words with proper stress at the required position."""
# This would ideally use a more sophisticated dictionary lookup,
# but here's a simple implementation with common word patterns
syllable_count = count_syllables_for_word(word)
# Common synonyms/replacements by syllable count with stress position
if syllable_count == 2:
if position_to_stress == 0: # Need stress on first syllable
first_stress = ["love-ly", "won-der", "beau-ty", "danc-ing", "dream-ing",
"heart-beat", "sun-light", "moon-light", "star-light"]
return ", ".join(first_stress[:3])
else: # Need stress on second syllable
second_stress = ["be-LIEVE", "a-BOVE", "a-ROUND", "to-DAY", "a-LIVE",
"a-LONE", "be-HOLD", "re-TURN", "de-LIGHT"]
return ", ".join(second_stress[:3])
elif syllable_count == 3:
if position_to_stress == 0: # First syllable stress
return "MEM-o-ry, WON-der-ful, BEAU-ti-ful"
elif position_to_stress == 1: # Second syllable stress
return "a-MAZE-ing, to-GE-ther, for-EV-er"
else: # Third syllable stress
return "un-der-STAND, o-ver-COME, ne-ver-MORE"
# For other cases, just provide general guidance
return f"a word with stress on syllable {position_to_stress + 1}"
def generate_lyrics(genre, duration, emotion_results, song_structure=None, lyrics_requirements=None):
"""
Generate lyrics based on the genre, emotion, and structure analysis with enhanced rhythmic alignment.
This improved version uses advanced template creation, better formatting, and verification with
potential refinement for lyrics that perfectly match the musical rhythm patterns.
Parameters:
genre: Musical genre of the audio
duration: Duration of the audio in seconds
emotion_results: Dictionary containing emotional analysis results
song_structure: Optional dictionary containing song structure analysis
lyrics_requirements: Optional user-provided requirements for the lyrics
Returns:
Generated lyrics aligned with the rhythm patterns of the music
"""
# Safety check for strings
def is_safe_dict_access(obj, key):
"""Safe dictionary key access with type checking"""
if not isinstance(obj, dict):
print(f"WARNING: Attempted to access key '{key}' on non-dictionary object of type {type(obj)}")
return False
return key in obj
# Ensure emotion_results is a dictionary with the expected structure
if not isinstance(emotion_results, dict):
emotion_results = {
"emotion_analysis": {"primary_emotion": "Unknown"},
"theme_analysis": {"primary_theme": "Unknown"},
"rhythm_analysis": {"tempo": 0},
"tonal_analysis": {"key": "Unknown", "mode": ""},
"summary": {"tempo": 0, "key": "Unknown", "mode": "", "primary_emotion": "Unknown", "primary_theme": "Unknown"}
}
# Ensure song_structure is properly structured
if song_structure is not None and not isinstance(song_structure, dict):
print(f"WARNING: song_structure is not a dict, it's {type(song_structure)}")
song_structure = None
print(f"DEBUG: Starting generate_lyrics with genre={genre}, duration={duration}")
print(f"DEBUG: Type of song_structure={type(song_structure)}")
print(f"DEBUG: Type of emotion_results={type(emotion_results)}")
# Helper function to safely access dictionary with string keys
def safe_dict_get(d, key, default=None):
"""Safely get a value from a dictionary, handling non-dictionary objects."""
if not isinstance(d, dict):
print(f"WARNING: Attempted to access key '{key}' in non-dictionary object of type {type(d)}")
return default
return d.get(key, default)
# Extract emotion and theme data with safe defaults
primary_emotion = safe_dict_get(safe_dict_get(emotion_results, "emotion_analysis", {}), "primary_emotion", "Unknown")
primary_theme = safe_dict_get(safe_dict_get(emotion_results, "theme_analysis", {}), "primary_theme", "Unknown")
# Extract numeric values safely with fallbacks
try:
tempo = float(safe_dict_get(safe_dict_get(emotion_results, "rhythm_analysis", {}), "tempo", 0.0))
except (ValueError, TypeError):
tempo = 0.0
key = safe_dict_get(safe_dict_get(emotion_results, "tonal_analysis", {}), "key", "Unknown")
mode = safe_dict_get(safe_dict_get(emotion_results, "tonal_analysis", {}), "mode", "")
# Format syllable templates for the prompt
syllable_guidance = ""
templates_for_verification = []
# Create a structure visualization to help with lyrics-music matching
structure_visualization = "=== MUSIC-LYRICS STRUCTURE MATCHING ===\n\n"
structure_visualization += f"Song Duration: {duration:.1f} seconds\n"
structure_visualization += f"Tempo: {tempo:.1f} BPM\n\n"
# Add second-level template guidance if available
if song_structure and is_safe_dict_access(song_structure, "second_level") and is_safe_dict_access(song_structure.get("second_level", {}), "templates"):
print(f"DEBUG: Using second-level templates")
second_level_templates = song_structure.get("second_level", {}).get("templates", [])
# Create second-level guidance
second_level_guidance = "\nSECOND-BY-SECOND RHYTHM INSTRUCTIONS:\n"
second_level_guidance += "Each line below corresponds to ONE SECOND of audio. Follow these rhythm patterns EXACTLY:\n\n"
# Format each second's template
formatted_second_templates = []
for i, template in enumerate(second_level_templates):
if i < min(60, len(second_level_templates)): # Limit to 60 seconds to avoid overwhelming the LLM
formatted_template = format_syllable_templates_for_prompt(template, arrow="→", line_wrap=0)
formatted_second_templates.append(f"Second {i+1}: {formatted_template}")
second_level_guidance += "\n".join(formatted_second_templates)
# Add critical instructions for second-level alignment
second_level_guidance += "\n\nCRITICAL: Create ONE LINE of lyrics for EACH SECOND, following the exact rhythm pattern."
second_level_guidance += "\nIf a second has no beats, use it for a breath or pause in the lyrics."
second_level_guidance += "\nThe first line of your lyrics MUST match Second 1, the second line matches Second 2, and so on."
# Add to syllable guidance
syllable_guidance = second_level_guidance
# Store templates for verification
templates_for_verification = second_level_templates
elif song_structure:
print(f"DEBUG: Checking flexible structure")
# Try to use flexible structure if available
if is_safe_dict_access(song_structure, "flexible_structure"):
print(f"DEBUG: Using flexible structure")
flexible = song_structure.get("flexible_structure", {})
if is_safe_dict_access(flexible, "segments") and len(flexible.get("segments", [])) > 0:
print(f"DEBUG: Found segments in flexible structure")
# Get the segments
segments = flexible.get("segments", [])
# Add structure visualization
structure_visualization += f"Total segments: {len(segments)}\n"
structure_visualization += "Each segment represents one musical phrase for which you should write ONE line of lyrics.\n\n"
# Process each segment to create enhanced rhythmic templates
enhanced_templates = []
for i, segment in enumerate(segments):
if i < 30: # Extend limit to 30 lines to handle longer songs
# Get the beat information for this segment
segment_start = segment["start"]
segment_end = segment["end"]
# Add segment info to visualization
structure_visualization += f"Segment {i+1}: {segment_start:.1f}s - {segment_end:.1f}s (duration: {segment_end-segment_start:.1f}s)\n"
# Find beats within this segment
segment_beats = []
# Add type checking for beat_times access
print(f"DEBUG: Checking beat_times in flexible structure")
if is_safe_dict_access(flexible, "beats") and is_safe_dict_access(flexible.get("beats", {}), "beat_times"):
beat_times = flexible.get("beats", {}).get("beat_times", [])
if isinstance(beat_times, list):
beat_strengths = flexible.get("beats", {}).get("beat_strengths", [])
for j, beat_time in enumerate(beat_times):
if segment_start <= beat_time < segment_end:
# Add this beat to the segment
segment_beats.append(j)
# Create segment-specific beat info
segment_beats_info = {
"beat_times": [beat_times[j] for j in segment_beats if j < len(beat_times)],
"tempo": flexible.get("beats", {}).get("tempo", 120)
}
if beat_strengths and isinstance(beat_strengths, list):
segment_beats_info["beat_strengths"] = [
beat_strengths[j] for j in segment_beats
if j < len(beat_strengths)
]
# Create a phrase structure for this segment
segment_beats_info["phrases"] = [segment_beats]
# Generate enhanced template with genre awareness and auto phrasing
print(f"DEBUG: Creating flexible syllable template for segment {i+1}")
enhanced_template = create_flexible_syllable_templates(
segment_beats_info,
genre=genre,
phrase_mode='auto' if i == 0 else 'default'
)
enhanced_templates.append(enhanced_template)
templates_for_verification.append(enhanced_template)
# Add template to visualization
structure_visualization += f" Template: {enhanced_template}\n"
else:
print(f"DEBUG: beat_times is not a list, it's {type(beat_times)}")
else:
print(f"DEBUG: beats or beat_times not found in flexible structure")
# Skip segment if we don't have beat information
continue
# Use these templates to determine rhythm patterns, without classifying as verse/chorus
pattern_groups = {}
for i, template in enumerate(enhanced_templates):
# Create simplified version for pattern matching
simple_pattern = template.replace("(", "").replace(")", "").replace(":", "")
# Check if this pattern is similar to any we've seen
found_match = False
for group, patterns in pattern_groups.items():
if any(simple_pattern == p.replace("(", "").replace(")", "").replace(":", "") for p in patterns):
pattern_groups[group].append(template)
found_match = True
break
if not found_match:
# New pattern type
group_name = f"Group_{len(pattern_groups) + 1}"
pattern_groups[group_name] = [template]
# Format templates with improved formatting for the prompt
syllable_guidance = "CRITICAL RHYTHM INSTRUCTIONS:\n"
syllable_guidance += "Each line of lyrics MUST match exactly with one musical phrase/segment.\n"
syllable_guidance += "Follow these rhythm patterns for each line (STRONG beats need stressed syllables):\n\n"
# Add formatted templates without section labels
formatted_templates = []
for i, template in enumerate(enhanced_templates):
formatted_templates.append(format_syllable_templates_for_prompt([template], arrow="→", line_wrap=8))
syllable_guidance += "\n".join(formatted_templates)
# Store info for later use in traditional sections approach
use_sections = True
# Use the detected section structure for traditional approach
if verse_lines > 0:
verse_lines = min(verse_lines, total_lines // 2) # Ensure reasonable limits
else:
verse_lines = total_lines // 2
if chorus_lines > 0:
chorus_lines = min(chorus_lines, total_lines // 3)
else:
chorus_lines = total_lines // 3
if bridge_lines > 0:
bridge_lines = min(bridge_lines, total_lines // 6)
else:
bridge_lines = 0
# Fallback to traditional sections if needed
elif song_structure and is_safe_dict_access(song_structure, "syllables") and song_structure.get("syllables"):
syllable_guidance = "RHYTHM PATTERN INSTRUCTIONS:\n"
syllable_guidance += "Follow these syllable patterns for each section. Each line should match ONE phrase:\n\n"
# Count sections for visualization
section_counts = {"verse": 0, "chorus": 0, "bridge": 0, "intro": 0, "outro": 0}
for section in song_structure.get("syllables", []):
if not isinstance(section, dict):
continue
section_type = section.get("type", "verse")
section_counts[section_type] = section_counts.get(section_type, 0) + 1
if is_safe_dict_access(section, "syllable_template"):
# Process to create enhanced template
if is_safe_dict_access(song_structure, "beats") and is_safe_dict_access(song_structure.get("beats", {}), "beat_times"):
section_beats_info = {
"beat_times": [beat for beat in song_structure.get("beats", {}).get("beat_times", [])
if section.get("start", 0) <= beat < section.get("end", 0)],
"tempo": song_structure.get("beats", {}).get("tempo", 120)
}
if is_safe_dict_access(song_structure.get("beats", {}), "beat_strengths"):
section_beats_info["beat_strengths"] = [
strength for i, strength in enumerate(song_structure.get("beats", {}).get("beat_strengths", []))
if i < len(song_structure.get("beats", {}).get("beat_times", [])) and
section.get("start", 0) <= song_structure.get("beats", {}).get("beat_times", [])[i] < section.get("end", 0)
]
# Create a phrase structure for this section
section_beats_info["phrases"] = [list(range(len(section_beats_info["beat_times"])))]
# Create a phrase structure for this section
section_beats_info["phrases"] = [list(range(len(section_beats_info["beat_times"])))]
# Generate enhanced template with genre awareness
enhanced_template = create_flexible_syllable_templates(
section_beats_info,
genre=genre,
phrase_mode='auto' if section['type'] == 'verse' else 'default'
)
syllable_guidance += f"[{section['type'].capitalize()}]:\n"
syllable_guidance += format_syllable_templates_for_prompt(
enhanced_template,
arrow="→",
line_wrap=6
) + "\n\n"
templates_for_verification.append(section)
elif "syllable_count" in section:
syllable_guidance += f"[{section['type'].capitalize()}]: ~{section['syllable_count']} syllables total\n"
# Create structure visualization
structure_visualization += "Using traditional section-based structure:\n"
for section_type, count in section_counts.items():
if count > 0:
structure_visualization += f"{section_type.capitalize()}: {count} sections\n"
# Set traditional section counts
verse_lines = max(2, section_counts.get("verse", 0) * 4)
chorus_lines = max(2, section_counts.get("chorus", 0) * 4)
bridge_lines = max(0, section_counts.get("bridge", 0) * 2)
# Use sections approach
use_sections = True
# If we couldn't get specific templates, use general guidance
if not syllable_guidance:
syllable_guidance = "RHYTHM ALIGNMENT INSTRUCTIONS:\n\n"
syllable_guidance += "1. Align stressed syllables with strong beats (usually beats 1 and 3 in 4/4 time)\n"
syllable_guidance += "2. Use unstressed syllables on weak beats (usually beats 2 and 4 in 4/4 time)\n"
syllable_guidance += "3. Use appropriate syllable counts based on tempo:\n"
syllable_guidance += " - Fast tempo (>120 BPM): 4-6 syllables per line\n"
syllable_guidance += " - Medium tempo (90-120 BPM): 6-8 syllables per line\n"
syllable_guidance += " - Slow tempo (<90 BPM): 8-10 syllables per line\n"
# Create basic structure visualization
structure_visualization += "Using estimated structure (no detailed analysis available):\n"
# Calculate rough section counts based on duration
estimated_lines = max(8, int(duration / 10))
structure_visualization += f"Estimated total lines: {estimated_lines}\n"
# Set traditional section counts based on duration
verse_lines = estimated_lines // 2
chorus_lines = estimated_lines // 3
bridge_lines = estimated_lines // 6 if estimated_lines > 12 else 0
# Use sections approach
use_sections = True
# Add examples of syllable-beat alignment with enhanced format
syllable_guidance += "\nEXAMPLES OF PERFECT RHYTHM ALIGNMENT:\n"
syllable_guidance += "Pattern: S(0.95):1 → w(0.4):1 → m(0.7):1 → w(0.3):1\n"
syllable_guidance += "Lyric: 'HEAR the MU-sic PLAY'\n"
syllable_guidance += " ↑ ↑ ↑ ↑\n"
syllable_guidance += " S w m w <- BEAT TYPE\n\n"
syllable_guidance += "Pattern: S(0.9):2 → w(0.3):1 → S(0.85):1 → w(0.4):2\n"
syllable_guidance += "Lyric: 'DANC-ing TO the RHYTHM of LOVE'\n"
syllable_guidance += " ↑ ↑ ↑ ↑ ↑ ↑\n"
syllable_guidance += " S S w S w w <- BEAT TYPE\n\n"
syllable_guidance += "Pattern: S(0.92):1 → m(0.65):2 → S(0.88):1 → w(0.35):1\n"
syllable_guidance += "Lyric: 'TIME keeps FLOW-ing ON and ON'\n"
syllable_guidance += " ↑ ↑ ↑ ↑ ↑ ↑\n"
syllable_guidance += " S m m S w w <- BEAT TYPE\n\n"
# Add genre-specific guidance based on the detected genre
genre_guidance = ""
if any(term in genre.lower() for term in ["rap", "hip-hop", "hip hop"]):
genre_guidance += "\nSPECIFIC GUIDANCE FOR RAP/HIP-HOP RHYTHMS:\n"
genre_guidance += "- Use more syllables per beat for rapid-fire sections\n"
genre_guidance += "- Create internal rhymes within lines, not just at line endings\n"
genre_guidance += "- Emphasize the first beat of each bar with strong consonants\n"
elif any(term in genre.lower() for term in ["electronic", "edm", "techno", "house", "dance"]):
genre_guidance += "\nSPECIFIC GUIDANCE FOR ELECTRONIC MUSIC RHYTHMS:\n"
genre_guidance += "- Use repetitive phrases that build and release tension\n"
genre_guidance += "- Match syllables precisely to the beat grid\n"
genre_guidance += "- Use short, percussive words on strong beats\n"
elif any(term in genre.lower() for term in ["rock", "metal", "punk", "alternative"]):
genre_guidance += "\nSPECIFIC GUIDANCE FOR ROCK RHYTHMS:\n"
genre_guidance += "- Use powerful, emotive words on downbeats\n"
genre_guidance += "- Create contrast between verse and chorus energy levels\n"
genre_guidance += "- Emphasize hooks with simple, memorable phrases\n"
elif any(term in genre.lower() for term in ["folk", "country", "acoustic", "ballad"]):
genre_guidance += "\nSPECIFIC GUIDANCE FOR FOLK/ACOUSTIC RHYTHMS:\n"
genre_guidance += "- Focus on storytelling with clear narrative flow\n"
genre_guidance += "- Use natural speech patterns that flow conversationally\n"
genre_guidance += "- Place important words at the start of phrases\n"
# Add genre guidance to the main guidance
syllable_guidance += genre_guidance
# Store the syllable guidance for later use
syllable_guidance_text = syllable_guidance
# Determine if we should use traditional sections or second-level alignment
use_sections = True
use_second_level = False
if song_structure and "second_level" in song_structure and song_structure["second_level"]:
use_second_level = True
# If we have second-level templates, prioritize those over traditional sections
if isinstance(song_structure["second_level"], dict) and "templates" in song_structure["second_level"]:
templates = song_structure["second_level"]["templates"]
if isinstance(templates, list) and len(templates) > 0:
use_sections = False
elif song_structure and "flexible_structure" in song_structure and song_structure["flexible_structure"]:
# If we have more than 4 segments, it's likely not a traditional song structure
if "segments" in song_structure["flexible_structure"]:
segments = song_structure["flexible_structure"]["segments"]
if len(segments) > 4:
use_sections = False
# Create enhanced prompt with better rhythm alignment instructions
if use_second_level:
# Second-level approach with per-second alignment
content = f"""
You are a talented songwriter who specializes in {genre} music.
Write original lyrics that match the rhythm of a {genre} music segment that is {duration:.1f} seconds long.
IMPORTANT: DO NOT include any thinking process, explanations, or analysis before the lyrics. Start directly with the song lyrics.
Music analysis has detected the following qualities:
- Tempo: {tempo:.1f} BPM
- Key: {key} {mode}
- Primary emotion: {primary_emotion}
- Primary theme: {primary_theme}
{syllable_guidance}
CRITICAL PRINCIPLES FOR RHYTHMIC ALIGNMENT:
1. STRESSED syllables MUST fall on STRONG beats (marked with STRONG in the pattern)
2. Natural word stress patterns must match the beat strength (strong words on strong beats)
3. Line breaks should occur at phrase endings for natural breathing
4. Consonant clusters should be avoided on fast notes and strong beats
5. Open vowels (a, e, o) work better for sustained notes and syllables
6. Pay attention to strength values in the pattern (higher values like 0.95 need stronger emphasis)
7. For half-syllable positions (like S1.5 or m2.5), use short, quick syllables or words with weak vowels
The lyrics should:
- Perfectly capture the essence and style of {genre} music
- Express the {primary_emotion} emotion and {primary_theme} theme
- Be completely original
- Maintain a consistent theme throughout
- Match the audio segment duration of {duration:.1f} seconds
Each line of lyrics must follow the corresponding segment's rhythm pattern EXACTLY.
IMPORTANT: Start immediately with the lyrics. DO NOT include any thinking process, analysis, or explanation before presenting the lyrics.
IMPORTANT: Your generated lyrics must be followed by a section titled "[RHYTHM_ANALYSIS_SECTION]"
where you analyze how well the lyrics align with the musical rhythm. This section MUST appear
even if there are no rhythm issues. Include the following in your analysis:
1. Syllable counts for each line and how they match the rhythm pattern
2. Where stressed syllables align with strong beats
3. Any potential misalignments or improvements
Your lyrics:
"""
# Add user requirements if provided
if lyrics_requirements and lyrics_requirements.strip():
content += f"""
USER REQUIREMENTS:
{lyrics_requirements.strip()}
The lyrics MUST incorporate these user requirements while still following the rhythm patterns.
"""
content += """
Each line of lyrics must follow the corresponding segment's rhythm pattern EXACTLY.
IMPORTANT: Start immediately with the lyrics. DO NOT include any thinking process, analysis, or explanation before presenting the lyrics.
IMPORTANT: Your generated lyrics must be followed by a section titled "[RHYTHM_ANALYSIS_SECTION]"
where you analyze how well the lyrics align with the musical rhythm. This section MUST appear
even if there are no rhythm issues. Include the following in your analysis:
1. Syllable counts for each line and how they match the rhythm pattern
2. Where stressed syllables align with strong beats
3. Any potential misalignments or improvements
Your lyrics:
"""
elif use_sections:
# Traditional approach with sections
content = f"""
You are a talented songwriter who specializes in {genre} music.
Write original {genre} song lyrics for a song that is {duration:.1f} seconds long.
IMPORTANT: DO NOT include any thinking process, explanations, or analysis before the lyrics. Start directly with the song lyrics.
Music analysis has detected the following qualities in the music:
- Tempo: {tempo:.1f} BPM
- Key: {key} {mode}
- Primary emotion: {primary_emotion}
- Primary theme: {primary_theme}
{syllable_guidance}
CRITICAL PRINCIPLES FOR RHYTHMIC ALIGNMENT:
1. STRESSED syllables MUST fall on STRONG beats (marked with STRONG in the pattern)
2. Natural word stress patterns must match the beat strength (strong words on strong beats)
3. Line breaks should occur at phrase endings for natural breathing
4. Consonant clusters should be avoided on fast notes and strong beats
5. Open vowels (a, e, o) work better for sustained notes and syllables
6. Pay attention to strength values in the pattern (higher values like 0.95 need stronger emphasis)
7. For half-syllable positions (like S1.5 or m2.5), use short, quick syllables or words with weak vowels
The lyrics should:
- Perfectly capture the essence and style of {genre} music
- Express the {primary_emotion} emotion and {primary_theme} theme
- Follow the structure patterns provided above
- Be completely original
- Match the song duration of {duration:.1f} seconds
"""
# Add user requirements if provided
if lyrics_requirements and lyrics_requirements.strip():
content += f"""
USER REQUIREMENTS:
{lyrics_requirements.strip()}
The lyrics MUST incorporate these user requirements while still following the rhythm patterns.
"""
content += """
IMPORTANT: Start immediately with the lyrics. DO NOT include any thinking process, analysis, or explanation before presenting the lyrics.
IMPORTANT: Your generated lyrics must be followed by a section titled "[RHYTHM_ANALYSIS_SECTION]"
where you analyze how well the lyrics align with the musical rhythm. This section MUST appear
even if there are no rhythm issues. Include the following in your analysis:
1. Syllable counts for each line and how they match the rhythm pattern
2. Where stressed syllables align with strong beats
3. Any potential misalignments or improvements
Your lyrics:
"""
else:
# Flexible approach without traditional sections
content = f"""
You are a talented songwriter who specializes in {genre} music.
Write original lyrics that match the rhythm of a {genre} music segment that is {duration:.1f} seconds long.
IMPORTANT: DO NOT include any thinking process, explanations, or analysis before the lyrics. Start directly with the song lyrics.
Music analysis has detected the following qualities:
- Tempo: {tempo:.1f} BPM
- Key: {key} {mode}
- Primary emotion: {primary_emotion}
- Primary theme: {primary_theme}
{syllable_guidance}
CRITICAL PRINCIPLES FOR RHYTHMIC ALIGNMENT:
1. STRESSED syllables MUST fall on STRONG beats (marked with STRONG in the pattern)
2. Natural word stress patterns must match the beat strength (strong words on strong beats)
3. Line breaks should occur at phrase endings for natural breathing
4. Consonant clusters should be avoided on fast notes and strong beats
5. Open vowels (a, e, o) work better for sustained notes and syllables
6. Pay attention to strength values in the pattern (higher values like 0.95 need stronger emphasis)
7. For half-syllable positions (like S1.5 or m2.5), use short, quick syllables or words with weak vowels
The lyrics should:
- Perfectly capture the essence and style of {genre} music
- Express the {primary_emotion} emotion and {primary_theme} theme
- Be completely original
- Maintain a consistent theme throughout
- Match the audio segment duration of {duration:.1f} seconds
"""
# Add user requirements if provided
if lyrics_requirements and lyrics_requirements.strip():
content += f"""
USER REQUIREMENTS:
{lyrics_requirements.strip()}
The lyrics MUST incorporate these user requirements while still following the rhythm patterns.
"""
content += """
Include any section labels like [Verse] or [Chorus] as indicated in the rhythm patterns above.
Each line of lyrics must follow the corresponding segment's rhythm pattern EXACTLY.
IMPORTANT: Start immediately with the lyrics. DO NOT include any thinking process, analysis, or explanation before presenting the lyrics.
IMPORTANT: Your generated lyrics must be followed by a section titled "[RHYTHM_ANALYSIS_SECTION]"
where you analyze how well the lyrics align with the musical rhythm. This section MUST appear
even if there are no rhythm issues. Include the following in your analysis:
1. Syllable counts for each line and how they match the rhythm pattern
2. Where stressed syllables align with strong beats
3. Any potential misalignments or improvements
Your lyrics:
"""
# Format as a chat message for the LLM
messages = [
{"role": "system", "content": "You are a professional songwriter. Create lyrics that match the specified rhythm patterns exactly. Start with the lyrics immediately without any explanation or thinking. Be concise and direct."},
{"role": "user", "content": content}
]
# Apply standard chat template without thinking enabled
text = llm_tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
# Generate lyrics using the LLM
model_inputs = llm_tokenizer([text], return_tensors="pt").to(llm_model.device)
# Configure generation parameters based on model capability
generation_params = {
"do_sample": True,
"temperature": 0.5, # Lower for more consistent and direct output
"top_p": 0.85, # Slightly lower for more predictable responses
"top_k": 50,
"repetition_penalty": 1.2,
"max_new_tokens": 2048,
"num_return_sequences": 1
}
# Add specific stop sequences to prevent excessive explanation
if hasattr(llm_model.generation_config, "stopping_criteria"):
thinking_stops = ["Let me think", "First, I need to", "Let's analyze", "I'll approach this", "Step 1:", "To start,"]
for stop in thinking_stops:
if stop not in llm_model.generation_config.stopping_criteria:
llm_model.generation_config.stopping_criteria.append(stop)
# Generate output
generated_ids = llm_model.generate(
**model_inputs,
**generation_params
)
# Extract output tokens
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# Get the raw output and strip any thinking process
lyrics = llm_tokenizer.decode(output_ids, skip_special_tokens=True).strip()
# Enhanced thinking process removal - handle multiple formats
# First check for standard thinking tags
if "" in lyrics and "" in lyrics:
lyrics = lyrics.split("")[1].strip()
# Check for alternative thinking indicators with improved detection
thinking_markers = [
"", "",
"[thinking]", "[/thinking]",
"I'll think step by step:",
"First, I need to understand",
"Let me think about",
"Let's tackle this query",
"Okay, let's tackle this query",
"First, I need to understand the requirements",
"Looking at the rhythm patterns"
]
# First try to find clear section breaks
for marker in thinking_markers:
if marker in lyrics:
parts = lyrics.split(marker)
if len(parts) > 1:
lyrics = parts[-1].strip() # Take the last part after any thinking marker
# Look for long analytical sections followed by clear lyrics
analytical_patterns = [
"Let me analyze",
"I need to understand",
"The tempo is",
"First, let's look at",
"Wait, maybe",
"Considering the emotional tone",
"Starting with the first line",
"Let me check the examples"
]
# Check if lyrics begin with any analytical patterns
for pattern in analytical_patterns:
if lyrics.startswith(pattern):
# Try to find where the actual lyrics start - look for common lyrics markers
lyrics_markers = [
"\n\n[Verse",
"\n\n[Chorus",
"\n\nVerse",
"\n\nChorus",
"\n\n[Verse 1]",
"\n\n[Intro]"
]
for marker in lyrics_markers:
if marker in lyrics:
lyrics = lyrics[lyrics.index(marker):].strip()
break
# One last effort to clean up - if the text is very long and contains obvious thinking
# before getting to actual lyrics, try to find a clear starting point
if len(lyrics.split()) > 100 and "\n\n" in lyrics:
paragraphs = lyrics.split("\n\n")
for i, paragraph in enumerate(paragraphs):
# Look for typical song structure indicators in a paragraph
if any(marker in paragraph for marker in ["[Verse", "[Chorus", "Verse 1", "Chorus:"]):
lyrics = "\n\n".join(paragraphs[i:])
break
# Clean up any remaining thinking artifacts at the beginning
lines = lyrics.split('\n')
clean_lines = []
lyrics_started = False
for line in lines:
# Skip initial commentary/thinking lines until we hit what looks like lyrics
if not lyrics_started:
if (line.strip().startswith('[') and ']' in line) or not any(thinking in line.lower() for thinking in ["i think", "let me", "maybe", "perhaps", "alternatively", "checking"]):
lyrics_started = True
if lyrics_started:
clean_lines.append(line)
# Only use the cleaning logic if we found some actual lyrics
if clean_lines:
lyrics = '\n'.join(clean_lines)
# Special handling for second-level templates
second_level_verification = None
if song_structure and "second_level" in song_structure and song_structure["second_level"]:
if isinstance(song_structure["second_level"], dict) and "templates" in song_structure["second_level"]:
second_level_verification = song_structure["second_level"]["templates"]
if not isinstance(second_level_verification, list):
second_level_verification = None
# Verify syllable counts with enhanced verification - pass second-level templates if available
if templates_for_verification:
# Convert any NumPy values to native types before verification - directly handle conversions
# Simple conversion for basic templates (non-recursive)
if isinstance(templates_for_verification, list):
safe_templates = []
for template in templates_for_verification:
if isinstance(template, dict):
processed_template = {}
for k, v in template.items():
if isinstance(v, np.ndarray):
if v.size == 1:
processed_template[k] = float(v.item())
else:
processed_template[k] = [float(x) if isinstance(x, np.number) else x for x in v]
elif isinstance(v, np.number):
processed_template[k] = float(v)
else:
processed_template[k] = v
safe_templates.append(processed_template)
else:
safe_templates.append(template)
else:
safe_templates = templates_for_verification
# Wrap verification in try-except to handle any potential string indices errors
try:
print(f"DEBUG: Calling verify_flexible_syllable_counts")
print(f"DEBUG: Type of lyrics: {type(lyrics)}")
print(f"DEBUG: Type of safe_templates: {type(safe_templates)}")
print(f"DEBUG: Type of second_level_verification: {type(second_level_verification)}")
verified_lyrics = verify_flexible_syllable_counts(lyrics, safe_templates, second_level_verification)
print(f"DEBUG: Type of verified_lyrics: {type(verified_lyrics)}")
except Exception as e:
print(f"ERROR in verify_flexible_syllable_counts: {str(e)}")
# Return the original lyrics if verification fails
return {
"lyrics": lyrics if isinstance(lyrics, str) else str(lyrics),
"rhythm_analysis": f"Error in rhythm analysis: {str(e)}",
"syllable_analysis": "Error performing syllable analysis",
"prompt_template": "Error generating prompt template"
}
if isinstance(verified_lyrics, str) and "[Note: Potential rhythm mismatches" in verified_lyrics and "Detailed Alignment Analysis" in verified_lyrics:
# Extract the original lyrics (before the notes section)
original_lyrics = lyrics.split("[Note:")[0].strip() if isinstance(lyrics, str) else str(lyrics)
# Extract the analysis
analysis = verified_lyrics.split("[Note:")[1] if "[Note:" in verified_lyrics else ""
# If we have serious alignment issues, consider a refinement step
if "stress misalignments" in analysis and len(templates_for_verification) > 0:
# Add a refinement prompt with the specific analysis
refinement_prompt = f"""
You need to fix rhythm issues in these lyrics. Here's the analysis of the problems:
{analysis}
Revise the lyrics to perfectly match the rhythm pattern while maintaining the theme.
Focus on fixing the stress misalignments by placing stressed syllables on STRONG beats.
Original lyrics:
{original_lyrics}
Improved lyrics with fixed rhythm:
"""
# Format as a chat message for refinement
refinement_messages = [
{"role": "user", "content": refinement_prompt}
]
# Use standard template for refinement (no thinking mode needed)
refinement_text = llm_tokenizer.apply_chat_template(
refinement_messages,
tokenize=False,
add_generation_prompt=True
)
try:
# Generate refined lyrics with more focus on rhythm alignment
refinement_inputs = llm_tokenizer([refinement_text], return_tensors="pt").to(llm_model.device)
# Use stricter parameters for refinement
refinement_params = {
"do_sample": True,
"temperature": 0.4, # Lower temperature for more precise refinement
"top_p": 0.9,
"repetition_penalty": 1.3,
"max_new_tokens": 1024
}
refined_ids = llm_model.generate(
**refinement_inputs,
**refinement_params
)
# Extract refined lyrics
refined_output_ids = refined_ids[0][len(refinement_inputs.input_ids[0]):].tolist()
refined_lyrics = llm_tokenizer.decode(refined_output_ids, skip_special_tokens=True).strip()
# Verify the refined lyrics
try:
refined_verified_lyrics = verify_flexible_syllable_counts(refined_lyrics, safe_templates, second_level_verification)
# Only use refined lyrics if they're better (fewer notes)
if "[Note: Potential rhythm mismatches" not in refined_verified_lyrics:
lyrics = refined_lyrics
elif refined_verified_lyrics.count("misalignments") < verified_lyrics.count("misalignments"):
lyrics = refined_verified_lyrics
else:
lyrics = verified_lyrics
except Exception as e:
print(f"Error in refined lyrics verification: {str(e)}")
lyrics = verified_lyrics
except Exception as e:
print(f"Error in lyrics refinement: {str(e)}")
lyrics = verified_lyrics
else:
# Minor issues, just use the verification notes
lyrics = verified_lyrics
else:
# No significant issues detected
lyrics = verified_lyrics
# Check if we have the [RHYTHM_ANALYSIS_SECTION] tag
if "[RHYTHM_ANALYSIS_SECTION]" in lyrics:
# Split at our custom marker
parts = lyrics.split("[RHYTHM_ANALYSIS_SECTION]")
clean_lyrics = parts[0].strip()
rhythm_analysis = parts[1].strip()
# Add our standard marker for compatibility with existing code
lyrics = clean_lyrics + "\n\n[Note: Rhythm Analysis]\n" + rhythm_analysis
# For backwards compatibility - if we have the old format, still handle it
elif "[Note: Potential rhythm mismatches" in lyrics:
# Keep it as is, the existing parsing code can handle this format
pass
else:
# No analysis found, add a minimal one
lyrics = lyrics + "\n\n[Note: Rhythm Analysis]\nNo rhythm issues detected. All syllables align well with the beat pattern."
# Before returning, add syllable analysis and prompt template
if isinstance(lyrics, str):
# Extract clean lyrics and analysis
if "[Note: Rhythm Analysis]" in lyrics:
clean_lyrics = lyrics.split("[Note: Rhythm Analysis]")[0].strip()
rhythm_analysis = lyrics.split("[Note: Rhythm Analysis]")[1]
elif "[Note: Potential rhythm mismatches" in lyrics:
clean_lyrics = lyrics.split("[Note:")[0].strip()
rhythm_analysis = "[Note:" + lyrics.split("[Note:")[1]
else:
clean_lyrics = lyrics
rhythm_analysis = "No rhythm analysis available"
# Create syllable analysis
syllable_analysis = "=== SYLLABLE ANALYSIS ===\n\n"
if templates_for_verification:
syllable_analysis += "Template Analysis:\n"
for i, template in enumerate(templates_for_verification):
if i < min(len(templates_for_verification), 30): # Limit to 30 to avoid overwhelming output
syllable_analysis += f"Line {i+1}:\n"
if isinstance(template, dict):
if "syllable_template" in template:
syllable_analysis += f" Template: {template['syllable_template']}\n"
if "syllable_count" in template:
syllable_analysis += f" Expected syllables: {template['syllable_count']}\n"
elif isinstance(template, str):
syllable_analysis += f" Template: {template}\n"
syllable_analysis += "\n"
if len(templates_for_verification) > 30:
syllable_analysis += f"... and {len(templates_for_verification) - 30} more lines\n\n"
# Add second-level analysis if available
if second_level_verification:
syllable_analysis += "\nSecond-Level Template Analysis:\n"
for i, template in enumerate(second_level_verification):
if i < min(len(second_level_verification), 30): # Limit to 30 seconds
syllable_analysis += f"Second {i+1}: {template}\n"
if len(second_level_verification) > 30:
syllable_analysis += f"... and {len(second_level_verification) - 30} more seconds\n"
# Add structure visualization to syllable analysis
syllable_analysis += "\n" + structure_visualization
# Create prompt template
prompt_template = "=== PROMPT TEMPLATE ===\n\n"
prompt_template += "Genre: " + genre + "\n"
prompt_template += f"Duration: {duration:.1f} seconds\n"
prompt_template += f"Tempo: {tempo:.1f} BPM\n"
prompt_template += f"Key: {key} {mode}\n"
prompt_template += f"Primary Emotion: {primary_emotion}\n"
prompt_template += f"Primary Theme: {primary_theme}\n\n"
prompt_template += "Syllable Guidance:\n" + syllable_guidance_text
# Return all components
return {
"lyrics": clean_lyrics,
"rhythm_analysis": rhythm_analysis,
"syllable_analysis": syllable_analysis,
"prompt_template": prompt_template
}
return {
"lyrics": lyrics,
"rhythm_analysis": "No rhythm analysis available",
"syllable_analysis": "No syllable analysis available",
"prompt_template": "No prompt template available"
}
def process_audio(audio_file, lyrics_requirements=None):
"""Main function to process audio file, classify genre, and generate lyrics with enhanced rhythm analysis."""
if audio_file is None:
return "Please upload an audio file.", None, None
try:
print("Step 1/5: Extracting audio features...")
# Extract audio features
audio_data = extract_audio_features(audio_file)
print("Step 2/5: Verifying audio contains music...")
# First check if it's music
try:
is_music, ast_results = detect_music(audio_data)
except Exception as e:
print(f"Error in music detection: {str(e)}")
return f"Error in music detection: {str(e)}", None, ast_results
if not is_music:
return "The uploaded audio does not appear to be music. Please upload a music file.", None, ast_results
print("Step 3/5: Classifying music genre...")
# Classify genre
try:
top_genres = classify_genre(audio_data)
# Format genre results using utility function
genre_results = format_genre_results(top_genres)
if not isinstance(top_genres, list) or len(top_genres) == 0:
# Fallback if we don't have valid top_genres
top_genres = [("rock", 1.0)]
except Exception as e:
print(f"Error in genre classification: {str(e)}")
top_genres = [("rock", 1.0)] # Ensure we have a default even when exception happens
return f"Error in genre classification: {str(e)}", None, ast_results
# Initialize default values
ast_results = ast_results if ast_results else []
song_structure = None
emotion_results = {
"emotion_analysis": {"primary_emotion": "Unknown"},
"theme_analysis": {"primary_theme": "Unknown"},
"rhythm_analysis": {"tempo": 0},
"tonal_analysis": {"key": "Unknown", "mode": ""},
"summary": {"tempo": 0, "key": "Unknown", "mode": "", "primary_emotion": "Unknown", "primary_theme": "Unknown"}
}
print("Step 4/5: Analyzing music emotions, themes, and structure...")
# Analyze music emotions and themes
try:
emotion_results = music_analyzer.analyze_music(audio_file)
except Exception as e:
print(f"Error in emotion analysis: {str(e)}")
# Continue with default emotion_results
# Calculate detailed song structure for better lyrics alignment
try:
# Load audio data
y, sr = load_audio(audio_file, SAMPLE_RATE)
# Analyze beats and phrases for music-aligned lyrics
beats_info = detect_beats(y, sr)
sections_info = detect_sections(y, sr)
# Create structured segments for precise line-by-line matching
segments = []
# Try to break audio into meaningful segments based on sections
# Each segment will correspond to one line of lyrics
if sections_info and len(sections_info) > 1:
min_segment_duration = 1.5 # Minimum 1.5 seconds per segment
for section in sections_info:
section_start = section["start"]
section_end = section["end"]
section_duration = section["duration"]
# For very short sections, add as a single segment
if section_duration < min_segment_duration * 1.5:
segments.append({
"start": section_start,
"end": section_end
})
else:
# Calculate ideal number of segments for this section
# based on its duration - aiming for 2-4 second segments
ideal_segment_duration = 3.0 # Target 3 seconds per segment
segment_count = max(1, int(section_duration / ideal_segment_duration))
# Create evenly-spaced segments within this section
segment_duration = section_duration / segment_count
for i in range(segment_count):
segment_start = section_start + i * segment_duration
segment_end = segment_start + segment_duration
segments.append({
"start": segment_start,
"end": segment_end
})
# If no good sections found, create segments based on beats
elif beats_info and len(beats_info["beat_times"]) > 4:
beats = beats_info["beat_times"]
time_signature = beats_info.get("time_signature", 4)
# Target one segment per musical measure (typically 4 beats)
measure_size = time_signature
for i in range(0, len(beats), measure_size):
if i + 1 < len(beats): # Need at least 2 beats for a meaningful segment
measure_start = beats[i]
# If we have enough beats for the full measure
if i + measure_size < len(beats):
measure_end = beats[i + measure_size]
else:
# Use available beats and extrapolate for the last measure
if i > 0:
beat_interval = beats[i] - beats[i-1]
measure_end = beats[-1] + (beat_interval * (measure_size - (len(beats) - i)))
else:
measure_end = audio_data["duration"]
segments.append({
"start": measure_start,
"end": measure_end
})
# Last resort: simple time-based segments
else:
# Create segments of approximately 3 seconds each
segment_duration = 3.0
total_segments = max(4, int(audio_data["duration"] / segment_duration))
segment_duration = audio_data["duration"] / total_segments
for i in range(total_segments):
segment_start = i * segment_duration
segment_end = segment_start + segment_duration
segments.append({
"start": segment_start,
"end": segment_end
})
# Create flexible structure with the segments
flexible_structure = {
"beats": beats_info,
"segments": segments
}
# Create song structure object
song_structure = {
"beats": beats_info,
"sections": sections_info,
"flexible_structure": flexible_structure,
"syllables": []
}
# Add syllable counts to each section
for section in sections_info:
# Create syllable templates for sections
section_beats_info = {
"beat_times": [beat for beat in beats_info["beat_times"]
if section["start"] <= beat < section["end"]],
"tempo": beats_info.get("tempo", 120)
}
if "beat_strengths" in beats_info:
section_beats_info["beat_strengths"] = [
strength for i, strength in enumerate(beats_info["beat_strengths"])
if i < len(beats_info["beat_times"]) and
section["start"] <= beats_info["beat_times"][i] < section["end"]
]
# Get a syllable count based on section duration and tempo
syllable_count = int(section["duration"] * (beats_info.get("tempo", 120) / 60) * 1.5)
section_info = {
"type": section["type"],
"start": section["start"],
"end": section["end"],
"duration": section["duration"],
"syllable_count": syllable_count,
"beat_count": len(section_beats_info["beat_times"])
}
# Try to create a more detailed syllable template
if len(section_beats_info["beat_times"]) >= 2:
# Ensure top_genres is a list with at least one element
if isinstance(top_genres, list) and len(top_genres) > 0 and isinstance(top_genres[0], tuple):
genre_name = top_genres[0][0]
else:
genre_name = "unknown" # Default genre if top_genres is invalid
section_info["syllable_template"] = create_flexible_syllable_templates(
section_beats_info,
genre=genre_name
)
song_structure["syllables"].append(section_info)
# Add second-level beat analysis
try:
# Get enhanced beat information with subbeats
subbeat_info = detect_beats_and_subbeats(y, sr, subdivision=4)
# Map beats to second-level windows
sec_map = map_beats_to_seconds(
subbeat_info["subbeat_times"],
audio_data["duration"]
)
# Create second-level templates
# Ensure top_genres is a list with at least one element
genre_name = "unknown"
if isinstance(top_genres, list) and len(top_genres) > 0 and isinstance(top_genres[0], tuple):
genre_name = top_genres[0][0]
second_level_templates = create_second_level_templates(
sec_map,
subbeat_info["tempo"],
genre_name # Use top genre with safety check
)
# Add to song structure
song_structure["second_level"] = {
"sec_map": sec_map,
"templates": second_level_templates
}
except Exception as e:
print(f"Error in second-level beat analysis: {str(e)}")
# Continue without second-level data
except Exception as e:
print(f"Error analyzing song structure: {str(e)}")
# Continue without song structure
print("Step 5/5: Generating rhythmically aligned lyrics...")
# Generate lyrics based on top genre, emotion analysis, and song structure
try:
# Ensure top_genres is a list with at least one element before accessing
primary_genre = "unknown"
if isinstance(top_genres, list) and len(top_genres) > 0 and isinstance(top_genres[0], tuple):
primary_genre, _ = top_genres[0]
# CRITICAL FIX: Create a sanitized version of song_structure to prevent string indices error
sanitized_song_structure = None
if song_structure:
sanitized_song_structure = {}
# Safely copy beats data
if "beats" in song_structure and isinstance(song_structure["beats"], dict):
sanitized_song_structure["beats"] = song_structure["beats"]
# Safely copy sections data
if "sections" in song_structure and isinstance(song_structure["sections"], list):
sanitized_song_structure["sections"] = song_structure["sections"]
# Safely handle flexible structure
if "flexible_structure" in song_structure and isinstance(song_structure["flexible_structure"], dict):
flex_struct = song_structure["flexible_structure"]
sanitized_flex = {}
# Safely handle segments
if "segments" in flex_struct and isinstance(flex_struct["segments"], list):
sanitized_flex["segments"] = flex_struct["segments"]
# Safely handle beats
if "beats" in flex_struct and isinstance(flex_struct["beats"], dict):
sanitized_flex["beats"] = flex_struct["beats"]
sanitized_song_structure["flexible_structure"] = sanitized_flex
# Safely handle syllables
if "syllables" in song_structure and isinstance(song_structure["syllables"], list):
sanitized_song_structure["syllables"] = song_structure["syllables"]
# Safely handle second-level
if "second_level" in song_structure and isinstance(song_structure["second_level"], dict):
second_level = song_structure["second_level"]
sanitized_second = {}
if "templates" in second_level and isinstance(second_level["templates"], list):
sanitized_second["templates"] = second_level["templates"]
if "sec_map" in second_level and isinstance(second_level["sec_map"], list):
sanitized_second["sec_map"] = second_level["sec_map"]
sanitized_song_structure["second_level"] = sanitized_second
try:
print("Calling generate_lyrics function...")
# Pass lyrics_requirements to generate_lyrics function
lyrics_result = generate_lyrics(primary_genre, audio_data["duration"], emotion_results,
sanitized_song_structure, lyrics_requirements)
print(f"Type of lyrics_result: {type(lyrics_result)}")
# Handle both old and new return formats with robust type checking
if isinstance(lyrics_result, dict) and all(k in lyrics_result for k in ["lyrics"]):
lyrics = lyrics_result.get("lyrics", "No lyrics generated")
rhythm_analysis = lyrics_result.get("rhythm_analysis", "No rhythm analysis available")
syllable_analysis = lyrics_result.get("syllable_analysis", "No syllable analysis available")
prompt_template = lyrics_result.get("prompt_template", "No prompt template available")
else:
# Convert to string regardless of the type
lyrics = str(lyrics_result) if lyrics_result is not None else "No lyrics generated"
rhythm_analysis = "No detailed rhythm analysis available"
syllable_analysis = "No syllable analysis available"
prompt_template = "No prompt template available"
except Exception as inner_e:
print(f"Inner error in lyrics generation: {str(inner_e)}")
# Create a simplified fallback result with just the error message
lyrics = f"Error generating lyrics: {str(inner_e)}"
rhythm_analysis = "Error in rhythm analysis"
syllable_analysis = "Error in syllable analysis"
prompt_template = "Error in prompt template generation"
except Exception as e:
print(f"Outer error in lyrics generation: {str(e)}")
lyrics = f"Error generating lyrics: {str(e)}"
rhythm_analysis = "No rhythm analysis available"
syllable_analysis = "No syllable analysis available"
prompt_template = "No prompt template available"
# Prepare results dictionary with additional rhythm analysis
results = {
"genre_results": genre_results,
"lyrics": lyrics,
"rhythm_analysis": rhythm_analysis,
"syllable_analysis": syllable_analysis,
"prompt_template": prompt_template,
"ast_results": ast_results
}
return results
except Exception as e:
error_msg = f"Error processing audio: {str(e)}"
print(error_msg)
return error_msg, None, []
def format_complete_beat_timeline(audio_file, lyrics=None):
"""Creates a complete formatted timeline showing all beat timings and their syllable patterns without truncation"""
if audio_file is None:
return "Please upload an audio file to see beat timeline."
try:
# Extract audio data
y, sr = load_audio(audio_file, SAMPLE_RATE)
# Get beat information
beats_info = detect_beats(y, sr)
# Helper function to convert numpy values to floats - FIXED
def ensure_float(value):
if isinstance(value, np.ndarray) or isinstance(value, np.number):
return float(value)
return value
# Format the timeline with enhanced scientific headers
timeline = "=== BEAT & SYLLABLE TIMELINE ===\n\n"
tempo = ensure_float(beats_info['tempo'])
tempo_confidence = ensure_float(beats_info.get('tempo_confidence', 90.0))
time_sig_confidence = ensure_float(beats_info.get('time_sig_confidence', 85.0))
beat_periodicity = ensure_float(beats_info.get('beat_periodicity', 60 / tempo))
timeline += f"Tempo: {tempo:.1f} BPM (±{tempo_confidence:.1f}%)\n"
timeline += f"Time Signature: {beats_info['time_signature']}/4 (Confidence: {time_sig_confidence:.1f}%)\n"
timeline += f"Beat Periodicity: {beat_periodicity:.3f}s\n"
timeline += f"Beat Entropy: {beats_info.get('beat_entropy', 'N/A')}\n"
timeline += f"Total Beats: {beats_info['beat_count']}\n"
# Add musicological context based on tempo classification
if tempo < 60:
tempo_class = "Largo (very slow)"
elif tempo < 76:
tempo_class = "Adagio (slow)"
elif tempo < 108:
tempo_class = "Andante (walking pace)"
elif tempo < 132:
tempo_class = "Moderato (moderate)"
elif tempo < 168:
tempo_class = "Allegro (fast)"
else:
tempo_class = "Presto (very fast)"
timeline += f"Tempo Classification: {tempo_class}\n\n"
# Create an enhanced table header with better column descriptions
timeline += "| Beat # | Time (s) | Beat Strength | Syllable Pattern |\n"
timeline += "|--------|----------|--------------|------------------|\n"
# Add beat-by-beat information with improved classification
for i, (time, strength) in enumerate(zip(beats_info['beat_times'], beats_info['beat_strengths'])):
# Convert numpy values to Python float if needed
time = ensure_float(time)
strength = ensure_float(strength)
# More scientific determination of beat type based on both strength and metrical position
metrical_position = i % beats_info['time_signature']
if metrical_position == 0: # Downbeat (first beat of measure)
beat_type = "STRONG"
syllable_value = 1.5
elif metrical_position == beats_info['time_signature'] // 2 and beats_info['time_signature'] > 2:
# Secondary strong beat (e.g., beat 3 in 4/4 time)
beat_type = "MEDIUM" if strength < 0.8 else "STRONG"
syllable_value = 1.0 if strength < 0.8 else 1.5
else:
# Other beats - classified by actual strength value
if strength >= 0.8:
beat_type = "STRONG"
syllable_value = 1.5
elif strength >= 0.5:
beat_type = "MEDIUM"
syllable_value = 1.0
else:
beat_type = "WEAK"
syllable_value = 1.0
# Determine pattern letter based on beat type for consistency
if beat_type == "STRONG":
pattern = "S"
elif beat_type == "MEDIUM":
pattern = "m"
else:
pattern = "w"
# Add row to table with the correct beat classification
timeline += f"| {i+1:<6} | {time:.2f}s | {beat_type:<12} | {pattern}:{syllable_value} |\n"
# No truncation - show all beats
# Add a visual timeline of beats
timeline += "\n=== VISUAL BEAT TIMELINE ===\n\n"
timeline += "Each character represents 0.5 seconds. Beats are marked as:\n"
timeline += "S = Strong beat | m = Medium beat | w = Weak beat | · = No beat\n\n"
# Calculate total duration and create time markers
if 'beat_times' in beats_info and len(beats_info['beat_times']) > 0:
# Get the max value safely
max_beat_time = max([ensure_float(t) for t in beats_info['beat_times']])
total_duration = max_beat_time + 2 # Add 2 seconds of padding
else:
total_duration = 30 # Default duration if no beats found
time_markers = ""
for i in range(0, int(total_duration) + 1, 5):
time_markers += f"{i:<5}"
timeline += time_markers + " (seconds)\n"
# Create a ruler for easier time tracking
ruler = ""
for i in range(0, int(total_duration) + 1):
if i % 5 == 0:
ruler += "+"
else:
ruler += "-"
ruler += "-" * 9 # Each second is 10 characters wide
timeline += ruler + "\n"
# Create a visualization of beats with symbols
beat_line = ["·"] * int(total_duration * 2) # 2 characters per second
for i, time in enumerate(beats_info['beat_times']):
if i >= len(beats_info['beat_strengths']):
break
# Convert to float if it's a numpy array
time_val = ensure_float(time)
# Determine position in the timeline
pos = int(time_val * 2) # Convert to position in the beat_line
if pos >= len(beat_line):
continue
# Determine beat type based on strength and position
strength = beats_info['beat_strengths'][i]
# Convert to float if it's a numpy array
strength = ensure_float(strength)
if i % beats_info['time_signature'] == 0:
beat_line[pos] = "S" # Strong beat at start of measure
elif strength >= 0.8:
beat_line[pos] = "S" # Strong beat
elif i % beats_info['time_signature'] == beats_info['time_signature'] // 2 and beats_info['time_signature'] > 3:
beat_line[pos] = "m" # Medium beat (3rd beat in 4/4)
elif strength >= 0.5:
beat_line[pos] = "m" # Medium beat
else:
beat_line[pos] = "w" # Weak beat
# Format and add to timeline
beat_visualization = ""
for i in range(0, len(beat_line), 10):
beat_visualization += "".join(beat_line[i:i+10])
if i + 10 < len(beat_line):
beat_visualization += " " # Add space every 5 seconds
timeline += beat_visualization + "\n\n"
# Add measure markers
timeline += "=== MEASURE MARKERS ===\n\n"
# Create a list to track measure start times
measure_starts = []
for i, time in enumerate(beats_info['beat_times']):
if i % beats_info['time_signature'] == 0: # Start of measure
# Convert to float if it's a numpy array
time_val = ensure_float(time)
measure_starts.append((i // beats_info['time_signature'] + 1, time_val))
# Format measure information
if measure_starts:
timeline += "| Measure # | Start Time | Duration |\n"
timeline += "|-----------|------------|----------|\n"
for i in range(len(measure_starts)):
measure_num, start_time = measure_starts[i]
# Calculate end time (start of next measure or end of song)
if i < len(measure_starts) - 1:
end_time = measure_starts[i+1][1]
elif 'beat_times' in beats_info and len(beats_info['beat_times']) > 0:
# Get the last beat time and convert to float if needed
last_beat = beats_info['beat_times'][-1]
end_time = ensure_float(last_beat)
else:
end_time = start_time + 2.0 # Default 2 seconds if no next measure
duration = end_time - start_time
timeline += f"| {measure_num:<9} | {start_time:.2f}s | {duration:.2f}s |\n"
# No truncation - show all measures
# Add phrase information
if 'phrases' in beats_info and beats_info['phrases']:
timeline += "\n=== MUSICAL PHRASES ===\n\n"
for i, phrase in enumerate(beats_info['phrases']):
# Show all phrases, not just the first 10
if not phrase:
continue
# Safely check phrase indices
if not (len(phrase) > 0 and len(beats_info['beat_times']) > 0):
continue
start_beat = min(phrase[0], len(beats_info['beat_times'])-1)
end_beat = min(phrase[-1], len(beats_info['beat_times'])-1)
# Convert to float if needed
phrase_start = ensure_float(beats_info['beat_times'][start_beat])
phrase_end = ensure_float(beats_info['beat_times'][end_beat])
timeline += f"Phrase {i+1}: Beats {start_beat+1}-{end_beat+1} ({phrase_start:.2f}s - {phrase_end:.2f}s)\n"
# Create syllable template for this phrase with simplified numpy handling
phrase_beats = {
"beat_times": [ensure_float(beats_info['beat_times'][j])
for j in phrase if j < len(beats_info['beat_times'])],
"beat_strengths": [ensure_float(beats_info['beat_strengths'][j])
for j in phrase if j < len(beats_info['beat_strengths'])],
"tempo": ensure_float(beats_info['tempo']),
"time_signature": beats_info['time_signature'],
"phrases": [list(range(len(phrase)))]
}
template = create_flexible_syllable_templates(phrase_beats)
timeline += f" Syllable Template: {template}\n"
# Create a visual representation of this phrase
if phrase_start < total_duration and phrase_end < total_duration:
# Create a timeline for this phrase
phrase_visualization = ["·"] * int(total_duration * 2)
# Mark the phrase boundaries
start_pos = int(phrase_start * 2)
end_pos = int(phrase_end * 2)
if start_pos < len(phrase_visualization):
phrase_visualization[start_pos] = "["
if end_pos < len(phrase_visualization):
phrase_visualization[end_pos] = "]"
# Mark the beats in this phrase
for j in phrase:
if j < len(beats_info['beat_times']):
beat_time = ensure_float(beats_info['beat_times'][j])
beat_pos = int(beat_time * 2)
if beat_pos < len(phrase_visualization) and beat_pos != start_pos and beat_pos != end_pos:
# Determine beat type
if j % beats_info['time_signature'] == 0:
phrase_visualization[beat_pos] = "S"
elif j % beats_info['time_signature'] == beats_info['time_signature'] // 2:
phrase_visualization[beat_pos] = "m"
else:
phrase_visualization[beat_pos] = "w"
# Format and add visualization
phrase_visual = ""
for k in range(0, len(phrase_visualization), 10):
phrase_visual += "".join(phrase_visualization[k:k+10])
if k + 10 < len(phrase_visualization):
phrase_visual += " "
timeline += f" Timeline: {phrase_visual}\n\n"
# Add second-level script display
try:
# Get second-level beat information
subbeat_info = detect_beats_and_subbeats(y, sr, subdivision=4)
duration = librosa.get_duration(y=y, sr=sr)
# Map to seconds
sec_map = map_beats_to_seconds(subbeat_info["subbeat_times"], duration)
# Create templates
templates = create_second_level_templates(sec_map, subbeat_info["tempo"])
# Add to timeline
timeline += "\n=== SECOND-LEVEL SCRIPT ===\n\n"
timeline += "Each line below represents ONE SECOND of audio with matching lyric content.\n"
timeline += "| Second | Beat Pattern | Lyric Content |\n"
timeline += "|--------|-------------|---------------|\n"
# Get clean lyrics (without analysis notes)
clean_lyrics = lyrics
if isinstance(lyrics, str):
if "[Note: Rhythm Analysis]" in lyrics:
clean_lyrics = lyrics.split("[Note: Rhythm Analysis]")[0].strip()
elif "[Note: Potential rhythm mismatches" in lyrics:
clean_lyrics = lyrics.split("[Note:")[0].strip()
# Get lyric lines
lines = clean_lyrics.strip().split('\n') if clean_lyrics else []
for i, template in enumerate(templates):
# Get corresponding lyric line if available
lyric = lines[i] if i < len(lines) else ""
if lyric.startswith('[') and ']' in lyric:
lyric = "" # Skip section headers
# Format nicely for display
timeline += f"| {i+1:<6} | {template:<30} | {lyric[:40]} |\n"
# Add ASCII visualization of second-level beats
timeline += "\n=== SECOND-LEVEL VISUALIZATION ===\n\n"
timeline += "Each row represents ONE SECOND. Beat types:\n"
timeline += "S = Strong beat | m = Medium beat | w = Weak beat | · = No beat\n\n"
for i, window in enumerate(sec_map):
beats = window["beats"]
# Create ASCII visualization
beat_viz = ["·"] * 20 # 20 columns for visualization
for beat in beats:
# Calculate position in visualization
pos = int(beat["relative_pos"] * 19) # Map 0-1 to 0-19
if 0 <= pos < len(beat_viz):
# Set marker based on beat type
if beat["type"] == "main":
beat_viz[pos] = "S"
elif beat["strength"] >= 0.7:
beat_viz[pos] = "m"
else:
beat_viz[pos] = "w"
# Get corresponding lyric
lyric = lines[i] if i < len(lines) else ""
if lyric.startswith('[') and ']' in lyric:
lyric = ""
# Format visualization line
viz_line = f"Second {i+1:2d}: [" + "".join(beat_viz) + "]"
if lyric:
viz_line += f" → {lyric[:40]}"
timeline += viz_line + "\n"
except Exception as e:
timeline += f"\n[Error generating second-level analysis: {str(e)}]"
# Add a section showing alignment if lyrics were generated
if lyrics and isinstance(lyrics, str):
timeline += "\n=== LYRICS-BEAT ALIGNMENT ===\n\n"
# Remove rhythm analysis notes from lyrics if present
if "[Note:" in lyrics:
clean_lyrics = lyrics.split("[Note:")[0].strip()
else:
clean_lyrics = lyrics
lines = clean_lyrics.strip().split('\n')
# Show alignment for ALL lines, not just the first 10
for i, line in enumerate(lines):
if not line.strip() or line.startswith('['):
continue
timeline += f"Line: \"{line}\"\n"
# Count syllables
syllable_count = count_syllables(line)
timeline += f" Syllables: {syllable_count}\n"
# Create adaptive phrase matching - if we don't have a direct phrase match,
# try to find the closest matching phrase by time or measure
matching_phrase = None
if 'phrases' in beats_info and beats_info['phrases']:
# First try direct index matching
if i < len(beats_info['phrases']) and beats_info['phrases'][i]:
matching_phrase = beats_info['phrases'][i]
else:
# If no direct match, try to find a phrase by musical position
# Calculate which section of the song we're in
if len(beats_info['phrases']) > 0:
section_size = max(1, len(beats_info['phrases']) // 4)
section_index = min(i // section_size, 3) # Limit to 4 sections
section_start = section_index * section_size
section_end = min(section_start + section_size, len(beats_info['phrases']))
# Try to find a phrase within this section
candidate_phrases = [phrase for j, phrase in enumerate(beats_info['phrases'])
if section_start <= j < section_end and phrase]
if candidate_phrases:
matching_phrase = candidate_phrases[min(i % section_size, len(candidate_phrases)-1)]
elif beats_info['phrases']:
# Fallback to cycling through available phrases
phrase_index = i % len(beats_info['phrases'])
if beats_info['phrases'][phrase_index]:
matching_phrase = beats_info['phrases'][phrase_index]
# Show timing and detailed alignment if we found a matching phrase
if matching_phrase and len(matching_phrase) > 0 and len(beats_info['beat_times']) > 0:
# Safely check if phrase has elements and indices are valid
if len(matching_phrase) > 0 and len(beats_info['beat_times']) > 0:
start_beat = min(matching_phrase[0], len(beats_info['beat_times'])-1)
end_beat = min(matching_phrase[-1], len(beats_info['beat_times'])-1)
start_time = ensure_float(beats_info['beat_times'][start_beat])
end_time = ensure_float(beats_info['beat_times'][end_beat])
timeline += f" Timing: {start_time:.2f}s - {end_time:.2f}s\n"
# Create an enhanced visualization of syllable alignment
timeline += " Alignment: "
# Create a timeline focused on just this phrase
phrase_duration = end_time - start_time
syllable_viz = []
# Initialize with beat markers for this phrase using improved algorithm
for j, beat_idx in enumerate(matching_phrase):
if beat_idx < len(beats_info['beat_times']):
beat_time = ensure_float(beats_info['beat_times'][beat_idx])
# Handle edge case where phrase_duration is very small
if phrase_duration > 0.001: # Avoid division by very small numbers
# Use non-linear mapping for more musical alignment
# This accounts for natural speech rhythms not being strictly linear
normalized_pos = (beat_time - start_time) / phrase_duration
# Apply slight curve to map syllable positions more naturally
curved_pos = min(1.0, normalized_pos * (1.0 + 0.1 * (normalized_pos - 0.5)))
relative_pos = int(curved_pos * syllable_count)
else:
relative_pos = j # Default to sequential if duration is too small
# Ensure we have enough space
while len(syllable_viz) <= relative_pos:
syllable_viz.append("·")
# Determine beat type with metrical context
metrical_pos = beat_idx % beats_info['time_signature']
beat_strength = beats_info['beat_strengths'][beat_idx] if beat_idx < len(beats_info['beat_strengths']) else 0
if metrical_pos == 0 or beat_strength >= 0.8:
syllable_viz[relative_pos] = "S" # Strong beat
elif metrical_pos == beats_info['time_signature'] // 2 or beat_strength >= 0.5:
syllable_viz[relative_pos] = "m" # Medium beat
else:
syllable_viz[relative_pos] = "w" # Weak beat
# Fill in any gaps
while len(syllable_viz) < syllable_count:
syllable_viz.append("·")
# Trim if too long
syllable_viz = syllable_viz[:syllable_count]
# Add alignment visualization with word stress analysis
timeline += "".join(syllable_viz) + "\n"
# Add word stress analysis
words = re.findall(r'\b[a-zA-Z]+\b', line.lower())
if words:
word_stresses = []
cumulative_syllables = 0
for word in words:
syllable_count_word = count_syllables_for_word(word)
stress_pattern = get_word_stress(word)
# Ensure stress pattern is as long as syllable count
while len(stress_pattern) < syllable_count_word:
stress_pattern += "0"
for j in range(syllable_count_word):
stress_char = "S" if j < len(stress_pattern) and stress_pattern[j] == "1" else "_"
word_stresses.append(stress_char)
cumulative_syllables += syllable_count_word
# Add word stress information
timeline += " Word stress: " + "".join(word_stresses) + "\n"
# Check if stressed syllables align with strong beats
alignment_score = 0
alignment_issues = []
for j, (stress, beat) in enumerate(zip(word_stresses, syllable_viz)):
if (stress == "S" and beat == "S") or (stress != "S" and beat != "S"):
alignment_score += 1
elif stress == "S" and beat != "S":
alignment_issues.append(f"Syllable {j+1} has stress but weak beat")
elif stress != "S" and beat == "S":
alignment_issues.append(f"Syllable {j+1} has no stress but strong beat")
if word_stresses:
alignment_percent = (alignment_score / len(word_stresses)) * 100
timeline += f" Stress alignment: {alignment_percent:.1f}% match\n"
if alignment_issues and len(alignment_issues) <= 3:
timeline += " Issues: " + "; ".join(alignment_issues) + "\n"
else:
timeline += " No matching phrase found for alignment\n"
timeline += "\n"
return timeline
except Exception as e:
print(f"Error generating complete beat timeline: {str(e)}")
return f"Error generating complete beat timeline: {str(e)}"
def display_results(audio_file, lyrics_requirements=None):
"""Process audio file and return formatted results for display in the UI."""
# Default error response
error_response = ("Please upload an audio file.",
"No emotion analysis available.",
"No audio classification available.",
"No lyrics generated.",
"No beat timeline available.")
if audio_file is None:
return error_response
try:
# Process audio and get results - pass user requirements
results = process_audio(audio_file, lyrics_requirements)
# Check if we got an error message
if isinstance(results, str) and "Error" in results:
return results, *error_response[1:]
elif isinstance(results, tuple) and isinstance(results[0], str) and "Error" in results[0]:
return results[0], *error_response[1:]
# Extract results
if isinstance(results, dict):
# New format
genre_results = results.get("genre_results", "Genre classification failed")
lyrics = results.get("lyrics", "Lyrics generation failed")
ast_results = results.get("ast_results", [])
else:
# Old tuple format
genre_results, lyrics, ast_results = results
# Get clean lyrics (without analysis notes)
clean_lyrics = lyrics
if isinstance(lyrics, str):
if "[Note: Rhythm Analysis]" in lyrics:
clean_lyrics = lyrics.split("[Note: Rhythm Analysis]")[0].strip()
elif "[Note: Potential rhythm mismatches" in lyrics:
clean_lyrics = lyrics.split("[Note:")[0].strip()
# Generate beat timeline - use the complete timeline function that shows all beats
beat_timeline = format_complete_beat_timeline(audio_file, clean_lyrics)
# Format emotion analysis results
emotion_text = "No emotion analysis available."
try:
emotion_results = music_analyzer.analyze_music(audio_file)
emotion_text = (f"Tempo: {emotion_results['summary']['tempo']:.1f} BPM\n"
f"Key: {emotion_results['summary']['key']} {emotion_results['summary']['mode']}\n"
f"Primary Emotion: {emotion_results['summary']['primary_emotion']}\n"
f"Primary Theme: {emotion_results['summary']['primary_theme']}")
# Keep basic beat analysis without section information
y, sr = load_audio(audio_file, SAMPLE_RATE)
beats_info = detect_beats(y, sr)
# Add beat analysis info
emotion_text += f"\n\nBeat Analysis:\n"
emotion_text += f"- Tempo: {beats_info.get('tempo', 0):.1f} BPM\n"
emotion_text += f"- Time Signature: {beats_info.get('time_signature', 4)}/4\n"
emotion_text += f"- Total Beats: {beats_info.get('beat_count', 0)}\n"
except Exception as e:
print(f"Error in emotion analysis: {str(e)}")
# Format audio classification results
ast_text = "No valid audio classification results available."
if ast_results and isinstance(ast_results, list):
ast_text = "Audio Classification Results:\n"
for result in ast_results[:5]: # Show top 5 results
ast_text += f"{result['label']}: {result['score']*100:.2f}%\n"
# Return all results
return genre_results, emotion_text, ast_text, clean_lyrics, beat_timeline
except Exception as e:
error_msg = f"Error: {str(e)}"
print(error_msg)
return error_msg, *error_response[1:]
# Create enhanced Gradio interface with tabs for better organization
with gr.Blocks(title="Music Genre Classifier & Lyrics Generator") as demo:
gr.Markdown("# Music Genre Classifier & Lyrics Generator")
gr.Markdown("Upload a music file to classify its genre, analyze its emotions, and generate perfectly aligned lyrics.")
with gr.Row():
with gr.Column(scale=1):
audio_input = gr.Audio(label="Upload Music", type="filepath")
# Add the new lyrics requirements input
lyrics_requirements_input = gr.Textbox(
label="Lyrics Requirements (optional)",
placeholder="Enter specific themes, topics, words, or styles you want in the lyrics",
lines=3
)
submit_btn = gr.Button("Analyze & Generate", variant="primary")
# Add genre info box
with gr.Accordion("About Music Genres", open=False):
gr.Markdown("""
The system recognizes various music genres including:
- Pop, Rock, Hip-Hop, R&B
- Electronic, Dance, Techno, House
- Jazz, Blues, Classical
- Folk, Country, Acoustic
- Metal, Punk, Alternative
- And many others!
For best results, use high-quality audio files (MP3, WAV, FLAC) with at least 10 seconds of music.
""")
with gr.Column(scale=2):
# Use tabs for better organization of outputs
with gr.Tabs():
with gr.TabItem("Analysis Results"):
genre_output = gr.Textbox(label="Detected Genres", lines=4)
# Create 2 columns for emotion and audio classification
with gr.Row():
with gr.Column():
emotion_output = gr.Textbox(label="Emotion & Structure Analysis", lines=8)
with gr.Column():
ast_output = gr.Textbox(label="Audio Classification", lines=8)
with gr.TabItem("Generated Lyrics"):
lyrics_output = gr.Textbox(label="Lyrics", lines=18)
with gr.TabItem("Beat & Syllable Timeline"):
beat_timeline_output = gr.Textbox(label="Beat Timings & Syllable Patterns", lines=40)
# Connect the button to the display function with updated inputs
submit_btn.click(
fn=display_results,
inputs=[audio_input, lyrics_requirements_input],
outputs=[genre_output, emotion_output, ast_output, lyrics_output, beat_timeline_output]
)
# Enhanced explanation of how the system works
with gr.Accordion("How it works", open=False):
gr.Markdown("""
## Advanced Lyrics Generation Process
1. **Audio Analysis**: The system analyzes your uploaded music file using multiple machine learning models.
2. **Genre Classification**: A specialized neural network identifies the musical genre, detecting subtle patterns in the audio.
3. **Emotional Analysis**: The system examines harmonic, rhythmic, and timbral features to determine the emotional qualities of the music.
4. **Rhythm Mapping**: Advanced beat detection algorithms create a detailed rhythmic map of the music, identifying:
- Strong and weak beats
- Natural phrase boundaries
- Time signature and tempo variations
- Beat subdivisions (half and quarter beats)
5. **Second-Level Alignment**: The system maps beats and subbeats to each second of audio, creating precise templates for perfect alignment.
6. **Syllable Template Creation**: For each second of audio, the system generates precise syllable templates that reflect:
- Beat stress patterns (strong, medium, weak)
- Appropriate syllable counts based on tempo
- Genre-specific rhythmic qualities
- Half-beat and quarter-beat subdivisions
7. **Lyrics Generation**: Using the detected genre, emotion, rhythm patterns, and your custom requirements, a large language model generates lyrics that:
- Match the emotional quality of the music
- Follow the precise syllable templates for each second
- Align stressed syllables with strong beats
- Maintain genre-appropriate style and themes
- Incorporate your specific requirements and preferences
8. **Rhythm Verification**: The system verifies the generated lyrics, analyzing:
- Syllable count accuracy
- Stress alignment with strong beats
- Word stress patterns
- Second-by-second alignment precision
9. **Refinement**: If significant rhythm mismatches are detected, the system can automatically refine the lyrics for better alignment.
This multi-step process creates lyrics that feel naturally connected to the music, as if they were written specifically for it.
""")
# Launch the app
demo.launch()