ss

README.md

@@ -28,6 +28,17 @@ This Hugging Face Space application analyzes music files and generates lyrics th
 3. View the analysis results showing tempo, key, emotion, theme, and genre
 4. Check the generated lyrics tailored to match your music
 
+## Supported Genres
+
+Lyrics generation is currently limited to the following genres:
+- **Pop**
+- **Rock**
+- **Country**
+- **Disco**
+- **Metal**
+
+These genres have consistent syllable-to-beat relationships that work well with our beat-matching algorithm. For other genres, the application will still provide music analysis, but lyrics generation will be disabled.
+
 ## Technical Details
 
 This application uses:
@@ -44,6 +55,7 @@ See requirements.txt for detailed dependencies.
 
 - Large audio files may take longer to process
 - The quality of lyrics generation depends on the clarity of the audio and the detected musical features
+- Lyrics generation is restricted to specific genres (see Supported Genres section)
 
 ## Credits
 

app.py

@@ -151,12 +151,6 @@ def process_audio(audio_file):
         genre_results_text = format_genre_results(top_genres)
         primary_genre = top_genres[0][0]
         
-        # Generate lyrics using LLM
-        lyrics = generate_lyrics(music_analysis, primary_genre, duration)
-        
-        # Create beat/stress/syllable matching analysis
-        beat_match_analysis = analyze_lyrics_rhythm_match(lyrics, lyric_templates, primary_genre)
-        
         # Prepare analysis summary
         analysis_summary = f"""
 ### Music Analysis Results
@@ -184,6 +178,19 @@ def process_audio(audio_file):
 - Phrase 2: {lyric_templates[1]['stress_pattern'] if len(lyric_templates) > 1 else 'N/A'}
 """
         
+        # Check if genre is supported for lyrics generation
+        # Use the supported_genres list from BeatAnalyzer
+        genre_supported = any(genre.lower() in primary_genre.lower() for genre in beat_analyzer.supported_genres)
+        
+        # Generate lyrics only for supported genres
+        if genre_supported:
+            lyrics = generate_lyrics(music_analysis, primary_genre, duration)
+            beat_match_analysis = analyze_lyrics_rhythm_match(lyrics, lyric_templates, primary_genre)
+        else:
+            supported_genres_str = ", ".join([genre.capitalize() for genre in beat_analyzer.supported_genres])
+            lyrics = f"Lyrics generation is only supported for the following genres: {supported_genres_str}.\n\nDetected genre '{primary_genre}' doesn't have strong syllable-to-beat patterns required for our lyric generation algorithm."
+            beat_match_analysis = "Lyrics generation not available for this genre."
+        
         return analysis_summary, lyrics, tempo, time_signature, emotion, theme, primary_genre, beat_match_analysis
     
     except Exception as e:
@@ -594,13 +601,23 @@ def create_interface():
                     emotion_output, theme_output, genre_output, beat_match_output]
         )
         
-        gr.Markdown("""
+        # Format supported genres for display
+        supported_genres_md = "\n".join([f"- {genre.capitalize()}" for genre in beat_analyzer.supported_genres])
+        
+        gr.Markdown(f"""
         ## How it works
         1. Upload or record a music file
         2. The system analyzes tempo, beats, time signature and other musical features
         3. It detects emotion, theme, and music genre
         4. Using beat patterns and syllable stress analysis, it generates perfectly aligned lyrics
         5. Each line of the lyrics is matched to the beat pattern of the corresponding musical phrase
+        
+        ## Supported Genres
+        **Note:** Lyrics generation is currently only supported for the following genres:
+        {supported_genres_md}
+        
+        These genres have consistent syllable-to-beat patterns that work well with our algorithm.
+        For other genres, only music analysis will be provided.
         """)
     
     return demo

beat_analysis.py

@@ -32,25 +32,32 @@ class BeatAnalyzer:
             
         # Genre-specific syllable-to-beat ratio guidelines
         self.genre_syllable_ratios = {
-            # Genre: (min_ratio, typical_ratio, max_ratio)
+            # Supported genres with strong syllable-to-beat patterns
             'pop': (0.9, 1.5, 2.2),        # Pop tends to have more syllables per beat
-            'rock': (0.8, 1.2, 1.8),       # Rock can vary widely
+            'rock': (0.8, 1.2, 1.8),       # Rock can vary widely but maintains beat alignment
+            'country': (0.8, 1.2, 1.6),    # Country tends to be moderate and clear in syllable matching
+            'disco': (1.0, 1.5, 2.0),      # Disco tends to have more syllables with clear beat patterns
+            'metal': (0.8, 1.5, 2.0),      # Metal often has more syllables on strong beats
+            
+            # Other genres (analysis only, no lyrics generation)
             'hiphop': (1.8, 2.5, 3.5),     # Hip hop often has many syllables per beat
             'rap': (2.0, 3.0, 4.0),        # Rap often has very high syllable counts
             'folk': (0.8, 1.0, 1.3),       # Folk often has close to 1:1 ratio
-            'country': (0.8, 1.2, 1.6),    # Country tends to be moderate
             'jazz': (0.7, 1.0, 1.5),       # Jazz can be very flexible
             'reggae': (0.7, 1.0, 1.3),     # Reggae often emphasizes specific beats
             'soul': (0.8, 1.2, 1.6),       # Soul music tends to be expressive
             'r&b': (1.0, 1.5, 2.0),        # R&B can have melisma
             'electronic': (0.7, 1.0, 1.5), # Electronic music varies widely
-            'disco': (1.0, 1.5, 2.0),      # Disco tends to have more syllables
             'classical': (0.7, 1.0, 1.4),  # Classical can vary by subgenre
-            'metal': (0.8, 1.5, 2.0),      # Metal often has more syllables on strong beats
             'blues': (0.6, 0.8, 1.2),      # Blues often extends syllables
             'default': (0.9, 1.5, 2.0)     # Default for unknown genres
         }
         
+        # List of genres supported for lyrics generation
+        # These genres have the most predictable and consistent syllable-to-beat relationships,
+        # making them ideal for our beat-matching algorithm
+        self.supported_genres = ['pop', 'rock', 'country', 'disco', 'metal']
+        
     @lru_cache(maxsize=128)
     def count_syllables(self, word):
         """Count syllables in a word using CMU dictionary if available, otherwise use rule-based method."""