run clustering

scripts/clusterprosody.py

@@ -423,8 +423,8 @@ def get_audio_part(start_time, end_time, id, path):
 
 def plot_pitch_tts(speech_data,tts_data, tts_align,words,seg_aligns,cluster_id, voice):
     colors = ["red", "green", "blue", "orange", "purple", "pink", "brown", "gray", "cyan"]
-    i = 0
-    fig = plt.figure(figsize=(6, 5))
+    cc = 0
+    fig = plt.figure(figsize=(10, 5))
     plt.title(f"{words} - Pitch - Cluster {cluster_id}")
     for k,v in speech_data.items():
 
@@ -447,12 +447,12 @@ def plot_pitch_tts(speech_data,tts_data, tts_align,words,seg_aligns,cluster_id,
         if len(word_times)>2:
             for i in range(1,len(word_times)-1):
                 bound_line = np.mean([word_times[i][2],word_times[i+1][1]])
-                plt.axvline(x=bound_line, color=colors[i], linestyle='--', linewidth=1, label=f"Speaker {spk} -> {word_times[i+1][0]}")
+                plt.axvline(x=bound_line, color=colors[cc], linestyle='--', linewidth=1, label=f"Speaker {spk} -> {word_times[i+1][0]}")
                 
-        plt.scatter(pitch_xvals, pitches, color=colors[i], label=f"Speaker {spk}")
-        i += 1
-        if i >= len(colors):
-            i=0
+        plt.scatter(pitch_xvals, pitches, color=colors[cc], label=f"Speaker {spk}")
+        cc += 1
+        if cc >= len(colors):
+            cc=0
 
     tpitches = [p for p,e in tts_data]
     t_xvals = [x*0.005 for x in range(len(tpitches))]
@@ -469,7 +469,7 @@ def plot_pitch_tts(speech_data,tts_data, tts_align,words,seg_aligns,cluster_id,
     plt.scatter(t_xvals, tpitches, color="black", label=f"TTS {voice}")
 
 
-    plt.legend()
+    #plt.legend()
     #plt.show()