more cleanup

main.py

@@ -12,7 +12,7 @@ import time
 import numpy as np
 
 from src.mediautils.audio import extract_audio_from_video
-from src.mediautils.video import render_moments
+from src.mediautils.video import render_moments, filter_moments
 from src.editors.amplitude.editor import AmplitudeEditor
 from src.editors.sentiment.editor import SentimentEditor
 from src.math.cost import quadratic_loss
@@ -58,10 +58,12 @@ def main(args):
         log.info("using cached audio file", cache_path=audio_path)
     else:
         log.info("extracted audio", cache_path=audio_path)
+    params = vars(args)
+    params["temp_file_name"] = temp_file_name
     
     # Initalize Editor
     log.info("initializing editor", editor=args.editor)
-    editor = EDITORS[args.editor](str(in_vid_path.resolve()), audio_path, vars(args))
+    editor = EDITORS[args.editor](str(in_vid_path.resolve()), audio_path, params)
     log.info("initialized editor", editor=args.editor)
     costfunc = ERROR_FUNCS[args.cost]
     desired = args.duration
@@ -74,7 +76,7 @@ def main(args):
     spread_multiplier = random.uniform(0.15, 0.18)
 
     # The decay rate, or how quickly our spread multiplier decreases as we approach the center of the gradient
-    spread_decay = random.uniform(0.0001, 0.001)
+    spread_decay = random.uniform(0.000001, 0.0001)
 
     parallelism = args.parallelism
 
@@ -89,7 +91,6 @@ def main(args):
     complete = False
     iterations = 0
     while not complete:
-        log.info("creating distributions", large_start=large_window_center, small_start=small_window_center, spread=spread_multiplier, decay=spread_decay)
         large_distribution = create_distribution(large_window_center, spread_multiplier, parallelism)
         np.random.shuffle(large_distribution)
         small_distribution = create_distribution(small_window_center, spread_multiplier, parallelism)
@@ -104,14 +105,14 @@ def main(args):
                 futures.append(
                     executor.submit(
                         editor.edit,
-                        pair[0],
+                        pair[0] if pair[0] > pair[1] else pair[1],
-                        pair[1],
+                        pair[1] if pair[0] > pair[1] else pair[0],
                         vars(args)
                     )
                 )
             for future in concurrent.futures.as_completed(futures):
                 try:
-                    moment_results.append(future.result())
+                    moment_results.append(list(future.result()))
                 except Exception:
                     log.exception("error during editing")
                     sys.exit(-2)
@@ -120,6 +121,7 @@ def main(args):
         durations = []
         for result in moment_results:
             total_duration = 0
+            result[0] = filter_moments(result[0], args.mindur, args.maxdur)
             for moment in result[0]:
                total_duration = total_duration + moment.get_duration()
             costs.append(costfunc(desired, total_duration))
@@ -147,12 +149,6 @@ def main(args):
             small_window_center = random.uniform(5, 15)
             spread_multiplier = random.uniform(0.15, 0.18)
             spread_decay = random.uniform(0.0001, 0.001)
-            
-
-
-            
-
-    
 
 
 if __name__ == "__main__":

src/editors/sentiment/editor.py

@@ -1,4 +1,7 @@
 import whisper
+import json
+from pathlib import Path
+import tempfile
 import numpy as np
 import structlog
 
@@ -19,10 +22,22 @@ class TextGlob:
 class SentimentEditor:
     def __init__(self, video_path, audio_path, params):
         self.logger = structlog.get_logger("sentiment")
-        self.logger.info("loading whisper model", size=params["model_size"])
+        tempdir = tempfile.gettempdir()
-        self.model = whisper.load_model(params["model_size"])
+        dest_location = f"{tempdir}/{params['temp_file_name']}-{params['model_size']}-sentiment.json"
-        self.logger.info("transcribing audio", path=audio_path)
+        if not Path(dest_location).is_file():
-        self.result = self.model.transcribe(audio_path)
+            self.logger.info("loading whisper model", size=params["model_size"])
+            self.model = whisper.load_model(params["model_size"])
+            self.logger.info("transcribing audio", path=audio_path)
+            self.result = self.model.transcribe(audio_path)
+
+            with open(dest_location, 'w') as fp:
+                json.dump(self.result, fp)
+        else:
+            self.logger.info("cached transcription found", path=dest_location)
+
+        with open(dest_location, 'r') as f:
+          self.result = json.load(f)
+
         self.segments = []
         for segment in self.result['segments']:
             self.segments.append(TextGlob(segment['start'], segment['end'], segment['text'], 0))
@@ -42,7 +57,7 @@ class SentimentEditor:
     def edit(self, large_window, small_window, params):
         end_time = self.segments[-1].stop
         window_factor = len(self.sentiments) / end_time
-        long_ma = np_moving_average(self.squared_subsample, large_window * window_factor)
+        long_ma = np_moving_average(self.sentiments, large_window)
-        short_ma = np_moving_average(self.squared_subsample, small_window * window_factor)
+        short_ma = np_moving_average(self.sentiments, small_window)
         highlights = find_moving_average_highlights(short_ma, long_ma, 1.0 / window_factor)
         return highlights, large_window, small_window

src/mediautils/video.py

@@ -9,5 +9,8 @@ def get_subclips(source_video_path, moments):
 
 def render_moments(moments, input_video_path, output_path):
         clips, vid = get_subclips(input_video_path, moments)
-        to_render = mp.concatenate_videoclips(clips, logger=None)
+        to_render = mp.concatenate_videoclips(clips)
         to_render.write_videofile(output_path, logger=None)
+
+def filter_moments(moments, min_length, max_length):
+    return [m for m in moments if m.get_duration() > min_length and m.get_duration() < max_length]