Update app.py

app.py

@@ -90,45 +90,45 @@ def detect_landmark(image, detector, predictor):
             coords[i] = (shape.part(i).x, shape.part(i).y)
     return coords
 
-def predict_and_save(process_video):
-    num_frames = int(cv2.VideoCapture(process_video).get(cv2.CAP_PROP_FRAME_COUNT))
-
-    tsv_cont = ["/\n", f"test-0\t{process_video}\t{None}\t{num_frames}\t{int(16_000*num_frames/25)}\n"]
-    label_cont = ["DUMMY\n"]
-    with open(f"{data_dir}/test.tsv", "w") as fo:
-      fo.write("".join(tsv_cont))
-    with open(f"{data_dir}/test.wrd", "w") as fo:
-      fo.write("".join(label_cont))
-    task.load_dataset(gen_subset, task_cfg=saved_cfg.task)
-
-    def decode_fn(x):
-        dictionary = task.target_dictionary
-        symbols_ignore = generator.symbols_to_strip_from_output
-        symbols_ignore.add(dictionary.pad())
-        return task.datasets[gen_subset].label_processors[0].decode(x, symbols_ignore)
-
-    itr = task.get_batch_iterator(dataset=task.dataset(gen_subset)).next_epoch_itr(shuffle=False)
-    sample = next(itr)
-    if torch.cuda.is_available():
-        sample = utils.move_to_cuda(sample)
-    hypos = task.inference_step(generator, models, sample)
-    ref = decode_fn(sample['target'][0].int().cpu())
-    hypo = hypos[0][0]['tokens'].int().cpu()
-    hypo = decode_fn(hypo)
+# def predict_and_save(process_video):
+#     num_frames = int(cv2.VideoCapture(process_video).get(cv2.CAP_PROP_FRAME_COUNT))
+
+#     tsv_cont = ["/\n", f"test-0\t{process_video}\t{None}\t{num_frames}\t{int(16_000*num_frames/25)}\n"]
+#     label_cont = ["DUMMY\n"]
+#     with open(f"{data_dir}/test.tsv", "w") as fo:
+#       fo.write("".join(tsv_cont))
+#     with open(f"{data_dir}/test.wrd", "w") as fo:
+#       fo.write("".join(label_cont))
+#     task.load_dataset(gen_subset, task_cfg=saved_cfg.task)
+
+#     def decode_fn(x):
+#         dictionary = task.target_dictionary
+#         symbols_ignore = generator.symbols_to_strip_from_output
+#         symbols_ignore.add(dictionary.pad())
+#         return task.datasets[gen_subset].label_processors[0].decode(x, symbols_ignore)
+
+#     itr = task.get_batch_iterator(dataset=task.dataset(gen_subset)).next_epoch_itr(shuffle=False)
+#     sample = next(itr)
+#     if torch.cuda.is_available():
+#         sample = utils.move_to_cuda(sample)
+#     hypos = task.inference_step(generator, models, sample)
+#     ref = decode_fn(sample['target'][0].int().cpu())
+#     hypo = hypos[0][0]['tokens'].int().cpu()
+#     hypo = decode_fn(hypo)
     
-    # Collect timestamps and texts
-    transcript = []
-    for i, (start, end) in enumerate(sample['net_input']['video_lengths'], 1):
-        start_time = float(start) / 16_000
-        end_time = float(end) / 16_000
-        text = hypo[i].strip()
-        transcript.append({"timestamp": [start_time, end_time], "text": text})
+#     # Collect timestamps and texts
+#     transcript = []
+#     for i, (start, end) in enumerate(sample['net_input']['video_lengths'], 1):
+#         start_time = float(start) / 16_000
+#         end_time = float(end) / 16_000
+#         text = hypo[i].strip()
+#         transcript.append({"timestamp": [start_time, end_time], "text": text})
     
-    # Save transcript to a JSON file
-    with open('speech_transcript.json', 'w') as outfile:
-        json.dump(transcript, outfile, indent=4)
+#     # Save transcript to a JSON file
+#     with open('speech_transcript.json', 'w') as outfile:
+#         json.dump(transcript, outfile, indent=4)
     
-    return hypo
+#     return hypo
 
 
 def preprocess_video(input_video_path):