Update submit.py

app/event_handlers/submit.py

@@ -6,7 +6,6 @@ License: MIT License
 """
 
 import spaces
-import time
 import torch
 import pandas as pd
 import cv2
@@ -15,6 +14,7 @@ import gradio as gr
 # Importing necessary components for the Gradio app
 from app.config import config_data
 from app.utils import (
+    Timer,
     convert_video_to_audio,
     readetect_speech,
     slice_audio,
@@ -55,130 +55,134 @@ def event_handler_submit(
     gr.Textbox,
     gr.Textbox,
 ]:
-    start_time = time.time()
-
-    if video:
-        if video.split(".")[-1] == "webm":
-            video = convert_webm_to_mp4(video)
-
-    audio_file_path = convert_video_to_audio(file_path=video, sr=config_data.General_SR)
-    wav, vad_info = readetect_speech(
-        file_path=audio_file_path,
-        read_audio=read_audio,
-        get_speech_timestamps=get_speech_timestamps,
-        vad_model=vad_model,
-        sr=config_data.General_SR,
-    )
+    with Timer() as timer:
+        if video:
+            if video.split(".")[-1] == "webm":
+                video = convert_webm_to_mp4(video)
 
-    audio_windows = slice_audio(
-        start_time=config_data.General_START_TIME,
-        end_time=int(len(wav)),
-        win_max_length=int(config_data.General_WIN_MAX_LENGTH * config_data.General_SR),
-        win_shift=int(config_data.General_WIN_SHIFT * config_data.General_SR),
-        win_min_length=int(config_data.General_WIN_MIN_LENGTH * config_data.General_SR),
-    )
+        audio_file_path = convert_video_to_audio(
+            file_path=video, sr=config_data.General_SR
+        )
+        wav, vad_info = readetect_speech(
+            file_path=audio_file_path,
+            read_audio=read_audio,
+            get_speech_timestamps=get_speech_timestamps,
+            vad_model=vad_model,
+            sr=config_data.General_SR,
+        )
 
-    intersections = find_intersections(
-        x=audio_windows,
-        y=vad_info,
-        min_length=config_data.General_WIN_MIN_LENGTH * config_data.General_SR,
-    )
+        audio_windows = slice_audio(
+            start_time=config_data.General_START_TIME,
+            end_time=int(len(wav)),
+            win_max_length=int(
+                config_data.General_WIN_MAX_LENGTH * config_data.General_SR
+            ),
+            win_shift=int(config_data.General_WIN_SHIFT * config_data.General_SR),
+            win_min_length=int(
+                config_data.General_WIN_MIN_LENGTH * config_data.General_SR
+            ),
+        )
+
+        intersections = find_intersections(
+            x=audio_windows,
+            y=vad_info,
+            min_length=config_data.General_WIN_MIN_LENGTH * config_data.General_SR,
+        )
 
-    vfe = VideoFeatureExtractor(video_model, file_path=video, with_features=False)
-    vfe.preprocess_video()
-
-    transcriptions, total_text = asr(wav, audio_windows)
-
-    window_frames = []
-    preds_emo = []
-    preds_sen = []
-    for w_idx, window in enumerate(audio_windows):
-        a_w = intersections[w_idx]
-        if not a_w["speech"]:
-            a_pred = None
-        else:
-            wave = wav[a_w["start"] : a_w["end"]].clone()
-            a_pred, _ = audio_model(wave)
-
-        v_pred, _ = vfe(window, config_data.General_WIN_MAX_LENGTH)
-
-        t_pred, _ = text_model(transcriptions[w_idx][0])
-
-        if a_pred:
-            pred_emo = (a_pred["emo"] + v_pred["emo"] + t_pred["emo"]) / 3
-            pred_sen = (a_pred["sen"] + v_pred["sen"] + t_pred["sen"]) / 3
-        else:
-            pred_emo = (v_pred["emo"] + t_pred["emo"]) / 2
-            pred_sen = (v_pred["sen"] + t_pred["sen"]) / 2
-
-        frames = list(
-            range(
-                int(window["start"] * vfe.fps / config_data.General_SR) + 1,
-                int(window["end"] * vfe.fps / config_data.General_SR) + 2,
+        vfe = VideoFeatureExtractor(video_model, file_path=video, with_features=False)
+        vfe.preprocess_video()
+
+        transcriptions, total_text = asr(wav, audio_windows)
+
+        window_frames = []
+        preds_emo = []
+        preds_sen = []
+        for w_idx, window in enumerate(audio_windows):
+            a_w = intersections[w_idx]
+            if not a_w["speech"]:
+                a_pred = None
+            else:
+                wave = wav[a_w["start"] : a_w["end"]].clone()
+                a_pred, _ = audio_model(wave)
+
+            v_pred, _ = vfe(window, config_data.General_WIN_MAX_LENGTH)
+
+            t_pred, _ = text_model(transcriptions[w_idx][0])
+
+            if a_pred:
+                pred_emo = (a_pred["emo"] + v_pred["emo"] + t_pred["emo"]) / 3
+                pred_sen = (a_pred["sen"] + v_pred["sen"] + t_pred["sen"]) / 3
+            else:
+                pred_emo = (v_pred["emo"] + t_pred["emo"]) / 2
+                pred_sen = (v_pred["sen"] + t_pred["sen"]) / 2
+
+            frames = list(
+                range(
+                    int(window["start"] * vfe.fps / config_data.General_SR) + 1,
+                    int(window["end"] * vfe.fps / config_data.General_SR) + 2,
+                )
             )
+            preds_emo.extend([torch.argmax(pred_emo).numpy()] * len(frames))
+            preds_sen.extend([torch.argmax(pred_sen).numpy()] * len(frames))
+            window_frames.extend(frames)
+
+        if max(window_frames) < vfe.frame_number:
+            missed_frames = list(range(max(window_frames) + 1, vfe.frame_number + 1))
+            window_frames.extend(missed_frames)
+            preds_emo.extend([preds_emo[-1]] * len(missed_frames))
+            preds_sen.extend([preds_sen[-1]] * len(missed_frames))
+
+        df_pred = pd.DataFrame(columns=["frames", "pred_emo", "pred_sent"])
+        df_pred["frames"] = window_frames
+        df_pred["pred_emo"] = preds_emo
+        df_pred["pred_sent"] = preds_sen
+
+        df_pred = df_pred.groupby("frames").agg(
+            {
+                "pred_emo": calculate_mode,
+                "pred_sent": calculate_mode,
+            }
         )
-        preds_emo.extend([torch.argmax(pred_emo).numpy()] * len(frames))
-        preds_sen.extend([torch.argmax(pred_sen).numpy()] * len(frames))
-        window_frames.extend(frames)
-
-    if max(window_frames) < vfe.frame_number:
-        missed_frames = list(range(max(window_frames) + 1, vfe.frame_number + 1))
-        window_frames.extend(missed_frames)
-        preds_emo.extend([preds_emo[-1]] * len(missed_frames))
-        preds_sen.extend([preds_sen[-1]] * len(missed_frames))
-
-    df_pred = pd.DataFrame(columns=["frames", "pred_emo", "pred_sent"])
-    df_pred["frames"] = window_frames
-    df_pred["pred_emo"] = preds_emo
-    df_pred["pred_sent"] = preds_sen
-
-    df_pred = df_pred.groupby("frames").agg(
-        {
-            "pred_emo": calculate_mode,
-            "pred_sent": calculate_mode,
-        }
-    )
 
-    frame_indices = get_evenly_spaced_frame_indices(vfe.frame_number, 9)
-    num_frames = len(wav)
-    time_axis = [i / config_data.General_SR for i in range(num_frames)]
-    plt_audio = plot_audio(time_axis, wav.unsqueeze(0), frame_indices, vfe.fps, (12, 2))
-
-    all_idx_faces = list(vfe.faces[1].keys())
-    need_idx_faces = find_nearest_frames(frame_indices, all_idx_faces)
-    faces = []
-    for idx_frame, idx_faces in zip(frame_indices, need_idx_faces):
-        cur_face = cv2.resize(
-            vfe.faces[1][idx_faces], (224, 224), interpolation=cv2.INTER_AREA
+        frame_indices = get_evenly_spaced_frame_indices(vfe.frame_number, 9)
+        num_frames = len(wav)
+        time_axis = [i / config_data.General_SR for i in range(num_frames)]
+        plt_audio = plot_audio(
+            time_axis, wav.unsqueeze(0), frame_indices, vfe.fps, (12, 2)
         )
-        faces.append(
-            display_frame_info(
-                cur_face, "Frame: {}".format(idx_frame + 1), box_scale=0.3
+
+        all_idx_faces = list(vfe.faces[1].keys())
+        need_idx_faces = find_nearest_frames(frame_indices, all_idx_faces)
+        faces = []
+        for idx_frame, idx_faces in zip(frame_indices, need_idx_faces):
+            cur_face = cv2.resize(
+                vfe.faces[1][idx_faces], (224, 224), interpolation=cv2.INTER_AREA
             )
+            faces.append(
+                display_frame_info(
+                    cur_face, "Frame: {}".format(idx_frame + 1), box_scale=0.3
+                )
+            )
+        plt_faces = plot_images(faces)
+
+        plt_emo = plot_predictions(
+            df_pred,
+            "pred_emo",
+            "Emotion",
+            list(config_data.General_DICT_EMO),
+            (12, 2.5),
+            [i + 1 for i in frame_indices],
+            2,
+        )
+        plt_sent = plot_predictions(
+            df_pred,
+            "pred_sent",
+            "Sentiment",
+            list(config_data.General_DICT_SENT),
+            (12, 1.5),
+            [i + 1 for i in frame_indices],
+            2,
         )
-    plt_faces = plot_images(faces)
-
-    plt_emo = plot_predictions(
-        df_pred,
-        "pred_emo",
-        "Emotion",
-        list(config_data.General_DICT_EMO),
-        (12, 2.5),
-        [i + 1 for i in frame_indices],
-        2,
-    )
-    plt_sent = plot_predictions(
-        df_pred,
-        "pred_sent",
-        "Sentiment",
-        list(config_data.General_DICT_SENT),
-        (12, 1.5),
-        [i + 1 for i in frame_indices],
-        2,
-    )
-
-    end_time = time.time()
-    inference_time = end_time - start_time
 
     return (
         gr.Textbox(
@@ -199,7 +203,7 @@ def event_handler_submit(
             visible=True,
         ),
         gr.Textbox(
-            value=config_data.OtherMessages_SEC.format(inference_time),
+            value=timer.execution_time,
             info=config_data.InformationMessages_INFERENCE_TIME,
             container=True,
             visible=True,