Update app.py

app.py

@@ -1,161 +1,161 @@
-import gradio as gr
-import whisper
-import os
-import shutil
-import cv2
-from moviepy.editor import ImageSequenceClip, AudioFileClip, VideoFileClip
-from tqdm import tqdm
-
-FONT = cv2.FONT_HERSHEY_SIMPLEX
-FONT_SCALE = 0.8
-FONT_THICKNESS = 2
-
-class VideoTranscriber:
-    def __init__(self, model_path, video_path):
-        self.model = whisper.load_model(model_path)
-        self.video_path = video_path
-        self.audio_path = ''
-        self.text_array = []
-        self.fps = 0
-        self.char_width = 0
-
-    def transcribe_video(self):
-        print('Transcribing video')
-        result = self.model.transcribe(self.audio_path)
-        text = result["segments"][0]["text"]
-        textsize = cv2.getTextSize(text, FONT, FONT_SCALE, FONT_THICKNESS)[0]
-        cap = cv2.VideoCapture(self.video_path)
-        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-        asp = 16/9
-        ret, frame = cap.read()
-        width = frame[:, int(int(width - 1 / asp * height) / 2):width - int((width - 1 / asp * height) / 2)].shape[1]
-        width = width - (width * 0.1)
-        self.fps = cap.get(cv2.CAP_PROP_FPS)
-        self.char_width = int(textsize[0] / len(text))
-
-        for j in tqdm(result["segments"]):
-            lines = []
-            text = j["text"]
-            end = j["end"]
-            start = j["start"]
-            total_frames = int((end - start) * self.fps)
-            start = start * self.fps
-            total_chars = len(text)
-            words = text.split(" ")
-            i = 0
-
-            while i < len(words):
-                words[i] = words[i].strip()
-                if words[i] == "":
-                    i += 1
-                    continue
-                length_in_pixels = (len(words[i]) + 1) * self.char_width
-                remaining_pixels = width - length_in_pixels
-                line = words[i]
-
-                while remaining_pixels > 0:
-                    i += 1
-                    if i >= len(words):
-                        break
-                    length_in_pixels = (len(words[i]) + 1) * self.char_width
-                    remaining_pixels -= length_in_pixels
-                    if remaining_pixels < 0:
-                        continue
-                    else:
-                        line += " " + words[i]
-
-                line_array = [line, int(start) + 15, int(len(line) / total_chars * total_frames) + int(start) + 15]
-                start = int(len(line) / total_chars * total_frames) + int(start)
-                lines.append(line_array)
-                self.text_array.append(line_array)
-
-        cap.release()
-        print('Transcription complete')
-
-    def extract_audio(self):
-        print('Extracting audio')
-        audio_path = os.path.join(os.path.dirname(self.video_path), "audio.mp3")
-        video = VideoFileClip(self.video_path)
-        audio = video.audio
-        audio.write_audiofile(audio_path)
-        self.audio_path = audio_path
-        print('Audio extracted')
-
-    def extract_frames(self, output_folder):
-        print('Extracting frames')
-        cap = cv2.VideoCapture(self.video_path)
-        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-        asp = width / height
-        N_frames = 0
-
-        while True:
-            ret, frame = cap.read()
-            if not ret:
-                break
-
-            frame = frame[:, int(int(width - 1 / asp * height) / 2):width - int((width - 1 / asp * height) / 2)]
-
-            for i in self.text_array:
-                if N_frames >= i[1] and N_frames <= i[2]:
-                    text = i[0]
-                    text_size, _ = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)
-                    text_x = int((frame.shape[1] - text_size[0]) / 2)
-                    text_y = int(height/2)
-                    cv2.putText(frame, text, (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
-                    break
-
-            cv2.imwrite(os.path.join(output_folder, str(N_frames) + ".jpg"), frame)
-            N_frames += 1
-
-        cap.release()
-        print('Frames extracted')
-
-    def create_video(self, output_video_path):
-        print('Creating video')
-        image_folder = os.path.join(os.path.dirname(self.video_path), "frames")
-        if not os.path.exists(image_folder):
-            os.makedirs(image_folder)
-
-        self.extract_frames(image_folder)
-
-        images = [img for img in os.listdir(image_folder) if img.endswith(".jpg")]
-        images.sort(key=lambda x: int(x.split(".")[0]))
-
-        frame = cv2.imread(os.path.join(image_folder, images[0]))
-        height, width, layers = frame.shape
-
-        clip = ImageSequenceClip([os.path.join(image_folder, image) for image in images], fps=self.fps)
-        audio = AudioFileClip(self.audio_path)
-        clip = clip.set_audio(audio)
-        clip.write_videofile(output_video_path)
-        shutil.rmtree(image_folder)
-        os.remove(os.path.join(os.path.dirname(self.video_path), "audio.mp3"))
-
-def process_video(video_path):
-    model_path = "base"
-    output_video_path = "output.mp4"
-
-    transcriber = VideoTranscriber(model_path, video_path)
-    transcriber.extract_audio()
-    transcriber.transcribe_video()
-    transcriber.create_video(output_video_path)
-
-    return output_video_path
-
-# Gradio Interface
-def gradio_interface(video):
-    output_video_path = process_video(video)
-    return output_video_path
-
-iface = gr.Interface(
-    fn=gradio_interface,
-    inputs=gr.inputs.Video(label="Upload Video"),
-    outputs=gr.outputs.Video(label="Transcribed Video"),
-    title="Video Transcription App",
-    description="Upload a video to transcribe and generate a new video with subtitles."
-)
-
-if __name__ == "__main__":
+import gradio as gr
+import whisper
+import os
+import shutil
+import cv2
+from moviepy import ImageSequenceClip, AudioFileClip, VideoFileClip
+from tqdm import tqdm
+
+FONT = cv2.FONT_HERSHEY_SIMPLEX
+FONT_SCALE = 0.8
+FONT_THICKNESS = 2
+
+class VideoTranscriber:
+    def __init__(self, model_path, video_path):
+        self.model = whisper.load_model(model_path)
+        self.video_path = video_path
+        self.audio_path = ''
+        self.text_array = []
+        self.fps = 0
+        self.char_width = 0
+
+    def transcribe_video(self):
+        print('Transcribing video')
+        result = self.model.transcribe(self.audio_path)
+        text = result["segments"][0]["text"]
+        textsize = cv2.getTextSize(text, FONT, FONT_SCALE, FONT_THICKNESS)[0]
+        cap = cv2.VideoCapture(self.video_path)
+        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        asp = 16/9
+        ret, frame = cap.read()
+        width = frame[:, int(int(width - 1 / asp * height) / 2):width - int((width - 1 / asp * height) / 2)].shape[1]
+        width = width - (width * 0.1)
+        self.fps = cap.get(cv2.CAP_PROP_FPS)
+        self.char_width = int(textsize[0] / len(text))
+
+        for j in tqdm(result["segments"]):
+            lines = []
+            text = j["text"]
+            end = j["end"]
+            start = j["start"]
+            total_frames = int((end - start) * self.fps)
+            start = start * self.fps
+            total_chars = len(text)
+            words = text.split(" ")
+            i = 0
+
+            while i < len(words):
+                words[i] = words[i].strip()
+                if words[i] == "":
+                    i += 1
+                    continue
+                length_in_pixels = (len(words[i]) + 1) * self.char_width
+                remaining_pixels = width - length_in_pixels
+                line = words[i]
+
+                while remaining_pixels > 0:
+                    i += 1
+                    if i >= len(words):
+                        break
+                    length_in_pixels = (len(words[i]) + 1) * self.char_width
+                    remaining_pixels -= length_in_pixels
+                    if remaining_pixels < 0:
+                        continue
+                    else:
+                        line += " " + words[i]
+
+                line_array = [line, int(start) + 15, int(len(line) / total_chars * total_frames) + int(start) + 15]
+                start = int(len(line) / total_chars * total_frames) + int(start)
+                lines.append(line_array)
+                self.text_array.append(line_array)
+
+        cap.release()
+        print('Transcription complete')
+
+    def extract_audio(self):
+        print('Extracting audio')
+        audio_path = os.path.join(os.path.dirname(self.video_path), "audio.mp3")
+        video = VideoFileClip(self.video_path)
+        audio = video.audio
+        audio.write_audiofile(audio_path)
+        self.audio_path = audio_path
+        print('Audio extracted')
+
+    def extract_frames(self, output_folder):
+        print('Extracting frames')
+        cap = cv2.VideoCapture(self.video_path)
+        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        asp = width / height
+        N_frames = 0
+
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            frame = frame[:, int(int(width - 1 / asp * height) / 2):width - int((width - 1 / asp * height) / 2)]
+
+            for i in self.text_array:
+                if N_frames >= i[1] and N_frames <= i[2]:
+                    text = i[0]
+                    text_size, _ = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)
+                    text_x = int((frame.shape[1] - text_size[0]) / 2)
+                    text_y = int(height/2)
+                    cv2.putText(frame, text, (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
+                    break
+
+            cv2.imwrite(os.path.join(output_folder, str(N_frames) + ".jpg"), frame)
+            N_frames += 1
+
+        cap.release()
+        print('Frames extracted')
+
+    def create_video(self, output_video_path):
+        print('Creating video')
+        image_folder = os.path.join(os.path.dirname(self.video_path), "frames")
+        if not os.path.exists(image_folder):
+            os.makedirs(image_folder)
+
+        self.extract_frames(image_folder)
+
+        images = [img for img in os.listdir(image_folder) if img.endswith(".jpg")]
+        images.sort(key=lambda x: int(x.split(".")[0]))
+
+        frame = cv2.imread(os.path.join(image_folder, images[0]))
+        height, width, layers = frame.shape
+
+        clip = ImageSequenceClip([os.path.join(image_folder, image) for image in images], fps=self.fps)
+        audio = AudioFileClip(self.audio_path)
+        clip = clip.set_audio(audio)
+        clip.write_videofile(output_video_path)
+        shutil.rmtree(image_folder)
+        os.remove(os.path.join(os.path.dirname(self.video_path), "audio.mp3"))
+
+def process_video(video_path):
+    model_path = "base"
+    output_video_path = "output.mp4"
+
+    transcriber = VideoTranscriber(model_path, video_path)
+    transcriber.extract_audio()
+    transcriber.transcribe_video()
+    transcriber.create_video(output_video_path)
+
+    return output_video_path
+
+# Gradio Interface
+def gradio_interface(video):
+    output_video_path = process_video(video)
+    return output_video_path
+
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=gr.inputs.Video(label="Upload Video"),
+    outputs=gr.outputs.Video(label="Transcribed Video"),
+    title="Video Transcription App",
+    description="Upload a video to transcribe and generate a new video with subtitles."
+)
+
+if __name__ == "__main__":
     iface.launch()