first

.vscode/settings.json

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+{
+    "editor.trimAutoWhitespace": true,
+    "files.trimTrailingWhitespace": true, // 儲存的時候，會幫你自動過濾多餘的空格
+    "files.autoSave": "onFocusChange", // 是否自動儲存檔案
+    "editor.formatOnSave": true,
+    // "editor.defaultFormatter": "esbenp.prettier-vscode",
+    // "[javascriptreact]": {
+    //     "editor.defaultFormatter": "esbenp.prettier-vscode",
+    //     "editor.formatOnPaste": false, //Black didn;t support format selection
+    //     "editor.formatOnSave": true,
+    //     "editor.insertSpaces": true,
+    //     "editor.detectIndentation": true,
+    //     "editor.tabSize": 2
+    // },
+    "editor.wordWrapColumn": 240,
+    "python.linting.enabled": false,
+    "python.linting.pylintEnabled": true,
+    "python.linting.pylintPath": "pylint",
+    "python.linting.pylintArgs": [
+        "--max-line-length",
+        "240",
+        "--aggressive"
+    ],
+    // "python.formatting.provider": "black", // or "black" here
+    // "python.formatting.blackArgs": [
+    //     "--line-length",
+    //     "80"
+    // ],
+    "[python]": {
+        // 加入這幾行
+        "editor.formatOnPaste": false, //Black didn;t support format selection
+        "editor.formatOnSave": true,
+        // "editor.renderIndentGuides": true,
+        "editor.insertSpaces": true,
+        "editor.detectIndentation": true,
+        "editor.tabSize": 4
+    },
+    // "python.analysis.typeCheckingMode": "basic",
+}

app.py

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+import gradio as gr
+import openai
+from typing import Iterator, TextIO
+import tempfile
+from pydub import AudioSegment
+
+
+def audio_from_file(filename: str) -> AudioSegment:
+    try:
+        audio = AudioSegment.from_file(filename)
+    except FileNotFoundError:
+        raise ValueError(
+            f"Cannot load audio from file: `{filename}` not found. Do you forgot to install `ffmpeg`."
+        )
+
+    return audio
+
+
+def format_timestamp(seconds: float, always_include_hours: bool = False):
+    assert seconds >= 0, "non-negative timestamp expected"
+    milliseconds = round(seconds * 1000.0)
+
+    hours = milliseconds // 3_600_000
+    milliseconds -= hours * 3_600_000
+
+    minutes = milliseconds // 60_000
+    milliseconds -= minutes * 60_000
+
+    seconds = milliseconds // 1_000
+    milliseconds -= seconds * 1_000
+
+    hours_marker = f"{hours}:" if always_include_hours or hours > 0 else ""
+    return f"{hours_marker}{minutes:02d}:{seconds:02d}.{milliseconds:03d}"
+
+
+def write_srt(transcript: Iterator[dict], file: TextIO):
+    """
+    Write a transcript to a file in SRT format.
+    Example usage:
+        from pathlib import Path
+        from whisper.utils import write_srt
+        result = transcribe(model, audio_path, temperature=temperature, **args)
+        # save SRT
+        audio_basename = Path(audio_path).stem
+        with open(Path(output_dir) / (audio_basename + ".srt"), "w", encoding="utf-8") as srt:
+            write_srt(result["segments"], file=srt)
+    """
+    with open(file, "w", encoding="UTF-8") as f:
+        for segment in transcript:
+            # write srt lines
+            id = segment["id"]
+            start = format_timestamp(segment["start"], always_include_hours=True)
+            end = format_timestamp(segment["end"], always_include_hours=True)
+            text = segment["text"].strip().replace("-->", "->")
+
+            f.write(f"{id}\n{start} --> {end}\n{text}\n\n")
+
+
+def create_main_tab():
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column():
+                api_text = gr.Textbox(label="OpenAI API Key")
+
+                file_type = gr.Radio(
+                    ["Video", "Audio"],
+                    value="Video",
+                    label="File Type",
+                    interactive=True,
+                )
+                video = gr.Video()
+                audio = gr.Audio(visible=False)
+                with gr.Row():
+                    compress_btn = gr.Button("Compress")
+                    submit_btn = gr.Button("Submit")
+            with gr.Column():
+                compress_file = gr.File(label="Compress file", interactive=False)
+                subtitle_file = gr.File(label="Subtitle")
+                message_text = gr.Textbox(label="Info")
+
+            def handle_file_type_change(evt: gr.SelectData):
+                if evt.index == 0:
+                    # Video
+                    return [gr.update(visible=True), gr.update(visible=False)]
+                elif evt.index == 1:
+                    # Audio
+                    return [gr.update(visible=False), gr.update(visible=True)]
+
+            file_type.select(
+                handle_file_type_change,
+                None,
+                [video, audio],
+            )
+
+        def handle_compress_btn_submit(file_type, video, audio):
+            if file_type == "Video":
+                audio_data = audio_from_file(video)
+            elif file_type == "Audio":
+                audio_data = audio_from_file(audio)
+
+            with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as tmp_file:
+                audio_data.export(tmp_file.name, format="mp3", bitrate="96k")
+
+            return tmp_file.name
+
+        compress_btn.click(
+            fn=handle_compress_btn_submit,
+            inputs=[file_type, video, audio],
+            outputs=[compress_file],
+        )
+
+        def handle_btn_submit(compress_file, api_text):
+            def transcribe_audio(input_file, output_file):
+                with open(input_file, "rb") as f:
+                    try:
+                        result = openai.Audio.transcribe("whisper-1", f)
+                        write_srt(result["segments"], output_file)
+                        return "Success! The subtitle file will be named: {output_file}"
+                    except Exception as e:
+                        return f"Error. OpenAI API unavailable. Received: {e}"
+
+            openai.api_key = api_text
+
+            with tempfile.NamedTemporaryFile(suffix=".srt", delete=False) as out_file:
+                out_message = transcribe_audio(compress_file.name, out_file.name)
+
+            return out_file.name, out_message
+
+        submit_btn.click(
+            fn=handle_btn_submit,
+            inputs=[compress_file, api_text],
+            outputs=[subtitle_file, message_text],
+        )
+
+    return demo
+
+
+demo = create_main_tab()
+if __name__ == "__main__":
+    demo.launch()

gitignore

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+
+.vscode/
+build/
+dist/
+dist_onefile/
+flagged/
+
+venv/
+
+test/
+img/
+mp4/
+out/
+wav/
+tmp/
+project/
+repositories/
+
+__pycache__/
+pretrained_models/
+
+# *.srt
+
+test.py
+**/.openai

requirements.txt

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+gradio
+openai
+pydub