Subtitle Generator

Generate synchronized subtitles (SRT/VTT/ASS) from video audio with precise timestamps. Use when users need subtitles, captions, or video transcription with timing.

SenseAudio Subtitle Generator

Create accurate, synchronized subtitles for videos with proper timing, formatting, and multi-language support.

What This Skill Does

• Extract audio from video files
• Transcribe audio with precise timestamps
• Generate subtitle files (SRT, VTT, ASS)
• Support multiple languages and translations
• Format subtitles with proper line breaks and timing

Prerequisites

Install required Python packages:

bash

pip install requests pydub

Note: You'll also need ffmpeg installed for video audio extraction:

bash

# Ubuntu/Debian
sudo apt-get install ffmpeg

# macOS
brew install ffmpeg

Implementation Guide

Step 1: Extract Audio from Video

python

from pydub import AudioSegment
import subprocess

def extract_audio_from_video(video_file, output_audio="temp_audio.wav"):
    # Use ffmpeg to extract audio
    cmd = [
        "ffmpeg", "-i", video_file,
        "-vn",  # No video
        "-acodec", "pcm_s16le",  # PCM format
        "-ar", "16000",  # 16kHz sample rate
        "-ac", "1",  # Mono
        output_audio
    ]
    subprocess.run(cmd, check=True)
    return output_audio

Step 2: Transcribe with Word-Level Timestamps

python

import os
import requests

API_KEY = os.environ["SENSEAUDIO_API_KEY"]

def transcribe_for_subtitles(audio_file, language="zh"):
    url = "https://api.senseaudio.cn/v1/audio/transcriptions"

    headers = {"Authorization": f"Bearer {API_KEY}"}
    files = {"file": open(audio_file, "rb")}
    data = {
        "model": "sense-asr-pro",
        "language": language,
        "response_format": "verbose_json",
        "timestamp_granularities[]": ["word", "segment"],
        "enable_punctuation": "true"
    }

    response = requests.post(url, headers=headers, files=files, data=data)
    return response.json()

Step 3: Generate Subtitle Segments

python

def create_subtitle_segments(transcript_data, max_chars_per_line=42, max_duration=7):
    words = transcript_data.get("words", [])
    segments = []

    current_segment = {
        "start": 0,
        "end": 0,
        "text": ""
    }

    for word in words:
        word_text = word["word"]
        word_start = word["start"]
        word_end = word["end"]

        # Check if adding this word exceeds limits
        potential_text = current_segment["text"] + " " + word_text if current_segment["text"] else word_text

        if (len(potential_text) > max_chars_per_line or
            (current_segment["start"] > 0 and word_end - current_segment["start"] > max_duration)):
            # Save current segment
            if current_segment["text"]:
                segments.append(current_segment.copy())

            # Start new segment
            current_segment = {
                "start": word_start,
                "end": word_end,
                "text": word_text
            }
        else:
            # Add word to current segment
            if not current_segment["text"]:
                current_segment["start"] = word_start
            current_segment["end"] = word_end
            current_segment["text"] = potential_text

    # Add last segment
    if current_segment["text"]:
        segments.append(current_segment)

    return segments

Step 4: Format as SRT

python

def format_srt(segments):
    srt_content = ""

    for i, segment in enumerate(segments, 1):
        start_time = format_timestamp_srt(segment["start"])
        end_time = format_timestamp_srt(segment["end"])

        srt_content += f"{i}\n"
        srt_content += f"{start_time} --> {end_time}\n"
        srt_content += f"{segment['text']}\n\n"

    return srt_content

def format_timestamp_srt(seconds):
    hours = int(seconds // 3600)
    minutes = int((seconds % 3600) // 60)
    secs = int(seconds % 60)
    millis = int((seconds % 1) * 1000)
    return f"{hours:02d}:{minutes:02d}:{secs:02d},{millis:03d}"

Step 5: Format as VTT

python

def format_vtt(segments):
    vtt_content = "WEBVTT\n\n"

    for segment in segments:
        start_time = format_timestamp_vtt(segment["start"])
        end_time = format_timestamp_vtt(segment["end"])

        vtt_content += f"{start_time} --> {end_time}\n"
        vtt_content += f"{segment['text']}\n\n"

    return vtt_content

def format_timestamp_vtt(seconds):
    hours = int(seconds // 3600)
    minutes = int((seconds % 3600) // 60)
    secs = int(seconds % 60)
    millis = int((seconds % 1) * 1000)
    return f"{hours:02d}:{minutes:02d}:{secs:02d}.{millis:03d}"

Advanced Features

Multi-Language Subtitles

Generate subtitles in multiple languages:

python

def generate_multilingual_subtitles(video_file, languages=["zh", "en"]):
    audio_file = extract_audio_from_video(video_file)
    subtitles = {}

    for lang in languages:
        # Transcribe in original language
        transcript = transcribe_for_subtitles(audio_file, language=lang)

        # If translating, use target_language parameter
        if lang != languages[0]:
            transcript = transcribe_for_subtitles(
                audio_file,
                language=languages[0],
                target_language=lang
            )

        segments = create_subtitle_segments(transcript)
        subtitles[lang] = format_srt(segments)

    return subtitles

Subtitle Styling (ASS Format)

python

def format_ass(segments, style="Default"):
    ass_header = """[Script Info]
Title: Generated Subtitles
ScriptType: v4.00+

[V4+ Styles]
Format: Name, Fontname, Fontsize, PrimaryColour, SecondaryColour, OutlineColour, BackColour, Bold, Italic, Underline, StrikeOut, ScaleX, ScaleY, Spacing, Angle, BorderStyle, Outline, Shadow, Alignment, MarginL, MarginR, MarginV, Encoding
Style: Default,Arial,20,&H00FFFFFF,&H000000FF,&H00000000,&H00000000,0,0,0,0,100,100,0,0,1,2,0,2,10,10,10,1

[Events]
Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text
"""

    ass_content = ass_header

    for segment in segments:
        start = format_timestamp_ass(segment["start"])
        end = format_timestamp_ass(segment["end"])
        text = segment["text"]

        ass_content += f"Dialogue: 0,{start},{end},{style},,0,0,0,,{text}\n"

    return ass_content

def format_timestamp_ass(seconds):
    hours = int(seconds // 3600)
    minutes = int((seconds % 3600) // 60)
    secs = seconds % 60
    return f"{hours:01d}:{minutes:02d}:{secs:05.2f}"

Subtitle Optimization

python

def optimize_subtitles(segments):
    optimized = []

    for segment in segments:
        text = segment["text"]

        # Split long lines
        if len(text) > 42:
            words = text.split()
            mid = len(words) // 2
            line1 = " ".join(words[:mid])
            line2 = " ".join(words[mid:])
            text = f"{line1}\n{line2}"

        # Ensure minimum display time (1 second)
        duration = segment["end"] - segment["start"]
        if duration < 1.0:
            segment["end"] = segment["start"] + 1.0

        segment["text"] = text
        optimized.append(segment)

    return optimized

Burn Subtitles into Video

python

def burn_subtitles(video_file, subtitle_file, output_file):
    cmd = [
        "ffmpeg", "-i", video_file,
        "-vf", f"subtitles={subtitle_file}",
        "-c:a", "copy",
        output_file
    ]
    subprocess.run(cmd, check=True)

Output Format

• SRT subtitle file
• VTT subtitle file (for web)
• ASS subtitle file (with styling)
• JSON with timing data
• Video with burned-in subtitles (optional)

Tips for Best Results

• Use high-quality audio for better transcription
• Adjust max_chars_per_line for different video sizes
• Review and edit timestamps for perfect sync
• Test subtitles with video player before finalizing
• Consider reading speed (aim for 15-20 chars per second)

Example Usage

User request: "Generate English subtitles for this video"

Skill actions:

1. Extract audio from video
2. Transcribe with word-level timestamps
3. Create subtitle segments with proper timing
4. Format as SRT file
5. Optionally create VTT for web use
6. Provide subtitle files for download

Reference

API docs: https://senseaudio.cn/docs/speech_recognition