Build an auto-caption generator for videos

To build an auto-caption generator for videos, you’d need a combination of several techniques, including speech-to-text (STT) conversion, natural language processing (NLP) for sentence segmentation, and proper syncing of the generated captions with the video timeline. Here’s a step-by-step approach to creating an auto-caption generator:

1. Extract Audio from Video

First, you’ll need to extract the audio from the video file, as the main task is to transcribe the speech within the video.

• Libraries/Tools: You can use libraries like moviepy or ffmpeg to extract audio.

  python
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  from moviepy.editor import VideoFileClip
  
  video = VideoFileClip("video.mp4")
  video.audio.write_audiofile("audio.wav")
  

2. Transcribe Audio to Text

Once the audio is extracted, the next step is to convert it into text. This can be done using a speech-to-text engine.

• Google Speech-to-Text API: You can use Google’s API or any other STT services like Deepgram, IBM Watson, or Azure Speech.

• Libraries: SpeechRecognition, pydub, or whisper (from OpenAI) can be used for this purpose.

Example using speech_recognition library:

python
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import speech_recognition as sr

recognizer = sr.Recognizer()
audio_file = sr.AudioFile("audio.wav")

with audio_file as source:
    audio = recognizer.record(source)

# Use Google Web Speech API (or any available recognizer)
text = recognizer.recognize_google(audio)
print(text)

This will transcribe the audio into text, but the text will be unsegmented and lack timestamps.

3. Sentence Segmentation and Punctuation

Once you have the raw transcription, the next step is to clean up the text. This involves adding punctuation, splitting the text into sentences, and making it more readable.

• Libraries for NLP: nltk, spaCy, or transformers-based models (like BERT or GPT) can help in structuring the text.

For instance:

python
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import nltk
nltk.download('punkt')

sentences = nltk.sent_tokenize(text)  # Tokenize text into sentences

4. Timestamping the Captions

For the captions to sync with the video, you need to identify when each sentence starts and ends. There are several ways to do this:

• Speech-to-Text with Timestamps: Some STT APIs, such as Google’s, provide timestamps with the transcribed text. If you’re using an API like whisper by OpenAI, it provides timestamps as well.

Example with whisper:

python
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import whisper

model = whisper.load_model("base")
result = model.transcribe("audio.wav", word_timestamps=True)

for segment in result["segments"]:
    print(f"Start: {segment['start']}s - End: {segment['end']}s - Text: {segment['text']}")

5. Generate Caption File (SRT/ASS format)

The final step is to convert the text and timestamps into a caption file format, such as SRT or ASS. The SRT format is a simple text file where each caption is listed along with its timestamp.

Example for generating SRT:

python
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def create_srt(segments, filename="captions.srt"):
    with open(filename, "w") as file:
        for i, segment in enumerate(segments, start=1):
            start_time = segment['start']
            end_time = segment['end']
            text = segment['text']

            # Convert timestamps to SRT format
            start_time_str = str(int(start_time // 60)).zfill(2) + ":" + str(int(start_time % 60)).zfill(2) + ":" + str(int((start_time % 1) * 1000)).zfill(3)
            end_time_str = str(int(end_time // 60)).zfill(2) + ":" + str(int(end_time % 60)).zfill(2) + ":" + str(int((end_time % 1) * 1000)).zfill(3)

            file.write(f"{i}n{start_time_str} --> {end_time_str}n{text}nn")

# Create the SRT file
create_srt(result["segments"])

6. Fine-tuning

Once you have the basic functionality set up, you can fine-tune your caption generator:

• Error Handling: Improve transcription accuracy by training your model or using a better speech-to-text service for specific accents, environments, etc.

• Punctuation and Formatting: Add NLP models to improve punctuation or sentence segmentation, as raw transcriptions can often miss these.

• Handling Multiple Speakers: For videos with multiple speakers, you can integrate speaker diarization, which segments the speech based on the speaker.

7. Optional Enhancements

• User Interface: Create a simple front-end UI where users can upload their videos and get captions generated.

• Support for Multiple Languages: Extend the tool to handle different languages by switching between STT models or APIs.

Example Workflow for the Complete Script:

1. Extract audio from video.

2. Transcribe audio to text with timestamps.

3. Split the text into sentences and clean it up.

4. Generate captions in SRT format with the correct timestamps.

5. Output the SRT file ready to be used with the video.

This provides a general approach to creating an auto-caption generator.