Subtitle Generator Guide: Transform Speech into Text

Last Updated on 07/12/2025 by Eran Feit

Introduction

A subtitle generator has become an essential tool for anyone working with audio or video content. As digital communication continues to grow, subtitles help bridge gaps in accessibility, comprehension, and language diversity. Whether you’re creating educational videos, online tutorials, podcasts, or professional presentations, accurately transforming speech into text ensures your message reaches more people. Modern AI-powered systems can produce subtitles quickly and with impressive precision, making the process easier than ever.

In today’s fast-moving world, viewers often watch videos on mute, rely on captions in noisy environments, or prefer reading along to improve clarity. A subtitle generator solves these challenges by automatically converting spoken words into readable text, offering a smoother and more engaging viewing experience. As creators adopt more sophisticated tools, subtitles are no longer just optional—they’ve become a standard part of quality content production.

AI-based transcription models, such as Faster-Whisper, have revolutionized how subtitles are created. Instead of manually typing every spoken word or relying on slow traditional tools, AI models can process audio instantly, recognize speech patterns, and produce accurate transcripts. This automation significantly reduces workload while maintaining excellent accuracy, even across different accents and languages.

The rise of multilingual content has also increased the importance of subtitle generation. With the right subtitle generator, creators can instantly translate subtitles into multiple languages, making their message accessible to worldwide audiences. This seamless workflow allows content creators, educators, marketers, and businesses to scale their reach effortlessly while maintaining clarity and professionalism.

What is Faster-Whisper — and why it matters

Faster-Whisper is a re-implementation of Whisper (the speech-to-text model by OpenAI), but optimized for much faster inference by using CTranslate2 — a highly efficient inference engine for Transformer models.

By using CTranslate2, Faster-Whisper can run audio transcription up to 4 times faster than the original Whisper implementation — while maintaining the same level of accuracy.
Because of its optimized inference engine and support for quantization (e.g. 8-bit), Faster-Whisper also consumes significantly less memory and GPU/CPU resources.

This combination — speed, efficiency, and lower resource usage — makes Faster-Whisper especially useful when you want to build subtitle-generators, real-time transcription services, or batch-process large volumes of audio/video without requiring heavy infrastructure.

Key Features & Benefits of Faster-Whisper

🔹 High performance with low latency

Because Faster-Whisper uses efficient CTranslate2 inference (with support for GPU or CPU, quantization, batched processing, etc.), it dramatically reduces the time needed to transcribe — including large or long audio/video files.

For example, in a benchmark: to transcribe 13 minutes of audio using the large model on GPU, Faster-Whisper completed in about 1 minute 3 seconds, whereas the original Whisper took significantly longer.

🔹 Lower memory and resource usage

By using quantization and optimized inference routines, Faster-Whisper needs less VRAM / RAM compared to Vanilla Whisper — making it feasible even on hardware with modest specs.

🔹 Flexibility: speech-to-text, translation, streaming, and more

Faster-Whisper isn’t limited to offline audio files. Thanks to additional tools and wrappers, it supports:

• Real-time or streaming transcription (good for live captions).
• Language detection and translation capabilities, enabling subtitle generation in multiple languages.
• Batch processing of multiple files — making it ideal for workflows that require many transcriptions (e.g. podcasts, video archives, lecture series).

🔹 Easy integration and deployment

Because Faster-Whisper aims to be compatible with Whisper (input/output APIs are similar), migrating existing Whisper-based projects is straightforward. Many open-source tools, Docker images, and even server wrappers (self-hosted transcription servers) rely on Faster-Whisper as their backend.

This means you can build your own automated transcription / subtitle-generation service without heavy cloud dependencies — leveraging local or on-premise computing resources.

How Faster-Whisper Fits Into Subtitle Generation Use-Cases

When your goal is to generate subtitles (SRT files) for audio or video, Faster-Whisper becomes a natural choice because:

• It converts speech to text much faster, saving time especially for long recordings (lectures, webinars, movies, etc.).
• It uses less memory/VRAM, enabling subtitle generation on modest hardware (workstations, older GPUs, or even CPU-only machines).
• It supports batch processing and streaming, ideal for automated pipelines: for instance, automatically transcribe a directory of videos, generate SRTs, then optionally translate or format them.
• Its output is precise and compatible with standard subtitle formats (timestamps, segments, text), making it easy to integrate with subtitle-conversion or video-editing workflows.

Because of these advantages, Faster-Whisper transforms subtitle generation from a slow, manual, or resource-heavy task into an efficient, scalable, and accessible process.

Building a Practical Subtitle Generator with Python and Faster-Whisper

In this tutorial, the code is designed to walk you step by step through building a working subtitle generator using Python and Faster-Whisper. Instead of staying at the theoretical level, the script actually loads a pre-trained speech-to-text model, feeds it real audio and video files, and then turns the spoken dialogue into structured text segments. Each part of the code has a clear role: setting up the environment, loading the model to GPU, transcribing files, and finally converting the results into standard SRT subtitle files. By the end, you’re not just reading about AI transcription—you’re running it yourself.

The first section of the code focuses on installation and environment setup. It creates a dedicated Conda environment, installs the correct Python version, and pulls in the Faster-Whisper library with GPU support. This preparation ensures that the model can run efficiently using cuda and float16 precision, which is important when you work with larger models such as large-v3. The goal here is to give you a reliable, repeatable setup so that performance is fast and results are stable across different machines.

Next, the tutorial code demonstrates how to transcribe a simple audio file and then a full video file. Using the same WhisperModel object, it calls model.transcribe() on an MP3 file first, then on an M4V movie clip. The output is a series of segments, each containing start time, end time, and the recognized text. This structure matches exactly what you need for subtitles. The code also prints the detected language and its confidence score, so you can see how the model understands the input and verify that it picked the correct language before generating subtitles.

The most important part of the tutorial is where the transcription is converted into real SRT subtitle files. The code loops over each segment, formats the timestamps into the hh:mm:ss,ms style required by SRT, and writes them to disk with the correct numbering, timing, and text. This turns the raw model output into a file that can be loaded directly into video players, editors, or platforms like YouTube. In other words, the code bridges the gap between AI transcription and something you can plug into your everyday video workflow.

Finally, the script extends the subtitle generator into a multilingual tool by adding automatic translation. After creating the English SRT file, it uses a translator object to convert each subtitle line into another language and writes a second SRT file with the translated text. This shows how the same pipeline can be reused to support multiple languages without re-transcribing the audio. The overall target of the code is to provide a complete, end-to-end solution: from installing the environment, through transcribing audio and video, to generating and translating professional subtitle files you can use in real projects.

Link to the video tutorial : https://youtu.be/L75gpmkxY1I

Link to the code here : https://eranfeit.lemonsqueezy.com/buy/69b80e54-71fb-4da9-9066-063e2104dd3b or here : https://ko-fi.com/s/e895429f34

Link for Medium users : https://medium.com/@feitgemel/subtitle-generator-guide-transform-speech-into-text-2886e33c30bf

You can follow my blog here : https://eranfeit.net/blog/

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Subtitle Generator Guide: Transform Speech into Text

Creating accurate subtitles is now easier than ever thanks to Python and Faster-Whisper. In this tutorial, we walk through a full pipeline—from installation, to audio and video transcription, to generating English and translated SRT subtitle files. The goal is to provide a friendly, step-by-step introduction to building your own subtitle generator that automatically converts speech into clean, timestamped text.

Faster-Whisper is a highly optimized version of OpenAI’s Whisper model. It gives you the same accuracy but runs up to several times faster and with less GPU memory. This performance boost makes it perfect for creators, developers, and educators who want reliable subtitles without long processing times.

By the end of this tutorial, you’ll have a complete Python project that detects languages, transcribes audio and video, generates SRT subtitles, and even translates them. Each part of the code is broken down into simple steps so you can follow along comfortably.

Setting Up the Environment and Installing Faster-Whisper

Before generating subtitles, we create a clean environment and install the required libraries. Using a dedicated environment ensures stable versions, GPU compatibility, and smooth execution.

### Create a new Conda environment with Python 3.12 conda create -n fastw python=3.12  ### Activate the environment so we can install packages into it conda activate fastw  ### Install Faster-Whisper with GPU support pip install faster-whisper==1.0.3 

This section prepares your system, installs Faster-Whisper, and ensures that CUDA support is enabled for optimal performance.

Loading the Whisper Model and Transcribing an Audio File

Once the environment is ready, we load the Faster-Whisper model. The goal of this section is to transcribe a simple audio file and display each detected segment along with timestamps.

### Import the WhisperModel class from faster_whisper import WhisperModel  ### Choose the model size that balances speed and accuracy model_size = "large-v3"  ### Load the model to GPU with float16 precision for faster inference model = WhisperModel(model_size, device="cuda", compute_type="float16")  ### Path to a demo audio file we want to transcribe file = "Python-Code-Cool-Stuff/Fast-Whisper/a.mp3"  ### Transcribe the audio using beam search for accuracy segments , info = model.transcribe(file , beam_size=5)  ### Print the detected language and confidence print("detected language '%s' with probability %f" % (info.language, info.language_probability))  ### Loop through each segment and display timestamps and text for segment in segments:     print("[%.2fs -> %.2fs] %s" % (segment.start , segment.end, segment.text )) 

This part verifies your installation and shows how the model processes a straightforward audio file.

Processing a Video File and Extracting Spoken Dialogue

This section applies the same logic to a video clip instead of audio. Faster-Whisper handles both seamlessly, making it great for film, tutorials, or online course content.

### Import the Faster-Whisper model from faster_whisper import WhisperModel  ### Select the high-accuracy model model_size = "large-v3"  ### Load model to GPU model = WhisperModel(model_size, device="cuda", compute_type="float16")  ### Define the video file we want to transcribe file = "Python-Code-Cool-Stuff/Fast-Whisper/StarTrek-Origin.m4v"  ### Run transcription on the video segments , info = model.transcribe(file , beam_size=5)  ### Display detected language print("detected language '%s' with probability %f" % (info.language, info.language_probability))  ### Loop through transcription segments for segment in segments:     print("[%.2fs -> %.2fs] %s" % (segment.start , segment.end, segment.text )) 

You’ll see how to transcribe an entire video and read its spoken content line by line.

Generating English SRT Subtitle Files

After getting transcription segments, we convert them into an .srt subtitle file. This involves formatting timestamps, numbering each entry, and writing the results to disk.

### Import the model and translation library from faster_whisper import WhisperModel   from googletrans import Translator  ### Model size model_size = "large-v3"  ### Load the model to GPU model = WhisperModel(model_size, device="cuda", compute_type="float16")  ### Path to video file starFile = "Python-Code-Cool-Stuff/Fast-Whisper/StarTrek-Origin.m4v"  ### Transcribe the video with beam search segments, info = model.transcribe(starFile, beam_size=5)  ### Convert generator to list segments = list(segments)  ### Display language detection print("Detected language '%s' with probability %f" % (info.language, info.language_probability))  ### Helper function to convert seconds to SRT timestamp formatting def format_timestamp(seconds):       hours = int(seconds // 3600)       minutes = int((seconds % 3600) // 60)       seconds = int(seconds % 60)       milliseconds = int((seconds % 1) * 1000)       return f"{hours:02}:{minutes:02}:{seconds:02},{milliseconds:03}"  ### Write English subtitles to an .srt file with open("Python-Code-Cool-Stuff/Fast-Whisper/StarTrek-Origin.srt", "w", encoding="utf-8") as srt_file:       for i, segment in enumerate(segments, start=1):           start_time = format_timestamp(segment.start)           end_time = format_timestamp(segment.end)           text = segment.text            print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))          srt_file.write(f"{i}\n")           srt_file.write(f"{start_time} --> {end_time}\n")           srt_file.write(f"{text}\n\n")  ### Confirmation message print("English SRT file generated successfully.")   print("*******************************") 

Here you learn how to convert raw model output into a valid SRT file ready to import into any video editor.

Here is the result :

1 00:00:00,000 --> 00:00:06,000  Captain's log, stardate 1324.1.  2 00:00:07,000 --> 00:00:11,000  On planet M113, we encounter a killer from a lost world.  3 00:00:12,000 --> 00:00:14,000  Red modeling all over his face.  4 00:00:15,000 --> 00:00:16,000  What happened?  5 00:00:16,000 --> 00:00:17,000  What do you suppose happened, Captain?  6 00:00:17,000 --> 00:00:20,000  You beamed down a crewman who doesn't know better than to eat a...  7 00:00:20,000 --> 00:00:22,000  I've just lost a crewman, Mrs. Crater. I want to know what happened.  8 00:00:22,000 --> 00:00:23,000  And what kills a healthy man?  9 00:00:23,000 --> 00:00:25,000  I'll tell you something else.  10 00:00:25,000 --> 00:00:26,000  This man shouldn't be dead.  11 00:00:26,000 --> 00:00:28,000  I can't find anything wrong with him.  12 00:00:28,000 --> 00:00:31,000  According to all the tests, he should get up and just walk away from here.  13 00:00:31,000 --> 00:00:33,000  Can you recognize this thing when you see it?  14 00:00:36,000 --> 00:00:40,000  Professor, I'll forego charges up to this point.  15 00:00:41,000 --> 00:00:43,000  But this creature's aboard my ship.  16 00:00:43,000 --> 00:00:45,000  And I'll have it. Or I'll have your skin. Or both.  17 00:00:45,000 --> 00:00:46,000  Now, where is it?  18 00:00:46,000 --> 00:00:48,000  I'll kill to stay alone.  19 00:00:49,000 --> 00:00:50,000  You hear that, Crack?  20 00:00:50,000 --> 00:00:53,000  Crater knows the creature. If we can take him alive...  21 00:00:53,000 --> 00:00:55,000  We don't want you here!  22 00:00:55,000 --> 00:00:56,000  Let's get him.  23 00:00:58,000 --> 00:01:01,000  To be continued... 

Translating the Subtitle File into Another Language (Example: French)

This final section demonstrates how to translate every subtitle line into another language without re-transcribing the video.

### Initialize translator translator = Translator()  ### Write translated subtitles (example: French) with open("Python-Code-Cool-Stuff/Fast-Whisper/StarTrek-Origin-French.srt", "w", encoding="utf-8") as srt_file_es:       for i, segment in enumerate(segments, start=1):           start_time = format_timestamp(segment.start)           end_time = format_timestamp(segment.end)           text = segment.text            translated_text = translator.translate(text, src='en', dest='fr').text           print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, translated_text))          srt_file_es.write(f"{i}\n")           srt_file_es.write(f"{start_time} --> {end_time}\n")           srt_file_es.write(f"{translated_text}\n\n")  ### Translation completion message print("French SRT file generated successfully.")   print("*******************************") 

You can now create subtitles in English, French, Spanish, or nearly any language—perfect for international audiences.

Here is the result :

1 00:00:00,000 --> 00:00:06,000 Journal du capitaine, Stardate 1324.1.  2 00:00:07,000 --> 00:00:11,000 Sur la planète M113, nous rencontrons un tueur d'un monde perdu.  3 00:00:12,000 --> 00:00:14,000 Modélisation rouge sur son visage.  4 00:00:15,000 --> 00:00:16,000 Ce qui s'est passé?  5 00:00:16,000 --> 00:00:17,000 Que pensez-vous que vous êtes arrivé, capitaine?  6 00:00:17,000 --> 00:00:20,000 Vous avez rayonné un membre d'équipage qui ne sait pas mieux que de manger un ...  7 00:00:20,000 --> 00:00:22,000 Je viens de perdre un équipage, Mme Crater.Je veux savoir ce qui s'est passé.  8 00:00:22,000 --> 00:00:23,000 Et qu'est-ce qui tue un homme en bonne santé?  9 00:00:23,000 --> 00:00:25,000 Je vais vous dire autre chose.  10 00:00:25,000 --> 00:00:26,000 Cet homme ne devrait pas être mort.  11 00:00:26,000 --> 00:00:28,000 Je ne trouve rien de mal avec lui.  12 00:00:28,000 --> 00:00:31,000 Selon tous les tests, il devrait se lever et s'éloigner d'ici.  13 00:00:31,000 --> 00:00:33,000 Pouvez-vous reconnaître cette chose lorsque vous le voyez?  14 00:00:36,000 --> 00:00:40,000 Professeur, je renoncerai aux charges jusqu'à ce point.  15 00:00:41,000 --> 00:00:43,000 Mais cette créature est à bord de mon navire.  16 00:00:43,000 --> 00:00:45,000 Et je vais l'avoir.Ou j'aurai votre peau.Ou les deux.  17 00:00:45,000 --> 00:00:46,000 Maintenant, où est-il?  18 00:00:46,000 --> 00:00:48,000 Je vais tuer pour rester seul.  19 00:00:49,000 --> 00:00:50,000 Tu entends ça, crack?  20 00:00:50,000 --> 00:00:53,000 Crater connaît la créature.Si nous pouvons le prendre vivant ...  21 00:00:53,000 --> 00:00:55,000 Nous ne voulons pas de toi ici!  22 00:00:55,000 --> 00:00:56,000 Gettons-le.  23 00:00:58,000 --> 00:01:01,000 À suivre... 

FAQ — Subtitle Generator Using Faster-Whisper

Conclusion

Building your own subtitle generator opens the door to powerful automation, enhanced accessibility, and a streamlined content-creation workflow. Faster-Whisper provides the accuracy and speed needed to handle real-world transcription tasks, while Python makes the entire pipeline flexible enough for creators, educators, and developers. Whether you are processing podcasts, films, tutorials, or online courses, this project gives you a foundation to generate multilingual subtitles with precision and efficiency.

The steps in this tutorial—from installation to transcription to SRT generation and translation—give you everything you need to integrate automated subtitles into your projects. With just a few lines of Python, you can bring professional-grade captioning into your workflow and scale it effortlessly. As you continue experimenting, you can extend this pipeline into live captioning, batch processing, or even building your own subtitle generation service.

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