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https://github.com/milahu/autosub-by-abhirooptalasila
A CLI script to generate subtitle files (SRT/VTT/TXT) for any video using either DeepSpeech or Coqui
https://github.com/milahu/autosub-by-abhirooptalasila
autosub deepspeech offline speech-recognition srt subtitle-generator subtitles-generator tensorflow vtt
Last synced: 3 months ago
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A CLI script to generate subtitle files (SRT/VTT/TXT) for any video using either DeepSpeech or Coqui
- Host: GitHub
- URL: https://github.com/milahu/autosub-by-abhirooptalasila
- Owner: milahu
- License: mit
- Fork: true (abhirooptalasila/AutoSub)
- Created: 2022-03-05T15:36:58.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2023-12-15T10:51:18.000Z (11 months ago)
- Last Synced: 2024-06-28T11:33:31.330Z (5 months ago)
- Topics: autosub, deepspeech, offline, speech-recognition, srt, subtitle-generator, subtitles-generator, tensorflow, vtt
- Language: Python
- Homepage:
- Size: 101 KB
- Stars: 15
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# AutoSub
- [AutoSub](#autosub)
- [About](#about)
- [Installation](#installation)
- [Docker](#docker)
- [How-to example](#how-to-example)
- [How it works](#how-it-works)
- [Motivation](#motivation)
- [Contributing](#contributing)
- [References](#references)
## About
AutoSub is a CLI application to generate subtitle files (.srt, .vtt, and .txt transcript) for any video file using either [Mozilla DeepSpeech](https://github.com/mozilla/DeepSpeech) or [Coqui STT](https://github.com/coqui-ai/STT). I use their open-source models to run inference on audio segments and [pyAudioAnalysis](https://github.com/tyiannak/pyAudioAnalysis) to split the initial audio on silent segments, producing multiple smaller files (makes inference easy).
⭐ Featured in [DeepSpeech Examples](https://github.com/mozilla/DeepSpeech-examples) by Mozilla
## Installation
* Clone the repo
```bash
$ git clone https://github.com/abhirooptalasila/AutoSub
$ cd AutoSub
```
* Create a virtual environment to install the required packages. All further steps should be performed while in the `AutoSub/` directory
```bash
$ python3 -m pip install --user virtualenv
$ virtualenv sub
$ source sub/bin/activate
```
* Use the corresponding requirements file depending on whether you have a GPU or not. Make sure you have the appropriate [CUDA](https://deepspeech.readthedocs.io/en/v0.9.3/USING.html#cuda-dependency-inference) version
```bash
$ pip3 install -r requirements.txt
OR
$ pip3 install -r requirements-gpu.txt
```
* Install FFMPEG. If you're on Ubuntu, this should work fine
```bash
$ sudo apt-get install ffmpeg
$ ffmpeg -version # I'm running 4.1.4
```
* By default, if no model files are found in the root directory, the script will download v0.9.3 models for DeepSpeech or TFLITE model and Huge Vocab for Coqui. Use `getmodels.sh` to download DeepSpeech model and scorer files with the version number as argument. For Coqui, download from [here](https://coqui.ai/models)
```bash
$ ./getmodels.sh 0.9.3
```
* For .tflite models with DeepSpeech, follow [this](https://github.com/abhirooptalasila/AutoSub/issues/41#issuecomment-968847604)
## Docker
* If you don't have the model files, get them
```bash
$ ./getmodels.sh 0.9.3
```
* For a CPU build
```bash
$ docker build -t autosub .
$ docker run --volume=`pwd`/input:/input --name autosub autosub --file /input/video.mp4
$ docker cp autosub:/output/ .
```
* For a GPU build that is reusable (saving time on instantiating the program)
```bash
$ docker build --build-arg BASEIMAGE=nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04 --build-arg DEPSLIST=requirements-gpu.txt -t autosub-base . && \
docker run --gpus all --name autosub-base autosub-base --dry-run || \
docker commit --change 'CMD []' autosub-base autosub-instance
```
* Finally
```bash
$ docker run --volume=`pwd`/input:/input --name autosub autosub-instance --file ~/video.mp4
$ docker cp autosub:/output/ .
```
## How-to example
* The model files should be in the repo root directory and will be loaded/downloaded automatically. Incase you have multiple versions, use the `--model` and `--scorer` args while executing
* By default, Coqui is used for inference. You can change this by using the `--engine` argument with value `"ds"` for DeepSpeech
* For languages other than English, you'll need to manually download the model and scorer files. Check [here](https://discourse.mozilla.org/t/links-to-pretrained-models/62688) for DeepSpeech and [here](https://coqui.ai/models) for Coqui.
* After following the installation instructions, you can run `autosub/main.py` as given below. The `--file` argument is the video file for which subtitles are to be generated
```bash
$ python3 autosub/main.py --file ~/movie.mp4
```
* After the script finishes, the SRT file is saved in `output/`
* The optional `--split-duration` argument allows customization of the maximum number of seconds any given subtitle is displayed for. The default is 5 seconds
```bash
$ python3 autosub/main.py --file ~/movie.mp4 --split-duration 8
```
* By default, AutoSub outputs SRT, VTT and TXT files. To only produce the file formats you want, use the `--format` argument
```bash
$ python3 autosub/main.py --file ~/movie.mp4 --format srt txt
```
* Open the video file and add this SRT file as a subtitle. You can just drag and drop in VLC.
## How it works
Mozilla DeepSpeech is an open-source speech-to-text engine with support for fine-tuning using custom datasets, external language models, exporting memory-mapped models and a lot more. You should definitely check it out for STT tasks. So, when you run the script, I use FFMPEG to **extract the audio** from the video and save it in `audio/`. By default DeepSpeech is configured to accept 16kHz audio samples for inference, hence while extracting I make FFMPEG use 16kHz sampling rate.
Then, I use [pyAudioAnalysis](https://github.com/tyiannak/pyAudioAnalysis) for silence removal - which basically takes the large audio file initially extracted, and splits it wherever silent regions are encountered, resulting in smaller audio segments which are much easier to process. I haven't used the whole library, instead I've integrated parts of it in `autosub/featureExtraction.py` and `autosub/trainAudio.py`. All these audio files are stored in `audio/`. Then for each audio segment, I perform DeepSpeech inference on it, and write the inferred text in a SRT file. After all files are processed, the final SRT file is stored in `output/`.
When I tested the script on my laptop, it took about **40 minutes to generate the SRT file for a 70 minutes video file**. My config is an i5 dual-core @ 2.5 Ghz and 8GB RAM. Ideally, the whole process shouldn't take more than 60% of the duration of original video file.
## Motivation
In the age of OTT platforms, there are still some who prefer to download movies/videos from YouTube/Facebook or even torrents rather than stream. I am one of them and on one such occasion, I couldn't find the subtitle file for a particular movie I had downloaded. Then the idea for AutoSub struck me and since I had worked with DeepSpeech previously, I decided to use it.
## Contributing
I would love to follow up on any suggestions/issues you find :)
## References
1. https://github.com/mozilla/DeepSpeech/
2. https://github.com/tyiannak/pyAudioAnalysis
3. https://deepspeech.readthedocs.io/