This repo shows how to translate and automatically caption videos using Whisper and MoviePy.
Launch this in Paperspace Gradient by clicking the link below.
The subtitle_video function can be accessed through the whisper-caption.ipynb Notebook. This function uses Whisper and MoviePy to take in a video, extract its audio, convert its speech into text captions, and then add those captions at the correct timeslots back to the original video.
subtitle_video takes in the following parameters:
download: bool, this tells your function if you are downloading a youtube video
url: str, str, the URL of youtube video to download if download is True
aud_opts: dict, audio file youtube-dl options
vid_opts: dict, video file youtube-dl options
model_type: str, which pretrained model to download. Options are:
['tiny', 'small', 'base', 'medium','large','tiny.en', 'small.en', 'base.en', 'medium.en']
More details about model_types can be found in table in original repo here:
https://github.com/openai/whisper#Available-models-and-languages
name: str, name of directory to store files in in experiments folder
audio_file: str, path to extracted audio file for Whisper
input_file: str, path to video file for MoviePy to caption
output: str, destination of final output video file
uploaded_vid: str, path to uploaded video file if download is False
To deploy Whisper AutoCaption in the Flask web application, go to Gradient Deployments, and create a new deployment. Then fill in the values, and create the deployment. From there, all you need to do is click the API endpoint URL in the Deployment's details page.
From there, you can directly input any video from your local computer or Youtube URL.
image: paperspace/whisper-autocaption:v1.01
port: 5000
resources:
replicas: 1
instanceType: RTX4000
The full spec is as follows:
enabled: true
image: paperspace/whisper-autocaption:v1.01
port: 5000
resources:
replicas: 1
instanceType: RTX4000
autoscaling:
enabled: true
maxReplicas: 5
metrics:
- metric: requestDuration
summary: average
value: 0.15
- metric: cpu
summary: average
value: 30
- metric: memory
summary: average
value: 45
Future plans:
- API version
Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification.
A Transformer sequence-to-sequence model is trained on various speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection. All of these tasks are jointly represented as a sequence of tokens to be predicted by the decoder, allowing for a single model to replace many different stages of a traditional speech processing pipeline. The multitask training format uses a set of special tokens that serve as task specifiers or classification targets.
We used Python 3.9.9 and PyTorch 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.7 or later and recent PyTorch versions. The codebase also depends on a few Python packages, most notably HuggingFace Transformers for their fast tokenizer implementation and ffmpeg-python for reading audio files. The following command will pull and install the latest commit from this repository, along with its Python dependencies
pip install git+https://github.com/openai/whisper.git
It also requires the command-line tool ffmpeg to be installed on your system, which is available from most package managers:
# on Ubuntu or Debian
sudo apt update && sudo apt install ffmpeg
# on Arch Linux
sudo pacman -S ffmpeg
# on MacOS using Homebrew (https://brew.sh/)
brew install ffmpeg
# on Windows using Chocolatey (https://chocolatey.org/)
choco install ffmpeg
# on Windows using Scoop (https://scoop.sh/)
scoop install ffmpegYou may need rust installed as well, in case tokenizers does not provide a pre-built wheel for your platform. If you see installation errors during the pip install command above, please follow the Getting started page to install Rust development environment. Additionally, you may need to configure the PATH environment variable, e.g. export PATH="$HOME/.cargo/bin:$PATH". If the installation fails with No module named 'setuptools_rust', you need to install setuptools_rust, e.g. by running:
pip install setuptools-rustThere are five model sizes, four with English-only versions, offering speed and accuracy tradeoffs. Below are the names of the available models and their approximate memory requirements and relative speed.
| Size | Parameters | English-only model | Multilingual model | Required VRAM | Relative speed |
|---|---|---|---|---|---|
| tiny | 39 M | tiny.en |
tiny |
~1 GB | ~32x |
| base | 74 M | base.en |
base |
~1 GB | ~16x |
| small | 244 M | small.en |
small |
~2 GB | ~6x |
| medium | 769 M | medium.en |
medium |
~5 GB | ~2x |
| large | 1550 M | N/A | large |
~10 GB | 1x |
For English-only applications, the .en models tend to perform better, especially for the tiny.en and base.en models. We observed that the difference becomes less significant for the small.en and medium.en models.
Whisper's performance varies widely depending on the language. The figure below shows a WER breakdown by languages of Fleurs dataset, using the large model. More WER and BLEU scores corresponding to the other models and datasets can be found in Appendix D in the paper.
The following command will transcribe speech in audio files, using the medium model:
whisper audio.flac audio.mp3 audio.wav --model medium
The default setting (which selects the small model) works well for transcribing English. To transcribe an audio file containing non-English speech, you can specify the language using the --language option:
whisper japanese.wav --language Japanese
Adding --task translate will translate the speech into English:
whisper japanese.wav --language Japanese --task translate
Run the following to view all available options:
whisper --help
See tokenizer.py for the list of all available languages.
Transcription can also be performed within Python:
import whisper
model = whisper.load_model("base")
result = model.transcribe("audio.mp3")
print(result["text"])Internally, the transcribe() method reads the entire file and processes the audio with a sliding 30-second window, performing autoregressive sequence-to-sequence predictions on each window.
Below is an example usage of whisper.detect_language() and whisper.decode() which provide lower-level access to the model.
import whisper
model = whisper.load_model("base")
# load audio and pad/trim it to fit 30 seconds
audio = whisper.load_audio("audio.mp3")
audio = whisper.pad_or_trim(audio)
# make log-Mel spectrogram and move to the same device as the model
mel = whisper.log_mel_spectrogram(audio).to(model.device)
# detect the spoken language
_, probs = model.detect_language(mel)
print(f"Detected language: {max(probs, key=probs.get)}")
# decode the audio
options = whisper.DecodingOptions()
result = whisper.decode(model, mel, options)
# print the recognized text
print(result.text)Please use the 🙌 Show and tell category in Discussions for sharing more example usages of Whisper and third-party extensions such as web demos, integrations with other tools, ports for different platforms, etc.
The code and the model weights of Whisper are released under the MIT License. See LICENSE for further details.