open-dubbing 0.2.3

AI dubbing system which uses machine learning models to automatically translate and synchronize audio dialogue into different languages.

Introduction

Open dubbing is an AI dubbing system uses machine learning models to automatically translate and synchronize audio dialogue into different languages. It is designed as a command line tool.

At the moment, it is pure experimental and an excuse to help me to understand better STT, TTS and translation systems combined together.

If you want to see a live system running you can do it at https://www.softcatala.org/doblatge/ (accepts only English and Spanish an dubs only to Catalan). It combines this project, https://github.com/Softcatala/subdub-editor (an editor) and https://github.com/Softcatala/dubbing-service (web service).

Features

• Build on top of open source models and able to run it locally
• Dubs automatically a video from a source to a target language
• Supports multiple Text To Speech (TTS): Coqui, MMS, Edge, OpenAI TTS
• Allows to use any non-supported one by configuring an API or CLI
• Gender voice detection to allow to assign properly synthetic voice
• Support for multiple translation engines (Meta's NLLB, Apertium API, etc)
• Automatic detection of the source language of the video (using Whisper)

Roadmap

Areas what we will like to explore:

• Better control of voice used for dubbing
• Optimize it for long videos and less resource usage
• Support for multiple video input formats

Demo

This video on propose shows the strengths and limitations of the system.

Original English video

https://github.com/user-attachments/assets/54c0d37f-0cc8-4ea2-8f8d-fd2d2f4eeccc

Automatic dubbed video in Catalan

https://github.com/user-attachments/assets/99936655-5851-4d0c-827b-f36f79f56190

Limitations

• This is an experimental project
• Automatic video dubbing includes speech recognition, translation, vocal recognition, etc. At each one of these steps errors can be introduced

Supported languages

The support languages depends on the combination of text to speech, translation system and text to speech system used. With Coqui TTS, these are the languages supported (I only tested a very few of them):

Supported source languages: Afrikaans, Amharic, Armenian, Assamese, Bashkir, Basque, Belarusian, Bengali, Bosnian, Bulgarian, Burmese, Catalan, Chinese, Croatian, Czech, Danish, Dutch, English, Estonian, Faroese, Finnish, French, Galician, Georgian, German, Gujarati, Haitian, Hausa, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Lao, Lingala, Lithuanian, Luxembourgish, Macedonian, Malayalam, Maltese, Maori, Marathi, Modern Greek (1453-), Norwegian Nynorsk, Occitan (post 1500), Panjabi, Polish, Portuguese, Romanian, Russian, Sanskrit, Serbian, Shona, Sindhi, Sinhala, Slovak, Slovenian, Somali, Spanish, Sundanese, Swedish, Tagalog, Tajik, Tamil, Tatar, Telugu, Thai, Tibetan, Turkish, Turkmen, Ukrainian, Urdu, Vietnamese, Welsh, Yoruba, Yue Chinese

Supported target languages: Achinese, Akan, Amharic, Assamese, Awadhi, Ayacucho Quechua, Balinese, Bambara, Bashkir, Basque, Bemba (Zambia), Bengali, Bulgarian, Burmese, Catalan, Cebuano, Central Aymara, Chhattisgarhi, Crimean Tatar, Dutch, Dyula, Dzongkha, English, Ewe, Faroese, Fijian, Finnish, Fon, French, Ganda, German, Guarani, Gujarati, Haitian, Hausa, Hebrew, Hindi, Hungarian, Icelandic, Iloko, Indonesian, Javanese, Kabiyè, Kabyle, Kachin, Kannada, Kazakh, Khmer, Kikuyu, Kinyarwanda, Kirghiz, Korean, Lao, Magahi, Maithili, Malayalam, Marathi, Minangkabau, Modern Greek (1453-), Mossi, North Azerbaijani, Northern Kurdish, Nuer, Nyanja, Odia, Pangasinan, Panjabi, Papiamento, Polish, Portuguese, Romanian, Rundi, Russian, Samoan, Sango, Shan, Shona, Somali, South Azerbaijani, Southwestern Dinka, Spanish, Sundanese, Swahili (individual language), Swedish, Tagalog, Tajik, Tamasheq, Tamil, Tatar, Telugu, Thai, Tibetan, Tigrinya, Tok Pisin, Tsonga, Turkish, Turkmen, Uighur, Ukrainian, Urdu, Vietnamese, Waray (Philippines), Welsh, Yoruba

Installation

To install the open_dubbing in all platforms:

pip install open_dubbing

If you want to install also Coqui-tts, do:

pip install open_dubbing[coqui]

If you want to install also OpenIA support, do:

pip install open_dubbing[openai]

Linux additional dependencies

In Linux you also need to install:

sudo apt install ffmpeg

If you are going to use Coqui-tts you also need to install espeak-ng:

sudo apt install espeak-ng

macOS additional dependencies

In macOS you also need to install:

brew install ffmpeg

If you are going to use Coqui-tts you also need to install espeak-ng:

brew install espeak-ng

Windows additional dependencies

Windows currently works but it has not been tested extensively.

You also need to install ffmpeg for Windows. Make sure that is the system path.

Accept pyannote license

1. Go to and Accept pyannote/segmentation-3.0 user conditions
2. Accept pyannote/speaker-diarization-3.1 user conditions
3. Go to and access token at hf.co/settings/tokens.

Quick start

Quick start

 open-dubbing --input_file video.mp4 --target_language=cat --hugging_face_token=TOKEN

Where:

• TOKEN is the HuggingFace token that allows to access the models
• cat in this case is the target language using iso ISO 639-3 language codes

By default, the source language is predicted using the first 30 seconds of the video. If this does not work (e.g. there is only music at the beginning), use the parameter source_language to specify the source language using ISO 639-3 language codes (e.g. 'eng' for English).

To get a list of available options:

open-dubbing --help

Post editing automatic generated dubbed files

There are cases where you want to manually adjust the text generated automatically for dubbing, the voice used or the timings.

After you have executed open-dubbing you have the intermediate files and the outcome dubbed file in the selected output directory.

You can edit the file utterance_metadata_XXX.json (where XXX is the target language code), make manual adjustments, and generate the video again.

See an example JSON:

    "utterances": [
        {
            "start": 7.607843750000001,
            "end": 8.687843750000003,
            "speaker_id": "SPEAKER_00",
            "path": "short/chunk_7.607843750000001_8.687843750000003.mp3",
            "text": "And I love this city.",
            "for_dubbing": true,
            "gender": "Male",
            "translated_text": **"I m'encanta aquesta ciutat."**,
            "assigned_voice": "ca-ES-EnricNeural",
            "speed": 1.3,
            "dubbed_path": "short/dubbed_chunk_7.607843750000001_8.687843750000003.mp3",
            "hash": "b11d7f0e2aa5475e652937469d89ef0a178fecea726f076095942d552944089f"
        },

Imagine that you have changed the translated_text. To generated the post-edited video:

 open-dubbing --input_file video.mp4 --target_language=cat --hugging_face_token=TOKEN --update

The update parameter changes the behavior of open-dubbing and instead of producing a full dubbing it rebuilds the already existing dubbing incorporating any change made into the JSON file.

Fields that are usefull to modify are: translated_text, gender (of the voice) or speed.

Documentation

For more detailed documentation on how the tool works and how to use it, see our documentation page.

Appreciation

Core libraries used:

• demucs to separate vocals from the audio
• pyannote-audio to diarize speakers
• faster-whisper for audio to speech
• NLLB-200 for machine translation
• TTS
  • coqui-tts
  • Meta mms
  • Microsoft Edge TTS
  • OpenAI TTS

And very special thanks to ariel from which we leveraged parts of their code base.

License

See license

Contact

Email address: Jordi Mas: [e-mail]