I Created Another App To REVOLUTIONIZE YouTube

The video introduces a revolutionary app that transforms YouTube by allowing viewers to switch audio tracks to multiple languages, offering dubbed versions instead of subtitles. The creator developed an open-source Python program called 'Auto Synced and Translated Dubs' to automate this process, addressing limitations of Google's 'Aloud' project. The program uses Google API for translation, Microsoft Azure for high-quality AI voices, and FFmpeg for adding audio tracks to videos. It also includes scripts for translating titles and descriptions. Despite the high cost of custom voice models, the creator predicts AI will eventually automate transcription and dubbing for all videos. The workflow uses OpenAI's 'Whisper' model and Descript for transcription editing, emphasizing the importance of accurate timing for dubbed audio.

Takeaways

• 🔍 The video introduces a new feature on YouTube that allows switching audio tracks to different languages, offering dubbed versions instead of subtitles.
• 📝 The creator discusses the limitations of current dubbed translations, which are not automated, and the inspiration behind creating an open-source solution.
• 🛠️ The program 'Auto Synced and Translated Dubs' is an open-source Python tool developed to automate the translation and dubbing process for videos.
• 🌐 The tool uses Google API for translations and can generate subtitle files, offering a more efficient alternative to existing methods.
• 🎧 The program addresses the synchronization issue by using subtitle timings to match the dubbed speech with the original content.
• 🎙️ It provides options for audio clip synchronization, including stretching/shrinking audio and a two-pass synthesis for better quality.
• 📈 Two-pass synthesis is a more resource-intensive method but yields higher quality audio by adjusting the speed of speech synthesis.
• 📑 The tool also includes scripts for attaching translated audio tracks to video files and translating video titles and descriptions.
• 📈 The creator discusses the cost implications of using custom voice models and the current state of AI transcription and dubbing technology.
• 📉 The video mentions the potential future where AI advancements make transcription and dubbing fully automated and accessible for all YouTube videos.
• 📚 The creator shares their personal workflow for transcribing videos, including the use of OpenAI's 'Whisper' model and Descript for transcription editing.

Q & A

What is the new feature on YouTube that allows viewers to switch the audio track to different languages?

The new feature on YouTube is an audio track switcher that lets viewers listen to dubbed versions of videos in several languages instead of just reading translated subtitles.

Why did the creator request access to the limited feature for YouTube videos?

The creator requested access to the limited feature because it has the potential to significantly change how YouTube works for international audiences once it becomes widely available.

What is the name of the open source Python program created by the speaker?

The open source Python program created by the speaker is called 'Auto Synced and Translated Dubs' and is available on GitHub.

What are the limitations of Google's experimental project 'Aloud'?

Google's 'Aloud' project is invite-only, currently supports only Spanish and Portuguese, requires manual synchronization, and uses AI voices that, according to the speaker, are not the highest quality available.

How does the program ensure that the dubbed translations are synchronized with the original video?

The program uses the subtitle SRT file's timings to align each group of words with the corresponding audio clips, ensuring that the dubbed translations are synchronized with the original video.

What is the 'two-pass synthesis' feature of the program?

The 'two-pass synthesis' feature allows the program to adjust the speed of the AI voice to match the required duration of each subtitle segment, resulting in a higher quality audio clip that is the exact correct length without the need for time-stretching.

What is the downside of using the time-stretching technique in the program?

The downside of using the time-stretching technique is that it significantly degrades the audio quality, even when using the best freely available time stretcher algorithm.

How does the separate script mentioned by the speaker help with uploading videos to YouTube with multiple audio tracks?

The separate script uses FFmpeg to add the audio tracks with proper language tagging to the video file without converting the video, ensuring that all the languages are included in the uploaded video on YouTube.

What additional feature does the program offer for translated titles and descriptions on YouTube?

The program includes a script that translates titles and descriptions into the languages set by the user, utilizing the Google Translate API, and outputs them into a text file for easy copying and pasting.

Why is the creator not currently using a custom voice model for the dubbed translations?

The creator is not using a custom voice model because it is currently too expensive, with training costs ranging from $1,000 to $2,000 and additional costs for using the model and hosting it.

What is the creator's prediction about the future of AI and YouTube?

The creator predicts that AI will become so advanced and affordable that YouTube will automatically transcribe and dub videos in all languages, making the process seamless and effortless for content creators.

What tools does the creator use for transcribing videos?

The creator uses OpenAI's 'Whisper' model for transcription and Descript for transcription editing, finding them more accurate and easier to work with than other available options.

Outlines

00:00

🌐 Language Dubbing on YouTube

The video introduces a new YouTube feature that allows users to switch audio tracks to different languages, offering dubbed versions of videos instead of just subtitles. This feature is currently limited to certain channels and requires access permission. The creator discusses the challenges of producing dubbed translations and shares their journey in developing an open-source Python program called 'Auto Synced and Translated Dubs' to automate the process. The program leverages AI tools for transcription, translation, and voice synthesis, and it addresses several limitations of existing solutions, such as the need for precise synchronization and higher quality AI voices.

05:02

🔍 How the Dubbing Program Works

The video script explains the technical process behind the dubbing program. It starts with the necessity of a well-edited SRT subtitle file for accurate timing and translation. The program uses Google API for text translation and generates a new subtitle file. It then uses a text-to-speech service to create audio clips for each subtitle, which are synchronized with the original video using either a time-stretching technique or a two-pass synthesis method for better audio quality. The program also includes a script for attaching the dubbed audio tracks to the video file using FFmpeg and offers options for translating titles and descriptions for a fully localized experience.

10:05

💸 Costs and Limitations of Custom Voice Models

The video discusses the high costs and limitations associated with creating custom voice models for multilingual dubbing. It outlines the expenses for training and using such models on platforms like Microsoft Azure and Google Cloud. The creator also shares their prediction that AI will eventually become advanced and affordable enough for YouTube to offer automatic transcription and dubbing for all videos. The video workflow includes using OpenAI's 'Whisper' model for transcription and Descript for transcription editing, which provides more accurate and easily editable subtitles suitable for dubbing.

15:09

📢 Conclusion and Next Steps

The video concludes with the creator's intention to apply the dubbing process to most of their future videos and an invitation for viewers to give a thumbs up if they found the content interesting. The creator also recommends the next video about a speech enhancer AI tool by Adobe and provides a link for viewers to continue watching.

Keywords

Auto Synced and Translated Dubs

This is the name of the open-source Python program created by the video's author to automate the process of translating and dubbing videos into different languages. It uses AI voices and subtitle timings to create a dubbed version of the video that is synchronized with the original content. This program is significant as it addresses the limitations of existing tools and aims to revolutionize how content is made accessible to international audiences on platforms like YouTube.

Text-to-Speech Services

Text-to-speech (TTS) services are AI technologies that convert written text into spoken audio. In the context of the video, these services are used to generate audio clips in various languages by taking the translated text from the subtitle file. The program then synchronizes these audio clips with the video, ensuring that the dubbed speech aligns with the original content both in timing and pace.

Subtitle SRT File

An SRT file is a SubRip subtitle file format that contains the text of the subtitles along with timing information. It is crucial for the program discussed in the video because it provides the necessary timings for each group of words to be spoken. The SRT file is used to ensure that the dubbed audio aligns perfectly with the video, making the translated version as seamless as possible.

Google API

Google API, specifically referred to in the video, likely includes the Google Translate API which is used to translate the text from the SRT file into the desired language. This translation is a fundamental step in the process of creating dubbed versions of videos, allowing content to be accessible to speakers of different languages.

Time-Stretching

Time-stretching is a technique used to alter the duration of an audio clip without changing its pitch. In the video, it is mentioned as one of the methods to match the length of the audio clips to the timings specified in the SRT file. However, the author notes that this technique can degrade audio quality, hence the development of a more sophisticated two-pass synthesis method.

Two-Pass Synthesis

This is an advanced feature of the program that improves the quality of the dubbed audio. It involves synthesizing the audio clip at a default speed, measuring its length, and then synthesizing it again at an adjusted speed to match the desired duration exactly. This method ensures that the final audio clip is of higher quality compared to simple time-stretching.

FFmpeg

FFmpeg is a popular, open-source multimedia framework that can handle various tasks, including the conversion, streaming, and manipulation of audio and video files. In the context of the video, the author uses a script that leverages FFmpeg to add the translated audio tracks to the video file without the need for video conversion, streamlining the process of preparing videos for upload to platforms like YouTube.

Google Translate API

The Google Translate API is used within the program to translate not only the video's subtitles but also the video's titles and descriptions into different languages. This enables the video to be presented with translated text that is relevant to the viewer's language settings, enhancing the international viewer's experience.

Aloud

Aloud is an experimental project by YouTube that aims to provide dubbed audio tracks for videos. However, the author of the video mentions that Aloud has limitations such as being invite-only, supporting only Spanish and Portuguese, and requiring manual synchronization. The author's program, Auto Synced and Translated Dubs, was created to overcome these limitations.

Custom Voice Model

A custom voice model refers to a personalized AI voice that can be trained to mimic a specific person's voice. The video's author expresses a desire to create such a model so that dubbed videos could be spoken in their own voice across multiple languages. However, the cost and complexity of creating and using custom voice models are currently prohibitive.

OpenAI's Whisper

OpenAI's Whisper is a transcription model that the author uses for transcribing videos. It is noted for its high accuracy and ability to transcribe even complex speech with technical jargon. The author uses Whisper as part of their workflow to create a more accurate starting point for subtitle creation before editing and syncing them with the video content.

Highlights

A new feature on YouTube allows switching the audio track to several languages, offering dubbed versions instead of subtitles.

The feature is currently limited and requires special access, potentially changing how YouTube serves international audiences.

Dubbed translations are not automated, prompting the creation of an open-source Python program called Auto Synced and Translated Dubs.

The program uses AI to transcribe, translate, and sync audio with subtitles, even allowing for custom voice training, although it's currently expensive.

Auto Synced and Translated Dubs addresses limitations of Google's similar 'Aloud' project, including language support and synchronization precision.

The program requires a human-edited subtitle SRT file for accurate timing and text.

Google API is utilized to translate text into the desired language and generate a new subtitle file.

Text-to-speech services are used to synthesize audio clips, with a two-pass synthesis method to achieve correct audio length and high quality.

The program offers an option to stretch audio clips to the desired length, though it can degrade quality.

A separate script is included to attach the translated audio tracks to the video file for uploading to YouTube.

FFmpeg is used to add audio tracks without video conversion and supports merging a sound effects track into each dub.

YouTube allows adding translated titles and descriptions, which the program also automates using Google Translate API.

The video creator plans to apply this method to most future videos, enhancing accessibility for non-English speakers.

Custom voice models, although desirable, are currently cost-prohibitive due to training and hosting expenses.

AI is expected to become advanced and affordable enough for YouTube to automate transcription and dubbing for all videos.

The current limitation is transcription accuracy, especially for fast speech and technical jargon.

OpenAI's 'Whisper' model is used for transcription, offering high accuracy and punctuation recognition.

Descript is utilized for transcription editing, allowing for quick punctuation and capitalization adjustments.

The program includes additional configuration options for fine-tuning the dubbing process.