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abogen

Tue, 02 Sep 2025 15:30:10 +0800

denizsafak/abogen

abogen

Abogen is a powerful text-to-speech conversion tool that makes it easy to turn ePub, PDF, or text files into high-quality audio with matching subtitles in seconds. Use it for audiobooks, voiceovers for Instagram, YouTube, TikTok, or any project that needs natural-sounding text-to-speech, using Kokoro-82M.

Demo

https://github.com/user-attachments/assets/094ba3df-7d66-494a-bc31-0e4b41d0b865

This demo was generated in just 5 seconds, producing ∼1 minute of audio with perfectly synced subtitles. To create a similar video, see the demo guide.

`How to install?`

Windows

Go to espeak-ng latest release download and run the *.msi file.

OPTION 1: Install using script

Download the repository
Extract the ZIP file
Run WINDOWS_INSTALL.bat by double-clicking it

This method handles everything automatically - installing all dependencies including CUDA in a self-contained environment without requiring a separate Python installation. (You still need to install espeak-ng.)

[!NOTE] You don’t need to install Python separately. The script will install Python automatically.

OPTION 2: Install using pip

# Create a virtual environment (optional)
mkdir abogen && cd abogen
python -m venv venv
venv\Scripts\activate

# For NVIDIA GPUs:
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128

# For AMD GPUs:
# Not supported yet, because ROCm is not available on Windows. Use Linux if you have AMD GPU.

# Install abogen
pip install abogen

Mac

# Install espeak-ng
brew install espeak-ng

# Create a virtual environment (recommended)
mkdir abogen && cd abogen
python3 -m venv venv
source venv/bin/activate

# Install abogen
pip3 install abogen

# For Silicon Mac (M1, M2 etc.)
# After installing abogen, we need to install Kokoro's development version which includes MPS support.
pip3 install git+https://github.com/hexgrad/kokoro.git

Linux

# Install espeak-ng
sudo apt install espeak-ng # Ubuntu/Debian
sudo pacman -S espeak-ng # Arch Linux
sudo dnf install espeak-ng # Fedora

# Create a virtual environment (recommended)
mkdir abogen && cd abogen
python3 -m venv venv
source venv/bin/activate

# Install abogen
pip3 install abogen

# For NVIDIA GPUs:
# Already supported, no need to install CUDA separately.

# For AMD GPUs:
# After installing abogen, we need to uninstall the existing torch package
pip3 uninstall torch 
pip3 install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.4

[!TIP] If you get WARNING: The script abogen-cli is installed in '/home/username/.local/bin' which is not on PATH. error, run the following command to add it to your PATH:
1
echo "export PATH=\"/home/$USER/.local/bin:\$PATH\"" >> ~/.bashrc && source ~/.bashrc

[!TIP] If you get “No matching distribution found” error, try installing it on supported Python (3.10 to 3.12). You can use pyenv to manage multiple Python versions easily in Linux. Watch this video by NetworkChuck for a quick guide.

Special thanks to @hg000125 for his contribution in #23. AMD GPU support is possible thanks to his work.

`How to run?`

If you installed using pip, you can simply run the following command to start Abogen:

abogen

[!TIP] If you installed using the Windows installer (WINDOWS_INSTALL.bat), It should have created a shortcut in the same folder, or your desktop. You can run it from there. If you lost the shortcut, Abogen is located in python_embedded/Scripts/abogen.exe. You can run it from there directly.

`How to use?`

Drag and drop any ePub, PDF, or text file (or use the built-in text editor)
Configure the settings:
- Set speech speed
- Select a voice (or create a custom voice using voice mixer)
- Select subtitle generation style (by sentence, word, etc.)
- Select output format
- Select where to save the output
Hit Start

`In action`

Here’s Abogen in action: in this demo, it processes ∼3,000 characters of text in just 11 seconds and turns it into 3 minutes and 28 seconds of audio, and I have a low-end RTX 2060 Mobile laptop GPU. Your results may vary depending on your hardware.

`Configuration`

Options	Description
Input Box	Drag and drop `ePub`, `PDF`, or `.TXT` files (or use built-in text editor)
Queue options	Add multiple files to a queue and process them in batch, with individual settings for each file. See Queue mode for more details.
Speed	Adjust speech rate from `0.1x` to `2.0x`
Select Voice	First letter of the language code (e.g., `a` for American English, `b` for British English, etc.), second letter is for `m` for male and `f` for female.
Voice mixer	Create custom voices by mixing different voice models with a profile system. See Voice Mixer for more details.
Voice preview	Listen to the selected voice before processing.
Generate subtitles	`Disabled`, `Sentence`, `Sentence + Comma`, `Sentence + Highlighting`, `1 word`, `2 words`, `3 words`, etc. (Represents the number of words in each subtitle entry)
Output voice format	`.WAV`, `.FLAC`, `.MP3`, `.OPUS (best compression)` and `M4B (with chapters)` (Special thanks to @jborza for chapter support in PR #10)
Output subtitle format	Configures the subtitle format as `SRT (standard)`, `ASS (wide)`, `ASS (narrow)`, `ASS (centered wide)`, or `ASS (centered narrow)`.
Replace single newlines with spaces	Replaces single newlines with spaces in the text. This is useful for texts that have imaginary line breaks.
Save location	`Save next to input file`, `Save to desktop`, or `Choose output folder`

Book handler options	Description
Chapter Control	Select specific `chapters` from ePUBs or `chapters + pages` from PDFs.
Save each chapter separately	Save each chapter in e-books as a separate audio file.
Create a merged version	Create a single audio file that combines all chapters. (If `Save each chapter separately` is disabled, this option will be the default behavior.)
Save in a project folder with metadata	Save the converted items in a project folder with available metadata files.

Menu options	Description
Theme	Change the application’s theme using `System`, `Light`, or `Dark` options.
Configure max words per subtitle	Configures the maximum number of words per subtitle entry.
Configure max lines in log window	Configures the maximum number of lines to display in the log window.
Separate chapters audio format	Configures the audio format for separate chapters as `wav`, `flac`, `mp3`, or `opus`.
Create desktop shortcut	Creates a shortcut on your desktop for easy access.
Open config directory	Opens the directory where the configuration file is stored.
Open cache directory	Opens the cache directory where converted text files are stored.
Clear cache files	Deletes cache files created during the conversion or preview.
Check for updates at startup	Automatically checks for updates when the program starts.
Disable Kokoro’s internet access	Prevents Kokoro from downloading models or voices from HuggingFace Hub, useful for offline use.
Reset to default settings	Resets all settings to their default values.

Special thanks to @robmckinnon for adding Sentence + Highlighting feature in PR #65

`Voice Mixer`

With voice mixer, you can create custom voices by mixing different voice models. You can adjust the weight of each voice and save your custom voice as a profile for future use. The voice mixer allows you to create unique and personalized voices. (Huge thanks to @jborza for making this possible through his contributions in #5)

`Queue Mode`

Abogen supports queue mode, allowing you to add multiple files to a processing queue. This is useful if you want to convert several files in one batch.

You can add text files (.txt) directly using the Add files button in the Queue Manager. To add PDF or EPUB files, use the input box in the main window and click the Add to Queue button.
Each file in the queue keeps the configuration settings that were active when it was added. Changing the main window configuration afterward does not affect files already in the queue.
You can view each file’s configuration by hovering over them.

Abogen will process each item in the queue automatically, saving outputs as configured.

Special thanks to @jborza for adding queue mode in PR #35

`About Chapter Markers`

When you process ePUB or PDF files, Abogen converts them into text files stored in your cache directory. When you click “Edit,” you’re actually modifying these converted text files. In these text files, you’ll notice tags that look like this:

`1`	`<<CHAPTER_MARKER:Chapter Title>>`

These are chapter markers. They are automatically added when you process ePUB or PDF files, based on the chapters you select. They serve an important purpose:

Allow you to split the text into separate audio files for each chapter
Save time by letting you reprocess only specific chapters if errors occur, rather than the entire file

You can manually add these markers to plain text files for the same benefits. Simply include them in your text like this:

<<CHAPTER_MARKER:Introduction>>
This is the beginning of my text...  

<<CHAPTER_MARKER:Main Content>> 
Here's another part...

When you process the text file, Abogen will detect these markers automatically and ask if you want to save each chapter separately and create a merged version.

`About Metadata Tags`

Similar to chapter markers, it is possible to add metadata tags for M4B files. This is useful for audiobook players that support metadata, allowing you to add information like title, author, year, etc. Abogen automatically adds these tags when you process ePUB or PDF files, but you can also add them manually to your text files. Add metadata tags at the beginning of your text file like this:

<<METADATA_TITLE:Title>>
<<METADATA_ARTIST:Author>>
<<METADATA_ALBUM:Album Title>>
<<METADATA_YEAR:Year>>
<<METADATA_ALBUM_ARTIST:Album Artist>>
<<METADATA_COMPOSER:Narrator>>
<<METADATA_GENRE:Audiobook>>

`Supported Languages`

# 🇺🇸 'a' => American English, 🇬🇧 'b' => British English
# 🇪🇸 'e' => Spanish es
# 🇫🇷 'f' => French fr-fr
# 🇮🇳 'h' => Hindi hi
# 🇮🇹 'i' => Italian it
# 🇯🇵 'j' => Japanese: pip install misaki[ja]
# 🇧🇷 'p' => Brazilian Portuguese pt-br
# 🇨🇳 'z' => Mandarin Chinese: pip install misaki[zh]

For a complete list of supported languages and voices, refer to Kokoro’s VOICES.md. To listen to sample audio outputs, see SAMPLES.md.

[!NOTE] Japanese audio may require additional configuration. Please check #56 for more information.

`MPV Config`

I highly recommend using MPV to play your audio files, as it supports displaying subtitles even without a video track. Here’s my mpv.conf:

# --- MPV Settings ---
save-position-on-quit
keep-open=yes
# --- Subtitle ---
sub-ass-override=no
sub-margin-y=50
sub-margin-x=50
# --- Audio Quality ---
audio-spdif=ac3,dts,eac3,truehd,dts-hd
audio-channels=auto
audio-samplerate=48000
volume-max=200

`Docker Guide`

If you want to run Abogen in a Docker container:

Download the repository and extract, or clone it using git.
Go to abogen folder. You should see Dockerfile there.
Open your termminal in that directory and run the following commands:

# Build the Docker image:
docker build --progress plain -t abogen .

# Note that building the image may take a while.
# After building is complete, run the Docker container:

# Windows
docker run --name abogen -v %cd%:/shared -p 5800:5800 -p 5900:5900 --gpus all abogen

# Linux
docker run --name abogen -v $(pwd):/shared -p 5800:5800 -p 5900:5900 --gpus all abogen

# MacOS
docker run --name abogen -v $(pwd):/shared -p 5800:5800 -p 5900:5900 abogen

# We expose port 5800 for use by a web browser, 5900 if you want to connect with a VNC client.

Abogen launches automatically inside the container.

You can access it via a web browser at http://localhost:5800 or connect to it using a VNC client at localhost:5900.
You can use /shared directory to share files between your host and the container.
For later use, start it with docker start abogen and stop it with docker stop abogen.

Known issues:

Audio preview is not working inside container (ALSA error).
Open cache directory and Open configuration directory options in settings not working. (Tried pcmanfm, did not work with Abogen).

(Special thanks to @geo38 from Reddit, who provided the Dockerfile and instructions in this comment.)

`Similar Projects`

Abogen is a standalone project, but it is inspired by and shares some similarities with other projects. Here are a few:

audiblez: Generate audiobooks from e-books. (Has CLI and GUI support)
autiobooks: Automatically convert epubs to audiobooks
pdf-narrator: Convert your PDFs and EPUBs into audiobooks effortlessly.
epub_to_audiobook: EPUB to audiobook converter, optimized for Audiobookshelf
ebook2audiobook: Convert ebooks to audiobooks with chapters and metadata using dynamic AI models and voice cloning

`Roadmap`

Add OCR scan feature for PDF files using docling/teserract.
Add chapter metadata for .m4a files. (Issue #9, PR #10)
Add support for different languages in GUI.
Add voice formula feature that enables mixing different voice models. (Issue #1, PR #5)
Add support for kokoro-onnx (If it’s necessary).
Add dark mode.

`Troubleshooting`

If you encounter any issues while running Abogen, try launching it from the command line with:

`1`	`abogen-cli`

This will start Abogen in command-line mode and display detailed error messages. Please open a new issue on the Issues page with the error message and a description of your problem.

`Contributing`

I welcome contributions! If you have ideas for new features, improvements, or bug fixes, please fork the repository and submit a pull request.

For developers and contributors

If you’d like to modify the code and contribute to development, you can download the repository, extract it and run the following commands to build or install the package:

# Go to the directory where you extracted the repository and run:
pip install -e .      # Installs the package in editable mode
pip install build     # Install the build package
python -m build       # Builds the package in dist folder (optional)
abogen                # Opens the GUI

Feel free to explore the code and make any changes you like.

`Credits`

Abogen uses Kokoro for its high-quality, natural-sounding text-to-speech synthesis. Huge thanks to the Kokoro team for making this possible.
Thanks to @wojiushixiaobai for Embedded Python packages. These modified packages include pip pre-installed, enabling Abogen to function as a standalone application without requiring users to separately install Python in Windows.
Thanks to creators of EbookLib, a Python library for reading and writing ePub files, which is used for extracting text from ePub files.
Special thanks to the PyQt team for providing the cross-platform GUI toolkit that powers Abogen’s interface.
Icons: US, Great Britain, Spain, France, India, Italy, Japan, Brazil, China, Female, Male, Adjust and Voice Id icons by Icons8.

`License`

This project is available under the MIT License - see the LICENSE file for details. Kokoro is licensed under Apache-2.0 which allows commercial use, modification, distribution, and private use.

[!IMPORTANT] Subtitle generation currently works only for English. This is because Kokoro provides timestamp tokens only for English text. If you want subtitles in other languages, please request this feature in the Kokoro project. For more technical details, see this line in the Kokoro’s code.

Tags: audiobook, kokoro, text-to-speech, TTS, audiobook generator, audiobooks, text to speech, audiobook maker, audiobook creator, audiobook generator, voice-synthesis, text to audio, text to audio converter, text to speech converter, text to speech generator, text to speech software, text to speech app, epub to audio, pdf to audio, content-creation, media-generation

youtube-transcript-api

Mon, 16 Jun 2025 15:31:14 +0800

jdepoix/youtube-transcript-api

✨ YouTube Transcript API ✨

This is a python API which allows you to retrieve the transcript/subtitles for a given YouTube video. It also works for automatically generated subtitles, supports translating subtitles and it does not require a headless browser, like other selenium based solutions do!

Maintenance of this project is made possible by all the contributors and sponsors. If you'd like to sponsor this project and have your avatar or company logo appear below click here. 💖

Install

It is recommended to install this module by using pip:

`1`	`pip install youtube-transcript-api`

You can either integrate this module into an existing application or just use it via a CLI.

API

The easiest way to get a transcript for a given video is to execute:

from youtube_transcript_api import YouTubeTranscriptApi

ytt_api = YouTubeTranscriptApi()
ytt_api.fetch(video_id)

Note: By default, this will try to access the English transcript of the video. If your video has a different language, or you are interested in fetching a transcript in a different language, please read the section below.

Note: Pass in the video ID, NOT the video URL. For a video with the URL https://www.youtube.com/watch?v=12345 the ID is 12345.

This will return a FetchedTranscript object looking somewhat like this:

FetchedTranscript(
    snippets=[
        FetchedTranscriptSnippet(
            text="Hey there",
            start=0.0,
            duration=1.54,
        ),
        FetchedTranscriptSnippet(
            text="how are you",
            start=1.54,
            duration=4.16,
        ),
        # ...
    ],
    video_id="12345",
    language="English",
    language_code="en",
    is_generated=False,
)

This object implements most interfaces of a List:

ytt_api = YouTubeTranscriptApi()
fetched_transcript = ytt_api.fetch(video_id)

# is iterable
for snippet in fetched_transcript:
    print(snippet.text)

# indexable
last_snippet = fetched_transcript[-1]

# provides a length
snippet_count = len(fetched_transcript)

If you prefer to handle the raw transcript data you can call fetched_transcript.to_raw_data(), which will return a list of dictionaries:

[
    {
        'text': 'Hey there',
        'start': 0.0,
        'duration': 1.54
    },
    {
        'text': 'how are you',
        'start': 1.54
        'duration': 4.16
    },
    # ...
]

Retrieve different languages

You can add the languages param if you want to make sure the transcripts are retrieved in your desired language (it defaults to english).

`1`	`YouTubeTranscriptApi().fetch(video_id, languages=['de', 'en'])`

It’s a list of language codes in a descending priority. In this example it will first try to fetch the german transcript ('de') and then fetch the english transcript ('en') if it fails to do so. If you want to find out which languages are available first, have a look at list().

If you only want one language, you still need to format the languages argument as a list

`1`	`YouTubeTranscriptApi().fetch(video_id, languages=['de'])`

Preserve formatting

You can also add preserve_formatting=True if you’d like to keep HTML formatting elements such as <i> (italics) and <b> (bold).

`1`	`YouTubeTranscriptApi().fetch(video_ids, languages=['de', 'en'], preserve_formatting=True)`

List available transcripts

If you want to list all transcripts which are available for a given video you can call:

1
2

ytt_api = YouTubeTranscriptApi()
transcript_list = ytt_api.list(video_id)

This will return a TranscriptList object which is iterable and provides methods to filter the list of transcripts for specific languages and types, like:

`1`	`transcript = transcript_list.find_transcript(['de', 'en'])`

By default this module always chooses manually created transcripts over automatically created ones, if a transcript in the requested language is available both manually created and generated. The TranscriptList allows you to bypass this default behaviour by searching for specific transcript types:

# filter for manually created transcripts
transcript = transcript_list.find_manually_created_transcript(['de', 'en'])

# or automatically generated ones
transcript = transcript_list.find_generated_transcript(['de', 'en'])

The methods find_generated_transcript, find_manually_created_transcript, find_transcript return Transcript objects. They contain metadata regarding the transcript:

print(
    transcript.video_id,
    transcript.language,
    transcript.language_code,
    # whether it has been manually created or generated by YouTube
    transcript.is_generated,
    # whether this transcript can be translated or not
    transcript.is_translatable,
    # a list of languages the transcript can be translated to
    transcript.translation_languages,
)

and provide the method, which allows you to fetch the actual transcript data:

`1`	`transcript.fetch()`

This returns a FetchedTranscript object, just like YouTubeTranscriptApi().fetch() does.

Translate transcript

YouTube has a feature which allows you to automatically translate subtitles. This module also makes it possible to access this feature. To do so Transcript objects provide a translate() method, which returns a new translated Transcript object:

1
2
3

transcript = transcript_list.find_transcript(['en'])
translated_transcript = transcript.translate('de')
print(translated_transcript.fetch())

By example

from youtube_transcript_api import YouTubeTranscriptApi

ytt_api = YouTubeTranscriptApi()

# retrieve the available transcripts
transcript_list = ytt_api.list('video_id')

# iterate over all available transcripts
for transcript in transcript_list:

    # the Transcript object provides metadata properties
    print(
        transcript.video_id,
        transcript.language,
        transcript.language_code,
        # whether it has been manually created or generated by YouTube
        transcript.is_generated,
        # whether this transcript can be translated or not
        transcript.is_translatable,
        # a list of languages the transcript can be translated to
        transcript.translation_languages,
    )

    # fetch the actual transcript data
    print(transcript.fetch())

    # translating the transcript will return another transcript object
    print(transcript.translate('en').fetch())

# you can also directly filter for the language you are looking for, using the transcript list
transcript = transcript_list.find_transcript(['de', 'en'])  

# or just filter for manually created transcripts  
transcript = transcript_list.find_manually_created_transcript(['de', 'en'])  

# or automatically generated ones  
transcript = transcript_list.find_generated_transcript(['de', 'en'])

Working around IP bans (`RequestBlocked` or `IpBlocked` exception)

Unfortunately, YouTube has started blocking most IPs that are known to belong to cloud providers (like AWS, Google Cloud Platform, Azure, etc.), which means you will most likely run into ReuquestBlocked or IpBlocked exceptions when deploying your code to any cloud solutions. Same can happen to the IP of your self-hosted solution, if you are doing too many requests. You can work around these IP bans using proxies. However, since YouTube will ban static proxies after extended use, going for rotating residential proxies provide is the most reliable option.

There are different providers that offer rotating residential proxies, but after testing different offerings I have found Webshare to be the most reliable and have therefore integrated it into this module, to make setting it up as easy as possible.

Using Webshare

Once you have created a Webshare account and purchased a “Residential” proxy package that suits your workload (make sure NOT to purchase “Proxy Server” or “Static Residential”!), open the Webshare Proxy Settings to retrieve your “Proxy Username” and “Proxy Password”. Using this information you can initialize the YouTubeTranscriptApi as follows:

from youtube_transcript_api import YouTubeTranscriptApi
from youtube_transcript_api.proxies import WebshareProxyConfig

ytt_api = YouTubeTranscriptApi(
    proxy_config=WebshareProxyConfig(
        proxy_username="<proxy-username>",
        proxy_password="<proxy-password>",
    )
)

# all requests done by ytt_api will now be proxied through Webshare
ytt_api.fetch(video_id)

Using the WebshareProxyConfig will default to using rotating residential proxies and requires no further configuration.

Note that referral links are used here and any purchases made through these links will support this Open Source project, which is very much appreciated! 💖😊🙏💖

However, you are of course free to integrate your own proxy solution using the GenericProxyConfig class, if you prefer using another provider or want to implement your own solution, as covered by the following section.

Using other Proxy solutions

Alternatively to using Webshare, you can set up any generic HTTP/HTTPS/SOCKS proxy using the GenericProxyConfig class:

from youtube_transcript_api import YouTubeTranscriptApi
from youtube_transcript_api.proxies import GenericProxyConfig

ytt_api = YouTubeTranscriptApi(
    proxy_config=GenericProxyConfig(
        http_url="http://user:pass@my-custom-proxy.org:port",
        https_url="https://user:pass@my-custom-proxy.org:port",
    )
)

# all requests done by ytt_api will now be proxied using the defined proxy URLs
ytt_api.fetch(video_id)

Be aware that using a proxy doesn’t guarantee that you won’t be blocked, as YouTube can always block the IP of your proxy! Therefore, you should always choose a solution that rotates through a pool of proxy addresses, if you want to maximize reliability.

Overwriting request defaults

When initializing a YouTubeTranscriptApi object, it will create a requests.Session which will be used for all HTTP(S) request. This allows for caching cookies when retrieving multiple requests. However, you can optionally pass a requests.Session object into its constructor, if you manually want to share cookies between different instances of YouTubeTranscriptApi, overwrite defaults, set custom headers, specify SSL certificates, etc.

from requests import Session

http_client = Session()

# set custom header
http_client.headers.update({"Accept-Encoding": "gzip, deflate"})

# set path to CA_BUNDLE file
http_client.verify = "/path/to/certfile"

ytt_api = YouTubeTranscriptApi(http_client=http_client)
ytt_api.fetch(video_id)

# share same Session between two instances of YouTubeTranscriptApi
ytt_api_2 = YouTubeTranscriptApi(http_client=http_client)
# now shares cookies with ytt_api
ytt_api_2.fetch(video_id)

Some videos are age restricted, so this module won’t be able to access those videos without some sort of authentication. Unfortunately, some recent changes to the YouTube API have broken the current implementation of cookie based authentication, so this feature is currently not available.

Using Formatters

Formatters are meant to be an additional layer of processing of the transcript you pass it. The goal is to convert a FetchedTranscript object into a consistent string of a given “format”. Such as a basic text (.txt) or even formats that have a defined specification such as JSON (.json), WebVTT (.vtt), SRT (.srt), Comma-separated format (.csv), etc…

The formatters submodule provides a few basic formatters, which can be used as is, or extended to your needs:

JSONFormatter
PrettyPrintFormatter
TextFormatter
WebVTTFormatter
SRTFormatter

Here is how to import from the formatters module.

# the base class to inherit from when creating your own formatter.
from youtube_transcript_api.formatters import Formatter

# some provided subclasses, each outputs a different string format.
from youtube_transcript_api.formatters import JSONFormatter
from youtube_transcript_api.formatters import TextFormatter
from youtube_transcript_api.formatters import WebVTTFormatter
from youtube_transcript_api.formatters import SRTFormatter

Formatter Example

Let’s say we wanted to retrieve a transcript and store it to a JSON file. That would look something like this:

# your_custom_script.py

from youtube_transcript_api import YouTubeTranscriptApi
from youtube_transcript_api.formatters import JSONFormatter

ytt_api = YouTubeTranscriptApi()
transcript = ytt_api.fetch(video_id)

formatter = JSONFormatter()

# .format_transcript(transcript) turns the transcript into a JSON string.
json_formatted = formatter.format_transcript(transcript)

# Now we can write it out to a file.
with open('your_filename.json', 'w', encoding='utf-8') as json_file:
    json_file.write(json_formatted)

# Now should have a new JSON file that you can easily read back into Python.

Passing extra keyword arguments

Since JSONFormatter leverages json.dumps() you can also forward keyword arguments into .format_transcript(transcript) such as making your file output prettier by forwarding the indent=2 keyword argument.

`1`	`json_formatted = JSONFormatter().format_transcript(transcript, indent=2)`

Custom Formatter Example

You can implement your own formatter class. Just inherit from the Formatter base class and ensure you implement the format_transcript(self, transcript: FetchedTranscript, **kwargs) -> str and format_transcripts(self, transcripts: List[FetchedTranscript], **kwargs) -> str methods which should ultimately return a string when called on your formatter instance.

class MyCustomFormatter(Formatter):
    def format_transcript(self, transcript: FetchedTranscript, **kwargs) -> str:
        # Do your custom work in here, but return a string.
        return 'your processed output data as a string.'

    def format_transcripts(self, transcripts: List[FetchedTranscript], **kwargs) -> str:
        # Do your custom work in here to format a list of transcripts, but return a string.
        return 'your processed output data as a string.'

CLI

Execute the CLI script using the video ids as parameters and the results will be printed out to the command line:

`1`	`youtube_transcript_api <first_video_id> <second_video_id> ...`

The CLI also gives you the option to provide a list of preferred languages:

`1`	`youtube_transcript_api <first_video_id> <second_video_id> ... --languages de en`

You can also specify if you want to exclude automatically generated or manually created subtitles:

1
2

youtube_transcript_api <first_video_id> <second_video_id> ... --languages de en --exclude-generated
youtube_transcript_api <first_video_id> <second_video_id> ... --languages de en --exclude-manually-created

If you would prefer to write it into a file or pipe it into another application, you can also output the results as json using the following line:

`1`	`youtube_transcript_api <first_video_id> <second_video_id> ... --languages de en --format json > transcripts.json`

Translating transcripts using the CLI is also possible:

`1`	`youtube_transcript_api <first_video_id> <second_video_id> ... --languages en --translate de`

If you are not sure which languages are available for a given video you can call, to list all available transcripts:

`1`	`youtube_transcript_api --list-transcripts <first_video_id>`

If a video’s ID starts with a hyphen you’ll have to mask the hyphen using \ to prevent the CLI from mistaking it for a argument name. For example to get the transcript for the video with the ID -abc123 run:

`1`	`youtube_transcript_api "\-abc123"`

Working around IP bans using the CLI

If you are running into ReqestBlocked or IpBlocked errors, because YouTube blocks your IP, you can work around this using residential proxies as explained in Working around IP bans. To use Webshare “Residential” proxies through the CLI, you will have to create a Webshare account and purchase a “Residential” proxy package that suits your workload (make sure NOT to purchase “Proxy Server” or “Static Residential”!). Then you can use the “Proxy Username” and “Proxy Password” which you can find in your Webshare Proxy Settings, to run the following command:

`1`	`youtube_transcript_api <first_video_id> <second_video_id> --webshare-proxy-username "username" --webshare-proxy-password "password"`

If you prefer to use another proxy solution, you can set up a generic HTTP/HTTPS proxy using the following command:

`1`	`youtube_transcript_api <first_video_id> <second_video_id> --http-proxy http://user:pass@domain:port --https-proxy https://user:pass@domain:port`

To authenticate using cookies through the CLI as explained in Cookie Authentication run:

`1`	`youtube_transcript_api <first_video_id> <second_video_id> --cookies /path/to/your/cookies.txt`

Warning

This code uses an undocumented part of the YouTube API, which is called by the YouTube web-client. So there is no guarantee that it won’t stop working tomorrow, if they change how things work. I will however do my best to make things working again as soon as possible if that happens. So if it stops working, let me know!

Contributing

To setup the project locally run the following (requires poetry to be installed):

`1`	`poetry install --with test,dev`

There’s poe tasks to run tests, coverage, the linter and formatter (you’ll need to pass all of those for the build to pass):

poe test
poe coverage
poe format
poe lint

If you just want to make sure that your code passes all the necessary checks to get a green build, you can simply run:

`1`	`poe precommit`

Donations

If this project makes you happy by reducing your development time, you can make me happy by treating me to a cup of coffee, or become a Sponsor of this project :)

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How to use?

In action

Configuration

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youtube-transcript-api

jdepoix/youtube-transcript-api

✨ YouTube Transcript API ✨

Install

API

Retrieve different languages

Preserve formatting

List available transcripts

Translate transcript

By example

Working around IP bans (RequestBlocked or IpBlocked exception)

Using Webshare

Using other Proxy solutions

Overwriting request defaults

Cookie Authentication

Using Formatters

Formatter Example

Custom Formatter Example

CLI

Working around IP bans using the CLI

Cookie Authentication using the CLI

Warning

Contributing

Donations

`How to install?`

`How to run?`

`How to use?`

`In action`

`Configuration`

`Voice Mixer`

`Queue Mode`

`About Chapter Markers`

`About Metadata Tags`

`Supported Languages`

`MPV Config`

`Docker Guide`

`Similar Projects`

`Roadmap`

`Troubleshooting`

`Contributing`

`Credits`

`License`

Working around IP bans (`RequestBlocked` or `IpBlocked` exception)