Pandrator
Turn PDFs and EPUBs into audiobooks, subtitles or videos into dubbed videos (including translation), and more. For free. Pandrator uses local models, notably XTTS, including voice-cloning (instant, RVC-enhanced, XTTS fine-tuning) and LLM processing. It aspires to be a user-friendly app with a GUI, an installer and all-in-one packages.
Stars: 306
Pandrator is a GUI tool for generating audiobooks and dubbing using voice cloning and AI. It transforms text, PDF, EPUB, and SRT files into spoken audio in multiple languages. It leverages XTTS, Silero, and VoiceCraft models for text-to-speech conversion and voice cloning, with additional features like LLM-based text preprocessing and NISQA for audio quality evaluation. The tool aims to be user-friendly with a one-click installer and a graphical interface.
README:
[!TIP] TL;DR:
- Pandrator is not an AI model itself, but a GUI framework for Text-to-Speech projects. It can generate audiobooks and dubbing by leveraging several AI tools, custom workflows and algorithms. It works on Windows out of the box. It does work on Linux, but you have to perform a manual installation at the moment.
- The easiest way to use it is to download one of the precompiled archives - simply unpack them and use the included launcher. See this table for their contents and sizes.
This video shows the process of launching Pandrator, selecting a source file, starting generation, stopping it and previewing the saved file. It has not been sped up as it's intended to illustrate the real performance (you may skip the first 35s when the XTTS server is launching, and please remember to turn on the sound).
https://github.com/user-attachments/assets/7cab141a-e043-4057-8166-72cb29281c50
Pandrator aspires to be easy to use and install - it has a one-click installer and a graphical user interface. It is a tool designed to perform two tasks:
- transform text, PDF, EPUB and SRT files into spoken audio in multiple languages based chiefly on open source software run locally, including preprocessing to make the generated speech sound as natural as possible by, among other things, splitting the text into paragraphs, sentences and smaller logical text blocks (clauses), which the TTS models can process with minimal artifacts. Each sentence can be regenerated if the first attempt is not satisfacory. Voice cloning is possible for models that support it, and text can be additionally preprocessed using LLMs (to remove OCR artifacts or spell out things that the TTS models struggle with, like Roman numerals and abbreviations, for example),
- generate dubbing either directly from a video file, including transcription (using WhisperX), or from an .srt file. It includes a complete workflow from a video file to a dubbed video file with subtitles - including translation using a variety of APIs and techniques to improve the quality of translation. Subdub, a companion app developed for this purpose, can also be used on its own.
It leverages the XTTS, Silero and VoiceCraft model(s) for text-to-speech conversion and voice cloning, enhanced by RVC_CLI for quality improvement and better voice cloning results, and NISQA for audio quality evaluation. Additionally, it incorporates Text Generation Webui's API for local LLM-based text pre-processing, enabling a wide range of text manipulations before audio generation.
[!NOTE] Please note that Pandrator is still in an alpha stage and I'm not an experienced developer (I'm a noob, in fact), so the code is far from perfect in terms of optimisation, features and reliability. Please keep this in mind and contribute, if you want to help me make it better.
The samples were generated using the minimal settings - no LLM text processing, RVC or TTS evaluation, and no sentences were regenerated. Both XTTS and Silero generations were faster than playback speed.
https://github.com/user-attachments/assets/1c763c94-c66b-4c22-a698-6c4bcf3e875d
https://github.com/lukaszliniewicz/Pandrator/assets/75737665/bbb10512-79ed-43ea-bee3-e271b605580e
https://github.com/lukaszliniewicz/Pandrator/assets/75737665/118f5b9c-641b-4edd-8ef6-178dd924a883
Dubbing sample, including translation (video source):
https://github.com/user-attachments/assets/1ba8068d-986e-4dec-a162-3b7cc49052f4
Tool | CPU Requirements | GPU Requirements |
---|---|---|
XTTS | A reasonably modern CPU (for CPU-only generation) | NVIDIA GPU with 4GB+ of VRAM for good performance |
Silero | Performs well on most CPUs | N/A |
VoiceCraft | Usable on CPU, but generation will be slow | NVIDIA GPU with 8GB+ of VRAM for acceleration (4GB VRAM requires kv cache disabled) |
This project relies on several APIs and services (running locally) and libraries, notably:
- XTTS API Server by daswer123 for Text-to-Speech (TTS) generation using Coqui XTTSv2 OR Silero API Server by ouoertheo for TTS generaton using the Silero models OR VoiceCraft by jasonppy.
- FFmpeg for audio encoding.
-
Sentence Splitter by mediacloud for splitting
.txt
files into sentences, customtkinter by TomSchimansky, num2words by savoirfairelinux, and many others. For a full list, seerequirements.txt
.
- Subdub, a command line app that transcribes video files, translates subtitles and synchronises the generated speech with the video, made specially for Pandrator.
- WhisperX by m-bain, an enhanced implementation of OpenAI's Whisper model with improved alignment, used for dubbing and XTTS training.
- Easy XTTS Trainer, a command line app that enables XTTS fine-tuning using one or more audio files, made specially for Pandrator.
- RVC Python by daswer123 for enhancing voice quality and cloning results with Retrieval Based Voice Conversion.
- Text Generation Webui API by oobabooga for LLM-based text pre-processing.
- NISQA by gabrielmittag for evaluating TTS generations (using the FastAPI implementation).
I've prepared packages (archives) that you can simply unpack - everything is preinstalled in its own portable conda environment. You can use the launcher to start Pandrator, update it and install new features, depending on the version of the package you downloaded.
Package | Contents | Unpacked Size | Link |
---|---|---|---|
1 | Pandrator and Silero | 4GB | Download |
2 | Pandrator and XTTS | 14GB | Download |
3 | Pandrator, XTTS, RVC, WhisperX (for dubbing) and XTTS fine-tuning | 36GB | Download |
Run pandrator_installer_launcher.exe
with administrator priviliges. You will find it under Releases. The executable was created using pyinstaller from pandrator_installer_launcher.py
in the repository.
The file may be flagged as a threat by antivirus software, so you may have to add it as an exception.
You can choose which TTS engines to install and whether to install the software that enables RVC voice cloning (RVC Python), dubbing (WhisperX) and XTTS fine-tuning (Easy XTTS Trainer). You may install more components later.
The Installer/Launcher performs the following tasks:
- Creates the Pandrator folder
- Installs necessary tools if not already present:
- C++ Build Tools
- Calibre
- winget (if necessary)
- Installs Miniconda
- Clones the following repositories:
- Pandrator
- Subdub
- XTTS API Server (if selected)
- Silero API Server (if selected)
- VoiceCraft API (if selected)
- Creates required conda environments
- Installs all necessary dependencies
Note: You can use the Installer/Launcher to launch Pandrator and all the tools at any moment.
If you want to perform the setup again, remove the Pandrator folder it created. Please allow at least a couple of minutes for the initial setup process to download models and install dependencies. Depending on the options you've chosen, it may take up to 30 minutes.
For additional functionality not yet included in the installer:
- Install Text Generation Webui and remember to enable the API (add
--api
toCMD_FLAGS.txt
in the main directory of the Webui before starting it). - Set up NISQA API for automatic evaluation of generations.
Please refer to the repositories linked under Dependencies for detailed installation instructions. Remember that the APIs must be running to make use of the functionalities they offer.
- Git
- Miniconda or Anaconda (installed system-wide)
- Microsoft Visual C++ Build Tools
- Calibre
-
Install dependencies:
- Calibre: Download and install from https://calibre-ebook.com/download_windows
- Microsoft Visual C++ Build Tools:
winget install --id Microsoft.VisualStudio.2022.BuildTools --override "--quiet --wait --add Microsoft.VisualStudio.Workload.VCTools --includeRecommended" --accept-package-agreements --accept-source-agreements
-
Clone the repositories:
mkdir Pandrator cd Pandrator git clone https://github.com/lukaszliniewicz/Pandrator.git git clone https://github.com/lukaszliniewicz/Subdub.git
-
Create and activate a conda environment:
conda create -n pandrator_installer python=3.10 -y conda activate pandrator_installer
-
Install Pandrator and Subdub requirements:
cd Pandrator pip install -r requirements.txt cd ../Subdub pip install -r requirements.txt cd ..
-
(Optional) Install XTTS:
git clone https://github.com/daswer123/xtts-api-server.git conda create -n xtts_api_server_installer python=3.10 -y conda activate xtts_api_server_installer pip install torch==2.1.1+cu118 torchaudio==2.1.1+cu118 --extra-index-url https://download.pytorch.org/whl/cu118 pip install xtts-api-server
-
(Optional) Install Silero:
conda create -n silero_api_server_installer python=3.10 -y conda activate silero_api_server_installer pip install silero-api-server
-
(Optional) Install RVC (Retrieval-based Voice Conversion):
conda activate pandrator_installer pip install pip==24 pip install rvc-python pip install torch==2.1.1+cu118 torchaudio==2.1.1+cu118 --index-url https://download.pytorch.org/whl/cu118
-
(Optional) Install WhisperX:
conda create -n whisperx_installer python=3.10 -y conda activate whisperx_installer conda install git -c conda-forge -y pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu118 conda install cudnn=8.9.7.29 -c conda-forge -y conda install ffmpeg -c conda-forge -y pip install git+https://github.com/m-bain/whisperx.git
-
(Optional) Install XTTS Fine-tuning:
git clone https://github.com/lukaszliniewicz/easy_xtts_trainer.git conda create -n easy_xtts_trainer python=3.10 -y conda activate easy_xtts_trainer cd easy_xtts_trainer pip install -r requirements.txt pip install torch==2.1.1+cu118 torchaudio==2.1.1+cu118 --index-url https://download.pytorch.org/whl/cu118 cd ..
-
Run Pandrator:
conda activate pandrator_installer cd Pandrator python pandrator.py
-
Run XTTS API Server (if installed):
conda activate xtts_api_server_installer python -m xtts_api_server
Additional options:
- For CPU only: Add
--device cpu
- For low VRAM: Add
--lowvram
- To use DeepSpeed: Add
--deepspeed
- For CPU only: Add
-
Run Silero API Server (if installed):
conda activate silero_api_server_installer python -m silero_api_server
After installation, your folder structure should look like this:
Pandrator/
├── Pandrator/
├── Subdub/
├── xtts-api-server/ (if XTTS is installed)
├── easy_xtts_trainer/ (if XTTS Fine-tuning is installed)
For more detailed information on using specific components or troubleshooting, please refer to the documentation of each individual repository.
If you don't want to use the additional functionalities, you have everything you need in the Session tab.
- Either create a new session or load an existing one (select a folder in
Outputs
to do that). - Choose your
.txt
,.srt
,.pdf
orepub
file. If you choose a PDF or EPUB file, a preview window will open with the extracted text. You may edit it (OCRed books often have poorly recognized text from the title page, for example). Files that contain a lot of text, regardless of format, can take a long time to finish preprocessing before generation begins. The GUI will freeze, but as long as there is processor activity, it's simply working. For whole books, expect 10m+ for preprocessing. - Select the TTS server you want to use - XTTS, Silero or VoiceCraft - and the language from the dropdown (VoiceCraft currently supports only English).
- Choose the voice you want to use.
-
XTTS, voices are short, 6-12s
.wav
files (22050hz sample rate, mono) stored in thetts_voices
directory. The XTTS model uses the audio to clone the voice. It doesn't matter what language the sample is in, you will be able to generate speech in all supported languages, but the quality will be best if you provide a sample in your target language. You may use the sample one in the repository or upload your own. Please make sure that the audio is between 6 and 12s, mono, and the sample rate is 22050hz. You may use a tool like Audacity to prepare the files. The less noise, the better. You may use a tool like Resemble AI for denoising and/or enhancement of your samples on Hugging Face. - Silero offers a number of voices for each language it supports. It doesn't support voice cloning. Simply select a voice from the dropdown after choosing the language.
-
VoiceCraft works similarly to XTTS in that it clones the voice from a
.wav
sample. However, it needs both a properly formatted.wav
file (mono, 16000hz) and a.txt
file with the transcription of what is said in the sample. The files must have the same name (apart from the extension, of course). You need to upload them totts_voices/VoiceCraft
and you will be able to select them in the GUI. Currently only English is supported. If you generate with a new voice for the first time, the server will perform the alignment procedure, so the first sentence will be generated with a delay. This won't happen when you use that voice again.
-
XTTS, voices are short, 6-12s
- If you want, you can either slow down or speed up the generated audio (type in or choose a ratio, e.g. 1.1, which is 10% faster than generated; it may be especially useful for dubbing).
- If you chose an
.srt
file, you will be given the option to select a video file and one of its audio tracks to mix with the synchronized output, as well as weather you want to lower the volume of the original audio when subtitle audio is playing. - Start the generation. You may stop and resume it later, or close the programme and load the session later.
- You can play back the generated sentences, also as a playlist, edit them (the text for regeneration), regenerate or remove individual ones.
- "Save Output" concatenates the sentences generated so far an encodes them as one file (default is
.opus
at 64k bitrate; you may change it in the Audio tab to.wav
or.mp3
).
- You can change the lenght of silence appended to the end of sentences and paragraphs.
- You can enable a fade-in and -out effect and set the duration.
- You can choose the output format and bitrate.
- You can disable/enable splitting long sentences and set the max lenght a text fragment sent for TTS generation may have (enabled by default; it tries to split sentences whose lenght exceeds the max lenght value; it looks for punctuation marks (, ; : -) and chooses the one closest to the midpoint of the sentence; if there are no punctuation marks, it looks for conjunctions like "and"); it performs this operation twice as some sentence fragments may still be too long after just one split.
- You can disable/enable appending short sentences (to preceding or following sentences; disabled by default, may perhaps improve the flow as the lenght of text fragments sent to the model is more uniform).
- Remove diacritics (useful when generating a text that contains many foreign words or transliterations from foreign alphabets, e.g. Japanese). Do not enable this if you generate in a language that needs diacritics, like German or Polish! The pronounciation will be wrong then.
- Enable LLM processing to use language models for preprocessing the text before sending it to the TTS API. For example, you may ask the LLM to remove OCR artifacts, spell out abbreviations, correct punctuation etc.
- You can define up to three prompts for text optimization. Each prompt is sent to the LLM API separately, and the output of the last prompt is used for TTS generation.
- For each prompt, you can enable/disable it, set the prompt text, choose the LLM model to use, and enable/disable evaluation (if enabled, the LLM API will be called twice for each prompt, and then again for the model to choose the better result).
- Load the available LLM models using the "Load LLM Models" button in the Session tab.
- Enable RVC to enhance the generated audio quality and apply voice cloning.
- Select the RVC model file (.pth) and the corresponding index file using the "Select RVC Model" and "Select RVC Index" buttons in the Audio Processing tab.
- When RVC is enabled, the generated audio will be processed using the selected RVC model and index before being saved.
- Enable TTS evaluation to assess the quality of the generated audio using the NISQA (Non-Intrusive Speech Quality Assessment) model.
- Set the target MOS (Mean Opinion Score) value and the maximum number of attempts for each sentence.
- When TTS evaluation is enabled, the generated audio will be evaluated using the NISQA model, and the best audio (based on the MOS score) will be chosen for each sentence.
- If the target MOS value is not reached within the maximum number of attempts, the best audio generated so far will be used.
Contributions, suggestions for improvements, and bug reports are most welcome!
- You can find a collection of voice sample for example here. They are intended for use with ElevenLabs, so you will need to pick an 8-12s fragment and save it as 22050khz mono
.wav
usuing Audacity, for instance. - You can find a collection of RVC models for example here.
- [ ] Add support for Surya for PDF OCR, layout and redeaing order detection, plus preprocessing of chapters, headers, footers, footnotes and tables.
- [ ] Add support for StyleTTS2
- [ ] Add importing/exporting settings.
- [ ] Add support for proprietary APIs for text pre-processing and TTS generation.
- [ ] Include OCR for PDFs.
- [ ] Add support for a higher quality local TTS model, Tortoise.
- [ ] Add option to record a voice sample and use it for TTS to the GUI.
- [x] Add support for chapter segmentation
- [x] Add all API servers to the setup script.
- [x] Add support for custom XTTS models
- [x] Add workflow to create dubbing from
.srt
subtitle files. - [x] Include support for PDF files.
- [x] Integrate editing capabilities for processed sentences within the UI.
- [x] Add support for a lower quality but faster local TTS model that can easily run on CPU, e.g. Silero or Piper.
- [x] Add support for EPUB.
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VideoLingo is an all-in-one video translation and localization dubbing tool designed to generate Netflix-level high-quality subtitles. It aims to eliminate stiff machine translation, multiple lines of subtitles, and can even add high-quality dubbing, allowing knowledge from around the world to be shared across language barriers. Through an intuitive Streamlit web interface, the entire process from video link to embedded high-quality bilingual subtitles and even dubbing can be completed with just two clicks, easily creating Netflix-quality localized videos. Key features and functions include using yt-dlp to download videos from Youtube links, using WhisperX for word-level timeline subtitle recognition, using NLP and GPT for subtitle segmentation based on sentence meaning, summarizing intelligent term knowledge base with GPT for context-aware translation, three-step direct translation, reflection, and free translation to eliminate strange machine translation, checking single-line subtitle length and translation quality according to Netflix standards, using GPT-SoVITS for high-quality aligned dubbing, and integrating package for one-click startup and one-click output in streamlit.
1filellm
1filellm is a command-line data aggregation tool designed for LLM ingestion. It aggregates and preprocesses data from various sources into a single text file, facilitating the creation of information-dense prompts for large language models. The tool supports automatic source type detection, handling of multiple file formats, web crawling functionality, integration with Sci-Hub for research paper downloads, text preprocessing, and token count reporting. Users can input local files, directories, GitHub repositories, pull requests, issues, ArXiv papers, YouTube transcripts, web pages, Sci-Hub papers via DOI or PMID. The tool provides uncompressed and compressed text outputs, with the uncompressed text automatically copied to the clipboard for easy pasting into LLMs.
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RVC_CLI
**RVC_CLI: Retrieval-based Voice Conversion Command Line Interface** This command-line interface (CLI) provides a comprehensive set of tools for voice conversion, enabling you to modify the pitch, timbre, and other characteristics of audio recordings. It leverages advanced machine learning models to achieve realistic and high-quality voice conversions. **Key Features:** * **Inference:** Convert the pitch and timbre of audio in real-time or process audio files in batch mode. * **TTS Inference:** Synthesize speech from text using a variety of voices and apply voice conversion techniques. * **Training:** Train custom voice conversion models to meet specific requirements. * **Model Management:** Extract, blend, and analyze models to fine-tune and optimize performance. * **Audio Analysis:** Inspect audio files to gain insights into their characteristics. * **API:** Integrate the CLI's functionality into your own applications or workflows. **Applications:** The RVC_CLI finds applications in various domains, including: * **Music Production:** Create unique vocal effects, harmonies, and backing vocals. * **Voiceovers:** Generate voiceovers with different accents, emotions, and styles. * **Audio Editing:** Enhance or modify audio recordings for podcasts, audiobooks, and other content. * **Research and Development:** Explore and advance the field of voice conversion technology. **For Jobs:** * Audio Engineer * Music Producer * Voiceover Artist * Audio Editor * Machine Learning Engineer **AI Keywords:** * Voice Conversion * Pitch Shifting * Timbre Modification * Machine Learning * Audio Processing **For Tasks:** * Convert Pitch * Change Timbre * Synthesize Speech * Train Model * Analyze Audio
WavCraft
WavCraft is an LLM-driven agent for audio content creation and editing. It applies LLM to connect various audio expert models and DSP function together. With WavCraft, users can edit the content of given audio clip(s) conditioned on text input, create an audio clip given text input, get more inspiration from WavCraft by prompting a script setting and let the model do the scriptwriting and create the sound, and check if your audio file is synthesized by WavCraft.
Pandrator
Pandrator is a GUI tool for generating audiobooks and dubbing using voice cloning and AI. It transforms text, PDF, EPUB, and SRT files into spoken audio in multiple languages. It leverages XTTS, Silero, and VoiceCraft models for text-to-speech conversion and voice cloning, with additional features like LLM-based text preprocessing and NISQA for audio quality evaluation. The tool aims to be user-friendly with a one-click installer and a graphical interface.
transcriptionstream
Transcription Stream is a self-hosted diarization service that works offline, allowing users to easily transcribe and summarize audio files. It includes a web interface for file management, Ollama for complex operations on transcriptions, and Meilisearch for fast full-text search. Users can upload files via SSH or web interface, with output stored in named folders. The tool requires a NVIDIA GPU and provides various scripts for installation and running. Ports for SSH, HTTP, Ollama, and Meilisearch are specified, along with access details for SSH server and web interface. Customization options and troubleshooting tips are provided in the documentation.
LLMStack
LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.
daily-poetry-image
Daily Chinese ancient poetry and AI-generated images powered by Bing DALL-E-3. GitHub Action triggers the process automatically. Poetry is provided by Today's Poem API. The website is built with Astro.
exif-photo-blog
EXIF Photo Blog is a full-stack photo blog application built with Next.js, Vercel, and Postgres. It features built-in authentication, photo upload with EXIF extraction, photo organization by tag, infinite scroll, light/dark mode, automatic OG image generation, a CMD-K menu with photo search, experimental support for AI-generated descriptions, and support for Fujifilm simulations. The application is easy to deploy to Vercel with just a few clicks and can be customized with a variety of environment variables.
SillyTavern
SillyTavern is a user interface you can install on your computer (and Android phones) that allows you to interact with text generation AIs and chat/roleplay with characters you or the community create. SillyTavern is a fork of TavernAI 1.2.8 which is under more active development and has added many major features. At this point, they can be thought of as completely independent programs.