--- title: "IMS-Toucan" type: "tool" slug: "digitalphonetics-ims-toucan" canonical_url: "https://www.graphcanon.com/tools/digitalphonetics-ims-toucan" github_url: "https://github.com/DigitalPhonetics/IMS-Toucan" homepage_url: null stars: 2204 forks: 317 primary_language: "Python" license: "Apache-2.0" archived: false categories: ["model-training", "llm-frameworks", "inference-serving"] tags: ["deep-learning", "toolkit", "text-to-speech", "speech", "tts", "speech-processing", "pytorch", "speech-synthesis"] updated_at: "2026-07-11T12:09:18.066295+00:00" --- # IMS-Toucan > Controllable and fast Text-to-Speech for over 7000 languages! Controllable and fast Text-to-Speech for over 7000 languages! ## Facts - Repository: https://github.com/DigitalPhonetics/IMS-Toucan - Stars: 2,204 · Forks: 317 · Open issues: 3 · Watchers: 25 - Primary language: Python - License: Apache-2.0 - Last pushed: 2026-01-25T14:45:14+00:00 ## Trust & health _Signals computed from public GitHub metadata. Not a security guarantee._ - Maintenance: Slowing (computed 2026-07-11T12:09:13.025Z) - Security scan: No findings reported (0 critical, 0 high, 0 medium, 0 low) · last scan 2026-07-11T12:09:14.097Z - Full report: [trust report](/tools/digitalphonetics-ims-toucan/trust.md) · [JSON](https://www.graphcanon.com/api/graphcanon/tools/digitalphonetics-ims-toucan/trust) ## Categories - [Model Training](/categories/model-training.md) - [LLM Frameworks](/categories/llm-frameworks.md) - [Inference & Serving](/categories/inference-serving.md) ## Tags deep-learning, toolkit, text-to-speech, speech, tts, speech-processing, pytorch, speech-synthesis ## Category neighbours (exploratory) _Same-category tools for discovery only - not curated alternatives. 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If you're using Linux, you should have the following packages installed, or install them with apt-get if you haven't (on most distributions they come pre-installed): ``` libsndfile1 espeak-ng ffmpeg libasound-dev libportaudio2 libsqlite3-dev ``` Navigate to the directory you have cloned. We recommend creating and activating a [virtual environment](https://docs.python.org/3/library/venv.html) to install the basic requirements into. The commands below summarize everything you need to do under Linux. If you are running Windows, the second line needs to be changed, please have a look at the [venv documentation](https://docs.python.org/3/library/venv.html). ``` python -m venv source /bin/activate pip install --no-cache-dir -r requirements.txt ``` Run the second line everytime you start using the tool again to activate the virtual environment again, if you e.g. logged out in the meantime. To make use of a GPU, you don't need to do anything else on a Linux machine. On a Windows machine, have a look at [the official PyTorch website](https://pytorch.org/) for the install-command that enables GPU support. #### Storage configuration If you don't want the pretrained and trained models as well as the cache files resulting from preprocessing your datasets to be stored in the default subfolders, you can set corresponding directories globally by editing `Utility/storage_config.py` to suit your needs (the path can be relative to the repository root directory or absolute). #### Pretrained Models You don't need to use pretrained models, but it can speed things up tremendously. They will be downloaded on the fly automatically when they are needed, thanks to Hugging Face🤗 and [VB](https://github.com/Vaibhavs10) in particular. #### \[optional] eSpeak-NG eSpeak-NG is an optional requirement, that handles lots of special cases in many languages, so it's good to have. On most **Linux** environments it will be installed already, and if it is not, and you have the sufficient rights, you can install it by simply running ``` apt-get install espeak-ng ``` For **Windows**, they provide a convenient .msi installer file [on their GitHub release page](https://github.com/espeak-ng/espeak-ng/releases). After installation on non-linux systems, you'll also need to tell the phonemizer library where to find your espeak installation by setting the `PHONEMIZER_ESPEAK_LIBRARY` environment variable, which is discussed in [this issue](https://github.com/bootphon/phonemizer/issues/44#issuecomment-1008449718). For **Mac** it's unfortunately a lot more complicated. Thanks to Sang Hyun Park, here is a guide for installing it on Mac: For M1 Macs, the most convenient method to install espeak-ng onto your system is via a [MacPorts port of espeak-ng](https://ports.macports.org/port/espeak-ng/). MacPorts itself can be installed from the [MacPorts website](https://www.macports.org/install.php), which also requires Apple's [XCode](https://developer.apple.com/xcode/). Once XCode and MacPorts have been installed, you can install the port of espeak-ng via ``` sudo port install espeak-ng ``` As stated in the Windows install instructions, the espeak-ng installation will need to be set as a variable for the phonemizer library. The environment variable is `PHONEMIZER_ESPEAK_LIBRARY` as given in the [GitHub thread](https://github.com/bootphon/phonemizer/issues/44#issuecomment-1008449718) linked above. However, the espeak-ng installation file you need to set this variable to is a .dylib file rather than a .dll file on Mac. In order to locate the espeak-ng library file, you can run `port contents espeak-ng`. The ```` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/digitalphonetics-ims-toucan`](/api/graphcanon/tools/digitalphonetics-ims-toucan) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_