README.md

TorToiSe

Tortoise is a text-to-speech program built with the following priorities:

1. Strong multi-voice capabilities.
2. Highly realistic prosody and intonation.

This repo contains all the code needed to run Tortoise TTS in inference mode.

Colab

Colab is the easiest way to try this out. I've put together a notebook you can use here: https://colab.research.google.com/drive/1wVVqUPqwiDBUVeWWOUNglpGhU3hg_cbR?usp=sharing

Local Installation

If you want to use this on your own computer, you must have an NVIDIA GPU.

First, install pytorch using these instructions: https://pytorch.org/get-started/locally/. On Windows, I highly recommend using the Conda installation path. I have been told that if you do not do this, you will spend a lot of time chasing dependency problems.

Next, install TorToiSe and it's dependencies:

git clone https://github.com/neonbjb/tortoise-tts.git
cd tortoise-tts
python setup.py install

If you are on windows, you will also need to install pysoundfile: conda install -c conda-forge pysoundfile

do_tts.py

This script allows you to speak a single phrase with one or more voices.

python tortoise/do_tts.py --text "I'm going to speak this" --voice random --preset fast

read.py

This script provides tools for reading large amounts of text.

python tortoise/read.py --textfile <your text to be read> --voice random

This will break up the textfile into sentences, and then convert them to speech one at a time. It will output a series of spoken clips as they are generated. Once all the clips are generated, it will combine them into a single file and output that as well.

Sometimes Tortoise screws up an output. You can re-generate any bad clips by re-running read.py with the --regenerate argument.

API

Tortoise can be used programmatically, like so:

reference_clips = [utils.audio.load_audio(p, 22050) for p in clips_paths]
tts = api.TextToSpeech()
pcm_audio = tts.tts_with_preset("your text here", voice_samples=reference_clips, preset='fast')

Voice customization guide

Tortoise was specifically trained to be a multi-speaker model. It accomplishes this by consulting reference clips.

These reference clips are recordings of a speaker that you provide to guide speech generation. These clips are used to determine many properties of the output, such as the pitch and tone of the voice, speaking speed, and even speaking defects like a lisp or stuttering. The reference clip is also used to determine non-voice related aspects of the audio output like volume, background noise, recording quality and reverb.

Random voice

I've included a feature which randomly generates a voice. These voices don't actually exist and will be random every time you run it. The results are quite fascinating and I recommend you play around with it!

You can use the random voice by passing in 'random' as the voice name. Tortoise will take care of the rest.

For the those in the ML space: this is created by projecting a random vector onto the voice conditioning latent space.

Provided voices

This repo comes with several pre-packaged voices. Voices prepended with "train_" came from the training set and perform far better than the others. If your goal is high quality speech, I recommend you pick one of them. If you want to see what Tortoise can do for zero-shot mimicing, take a look at the others.

Adding a new voice

To add new voices to Tortoise, you will need to do the following:

1. Gather audio clips of your speaker(s). Good sources are YouTube interviews (you can use youtube-dl to fetch the audio), audiobooks or podcasts. Guidelines for good clips are in the next section.
2. Cut your clips into ~10 second segments. You want at least 3 clips. More is better, but I only experimented with up to 5 in my testing.
3. Save the clips as a WAV file with floating point format and a 22,050 sample rate.
4. Create a subdirectory in voices/
5. Put your clips in that subdirectory.
6. Run tortoise utilities with --voice=<your_subdirectory_name>.

Picking good reference clips

As mentioned above, your reference clips have a profound impact on the output of Tortoise. Following are some tips for picking good clips:

1. Avoid clips with background music, noise or reverb. These clips were removed from the training dataset. Tortoise is unlikely to do well with them.
2. Avoid speeches. These generally have distortion caused by the amplification system.
3. Avoid clips from phone calls.
4. Avoid clips that have excessive stuttering, stammering or words like "uh" or "like" in them.
5. Try to find clips that are spoken in such a way as you wish your output to sound like. For example, if you want to hear your target voice read an audiobook, try to find clips of them reading a book.
6. The text being spoken in the clips does not matter, but diverse text does seem to perform better.

Advanced Usage

Generation settings

Tortoise is primarily an autoregressive decoder model combined with a diffusion model. Both of these have a lot of knobs that can be turned that I've abstracted away for the sake of ease of use. I did this by generating thousands of clips using various permutations of the settings and using a metric for voice realism and intelligibility to measure their effects. I've set the defaults to the best overall settings I was able to find. For specific use-cases, it might be effective to play with these settings (and it's very likely that I missed something!)

These settings are not available in the normal scripts packaged with Tortoise. They are available, however, in the API. See api.tts for a full list.

Prompt engineering

Some people have discovered that it is possible to do prompt engineering with Tortoise! For example, you can evoke emotion by including things like "I am really sad," before your text. I've built an automated redaction system that you can use to take advantage of this. It works by attempting to redact any text in the prompt surrounded by brackets. For example, the prompt "[I am really sad,] Please feed me." will only speak the words "Please feed me" (with a sad tonality).

Playing with the voice latent

Tortoise ingests reference clips by feeding them through individually through a small submodel that produces a point latent, then taking the mean of all of the produced latents. The experimentation I have done has indicated that these point latents are quite expressive, affecting everything from tone to speaking rate to speech abnormalities.

This lends itself to some neat tricks. For example, you can combine feed two different voices to tortoise and it will output what it thinks the "average" of those two voices sounds like.

Generating conditioning latents from voices

Use the script get_conditioning_latents.py to extract conditioning latents for a voice you have installed. This script will dump the latents to a .pth pickle file. The file will contain a single tuple, (autoregressive_latent, diffusion_latent).

Alternatively, use the api.TextToSpeech.get_conditioning_latents() to fetch the latents.

Using raw conditioning latents to generate speech

After you've played with them, you can use them to generate speech by creating a subdirectory in voices/ with a single ".pth" file containing the pickled conditioning latents as a tuple (autoregressive_latent, diffusion_latent).

Tortoise-detect

Out of concerns that this model might be misused, I've built a classifier that tells the likelihood that an audio clip came from Tortoise.

This classifier can be run on any computer, usage is as follows:

python tortoise/is_this_from_tortoise.py --clip=<path_to_suspicious_audio_file>

This model has 100% accuracy on the contents of the results/ and voices/ folders in this repo. Still, treat this classifier as a "strong signal". Classifiers can be fooled and it is likewise not impossible for this classifier to exhibit false positives.

Model architecture

Tortoise TTS is inspired by OpenAI's DALLE, applied to speech data and using a better decoder. It is made up of 5 separate models that work together: https://nonint.com/2022/04/25/tortoise-architectural-design-doc/