Package: tortoise-tts
TorToiSeTortoise is a text-to-speech program built with the following priorities:Strong multi-voice capabilities.Highly realistic prosody and intonation.This repo contains all the code needed to run Tortoise TTS in inference mode.Manuscript:https://arxiv.org/abs/2305.07243Huggingface spacePlease duplicate space if you don't want to wait in a queue.https://huggingface.co/spaces/Manmay/tortoise-ttsVersion historyv3.0.0; 2023/10/18Added fast inference for tortoise with hifidecoder(inspired by xtts bycoquiTTS🐸, check their multi-lingual model)v2.8.0; 2023/9/13Added custom tokenizer for non-english modelsv2.7.0; 2023/7/26Bug fixesAdded Apple Silicon SupportUpdated Transformer versionv2.6.0; 2023/7/26Bug fixesv2.5.0; 2023/7/09Added kv_cache support 5x fasterAdded deepspeed support 10x fasterAdded half precision supportv2.4.0; 2022/5/17Removed CVVP model. Found that it does not, in fact, make an appreciable difference in the output.Add better debugging support; existing tools now spit out debug files which can be used to reproduce bad runs.v2.3.0; 2022/5/12New CLVP-large model for further improved decoding guidance.Improvements to read.py and do_tts.py (new options)v2.2.0; 2022/5/5Added several new voices from the training set.Automated redaction. Wrap the text you want to use to prompt the model but not be spoken in brackets.Bug fixesv2.1.0; 2022/5/2Added ability to produce totally random voices.Added ability to download voice conditioning latent via a script, and then use a user-provided conditioning latent.Added ability to use your own pretrained models.Refactored directory structures.Performance improvements & bug fixes.What's in a name?I'm naming my speech-related repos after Mojave desert flora and fauna. Tortoise is a bit tongue in cheek: this model
is insanely slow. It leverages both an autoregressive decoderanda diffusion decoder; both known for their low
sampling rates. On a K80, expect to generate a medium sized sentence every 2 minutes.DemosSeethis pagefor a large list of example outputs.Cool application of Tortoise+GPT-3 (not by me):https://twitter.com/lexman_aiUsage guideLocal InstallationIf you want to use this on your own computer, you must have an NVIDIA GPU.On Windows, Ihighlyrecommend 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.First, install miniconda:https://docs.conda.io/en/latest/miniconda.htmlThen run the following commands, using anaconda prompt as the terminal (or any other terminal configured to work with conda)This will:create conda environment with minimal dependencies specifiedactivate the environmentinstall pytorch with the command provided here:https://pytorch.org/get-started/locally/clone tortoise-ttschange the current directory to tortoise-ttsrun tortoise python setup install scriptcondacreate--nametortoisepython=3.9numbainflect
condaactivatetortoise
condainstallpytorchtorchvisiontorchaudiopytorch-cuda=11.7-cpytorch-cnvidia
condainstalltransformers=4.29.2
gitclonehttps://github.com/neonbjb/tortoise-tts.gitcdtortoise-tts
pythonsetup.pyinstallOptionally, pytorch can be installed in the base environment, so that other conda environments can use it too. To do this, simply send theconda install pytorch...line before activating the tortoise environment.Note:When you want to use tortoise-tts, you will always have to ensure thetortoiseconda environment is activated.If you are on windows, you may also need to install pysoundfile:conda install -c conda-forge pysoundfileDockerAn easy way to hit the ground running and a good jumping off point depending on your use case.gitclonehttps://github.com/neonbjb/tortoise-tts.gitcdtortoise-tts

dockerbuild.-ttts

dockerrun--gpusall\-eTORTOISE_MODELS_DIR=/models\-v/mnt/user/data/tortoise_tts/models:/models\-v/mnt/user/data/tortoise_tts/results:/results\-v/mnt/user/data/.cache/huggingface:/root/.cache/huggingface\-v/root:/work\-itttsThis gives you an interactive terminal in an environment that's ready to do some tts. Now you can explore the different interfaces that tortoise exposes for tts.For example:cdapp
condaactivatetortoisetimepythontortoise/do_tts.py\--output_path/results\--presetultra_fast\--voicegeralt\--text"Time flies like an arrow; fruit flies like a bananna."Apple SiliconOn MacOS 13+ with M1/M2 chips you need to install the nighly version of pytorch, as stated in the official page you can do:pip3install--pretorchtorchvisiontorchaudio--index-urlhttps://download.pytorch.org/whl/nightly/cpuBe sure to do that after you activate the environment. If you don't use conda the commands would look like this:python3.10-mvenv.venvsource.venv/bin/activate
pipinstallnumbainflectpsutil
pipinstall--pretorchtorchvisiontorchaudio--index-urlhttps://download.pytorch.org/whl/nightly/cpu
pipinstalltransformers
gitclonehttps://github.com/neonbjb/tortoise-tts.gitcdtortoise-tts
pipinstall.Be aware that DeepSpeed is disabled on Apple Silicon since it does not work. The flag--use_deepspeedis ignored.
You may need to prependPYTORCH_ENABLE_MPS_FALLBACK=1to the commands below to make them work since MPS does not support all the operations in Pytorch.do_tts.pyThis script allows you to speak a single phrase with one or more voices.pythontortoise/do_tts.py--text"I'm going to speak this"--voicerandom--presetfastfaster inference read.pyThis script provides tools for reading large amounts of text.pythontortoise/read_fast.py--textfile<yourtexttoberead>--voicerandomread.pyThis script provides tools for reading large amounts of text.pythontortoise/read.py--textfile<yourtexttoberead>--voicerandomThis 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-runningread.pywith the --regenerate
argument.APITortoise can be used programmatically, like so:reference_clips=[utils.audio.load_audio(p,22050)forpinclips_paths]tts=api.TextToSpeech()pcm_audio=tts.tts_with_preset("your text here",voice_samples=reference_clips,preset='fast')To use deepspeed:reference_clips=[utils.audio.load_audio(p,22050)forpinclips_paths]tts=api.TextToSpeech(use_deepspeed=True)pcm_audio=tts.tts_with_preset("your text here",voice_samples=reference_clips,preset='fast')To use kv cache:reference_clips=[utils.audio.load_audio(p,22050)forpinclips_paths]tts=api.TextToSpeech(kv_cache=True)pcm_audio=tts.tts_with_preset("your text here",voice_samples=reference_clips,preset='fast')To run model in float16:reference_clips=[utils.audio.load_audio(p,22050)forpinclips_paths]tts=api.TextToSpeech(half=True)pcm_audio=tts.tts_with_preset("your text here",voice_samples=reference_clips,preset='fast')for Faster runs use all three:reference_clips=[utils.audio.load_audio(p,22050)forpinclips_paths]tts=api.TextToSpeech(use_deepspeed=True,kv_cache=True,half=True)pcm_audio=tts.tts_with_preset("your text here",voice_samples=reference_clips,preset='fast')Voice customization guideTortoise 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.Provided voicesThis 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 mimicking, take a look at the others.Adding a new voiceTo add new voices to Tortoise, you will need to do the following: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.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.Save the clips as a WAV file with floating point format and a 22,050 sample rate.Create a subdirectory in voices/Put your clips in that subdirectory.Run tortoise utilities with --voice=<your_subdirectory_name>.Picking good reference clipsAs mentioned above, your reference clips have a profound impact on the output of Tortoise. Following are some tips for picking
good clips:Avoid clips with background music, noise or reverb. These clips were removed from the training dataset. Tortoise is unlikely to do well with them.Avoid speeches. These generally have distortion caused by the amplification system.Avoid clips from phone calls.Avoid clips that have excessive stuttering, stammering or words like "uh" or "like" in them.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.The text being spoken in the clips does not matter, but diverse text does seem to perform better.Advanced UsageGeneration settingsTortoise 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. Seeapi.ttsfor a full list.Prompt engineeringSome 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 latentTortoise 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 voicesUse the scriptget_conditioning_latents.pyto 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 speechAfter 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-detectOut 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 architectureTortoise 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. I've assembled a write-up of the system architecture here:https://nonint.com/2022/04/25/tortoise-architectural-design-doc/TrainingThese models were trained on my "homelab" server with 8 RTX 3090s over the course of several months. They were trained on a dataset consisting of
~50k hours of speech data, most of which was transcribed byocotillo. Training was done on my ownDLAStrainer.I currently do not have plans to release the training configurations or methodology. See the next section..Ethical ConsiderationsTortoise v2 works considerably better than I had planned. When I began hearing some of the outputs of the last few versions, I began
wondering whether or not I had an ethically unsound project on my hands. The ways in which a voice-cloning text-to-speech system
could be misused are many. It doesn't take much creativity to think up how.After some thought, I have decided to go forward with releasing this. Following are the reasons for this choice:It is primarily good at reading books and speaking poetry. Other forms of speech do not work well.It was trained on a dataset which does not have the voices of public figures. While it will attempt to mimic these voices if they are provided as references, it does not do so in such a way that most humans would be fooled.The above points could likely be resolved by scaling up the model and the dataset. For this reason, I am currently withholding details on how I trained the model, pending community feedback.I am releasing a separate classifier model which will tell you whether a given audio clip was generated by Tortoise or not. Seetortoise-detectabove.If I, a tinkerer with a BS in computer science with a ~$15k computer can build this, then any motivated corporation or state can as well. I would prefer that it be in the open and everyone know the kinds of things ML can do.DiversityThe diversity expressed by ML models is strongly tied to the datasets they were trained on.Tortoise was trained primarily on a dataset consisting of audiobooks. I made no effort to
balance diversity in this dataset. For this reason, Tortoise will be particularly poor at generating the voices of minorities
or of people who speak with strong accents.Looking forwardTortoise v2 is about as good as I think I can do in the TTS world with the resources I have access to. A phenomenon that happens when
training very large models is that as parameter count increases, the communication bandwidth needed to support distributed training
of the model increases multiplicatively. On enterprise-grade hardware, this is not an issue: GPUs are attached together with
exceptionally wide buses that can accommodate this bandwidth. I cannot afford enterprise hardware, though, so I am stuck.I want to mention here
that I think Tortoise could be alotbetter. The three major components of Tortoise are either vanilla Transformer Encoder stacks
or Decoder stacks. Both of these types of models have a rich experimental history with scaling in the NLP realm. I see no reason
to believe that the same is not true of TTS.AcknowledgementsThis project has garnered more praise than I expected. I am standing on the shoulders of giants, though, and I want to
credit a few of the amazing folks in the community that have helped make this happen:Hugging Face, who wrote the GPT model and the generate API used by Tortoise, and who hosts the model weights.Ramesh et alwho authored the DALLE paper, which is the inspiration behind Tortoise.Nichol and Dhariwalwho authored the (revision of) the code that drives the diffusion model.Jang et alwho developed and open-sourced univnet, the vocoder this repo uses.Kim and Jungwho implemented univnet pytorch model.lucidrainswho writes awesome open source pytorch models, many of which are used here.Patrick von Platenwhose guides on setting up wav2vec were invaluable to building my dataset.NoticeTortoise was built entirely by me using my own hardware. My employer was not involved in any facet of Tortoise's development.If you use this repo or the ideas therein for your research, please cite it! A bibtex entree can be found in the right pane on GitHub.