# Introduction 🐢

TorToiSe is a text-to-speech program built in April 2022 by jbetker@. TorToiSe is open source, with trained model weights available at https://github.com/neonbjb/tortoise-tts

This page demonstrates some of the results of TorToiSe.

# Handpicked results 🐢

Following are several particularly good results generated by the model.

## Short-form

## Long-form

# Comparisons (with the LJSpeech voice): 🐢

LJSpeech is a popular dataset used to train small-scale TTS models. TorToiSe is a multi-voice model, following is how it renders the LJSpeech voice with and without fine-tuning, compared with results for the same text from the popular Tacotron2 model paired with the Waveglow vocoder.

Tacotron2+Waveglow | TorToiSe | TorToiSe Finetuned

NaturalVoice is a SOTA TTS engine developed by Microsoft Research Asia in May 2022. It features realistic prosody and end-to-end generation with no need for a vocoder. While not much has actually been released about this model other than five samples, those samples are quite good and I would consider this the most competitive TTS engine out there right now.

Natural Voice | TorToiSe Finetuned

It is important to note that it is not actually fair to compare any of these models: Tortoise is a multi-voice probabilistic model trained on millions of hours of speech with an exceptionally slow inference time. Tacotron and NaturalVoice are efficient, fast, single-voice models trained on 24 hours of speech. Unfortunately, there isn't much in the way of actually comparable research to Tortoise.

# All Results 🐢

Following are all the results from which the hand-picked results were drawn from. Also included is the reference audio that the program is trying to mimic. This will give you a better sense of how TorToiSe really performs.

## Short-form

text | angie | daniel | deniro | emma | freeman | geralt | halle | jlaw | lj | myself | pat | snakes | tom | train_atkins | train_dotrice | train_kennard | weaver | william
reference clip |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
autoregressive_ml |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
bengio_it_needs_to_know_what_is_bad |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
dickinson_stop_for_death |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
espn_basketball |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
frost_oar_to_oar |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
frost_road_not_taken |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
gatsby_and_so_we_beat_on |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
harrypotter_differences_of_habit_and_language |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
i_am_a_language_model |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
melodie_kao |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
nyt_covid |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
real_courage_is_when_you_know_your_licked |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
rolling_stone_review |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
spacecraft_interview |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
tacotron2_sample1 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
tacotron2_sample2 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
tacotron2_sample3 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
tacotron2_sample4 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
watts_this_is_the_real_secret_of_life |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
wilde_nowadays_people_know_the_price |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |

## Long-form

Angelina:
Craig:
Deniro:
Emma:
Freeman:
Geralt:
Halle:
Jlaw:
LJ:
Myself:
Pat:
Snakes:
Tom:
Weaver:
William:

# Prompt Engineering 🐢

Tortoise is capable of "prompt-engineering" in that tone and prosody is affected by the emotions inflected in the words fed to the program. For example, prompting the model with "[I am so angry,] I went to the park and threw a ball" will result in it outputting "I went to the park and threw the ball" with an angry tone.

Following are a few examples of different prompts. The effect is subtle, but is definitely there. Many voices are less effected by this.

Angry:
Sad:
Happy:
Scared: