| # Tortoise-TTS | |
| Tortoise TTS is an experimental text-to-speech program that uses recent machine learning techniques to generate | |
| high-quality speech samples. | |
| This repo contains all the code needed to run Tortoise TTS in inference mode. | |
| ## 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 speech alignment model and a diffusion model, both of which | |
| are known for their slow inference. It also performs CLIP sampling, which slows things down even further. You can | |
| expect ~5 seconds of speech to take ~30 seconds to produce on the latest hardware. Still, the results are pretty cool. | |
| ## What the heck is this? | |
| Tortoise TTS is inspired by OpenAI's DALLE, applied to speech data. It is made up of 4 separate models that work together. | |
| These models are all derived from different repositories which are all linked. All the models have been modified | |
| for this use case (some substantially so). | |
| First, an autoregressive transformer stack predicts discrete speech "tokens" given a text prompt. This model is very | |
| similar to the GPT model used by DALLE, except it operates on speech data. | |
| Based on: [GPT2 from Transformers](https://huggingface.co/docs/transformers/model_doc/gpt2) | |
| Next, a CLIP model judges a batch of outputs from the autoregressive transformer against the provided text and stack | |
| ranks the outputs according to most probable. You could use greedy or beam-search decoding but in my experience CLIP | |
| decoding creates considerably better results. | |
| Based on [CLIP from lucidrains](https://github.com/lucidrains/DALLE-pytorch/blob/main/dalle_pytorch/dalle_pytorch.py) | |
| Next, the speech "tokens" are decoded into a low-quality MEL spectrogram using a VQVAE. | |
| Based on [VQVAE2 by rosinality](https://github.com/rosinality/vq-vae-2-pytorch) | |
| Finally, the output of the VQVAE is further decoded by a UNet diffusion model into raw audio, which can be placed in | |
| a wav file. | |
| Based on [ImprovedDiffusion by openai](https://github.com/openai/improved-diffusion) | |
| ## How do I use this? | |
| Check out the colab: https://colab.research.google.com/drive/1wVVqUPqwiDBUVeWWOUNglpGhU3hg_cbR?usp=sharing | |
| Or on a computer with a GPU (with >=16GB of VRAM): | |
| ```shell | |
| git clone https://github.com/neonbjb/tortoise-tts.git | |
| cd tortoise-tts | |
| pip install -r requirements.txt | |
| python do_tts.py | |
| ``` | |
| ## Hand-picked TTS samples | |
| I generated ~250 samples from 23 text prompts and 8 voices. The text prompts have never been seen by the model. The | |
| voices were pulled from the training set. | |
| All of the samples can be found in the results/ folder of this repo. I handpicked a few to show what the model is capable of: | |
| - [Atkins - Road not taken](results/favorites/atkins_road_not_taken.wav) | |
| - [Dotrice - Rolling Stone interview](results/favorites/dotrice_rollingstone.wav) | |
| - [Dotrice - 'Ornaments' from tacotron test set](results/favorites/dotrice_tacotron_samp1.wav) | |
| - [Kennard - 'Acute emotional intelligence' from tacotron test set](results/favorites/kennard_tacotron_samp2.wav) | |
| - [Mol - Because I could not stop for death](results/favorites/mol_dickenson.wav) | |
| - [Mol - Obama](results/favorites/mol_obama.wav) | |
| Prosody is remarkably good for poetry, despite the fact that it was never trained on poetry. | |
| ## How do I train this? | |
| Frankly - you don't. Building this model has been a labor of love for me, consuming most of my 6 RTX3090s worth of | |
| resources for the better part of 6 months. It uses a dataset I've gathered, refined and transcribed that consists of | |
| a lot of audio data which I cannot distribute because of copywrite or no open licenses. | |
| With that said, I'm willing to help you out if you really want to give it a shot. DM me. | |
| ## Looking forward | |
| I'm not satisfied with this yet. Treat this as a "sneak peek" and check back in a couple of months. I think the concept | |
| is sound, but there are a few hurdles to overcome to get sample quality up. I have been doing major tweaks to the | |
| diffusion model and should have something new and much better soon. |