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