🐸 TTS roadmap

[erogol]

These are the main dev plans for 🐸 TTS.

If you want to contribute to 🐸 TTS and don't know where to start you can pick one here and start with our Contribution Guideline. We're also always here to help.

Feel free to pick one or suggest a new one.

Contributions are always welcome 💪 .

v0.1.0 Milestones

• Better model config handling [Discussion] Ideas for better model config management #21
• TTS recipes for public datasets.
• TTS trainer API to unify all the model training scripts.
• TTS, Vocoder and SpeakerEncoder model abstractions and APIs.
• Documentation for
  • Implementing a new model using 🐸 TTS.
  • Training a model on a new dataset from gecko.
  • Using Synthesizer interface on CLI or Server.
  • Extracting Spectrograms for Vocoder training.
  • Contributing a new pre-trained 🐸 TTS model.
  • Explanation for Model config parameters/

v0.2.0 Milestones

• Grapheme 2 Phoneme in-house conversion. (Thx to gruut 👍 )
• Implement VITS model.

v0.3.0 Milestones

• Implement generic ForwardTTS API.
• Implement Fast Speech model.
• Implement Fast Pitch model.

v0.4.0 Milestones

• Trainer API v2 - join the discussion
• Multi-speaker VCTK recipes for all the TTS.tts models.

v0.5.0 Milestones

• Support for multi-lingual models
• YourTTS release 🚀

v0.6.0 Milestones

• Add ESpeak support
• New Tokenizer and Phonemizer APIs New Tokenizer API #937
• New Model API Update models (Rebased) #1078
• Splitting the trainer as a separate repo 👟Trainer
• Update VITS model API
• Gradient accumulation. Accumulate grads ( Larger batch size for low gpu memory) #560 (in 👟)

v0.7.0 Milestones

• Implement Capacitron 👑 @a-froghyar 👑 @WeberJulian
• Release pretrained Capacitron

v0.8.0 Milestones

• Separate numpy transforms
• Better data sampling for VITS
• New Thorsten DE models 👑 @thorstenMueller

🏃‍♀️ Milestones along the way

• Implement End-to-end training API for ForwardTTS models a vocoder. ForwardTTSE2E implementations and related API changes  #1510
• Implement a Python voice synthesis API.
• Inject phonemes to the input text at inference. SSML support #1452
• AdaSpeech1/2 https://arxiv.org/pdf/2104.09715 and https://arxiv.org/abs/2103.00993
• Let the user pass a custom text cleaner function.
• Refactor the text cleaners for a more flexible and transparent API.
• Implement HifiGAN2 (not the vocoder)
• Implement emotion and style adaptation.
• Implement FastSpeech2 (https://arxiv.org/abs/2006.04558).
• AutoTTS 🤖 (👑 @loganhart420)
• Watermarking TTS outputs to sign against DeepFakes.
• Implement SSML v0.0.1
• ONNX and TorchScript model exports.
• TensorFlow run-time for training models.

🤖 New TTS models

• AlignTTS (@erogol)
• HiFiGAN (Hifigan Vocoder #16 👑 @rishikksh20 and @erogol)
• UnivNet Vocoder ( 👑 @rishikksh20)
• VITS paper
• FastPitch source
• Alignment Network paper
• End2End TTS combining aligner + tts + vocoder.
• Multi-Lingual TTS (Add multilingual support #11 👑 @WeberJulian )
• ParallelTacotron paper (open for contribution)
• Efficient TTS paper (open for contribution)
• Gaussian length regulator from https://arxiv.org/pdf/2010.04301.pdf (open for contribution)
• LightSpeech from https://arxiv.org/pdf/2102.04040.pdf (open for contribution)
• AdaSpeech1/2 https://arxiv.org/pdf/2104.09715 and https://arxiv.org/abs/2103.00993

[lucascassiano]

great project! Excited to see this growing!

[AndrewBarfield]

I'm learning the code/API and performing experiments. I hope to contribute soon.

I'm also wondering if I can donate (money) to Coqui?

[kdavis-coqui]

I'm learning the code/API and performing experiments. I hope to contribute soon.

I'm also wondering if I can donate (money) to Coqui?

Wow! Thanks! Humbling.

We were setting up GitHub sponsors, but the tax implications were onerous.

We're currently exploring Patreon. So stay tuned!

[agrinh]

@erogol Thanks for sharing the plans!

Do you have any thoughts (or need help to) simplifying the dependencies a bit? I'm thinking that if TTS is used as a lib installed over pip it might be nice to remove visualisation dependencies only used in notebooks, removing test/dev dependencies and moving e.g. tensorflow into extras to reduce the footprint. Personally would love to use this as a dependency rather than maintaining my own fork.

[erogol]

@agrinh Why do you need to keep your own fork exactly? It'd be better to expand the conversation on gitter if you like.

[agrinh]

@agrinh Why do you need to keep your own fork exactly? It'd be better to expand the conversation on gitter if you like.

Wow, thanks for the super fast reply. Sure, we can move the discussion to gitter.

[Sadam1195]

Please add DC-TTS to the the list of models.

DC-TTS implementation available with MIT Licence code available here
EFFICIENTLY TRAINABLE TEXT-TO-SPEECH SYSTEM BASED ON DEEP CONVOLUTIONAL NETWORKS WITH GUIDED ATTENTION paper
@erogol

[will-rice]

What were you thinking about the "TensorFlow run-time for training models"? Like giving the user the option of using TensorFlow or PyTorch? I wouldn't mind taking a stab at the TensorFlow part.

[erogol]

@will-rice the plan is to mirror what we have in torch to TF as much as possible. It'd be great if you initiate the work