first, it got error in here:

phonemizer_backend.phonemize(
                    self._expand_abbreviations(t.lower()),
                    strip=True,
                )

latest need a [] of text as input.

And the return of new phoemizer is very strange:

['bˈɔːɹn tə mˈʌltɪplˌaɪ, bˈɔːɹn tə ɡˈeɪz ˌɪntʊ nˈaɪt skˈaɪz.']

it returned something like this.

Hi, can you help me. I'm trying this code on google colab and I'm getting this error message.

Your demo code: c, c_length, phoneme = nix.tokenize("Born to multiply, born to gaze into night skies.") error: input text to phonemize() is str but it must be list of str

I change the line like this: c, c_length, phoneme = nix.tokenize(["hi"]) error: 'list' object has no attribute 'lower'

Hi, would you be interested in adding nix-tts to Hugging Face as a Gradio Demo? I see there is already a streamlit demo and I think having a Gradio version would be cool as well.

Example from other organizations: Keras: https://huggingface.co/keras-io Microsoft: https://huggingface.co/microsoft Facebook: https://huggingface.co/facebook

Example spaces with repos: github: https://github.com/salesforce/BLIP Spaces: https://huggingface.co/spaces/salesforce/BLIP

github: https://github.com/facebookresearch/omnivore Spaces: https://huggingface.co/spaces/akhaliq/omnivore

and here are guides for adding spaces/models/datasets to your org

How to add a Space: https://huggingface.co/blog/gradio-spaces how to add models: https://huggingface.co/docs/hub/adding-a-model uploading a dataset: https://huggingface.co/docs/datasets/upload_dataset.html

Please let us know if you would be interested and if you have any questions, we can also help with the technical implementation.

Hello, according to your paper and github page, the parameter size is either 5.23M(deterministic predictor) or 6.03MB(stochastic predictor). However, the parameter size of the pretrained model far exceeds those. In the text & latent encoder, there are total 18 convs, each having a weight with shape 192x192x5, total (192x192x5)x18x4 Byte(since a float variable is 4 Byte) = 12.65625 MB. If we include bias of conv layers, an embedding layer, a projection layer, a duration predictor module, norm layers and a decoder module, the total parameter will be even bigger. Can you tell me how you calculated the parameter size?

EDIT I noticed that the given value was the number of parameters, not the size of parameters. Thank you.

Dear Developers, I spent some time trying to figure out how to compile NIX-TTS using Apache TVM. The idea was the same in concept to your compilation for Rasberry Pi. I discovered the following:

1. Apache TVM requires inputs to have static not dynamic shapes.
2. NIX-TTS models differ in how shapes of inputs and outputs are constructed based on being detoministic or stochastic model.

It is possible to change the input shape to static, using for example the Sclblonnx package. I am unfortunately not that much involved in neural network frameworks to be able to figure out the required fixes to allow compilation.

Since the detoministic model is faster and its voice quality is still fantastic, I had alook at the inputs:

1. The "encoder" network most important inputs are both dynamic: ** Input: "c" as INT64 with dimensions: [0, 0] ** Input: "c_lengths" as INT64 with dimension: [0]
2. The "decoder" network only input is as follows: ** Input: "z" as FLOAT with dimensions: [0, 0, 0]

What would be the procedure to make the inputs static, if it is possible for the detoministic model? What would be the same case for the stochastic model if it is possible. Last but not least: since the detoministic model's encoder output is a dynamic shape as the decoder input, would it be possible to merge both graphs as a single model file?

While I don't know if my case is possible to be implemented, I will be more than happy to descripe the procedure or publish the compiled model.

Thank you in advance for any hints and feedback...

Just curious - did you try training the same model architecture end-to-end from scratch (i.e. not distilling from VITS), and if so, are there any audio comparison samples available?

I just tested nix-tts on Raspberry Pi 4, pretty impressive :+1: Realtime factor is 0.5 btw (2x faster than realtime), but I had some trouble writing the audio buffer into a file because the example depends on IPython which is a) not in the requirements and b) probably meant for Jupyter or Huggingface (?) not some local test.

I tried to replace it with wave (because it is lightweight) like this:

import wave
...
wf = wave.open('test.wav', 'wb')
wf.setnchannels(1)
wf.setsampwidth(2)
wf.setframerate(22050)
wf.setnframes(len(xw[0,0]))
wf.writeframesraw(xw[0,0].tobytes())

I does work somehow but there is obviously something wrong in the encoding since I'm getting mostly noise from that code.

Since I couldn't solve the issue I gave up and used: scipy.io.wavfile.write("test.wav", 22050, xw[0,0]) in the end. It works but you have to install a bazillion more dependencies which takes forever on RPi4.

So can you recommend any working alternative to scipy (which is not librosa ^^)?

nix is quite impressive. I tried it's fast and natural compare with same params-level model.

However, seems the distillation part is not open sourced, Just wonder if these part can available or not? so that users can compress own trained model.