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nickovchinnikov committed Feb 8, 2024
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2 changes: 1 addition & 1 deletion advanced-demo.ipynb
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"from sklearn.preprocessing import StandardScaler\n",
"import torch\n",
"\n",
"from model.helpers.tools import get_mask_from_lengths\n",
"from models.helpers.tools import get_mask_from_lengths\n",
"from training.datasets import LibriTTSDatasetAcoustic\n",
"from training.loss import FastSpeech2LossGen, Metrics\n",
"from training.modules import AcousticModule\n",
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Expand Up @@ -17,28 +17,28 @@ Description of the training process. Docs, ideas and examples for the training p

## Model

### [Acoustic Model](model/acoustic_model/readme.md)
### [Acoustic Model](models/acoustic_model/readme.md)

The [DelightfulTTS: The Microsoft Speech Synthesis System for Blizzard Challenge 2021](https://arxiv.org/abs/2110.12612) AcousticModel class represents a PyTorch module for an acoustic model in text-to-speech (TTS).
The acoustic model is responsible for predicting speech signals from phoneme sequences.

The model comprises multiple sub-modules including encoder, decoder and various prosody encoders and predictors.
Additionally, a pitch and length adaptor are instantiated.

### [Reference Encoder](model/reference_encoder/readme.md)
### [Reference Encoder](models/reference_encoder/readme.md)

Similar to Tacotron model, the reference encoder is used to extract the high-level features from the reference

### [Convolution Blocks](model/conv_blocks/readme.md)
### [Convolution Blocks](models/conv_blocks/readme.md)

This part of the code responsible for the convolution blocks used in the model. Based on the FastSpeech models from [FastSpeech: Fast, Robust and Controllable Text to Speech](https://arxiv.org/abs/1905.09263) by Yi Ren et al and [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://arxiv.org/abs/2006.04558) by Yi Ren et al.

### [Attention](model/attention/readme.md)
### [Attention](models/attention/readme.md)

Attention mechanizm used in the model. The concept of "global style tokens" (GST) was introduced in
[Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis](https://arxiv.org/abs/1803.09017) by Yuxuan Wang et al.

### [Univnet](model/univnet/readme.md)
### [Univnet](models/univnet/readme.md)

[UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation](https://arxiv.org/abs/2106.07889v1)

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