# Enhancing Voice Cloning Quality through Data Selection and Alignment-Based Metrics +
## 4 Voice-Cloning System  
As discussed in Section 2, the voice-cloning community has introduced various systems within the text-to-speech and voice conversion frameworks. In this study, the primary system employed for training was Tacotron-2. Although there exist newer and faster architectures for voice cloning, Tacotron-2 is a well-established system in the TTS field, acknowledged by the community. Given that the focus of this study was not to compare different architectures, but rather to evaluate the training process of a specific one and considering that this work is an extension of [3], where Tacotron-2 was utilised, it was decided to maintain the use of this architecture in the experiments conducted for this study.  
Two different approaches were used for training the multiple models used in this work. For the corpus _XRev_ and the subsets derived from the random partition of 3 h of the HQ datasets (refer to Section 5), a fine-tuning approach was used. Each model started using the weights of a publicly available model ([[https://github.com/NVIDIA/tacotron2](https://github.com/NVIDIA/tacotron2)]([https://github.com/NVIDIA/tacotron2](https://github.com/NVIDIA/tacotron2)) accessed on 4 July 2023) trained on the IJ Speech corpus [51]. These models were trained on a single GPU with a batch size of 32 and a learning rate of \(10^{-4}\) for a total of 50,000 iterations.  
In the case of the whole datasets, the training setup corresponded to the original Tacotron-2 recipe [7]: they were trained from scratch on a single GPU with a batch size of 64, using a learning rate of \(10^{-3}\) decaying to \(10^{-5}\) after 50,000 iterations, for a total of 150,000 training steps. For both approaches, the Adam Optimiser [52] was used with the following parameters: \(\beta_{1}=0.9\), \(\beta_{2}=0.999\), and \(\epsilon=10^{-6}\).  
The acoustic data were sampled to 22,050 Hz prior to training, and due to the characteristics of the writing systems of these two languages, the text input of the Spanish corpora was left as characters, but phonemes were used for the English dataset.  
Regarding the vocoder, the Hi-Fi GAN [8] architecture was chosen, which is a GAN that employs a generator based on a feed-forward WaveNet [53] supplemented with a 12 1D  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Speaker** & **Min.** & **Max** & **Mean** & **Median** & **Stdev** \\ \hline XRev & 11.68 & 20.11 & 15.46 & 15.42 & 1.09 \\ Tux _valid_ & 6.25 & 22.22 & 12.20 & 12.23 & 1.49 \\ Hi-Fi TTS 92 & 7.46 & 26.32 & 15.03 & 15.14 & 1.46 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Minimum, maximum, average, median, and standard deviation values of utterance speed calculated on _XRev_, on the _valid_ subset of the _Tux_ corpus, and on speaker 92 of the _Hi-Fi TTS_ corpus.  
Figure 3: Density of utterance speed values of the speaker _XRev_ (green), the subset _valid_ of _Tux_ (orange), and speaker 92 from _Hi-Fi TTS_ (blue).  
convolutional postnet. In this study, the UNIVERSAL_V1 model was selected as the primary vocoder. This model was trained on the LibriSpeech [43], VCTK [54], and LJ Speech [51] datasets and was publicly released by the authors ([[https://github.com/jik876/hifi-gan](https://github.com/jik876/hifi-gan)]([https://github.com/jik876/hifi-gan](https://github.com/jik876/hifi-gan)) accessed on 4 July 2023).