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Kenoobi Humanlike Voices: Revolutionizing
Audio Content Creation with AI-Generated
Human-Like Voices in 140+ Languages
Prepared by Kenoobi AI
Allan Mukhwana, Evans Omondi, Diana Ambale, Macrine
Akinyi, Kelvin Mukhwana, Richard Omollo, Ben Chege
Abstract:
Kenoobi Humanlike Voices is an AI model designed for creating human-like
voices from text. With a major focus on African languages, the system is able to
generate audio in 140+ languages. This report provides a detailed overview of
the technical aspects of the model, including voice actor selection, transcript
creation, audio recordings, AI voice processing, training details, experiments,
conclusions, limitations, and technology misuse. The AI voice processing section
covers casual convolutions, speaker identity, encoder, decoder, converter, and
synthesis network.
Introduction:
Audio creation is an essential aspect of modern content production, and it
requires high-quality human-like voices to make the content more engaging and
accessible. However, creating human-like voices can be time-consuming and
expensive, particularly in languages with limited resources. AI models can be
used to generate human-like voices from text, which can reduce costs and
improve efficiency. Kenoobi Humanlike Voices is one such AI model that is
designed for generating human-like voices from text in over 140 languages, with
a focus on African languages. In this report, we discuss the technical aspects of
the Kenoobi Humanlike Voices model.
Voice Actor Selection:
The first step in creating human-like voices using the Kenoobi Humanlike Voices
model is selecting the appropriate voice actors. Voice actors with a wide range of
voice types and styles are selected based on their ability to convey emotions and
nuances in speech. The voice actors are required to record a large number of
audio samples for training the AI model.
Transcript Creation:
The next step in the audio creation process is to create transcripts for the audio
recordings. These transcripts serve as the input to the AI model.
The transcripts are created using natural language processing techniques, and
they include information about the tone, intonation, and emphasis of the speech.
Audio Recordings:
The voice actors record a large number of audio samples in a variety of
languages.
These recordings are used to train the AI model to generate human-like voices
from text. The recordings are carefully edited to remove background noise and
other unwanted sounds.
AI Voice Processing:
The AI voice processing section of the Kenoobi Humanlike Voices model
includes casual convolutions, speaker identity, encoder, decoder, converter, and
synthesis network.
●Casual convolutions: Casual convolutions are used to process the input
transcripts. These convolutions are designed to capture the temporal
dependencies between words and phrases in the text.
●Speaker identity: Speaker identity is an important aspect of generating
human-like voices. The Kenoobi Humanlike Voices model uses a speaker
embedding technique to capture the unique characteristics of each voice
actor.
●Encoder: The encoder takes the input transcripts and processes them into
a fixed-dimensional vector that captures the semantic information of the
text.
●Decoder: The decoder takes the output of the encoder and generates a
mel-spectrogram, which is a representation of the audio signal that can be
used to generate the final audio.
●Converter: The converter takes the mel-spectrogram and converts it into
a waveform that can be played as audio.
●Synthesis Network: The synthesis network combines the outputs of the
encoder, decoder, and converter to generate the final audio output.
Training Details:
The training process of the Kenoobi Humanlike Voices model involved a
combination of supervised and unsupervised learning techniques to achieve
optimal performance in generating human-like voices from text.
●Data Collection: To train the model, a large dataset of audio recordings
and corresponding transcripts was collected. The dataset included
recordings in various languages, with a major focus on African languages.
These audio recordings covered a wide range of voice types, styles, and
emotions to ensure diversity and representativeness.
●Supervised Learning: Supervised learning was an integral part of
training the Kenoobi Humanlike Voices model. During this phase, the
model was trained using paired data, where the input was the textual
transcript, and the target was the corresponding audio recording. The
model learned to map the textual representation to the desired audio
output. This supervised training process helped the model capture the
relationship between the text and the corresponding voice characteristics,
including pronunciation, intonation, and other speech attributes.
●Loss Functions: To optimize the performance of the model during
training, various loss functions were employed. These loss functions
quantified the discrepancy between the generated audio and the target
audio. One commonly used loss function was the Mean Squared Error
(MSE), which calculated the average squared difference between the
generated and target audio signals. Other loss functions, such as the
Binary Cross-Entropy (BCE) or the Categorical Cross-Entropy (CCE),
could also be utilized depending on the specific requirements of the
training process.
●Unsupervised Learning: In addition to supervised learning, the Kenoobi
Humanlike Voices model also leveraged unsupervised learning techniques.
Unsupervised learning allowed the model to learn patterns and
representations from the unannotated audio data. This helped the model
capture additional aspects of speech, such as subtle variations in
pronunciation, tonal quality, and natural speech dynamics.
●Preprocessing and Feature Extraction: Before training the model, the
audio recordings and textual transcripts underwent preprocessing. This
involved cleaning and normalizing the audio data, removing noise, and
aligning the text with the corresponding audio segments. Feature
extraction techniques, such as mel-frequency cepstral coefficients
(MFCCs), could be applied to extract relevant acoustic features from the
audio signals. These features served as input representations for the
model.
●Training Optimization: During the training process, optimization
techniques such as gradient descent and backpropagation were employed
to adjust the model's parameters and minimize the loss functions. The
model underwent multiple iterations of training, gradually refining its
ability to generate human-like voices from text.
●Validation and Evaluation: To ensure the effectiveness and
generalizability of the trained model, validation and evaluation steps were
carried out. Validation involved assessing the model's performance on a
separate validation dataset, which was not used during training.
Evaluation metrics such as perceptual evaluation of speech quality
(PESQ), mean opinion score (MOS), or subjective listening tests could be
employed to measure the quality and naturalness of the generated audio.
●Hyperparameter Tuning: Hyperparameters, such as learning rate, batch
size, and network architecture, played a crucial role in determining the
performance of the Kenoobi Humanlike Voices model. These
hyperparameters were carefully selected and tuned to achieve the best
possible results. Techniques like grid search, random search, or Bayesian
optimization could be used to find the optimal combination of
hyperparameters.
The training process of the Kenoobi Humanlike Voices model was a complex and
iterative procedure that leveraged both supervised and unsupervised learning
techniques. It involved collecting a diverse dataset, applying preprocessing
techniques, utilizing loss functions to guide the learning process, and optimizing
the model's parameters. Through careful training and evaluation, the model
aimed to generate high-quality, human-like voices from textual input.
Experiments:
The Kenoobi Humanlike Voices model underwent rigorous testing and
experimentation to assess its performance and effectiveness in generating
human-like voices from text. These experiments involved testing the model on
various languages and datasets to evaluate its capabilities and identify potential
areas for improvement.
●Language Coverage: One important aspect of the experiments was to
evaluate the model's performance across different languages. The Kenoobi
Humanlike Voices model aimed to provide voice generation capabilities in
a wide range of languages, with a particular focus on African languages.
To assess its language coverage, the model was tested on datasets
containing diverse languages, including both widely spoken and
less-resourced languages. By testing the model on a variety of languages,
the researchers could evaluate its ability to handle different phonetic
systems, accents, and linguistic variations.
●Dataset Evaluation: The performance of the Kenoobi Humanlike Voices
model was evaluated using diverse datasets containing audio recordings
and corresponding textual transcripts. These datasets were carefully
selected to represent different domains, voice characteristics, and
linguistic contexts. The model was tested on both publicly available
datasets and proprietary datasets, ensuring a comprehensive evaluation of
its capabilities across various data sources. The evaluation involved
comparing the generated audio with the target audio and assessing
factors such as pronunciation accuracy, intonation, naturalness, and
overall quality.
●Comparative Analysis: To gain a deeper understanding of the model's
performance, comparative analyses were conducted. The Kenoobi
Humanlike Voices model was benchmarked against other existing
text-to-speech systems, both traditional and AI-based, to evaluate its
competitive advantage. This involved comparing metrics such as speech
quality, naturalness, prosody, and linguistic accuracy. The comparative
analysis provided insights into the strengths and weaknesses of the
Kenoobi model, helping researchers identify areas for improvement and
innovation.
●Subjective Evaluation: In addition to objective metrics, subjective
evaluation methods were employed to assess the perceived quality and
naturalness of the generated audio. This involved conducting subjective
listening tests, where human evaluators listened to the generated voices
and provided ratings or feedback based on various criteria. These
subjective evaluations provided valuable insights into the perceptual
aspects of the generated audio and allowed for fine-tuning and refinement
of the model.
●Performance on Low-Resource Languages: One particular focus of the
experiments was to evaluate the model's performance on languages with
limited resources. These languages often pose challenges due to scarce
training data and less-developed language processing tools. By testing the
Kenoobi Humanlike Voices model on low-resource languages, researchers
could assess its ability to generalize and adapt to unfamiliar linguistic
contexts. The performance on low-resource languages was crucial for the
model's applicability in diverse linguistic environments.
●Fine-tuning and Iterative Improvement: The experimental phase also
involved fine-tuning and iterative improvement of the model based on the
evaluation results. Researchers analyzed the performance gaps, identified
areas for enhancement, and iteratively refined the model architecture,
training techniques, and hyperparameters. This iterative process aimed to
continually enhance the quality, naturalness, and language coverage of the
generated voices.
The experiments conducted on the Kenoobi Humanlike Voices model involved
comprehensive evaluations, comparative analyses, and subjective assessments.
These experiments demonstrated the model's high effectiveness in generating
human-like voices from text, particularly in languages with limited resources. By
testing the model on diverse languages and datasets, researchers were able to
assess its capabilities, identify areas for improvement, and enhance its overall
performance. The outcomes of these experiments paved the way for the model's
practical applications in various domains, including audio book production,
language learning, and content creation.
Conclusion:
Kenoobi Humanlike Voices is an AI model designed for generating human-like
voices from text in over 140 languages, with a major focus on African
languages. The model uses a combination of supervised and unsupervised
learning techniques, including casual convolutions, speaker identity, encoder,
decoder, converter, and synthesis network. The model has been tested
extensively and shown to be highly effective in generating human-like voices
from text, which makes it ideal for creating audio books, lectures, or other
content.
Limitations:
Despite its many benefits, the Kenoobi Humanlike Voices model has some
limitations. One of the primary limitations is the quality of the audio generated
from noisy input. The model may struggle to generate high-quality audio if the
input text is not clear or if there is a lot of background noise. Additionally, while
the model can generate human-like voices, it may not be able to capture the full
range of emotions and nuances that a human voice can convey. Finally, while the
model is capable of generating audio in over 140 languages, it may not be as
effective in languages with limited resources or in languages with very different
sound systems than those used in the model's training data.
Technology Misuse:
As with any technology, the Kenoobi Humanlike Voices model has the potential
for misuse. One potential concern is the use of the model for creating fake audio
content or for impersonating individuals. This could have serious consequences
for individuals or organizations if the fake audio is used for malicious purposes.
Additionally, the model could be used to create deepfake videos, which could
have severe ethical and legal implications. It is important that the model is used
ethically and responsibly, and that safeguards are put in place to prevent its
misuse.
Reference
1. Jaehyeon Kim, Sungwon Kim, Jungil Kong, and Sungroh Yoon. Glow-tts:
A generative flow for text-to-speech via monotonic alignment search.
Advances in Neural Information Processing Systems, 33, 2020.
2. Vadim Popov, Ivan Vovk, Vladimir Gogoryan, Tasnima Sadekova, and
Mikhail Kudinov. Gradtts: A diffusion probabilistic model for
text-to-speech. In International Conference on Machine Learning, pages
8599–8608. PMLR, 2021.
3. Jaehyeon Kim, Jungil Kong, and Juhee Son. Conditional variational
autoencoder with adversarial learning for end-to-end text-to-speech. In
International Conference on Machine Learning, pages 5530–5540.
PMLR, 2021.
4. Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Noury, Norman
Casagrande, Edward Lockhart, Florian Stimberg, Aaron Oord, Sander
Dieleman, and Koray Kavukcuoglu. Efficient neural audio synthesis. In
International Conference on Machine Learning, pages 2410–2419.
PMLR, 2018. 11
5. Jungil Kong, Jaehyeon Kim, and Jaekyoung Bae. Hifi-gan: Generative
adversarial networks for efficient and high fidelity speech synthesis.
Advances in Neural Information Processing Systems, 33, 2020.
6. Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan,
Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray
Kavukcuoglu. Wavenet: A generative model for raw audio. arXiv preprint
arXiv:1609.03499, 2016.
7. Xu Tan, Tao Qin, Frank Soong, and Tie-Yan Liu. A survey on neural
speech synthesis. arXiv preprint arXiv:2106.15561, 2021. 10
8. Jonathan Shen, Ruoming Pang, Ron J Weiss, Mike Schuster, Navdeep
Jaitly, Zongheng Yang, Zhifeng Chen, Yu Zhang, Yuxuan Wang, Rj
Skerrv-Ryan, et al. Natural tts synthesis by conditioning wavenet on mel
spectrogram predictions. In 2018 IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP), pages 4779–4783.
IEEE, 2018.
9. Yuxuan Wang, RJ Skerry-Ryan, Daisy Stanton, Yonghui Wu, Ron J
Weiss, Navdeep Jaitly, Zongheng Yang, Ying Xiao, Zhifeng Chen, Samy
Bengio, et al. Tacotron: Towards end-to-end speech synthesis. Proc.
Interspeech 2017, pages 4006–4010, 2017.
10. Sercan Ö Arık, Mike Chrzanowski, Adam Coates, Gregory Diamos,
Andrew Gibiansky, Yongguo Kang, Xian Li, John Miller, Andrew Ng,
Jonathan Raiman, et al. Deep voice: Real-time neural text-to-speech. In
International Conference on Machine Learning, pages 195–204. PMLR,
2017.
11. Andrew Gibiansky, Sercan Ömer Arik, Gregory Frederick Diamos,
John Miller, Kainan Peng, Wei Ping, Jonathan Raiman, and Yanqi Zhou.
Deep voice 2: Multi-speaker neural text-to-speech. In NIPS, 2017.
12. Wei Ping, Kainan Peng, Andrew Gibiansky, Sercan O Arik, Ajay
Kannan, Sharan Narang, Jonathan Raiman, and John Miller. Deep voice
3: 2000-speaker neural text-to-speech. Proc. ICLR, pages 214–217,
2018.
13. Hideyuki Tachibana, Katsuya Uenoyama, and Shunsuke Aihara.
Efficiently trainable text-tospeech system based on deep convolutional
networks with guided attention. In 2018 IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP), pages 4784–
4788. IEEE, 2018.
14. Naihan Li, Shujie Liu, Yanqing Liu, Sheng Zhao, and Ming Liu.
Neural speech synthesis with transformer network. In Proceedings of the
AAAI Conference on Artificial Intelligence, volume 33, pages
6706–6713, 2019. [10] Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng
Zhao, Zhou Zhao, and Tie-Yan Liu. Fastspeech: Fast, robust and
controllable text to speech. In NeurIPS, 2019.
15. Yanqing Liu, Zhihang Xu, Gang Wang, Kuan Chen, Bohan Li, Xu
Tan, Jinzhu Li, Lei He, and Sheng Zhao. Delightfultts: The microsoft
speech synthesis system for blizzard challenge 2021. arXiv preprint
arXiv:2110.12612, 2021.
16. Ryan Prenger, Rafael Valle, and Bryan Catanzaro. Waveglow: A
flow-based generative network for speech synthesis. In ICASSP
2019-2019 IEEE International Conference on Acoustics, Speech and
Signal Processing (ICASSP), pages 3617–3621. IEEE, 2019.
