How to Build Real Time Voice Cloning Pipelines

How to Build Real Time Voice Cloning Pipelines

Audio Cloning and TTS
⭐ All the code shown in this blog can be found in our here with all the necessary documentation and instructions to run.

In the evolving landscape of artificial intelligence and machine learning, real-time voice cloning has emerged as a groundbreaking technology. By leveraging advanced generative models and neural networks, it is now possible to create digital replicas of human voices with impressive accuracy. This technology not only captures the spoken words but also mimics the unique vocal characteristics, intonations, and emotions of the speaker. This groundbreaking technology is also being explored by top AI companies like Open AI,Lindy AI,Microsoft Cortana and much more.

What is Real Time Voice Cloning?

Real time voice cloning is the process of cloning the human voice using Generative model and Neural Network to create a digital copy of the True human voice. It gives a statistical representation of human voice using the process of spectrogram analysis. The neural network is the Machine learning algorithm which is responsible for the training of the generative model to produce the AI mimic Voice.

A spectrogram is a visual representation of the spectrum of frequencies in a sound signal as they vary with time. It is created by applying a Fast Fourier Transform (FFT) to short, overlapping segments of an audio signal, which provides information about the amplitude (or power) of different frequency components over time.

Why  Real Time Audio Generation?

Real time audio cloning technology instantly captures the human voice with such high fidelity not just the words spoken by them but also the unique vocal characteristics,intonation and emotions of the speaker.

Where this technology has great potential in sectors which have human- system communication streams, new forms of content creation and also act as accessible solutions to the society of blind and deaf people. Let’s see about the real time applicable sectors and the existing top technologies in them.

Voice Assistants

A natural and engaging experience can be introduced to the existing voice assistants like siri,alexa and google assistant and also the drawback of multilingual support can also be overcomed with the real time voice cloning architecture. By replicating tonal nuances and emotions in voice, Voice Assistants can respond more empathetic and appropriately to users, enhancing user satisfaction.

Moreover, the GPT-4o new release by OpenAI introduces advanced features in natural language understanding and generation. By integrating real-time voice cloning with GPT-4o, voice assistants can achieve even higher levels of sophistication in dialogue management, providing seamless and intuitive user experiences. This combination enables the creation of highly interactive and human-like voice assistants that can cater to a diverse range of needs and preferences.

Assistive Technology For Blind and Visually Impaired People

Real-time audio cloning can create more natural-sounding screen readers and navigation aids. These tools can read out text, describe surroundings, or provide directions in a voice that users find comforting and familiar. The user experience is ultimately improved due to the personalisation feature where it helps to choose the voice they wish or like.

Moreover, existing assistive technologies such as Aira, Be My Eyes, Lookout, and Seeing AI are revolutionizing support for the visually impaired. Aira connects users with trained agents who provide real-time assistance through live video calls, helping with navigation, reading, and other tasks. Be My Eyes also uses live video calls to link visually impaired individuals with sighted volunteers for assistance with various daily activities. Lookout, an Android app, provides spoken feedback about the user’s surroundings by utilizing the device's camera to recognize text, people, and objects. Seeing AI, available on iOS, narrates the world around the user by reading text, identifying products, recognizing faces, and describing scenes. Though the existing technology is useful, When we integrate real-time audio generation with advanced computer vision technology we don’t have to rely on human agents and the user experience would be smooth and consistent with their personal closed one voice navigating and exploring the world.

The new GPT-4o release by OpenAI offers advanced features in natural language understanding and generation, which can further enhance these assistive technologies. Integrating real-time audio cloning with GPT-4o allows for more sophisticated and intuitive interactions, providing a higher level of support and independence for visually impaired users. Feel free to watch the demo by OpenAI X Be My Eyes. This combination ensures that assistive tools not only convey information effectively but also do so in a voice that resonates personally with the user, making the technology more accessible and user-friendly.

Audiobooks and Stories On-Demand

Real-time audio cloning allows for the swift creation of audiobooks, enabling authors and publishers to meet the growing demand for audio content without the lengthy process of traditional recording.

Users can also listen to their audio books with the desired characteristics of voice either it can be their loved or inspirational persons.This personalization can make the listening experience more enjoyable and intimate.The ability to replicate the tonality and emotion in a voice helps convey the intended meaning more effectively. This is crucial in applications like virtual therapy, remote education, and customer service, where understanding subtleties in communication is essential.

A new tool in the market called AnyTopic which enables users to create their own audio book using the technique of GPT researcher concept where you will provide the keen topic you're interested in and then the agent will prepare a sole audiobook for the user's intended timing. The integration of real audio generation concepts over here will enhance the user personalisation concept drastically.

Scalable and  Reliable Customer Service

Customer Service Agent markets are huge and wide till date which can be replaced by the Real voice cloning architecture to answer the customer queries instead of agents which significantly make huge cost cutting and also not a compromisable solution as the tonality and characteristic of the voice is mimicked real. Cloned voices can be used to provide support in multiple languages, each with native-like pronunciation and tone. This can make non-native speakers feel more comfortable and understood.

Additionally, existing customer service platforms such as Bland.AI and Amazon Lex stand to benefit significantly from real-time voice cloning. Bland.AI can leverage this technology to enhance its user interactions, making conversations more fluid and natural across different languages and dialects. Amazon Lex, which powers Alexa's voice capabilities, can utilize real-time voice cloning to offer more personalized and context-aware interactions, improving user engagement and satisfaction.

Apart from the customer-agent perspective it is also well suitable for any line of  communication between humans. For example A healthcare provider can use voice cloning to offer compassionate support to patients, recognizing when a patient is stressed or anxious and responding with appropriate empathy and care.

How does Real Time Audio Cloning work?

Real-time audio cloning, also known as voice cloning, is a process that replicates a person's voice using artificial intelligence (AI) and machine learning (ML) techniques. This technology can generate synthetic speech that mimics the tone, pitch, and inflection of a target voice in real time. Here's a detailed explanation of how this process works:

Speaker Encoder

The feature extractor processes the input audio (speaker reference waveform) to extract essential features that capture the unique characteristics of the speaker's voice. These features are crucial for maintaining the speaker's identity in the synthesized speech.

The speaker encoder takes the features extracted by the feature extractor and encodes them into a latent representation. This encoded representation is a compact form that retains all the necessary speaker-specific information required for voice cloning.

Acoustic Model

The acoustic model receives input from two sources: the encoded speaker representation and the text (grapheme or phoneme sequence) to be spoken. It combines these inputs to generate intermediate acoustic representations that reflect both the content of the speech and the speaker's unique voice characteristics. The model leverages advanced neural network architectures, such as recurrent neural networks (RNNs) or transformer networks, to process the temporal and contextual aspects of the speech. By doing so, it ensures that the generated acoustic features accurately capture the nuances of the speaker's voice, including intonation, rhythm, and emotional tone. This allows for the production of synthetic speech that sounds natural, closely mimicking the reference speaker's vocal attributes while conveying the intended message clearly and effectively.


The vocoder is responsible for converting the intermediate acoustic representations generated by the acoustic model into a waveform. This waveform is the final synthesized speech output, designed to closely mimic the reference speaker's voice, including their unique tonal qualities, pitch, and inflections. By effectively translating the detailed acoustic features into a smooth, continuous audio signal, the vocoder ensures that the synthesized voice maintains a high degree of fidelity and naturalness, making it indistinguishable from the original speaker in both clarity and expressiveness.


The synthesizer is a critical component that includes the encoder, concatenation, attention mechanism, and decoder. The Encoder converts the input text (grapheme or phoneme sequence) into a high-dimensional representation. The concat concatenates the encoded text representation with the speaker's encoded voice features. Attention Mechanism ensures that the synthesizer focuses on the relevant parts of the text and speaker features at each step of the speech synthesis process.The Decoder converts the concatenated representation into a sequence of acoustic features that the vocoder can process.


The process begins with the Speaker Reference Waveform, where an audio sample of the speaker's voice is provided as input. The Speaker Encoder processes mel spectrograms from this audio sample to extract essential features, which are then encoded into speaker embeddings. These embeddings are optimized using a gradient-based approach and a GE2E (Generalized End-to-End) loss function.

Next, the system utilizes Dataset 2, which includes both text and mel spectrograms. The text input is processed by the Speaker Encoder to generate embeddings, and these are combined with the speaker embeddings in the Synthesizer. The synthesizer generates predicted mel spectrograms, which are refined using a spectral loss function to ensure they closely match the target spectrograms.

Finally, the Vocoder converts the predicted mel spectrograms into audio waveforms. These predicted audio waveforms are compared with the target audio using a waveform loss function, and the gradients from this comparison are used to further optimize the synthesizer and vocoder. This comprehensive process ensures that the final synthesized speech output closely mimics the reference speaker's voice, capturing their unique tonal qualities, pitch, and inflections. This innovative architecture allows for real-time audio cloning by effectively processing and integrating both textual and speaker-specific data, making it applicable for use in voice assistants, customer support services, interactive user interfaces, and more. To know more about the architecture of TTS refer to this paper.

How to build a Voice Cloning Pipeline

Building a voice cloning pipeline involves setting up a system that can take an audio input of a speaker's voice and generate new speech that mimics the same voice by matching with the text given by the user. 

Below is an iPython script that uses the TTS library to perform voice cloning. It initializes a text-to-speech (TTS) model, loads a pre-trained model checkpoint, and synthesizes speech using an input text and a specific speaker's voice characteristics extracted from audio files. Here's a detailed explanation of each part of the code and the purpose of the libraries used:

Importing Libraries

from TTS.tts.configs.bark_config import BarkConfig
from TTS.tts.models.bark import Bark

from import write as write_wav

import os

The TTS library is a powerful tool for text-to-speech conversion. It supports multiple TTS models, including Bark. These imports specifically bring in the configuration and model components required to set up and use the Bark TTS model.

SciPy is a scientific computing library in Python. Here, it is used to save the generated speech waveform to an audio file.

write_wav: This function writes a NumPy array to a WAV file, which is a common format for storing audio data.

The OS library provides a way to interact with the operating system. It is used for handling directory paths and file management.

Setting Up Configuration:

config = BarkConfig()
model = Bark.init_from_config(config)
model.load_checkpoint(config, checkpoint_dir="bark/", eval=True)

Initializes the configuration for the Bark model. This configuration includes various parameters that control the model's behavior during speech synthesis.

Initializes the Bark TTS model using the specified configuration. This sets up the model architecture and prepares it for loading pre-trained weights.Loads the pre-trained weights for the Bark model from the specified checkpoint directory. This is crucial for ensuring the model has learned to generate high-quality speech based on extensive training data.

Speech Synthesis

text = "Mercity ai is a leading AI innovator in India, with OpenAI planning collaboration."
voice_dirs = "/Users/username/Desktop/projects/AI voice Cloning/Speaker voice/"

Defines the text that will be converted into speech. This is the input that the TTS model will process to generate the corresponding audio output.

Specifies the directory containing the speaker's audio files. These files are used to extract speaker-specific characteristics (embeddings) for voice cloning.

Synthesizing Speech

output_dict = model.synthesize(text, config, speaker_id='speaker', voice_dirs="bark_voices", temperature=0.95)

Uses the Bark model to synthesize speech from the input text. The method combines the text with the speaker-specific embeddings extracted from the audio files in the voice_dirs directory.


text: The text to be converted to speech.

config: The model configuration.

speaker_id: An identifier for the speaker (not deeply detailed here but typically used to select the appropriate speaker embedding).

voice_dirs: Directory containing the speaker's audio files.

temperature: A parameter that controls the randomness of the output. Lower values make the output more deterministic, while higher values introduce more variation.

Saving the Generated Speech:

write_wav("SamAltman.wav", 24000, output_dict["wav"])

Saves the synthesized speech to a WAV file. The sample rate is set to 24,000 Hz. 

This guide should help you understand how to build a real-time voice cloning pipeline using the Bark TTS model.

What is Open Voice

Open voice is the innovative open-source project recently released by that provides instant voice cloning capabilities.It enables accurate voice capture functionalities like accurate tone color cloning, flexible voice style control, and zero-shot cross-lingual voice cloning.OpenVoice V1 supports multiple languages and accents, while OpenVoice V2, released in April 2024, offers improved audio quality and native multi-lingual support.

Open Voice vs Bark

The Bark library by Suno AI is a transformer-based text-to-audio model that offers a unique approach to generating audio content. Bark is not a conventional text-to-speech model but a fully generative text-to-audio model, capable of producing various types of audio, including highly realistic multilingual speech, music, background noise, and simple sound effects.

OpenVoice is chosen over Bark for real voice cloning tasks due to its superior capabilities in replicating the tone color of the reference speaker and achieving granular control over voice styles including accent, rhythm, intonation, pauses, and even emotions.

OpenVoice can mimic a speaker's voice using only a short audio clip, typically requiring less than 30 seconds to clone a voice. It generates a second of speech in just 85 milliseconds by decoupling tone color extraction from other voice attributes.. 

For zero-shot multi-language voice cloning, OpenVoice supports cross-lingual synthesis without needing the specific languages in the training dataset, ensuring high-quality voice cloning in various languages.

In contrast, Bark, although a powerful text-to-audio model, lacks the flexibility and control over voice styles that OpenVoice offers. Bark's probabilistic nature can lead to inconsistent generation results, which may not be suitable for real voice cloning tasks where high-quality and consistent voice replication is essential. Furthermore, Bark's requirements for massive-speaker multilingual datasets for cross-lingual voice cloning can be a significant limitation in certain applications.

How Open Voice Works?

The OpenVoice framework for instant voice cloning works by combining text content with style parameters (such as accent, emotion, and intonation) and processing them through a base speaker TTS (Text-to-Speech) model to control the overall speech styles and languages.This model processes the text and style parameters to generate an initial speech output, controlling the desired styles and languages. Simultaneously, the tone color of the reference speaker's voice is extracted to capture its unique characteristics. These elements are then encoded, passed through a flow-based model to remove the tone color while preserving other styles, and decoded to produce speech that integrates the reference speaker's tone color with controlled styles and languages. This process allows for high-quality, versatile speech synthesis.

This architecture ensures the produced speech closely mimics the reference speaker’s unique vocal characteristics while allowing for versatile style control, making it highly suitable for various applications like media content creation and personalized virtual assistants. Refer this paper to read more about Open voice.

How is OpenVoice different from others?

OpenVoice stands out from other text-to-speech (TTS) architectures due to its unique decoupled framework, which separates tone color cloning from other voice style and language controls. Unlike traditional TTS systems that often require extensive datasets to manage voice styles, accents, and emotions, OpenVoice leverages a two-step process. First, it uses a Base Speaker TTS model to generate initial speech with specific style parameters such as emotion, rhythm, and speed. Then, a Tone Color Converter applies the tone color of a reference speaker to this base speech. This decoupled approach allows for fine-grained control over voice attributes and enables high-quality voice cloning with minimal training data, making it more efficient and versatile compared to other architectures that do not separate these processes.

Additionally, OpenVoice supports zero-shot cross-lingual voice cloning, meaning it can clone voices in multiple languages even if the specific language was not included in the training dataset. This capability is powered by its innovative use of flow-based models for tone color conversion and advanced speaker embedding extraction techniques. Other TTS systems, like Google's Tacotron or DeepMind's WaveNet, typically require large multilingual datasets and extensive retraining to achieve similar results. OpenVoice’s architecture not only reduces the computational burden but also provides more flexibility in adjusting voice attributes on-the-fly, making it a powerful tool for applications requiring dynamic and contextually appropriate speech synthesis.

Why Open Voice

High-Fidelity Tone Color Cloning

OpenVoice excels at accurately replicating the tone color of a reference speaker, ensuring that the cloned voice sounds natural and true to the original. Tone color, also known as timbre, refers to the unique quality or character of a voice that distinguishes it from other voices, even when the pitch and loudness are the same. It encompasses the various nuances, overtones, and subtleties in a person's voice, such as warmth, brightness, or breathiness. This high-fidelity tone color cloning capability is achieved through the system's ability to maintain high audio quality even when generating speech in multiple languages.

Flexible Voice Style Control

OpenVoice allows users to finely control voice attributes such as emotion, accent, rhythm, pauses, and intonation. This flexibility enables the creation of diverse and contextually appropriate speech outputs, making it suitable for a wide range of applications.To add pauses, emotion, accent, and rhythm in OpenVoice, users can set specific parameters in the BaseSpeakerTTS and ToneColorConverter methods. For example, to synthesize speech with these attributes, you can define the speaker parameter for emotion (e.g., 'cheerful' or 'whispering'), the language parameter for accent (e.g., 'English' or 'Chinese'), and the speed parameter for rhythm (e.g., 0.9 for slower speech).

Zero-Shot Cross-Lingual Voice Cloning

OpenVoice can clone voices and generate speech in languages not included in the training data, eliminating the need for extensive multilingual datasets. This capability makes OpenVoice particularly valuable for applications requiring multilingual support without additional data collection and training.

User-Friendly and Accessible

OpenVoice is available as an open-source technology, facilitating easy adoption and integration into various projects. The community support and collaborative environment fostered by the open-source nature of OpenVoice encourage further research and development.

Wide Range of Applications

OpenVoice is suitable for a variety of applications, including content creation, customer support, and accessibility. It can generate voiceovers for videos, animations, and other multimedia content, enhance virtual assistants and chatbots with personalized and natural-sounding voices, and support assistive technologies for individuals with disabilities.

Ready to Transform Your Voice Solutions?

If you are seeking to enhance your projects with high-fidelity, real-time voice cloning, OpenVoice is your answer. Whether you need personalized voice assistants, assistive technology for the visually impaired, or scalable customer support solutions, OpenVoice offers unparalleled accuracy and flexibility. With, you can build innovative solutions and optimize your business processes using cutting-edge voice technology.

Contact us today to elevate your voice cloning applications and see immediate results. Let's create the future of voice technology together!

Subscribe to stay informed

Subscribe to our newsletter to stay updated on all things AI!
Awesome, you subscribed!
Error! Please try again.