What is Zonos TTS?

Zonos TTS AI is a text-to-speech (TTS) technology that enables the creation of highly expressive and natural-sounding speech from text inputs. It is designed to break language barriers by supporting multiple languages and offers advanced features like voice cloning, emotion control, and customization of speech parameters such as pitch and speaking rate.

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Overview of Zonos TTS

FeatureDescription
ModelZonos-v0.1
DescriptionOpen-weight TTS model
FunctionalityGenerates natural speech from text.
Audio Qualityspeech at 44kHz with control over rate, pitch, and emotions.
Multilingual SupportSupports English, Japanese, Chinese, French, and German.
Official Websiteplayground.zyphra.com/audio

Zonos TTS: Usage

Step 1: Import Libraries

Action: Import the necessary libraries to use Zonos TTS.

What Happens: You will need to import PyTorch, torchaudio, and the Zonos model to get started.

import torch
import torchaudio
from zonos.model import Zonos
from zonos.conditioning import make_cond_dict
from zonos.utils import DEFAULT_DEVICE as device

Step 2: Load the Model

Action: Load the pre-trained Zonos model.

What Happens: You can choose between different model versions. Here’s how to load the transformer model.

model = Zonos.from_pretrained("Zyphra/Zonos-v0.1-transformer", device=device)

Step 3: Prepare Audio Input

Action: Load your audio file and create a speaker embedding.

What Happens: The audio file is loaded, and a speaker embedding is created for further processing.

wav, sampling_rate = torchaudio.load("assets/exampleaudio.mp3")
speaker = model.make_speaker_embedding(wav, sampling_rate)

Step 4: Generate Speech

Action: Prepare the conditioning and generate the speech output.

What Happens: You create a conditioning dictionary and generate the speech based on the input text.

cond_dict = make_cond_dict(text="Hello, world!", speaker=speaker, language="en-us")
conditioning = model.prepare_conditioning(cond_dict)
codes = model.generate(conditioning)

Step 5: Save the Output

Action: Decode the generated codes and save the audio file.

What Happens: The generated audio is saved as a WAV file for playback or further use.

wavs = model.autoencoder.decode(codes).cpu()
torchaudio.save("sample.wav", wavs[0], model.autoencoder.sampling_rate)
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Gradio Interface (Recommended)

Action: Run the Gradio interface for an interactive experience.

What Happens: You can easily interact with the model through a web interface.

uv run gradio_interface.py
# python gradio_interface.py

Key Features of Zonos TTS

  • Zero-Shot TTS with Voice Cloning

    Zonos allows users to generate speech by providing a short voice sample, typically between 3 to 30 seconds, enabling accurate voice replication.

  • Multilingual Support

    It supports several major languages, including English, Chinese, Japanese, French, and German, making it versatile for global applications.

  • Emotion Control

    Users can adjust the emotional tone of the speech, allowing for dynamic content creation with emotions like happiness, sadness, and surprise.

  • High-Quality Output

    Zonos generates speech at a 44 kHz sample rate, ensuring high audio fidelity comparable to industry-leading solutions.

  • Open-Source Collaboration

    The models are released under the Apache 2.0 license, encouraging community contributions and improvements.

Pros and Cons

Pros

  • High-quality, expressive speech generation
  • Supports voice cloning with minimal audio input
  • Fine control over audio characteristics (pitch, rate, emotion)
  • Multilingual support for diverse applications
  • Fast processing with real-time performance on modern hardware

Cons

  • Requires initial audio sample for voice cloning
  • May be memory-intensive depending on usage
  • Performance can vary based on input complexity

How to Use Zonos TTS AI?

Step 1: Install Dependencies

Ensure you have Python and the required libraries installed. Use the command:

pip install -U uv

Step 2: Load the Model

Import the necessary libraries and load the Zonos model using:

model = Zonos.from_pretrained("Zyphra/Zonos-v0.1-transformer", device=device)

Step 3: Prepare Audio Input

Load your audio file using:

wav, sampling_rate = torchaudio.load("assets/exampleaudio.mp3")

Step 4: Create Speaker Embedding

Generate a speaker embedding with:

speaker = model.make_speaker_embedding(wav, sampling_rate)

Step 5: Prepare Conditioning

Create a conditioning dictionary and prepare it:

cond_dict = make_cond_dict(text="Hello, world!", speaker=speaker, language="en-us")
conditioning = model.prepare_conditioning(cond_dict)

Step 6: Generate Audio

Generate the audio output:

codes = model.generate(conditioning)

Step 7: Save the Output

Decode and save the generated audio:

wavs = model.autoencoder.decode(codes).cpu()
torchaudio.save("sample.wav", wavs[0], model.autoencoder.sampling_rate)

Step 8: Use Gradio Interface (Recommended)

For repeated sampling, run the Gradio interface:

uv run gradio_interface.py

Zonos TTS FAQs