Parler-TTS: Open Source Text-to-Speech for Developers

In the realm of artificial intelligence, text-to-speech (TTS) technology continues to advance, offering more natural and customizable voice solutions. One such innovation is Parler-TTS, an open-source system developed for developers, designed to provide amazing control over various voice attributes. This article explores the key features, functionality, and applications of Parler-TTS for AI enthusiasts and developers seeking powerful and flexible TTS solutions.

What is Parler-TTS?

Parler-TTS is an open-source text-to-speech (TTS) system designed to provide developers with granular control over various aspects of voice generation. Unlike many other TTS systems, Parler-TTS offers amazing flexibility and customization options, making it a compelling choice for projects requiring specific voice characteristics. While it might not have gained widespread traction, the system is among the more robust and powerful TTS solutions available to developers. Parler-TTS, developed by Hugging Face, offers different voice and attribute controls. This Text-to-Speech system did not gain enough traction but it's flexible. The flexibility is one of the best attributes of this system. Because the system has an apache 2.0 license, it is free to do whatever you want with this code.

The Core Philosophy: Flexibility and Control

The core philosophy behind Parler-TTS revolves around providing developers with an amazing level of flexibility and control over the generated voice.

This is achieved through a combination of several design choices, but one aspect that sets it apart is the use of a description or Prompt. The main characteristics of this system revolve around how flexible it is as it gives the user control of different voices and their attributes. With Parler-TTS, developers can adjust settings such as:

• Gender: Define the gender of the generated voice.
• Pitch: Adjust the pitch to create a higher or lower tone.

• Speaking style: Control the speaking style to convey a certain emotion or persona.

  By combining these elements, developers can craft audio that aligns perfectly with their project's specific requirements.

Open Source Advantages and Licensing

Parler-TTS distinguishes itself from other TTS models by being fully open source. All of the datasets, pre-processing tools, training code, and weights are available publicly under a permissive license. This allows developers to use the system freely, modify it, and build upon it for their specific needs without the restrictions often associated with proprietary software.

The model is released under the Apache 2.0 license. A 2.0 license means that you can do anything you want with this system. You can train the model or fine-tune the model using the training guide. The open nature of Parler-TTS fosters collaboration and innovation within the AI community, enabling developers to contribute to its improvement and create more powerful TTS models. It is important to give credit where credit is due and that the paper is not from Hugging Face, but the model is from Hugging Face.

Setting up Parler-TTS on Kaggle

To get started with Parler-TTS, developers can use platforms like Kaggle or Google Colab, which offer access to computational resources and a user-friendly environment for running code. Here's a step-by-step guide on how to set up Parler-TTS on Kaggle:

1. Enable GPU acceleration: Within the Kaggle notebook, enable GPU acceleration to improve the performance of the TTS model.
2. Select Python Language: Make sure that the notebook is set to the Python language.
3. Enable Internet Access: Enable internet access so that the notebook can install the necessary libraries.

By following these steps, you will be able to have your Kaggle collab setup to start using the Parler-TTS system.

Installing the Required Libraries

To run Parler-TTS, you'll need to install certain libraries. The most important ones are:

• parler_tts: Contains core functionality for Parler-TTS.
• transformers: Provides pre-trained models and tools for natural language processing.
• soundfile: Helps to process audio files.

All of these libraries come from GitHub so there is no need to run pip install for all the libraries. To install these, use the following command in your notebook, like in the video pip install git+https://github.com/huggingface/parler-tts.git transformers soundfile -q. These are lightweight dependencies so they can be installed in one line.

Code Implementation

The implementation code to run this powerful voice to text generator system can be broken down to the following:

1. Import Necessary Modules: Start by importing the necessary modules, including torch, ParlerTTSforConditionalGeneration, AutoTokenizer, and soundfile.
2. Device Selection: The code then determines whether to use GPU (CUDA) or CPU based on availability. If CUDA is available, the device is set to CUDA; otherwise, it uses the CPU.
3. Model Loading: The ParlerTTSforConditionalGeneration model is loaded from pre-trained weights. This model requires a description and tokenizer for use. Input and prompt IDs are tokenized.
4. Audio Generation: The generation, audio array, and generation CPU functions all work together to give the final audio output.
5. Wave File Generation: Finally, a file is created using the configuration and sampling rate information.

Generating Spoken Audio Step-by-step

These steps will give a spoken audio output:

1. First start by downloading the pre-trained weights.

   

   To do this add the following code model = ParlerTTSforConditionalGeneration.from_pretrained('parler-tts/parler-tts-mini-v1').to(device) tokenizer = AutoTokenizer.from_pretrained('parler-tts/parler-tts-mini-v1').to(device)

2. Next, configure the prompt and description code. To do this add the following code

   prompt = “Hey everyone, Welcome to one little coder, we are going to learn about an amazing text to speech System where you can control a lot of different voices and their attributes. John’s voice is exciting yet slightly fast in delivery, with a very close Recording that almost has no background noise.” 
   description = “John’s voice is exciting yet slightly fast in delivery, with a very close recording that almost has no background noise.”

3. Tokenize the input IDs . You must tokenize the input so that it can be understood and generated from the model. Add the following code:

   input_ids = tokenizer(description, return_tensors=”pt”).input_ids.to(device)
   prompt_input_ids = tokenizer(prompt, return_tensors=”pt”).input_ids.to(device)

4. Generate the audio and squeeze out any unwanted noise . To do this add the following code:

   generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
   audio_arr = generation.cpu().numpy().squeeze()

5. Create the wave file . To do this add the following code:

   sf.write(‘parler_tts_out.wav’, audio_arr, model.config.sampling_rate)

   With these simple coding steps, you can create high quality audio.

No pricing: Free to Use

Because this system is open source and the files use an Apache 2.0 license, there is no pricing to consider with Parler-TTS. All datasets, code, and tools are free to use with Parler-TTS, this is its defining characteristic.

What are the core features of Parler-TTS?

There are a number of core features to Parler-TTS that differentiate it from other, less flexible or customizable systems:

• Open Source: Free to use, Apache 2.0 license.
• Attribute adjustment: Easy to change pitch, speed, tone, or make adjustments for the way certain words are spoken.
• Voice selection: Pick from a variety of pre-configured voices or upload your own sample to train a new voice
• Kaggle or Google Colab implementation: Simple set up on a number of popular platforms.
• Streaming Option: The ability to generate words with voices almost as fast as they are typed, for real time voice streaming.

How to Use the Parler-TTS?

This powerful system can have the following use cases:

• Create Audiobooks: Audiobooks are simple to generate using this model because there is a specific speaker chosen for consistency.
• Simulate Situations: Create an environment where a crowd simulation can be generated for background noise.
• Live Streaming: Create a streaming service where, in real time, what is being typed can also be heard.
• Make Meditation Tools: Having soothing sounds from code can be achieved with Parler-TTS. With a soothing background tone, it is also possible to create sound that will help people be more at peace.