Parler-TTS has emerged as a robust text-to-speech (TTS) library, offering two powerful models: Parler-TTS Large v1 and Parler-TTS Mini v1. Both models are trained on an impressive 45,000 hours of audio data, allowing them to generate high-quality, natural-sounding speech with remarkable control over various characteristics. Users can manipulate aspects such as gender, background noise, speech rate, pitch, and reverberation through simple text prompts, providing unprecedented flexibility in speech generation.
The Parler-TTS Large v1 model boasts 2.2 billion parameters, making it a formidable tool for complex speech synthesis tasks. On the other hand, Parler-TTS Mini v1 serves as a lightweight alternative, offering similar capabilities in a more compact form factor. Both models are part of the broader Parler-TTS project, which aims to provide the community with comprehensive TTS training resources and dataset preprocessing code, fostering innovation and development in the field of speech synthesis.
One of the most notable features of both Parler-TTS models is their ability to ensure speaker consistency across generations. The models have been trained with 34 distinct speakers, each characterized by their name (e.g., Jon, Lea, Gary, Jenna, Mike, Laura). This feature allows users to specify a particular speaker in their text descriptions, allowing for the generation of consistent voice outputs across multiple instances. For example, users can create a description such as “Jon’s voice is monotone but slightly fast in delivery” to maintain the characteristics of a specific speaker.
The Parler-TTS project is distinguished from other TTS models by its commitment to open source principles. All datasets, preprocessing tools, training code, and model weights are released under permissive licenses. This approach allows the community to build on and extend the work, fostering the development of even more powerful TTS models. The project ecosystem includes the Parler-TTS repository for model training and tuning, the Data-Speech repository for dataset annotation, and the Parler-TTS organization for access to annotated datasets and future checkpoints.
To optimize the quality and characteristics of the generated speech, Parler-TTS offers several helpful tips for users. One key technique is to include specific terms in the text description to control audio clarity. For example, incorporating the phrase “very clear audio” will cause the model to generate the highest quality audio output. Conversely, using “very noisy audio” will introduce higher levels of background noise, allowing for more diverse and realistic speech environments when needed.
Punctuation plays a vital role in controlling the prosody of generated speech. Users can use this feature to add nuances and natural pauses to the output text. For example, strategic placement of commas in the input text will result in small pauses in the generated speech, mimicking the natural rhythm and flow of human conversation. This simple yet effective method allows for greater control over the pace and emphasis of the generated audio.
Other speech characteristics, such as gender, speaking rate, pitch and reverberation, can be manipulated directly through the text message. This level of control allows users to fine-tune the generated speech to fit specific requirements or preferences. By carefully crafting the input description, users can achieve a wide range of voice characteristics, from a slow, deep male voice to a fast, high-pitched female voice, with varying degrees of reverberation to simulate different acoustic environments.
Parler-TTS emerges as a cutting-edge text-to-speech library, featuring two models: Large v1 and Mini v1. Trained on 45,000 hours of audio, these models generate high-quality speech with controllable features. The library offers speaker consistency across 34 voices and embraces open-source principles, encouraging community innovation. Users can optimize output by specifying audio clarity, using punctuation for prosody control, and manipulating speech characteristics through text prompts. With its comprehensive ecosystem and easy-to-use approach, Parler-TTS represents a significant advancement in speech synthesis technology, providing powerful tools for both complex tasks and lightweight applications.
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Asjad is a consultant intern at Marktechpost. He is pursuing Bachelors in Mechanical Engineering from Indian Institute of technology, Kharagpur. Asjad is a Machine Learning and Deep Learning enthusiast who is always researching the applications of Machine Learning in the healthcare domain.
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