Orpheus TTS vs. Llama 2

Canopy Labs has introduced Orpheus, a groundbreaking collection of advanced speech large language models (LLMs) designed to replicate human-like speech generation. Built on the Llama-3 architecture, these models have been developed using a vast dataset of over 100,000 hours of English speech, enabling them to produce output with natural intonation, emotional nuance, and a rhythmic quality that surpasses current high-end closed-source models. One of the standout features of Orpheus is its zero-shot voice cloning capability, which allows users to replicate voices without needing any prior fine-tuning, alongside user-friendly tags that assist in manipulating emotion and intonation. Engineered for minimal latency, these models achieve around 200ms streaming latency for real-time applications, with potential reductions to approximately 100ms when input streaming is employed. Canopy Labs offers both pre-trained and fine-tuned models featuring 3 billion parameters under the adaptable Apache 2.0 license, and there are plans to develop smaller models with 1 billion, 400 million, and 150 million parameters to accommodate devices with limited processing power. This initiative is anticipated to enhance accessibility and expand the range of applications across diverse platforms and scenarios, making advanced speech generation technology more widely available. As technology continues to evolve, the implications of such advancements could significantly influence fields such as entertainment, education, and customer service.

We are excited to unveil the latest version of our open-source large language model, which includes model weights and initial code for the pretrained and fine-tuned Llama language models, ranging from 7 billion to 70 billion parameters. The Llama 2 pretrained models have been crafted using a remarkable 2 trillion tokens and boast double the context length compared to the first iteration, Llama 1. Additionally, the fine-tuned models have been refined through the insights gained from over 1 million human annotations. Llama 2 showcases outstanding performance compared to various other open-source language models across a wide array of external benchmarks, particularly excelling in reasoning, coding abilities, proficiency, and knowledge assessments. For its training, Llama 2 leveraged publicly available online data sources, while the fine-tuned variant, Llama-2-chat, integrates publicly accessible instruction datasets alongside the extensive human annotations mentioned earlier. Our project is backed by a robust coalition of global stakeholders who are passionate about our open approach to AI, including companies that have offered valuable early feedback and are eager to collaborate with us on Llama 2. The enthusiasm surrounding Llama 2 not only highlights its advancements but also marks a significant transformation in the collaborative development and application of AI technologies. This collective effort underscores the potential for innovation that can emerge when the community comes together to share resources and insights.