Large Language Models (LLMs) have been in the spotlight for a few months now. Being one of the best advances in the field of Artificial Intelligence, these models are transforming the way humans interact with machines. As all industries are embracing these models, they are the best example of how AI is taking over the world. LLMs excel at producing text for tasks involving complex interactions and knowledge retrieval, best exemplified by the famous chatbot developed by OpenAI, ChatGPT, based on the Transformer architecture of GPT 3.5 and GPT 4. Not just in generation text but also in models such as CLIP (Contrastive Language-Image Pretraining) has also been developed for image production, allowing the creation of text based on image content.
To advance audio generation and comprehension, a team of Google researchers introduced AudioPaLM, a large language model that can address speech generation and comprehension tasks. AudioPaLM combines the advantages of two existing models, ie the PaLM-2 model and the AudioLM model, to produce a unified multimodal architecture that can process and output both text and speech. This allows AudioPaLM to handle a variety of applications, from speech recognition to speech-to-text.
While AudioLM is excellent at maintaining paralinguistic information such as speaker identity and tone, PaLM-2, which is a text-based language model, specializes in text-specific linguistic knowledge. By combining these two models, AudioPaLM leverages the linguistic expertise of PaLM-2 and AudioLM’s preservation of paralinguistic information, leading to more complete understanding and creation of both text and speech.
AudioPaLM makes use of a joint vocabulary that can represent both speech and text using a limited number of discrete tokens. Combining this joint vocabulary with markup task descriptions allows a single decoder model to be trained on a variety of speech and text-based tasks. Tasks like speech recognition, text-to-speech synthesis, and speech-to-speech translation, which separate traditionally addressed models, can now be unified under a single architecture and training process.
Upon evaluation, AudioPaLM outperformed existing systems in speech translation by a significant margin. Demonstrated the ability to perform zero-trigger speech-to-text translation for language pairs, meaning you can accurately translate speech-to-text for languages you’ve never encountered before, opening up possibilities for broader language support . AudioPaLM can also transfer voices between languages based on brief spoken prompts and can capture and play back different voices in different languages, allowing for voice conversion and adaptation.
The key contributions mentioned by the team are:
- AudioPaLM uses the capabilities of PaLM and PaLM-2 from the text-only pretraining.
- It has achieved SOTA results in the Machine Speech Translation and Speech Translation benchmarks and competitive performance in the Machine Speech Recognition benchmarks.
- The model performs speech-to-speech translation with invisible speaker voice transfer, outperforming existing methods in speech quality and speech preservation.
- AudioPaLM demonstrates zero-throw capabilities by performing automatic speech translation with invisible language pairs.
In conclusion, AudioPaLM, which is a unified LLM that handles both speech and text by using the capabilities of text-based LLMs and incorporating audio prompting techniques, is a promising addition to the list of LLM.
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Tanya Malhotra is a final year student at the University of Petroleum and Power Studies, Dehradun, studying BTech in Computer Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a data science enthusiast with good analytical and critical thinking, along with a keen interest in acquiring new skills, leading groups, and managing work in an organized manner.
