VioLA: Unified Codec Language Models for Speech Recognition, Synthesis, and Translation

• URL: http://arxiv.org/abs/2305.16107v1
• Date: Thu, 25 May 2023 14:39:47 GMT
• Title: VioLA: Unified Codec Language Models for Speech Recognition, Synthesis, and Translation
• Authors: Tianrui Wang, Long Zhou, Ziqiang Zhang, Yu Wu, Shujie Liu, Yashesh Gaur, Zhuo Chen, Jinyu Li, Furu Wei
• Abstract summary: VioLA is a single auto-regressive Transformer decoder-only network that unifies various cross-modal tasks involving speech and text. We first convert all the speech utterances to discrete tokens using an offline neural encoder. We further integrate task IDs (TID) and language IDs (LID) into the proposed model to enhance the modeling capability of handling different languages and tasks.
• Score: 91.39949385661379
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent research shows a big convergence in model architecture, training objectives, and inference methods across various tasks for different modalities. In this paper, we propose VioLA, a single auto-regressive Transformer decoder-only network that unifies various cross-modal tasks involving speech and text, such as speech-to-text, text-to-text, text-to-speech, and speech-to-speech tasks, as a conditional codec language model task via multi-task learning framework. To accomplish this, we first convert all the speech utterances to discrete tokens (similar to the textual data) using an offline neural codec encoder. In such a way, all these tasks are converted to token-based sequence conversion problems, which can be naturally handled with one conditional language model. We further integrate task IDs (TID) and language IDs (LID) into the proposed model to enhance the modeling capability of handling different languages and tasks. Experimental results demonstrate that the proposed VioLA model can support both single-modal and cross-modal tasks well, and the decoder-only model achieves a comparable and even better performance than the strong baselines.

Related papers

• Investigating Decoder-only Large Language Models for Speech-to-text Translation [39.17113782374464]
  Large language models (LLMs) are known for their exceptional reasoning capabilities, generalizability, and fluency across diverse domains. We propose a decoder-only architecture that enables the LLM to directly consume the encoded speech representation and generate the text translation. Our model achieves state-of-the-art performance on CoVoST 2 and FLEURS among models trained without proprietary data.
  arXiv  Detail & Related papers  (2024-07-03T14:42:49Z)
• WavLLM: Towards Robust and Adaptive Speech Large Language Model [93.0773293897888]
  We introduce WavLLM, a robust and adaptive speech large language model with dual encoders, and a prompt-aware LoRA weight adapter. We validate the proposed model on universal speech benchmarks including tasks such as ASR, ST, SV, ER, and also apply it to specialized datasets like Gaokao English listening comprehension set for SQA, and speech Chain-of-Thought (CoT) evaluation set.
  arXiv  Detail & Related papers  (2024-03-31T12:01:32Z)
• SpeechComposer: Unifying Multiple Speech Tasks with Prompt Composition [67.08798754009153]
  Speech language models typically utilize task-dependent prompt tokens to unify various speech tasks in a single model. We propose a novel decoder-only speech language model, SpeechComposer, that can unify common speech tasks by composing a fixed set of prompt tokens.
  arXiv  Detail & Related papers  (2024-01-31T18:06:29Z)
• MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks [59.09343552273045]
  We propose a decoder-only model for multimodal tasks, which is surprisingly effective in jointly learning of these disparate vision-language tasks. We demonstrate that joint learning of these diverse objectives is simple, effective, and maximizes the weight-sharing of the model across these tasks. Our model achieves the state of the art on image-text and text-image retrieval, video question answering and open-vocabulary detection tasks, outperforming much larger and more extensively trained foundational models.
  arXiv  Detail & Related papers  (2023-03-29T16:42:30Z)
• Multilingual Speech Translation with Unified Transformer: Huawei Noah's Ark Lab at IWSLT 2021 [33.876412404781846]
  This paper describes the system submitted to the IWSLT 2021 Speech Translation (MultiST) task from Huawei Noah's Ark Lab. We use a unified transformer architecture for our MultiST model, so that the data from different modalities can be exploited to enhance the model's ability. We apply several training techniques to improve the performance, including multi-task learning, task-level curriculum learning, data augmentation, etc.
  arXiv  Detail & Related papers  (2021-06-01T02:50:49Z)
• VX2TEXT: End-to-End Learning of Video-Based Text Generation From Multimodal Inputs [103.99315770490163]
  We present a framework for text generation from multimodal inputs consisting of video plus text, speech, or audio. Experiments demonstrate that our approach based on a single architecture outperforms the state-of-the-art on three video-based text-generation tasks.
  arXiv  Detail & Related papers  (2021-01-28T15:22:36Z)
• Bridging the Modality Gap for Speech-to-Text Translation [57.47099674461832]
  End-to-end speech translation aims to translate speech in one language into text in another language via an end-to-end way. Most existing methods employ an encoder-decoder structure with a single encoder to learn acoustic representation and semantic information simultaneously. We propose a Speech-to-Text Adaptation for Speech Translation model which aims to improve the end-to-end model performance by bridging the modality gap between speech and text.
  arXiv  Detail & Related papers  (2020-10-28T12:33:04Z)
• Contextualized Spoken Word Representations from Convolutional Autoencoders [2.28438857884398]
  This paper proposes a Convolutional Autoencoder based neural architecture to model syntactically and semantically adequate contextualized representations of varying length spoken words. The proposed model was able to demonstrate its robustness when compared to the other two language-based models.
  arXiv  Detail & Related papers  (2020-07-06T16:48:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.