High-Fidelity Simultaneous Speech-To-Speech Translation

• URL: http://arxiv.org/abs/2502.03382v1
• Date: Wed, 05 Feb 2025 17:18:55 GMT
• Title: High-Fidelity Simultaneous Speech-To-Speech Translation
• Authors: Tom Labiausse, Laurent Mazaré, Edouard Grave, Patrick Pérez, Alexandre Défossez, Neil Zeghidour,
• Abstract summary: We introduce Hibiki, a decoder-only model for simultaneous speech translation. Hibiki leverages a multistream language model to synchronously process source and target speech, and jointly produces text and audio tokens to perform speech-to-text and speech-to-speech translation.
• Score: 75.69884829562591
• License:
• Abstract: We introduce Hibiki, a decoder-only model for simultaneous speech translation. Hibiki leverages a multistream language model to synchronously process source and target speech, and jointly produces text and audio tokens to perform speech-to-text and speech-to-speech translation. We furthermore address the fundamental challenge of simultaneous interpretation, which unlike its consecutive counterpart, where one waits for the end of the source utterance to start translating, adapts its flow to accumulate just enough context to produce a correct translation in real-time, chunk by chunk. To do so, we introduce a weakly-supervised method that leverages the perplexity of an off-the-shelf text translation system to identify optimal delays on a per-word basis and create aligned synthetic data. After supervised training, Hibiki performs adaptive, simultaneous speech translation with vanilla temperature sampling. On a French-English simultaneous speech translation task, Hibiki demonstrates state-of-the-art performance in translation quality, speaker fidelity and naturalness. Moreover, the simplicity of its inference process makes it compatible with batched translation and even real-time on-device deployment. We provide examples as well as models and inference code.

Related papers

• Ichigo: Mixed-Modal Early-Fusion Realtime Voice Assistant [0.0]
  Large Language Models (LLMs) have revolutionized natural language processing, but their application to speech-based tasks remains challenging. This paper introduces a mixed-modal model that seamlessly processes interleaved sequences of speech and text. We present a comprehensive training methodology, including pre-training on multilingual speech recognition datasets.
  arXiv  Detail & Related papers  (2024-10-20T07:03:49Z)
• TransVIP: Speech to Speech Translation System with Voice and Isochrony Preservation [97.54885207518946]
  We introduce a novel model framework TransVIP that leverages diverse datasets in a cascade fashion. We propose two separated encoders to preserve the speaker's voice characteristics and isochrony from the source speech during the translation process. Our experiments on the French-English language pair demonstrate that our model outperforms the current state-of-the-art speech-to-speech translation model.
  arXiv  Detail & Related papers  (2024-05-28T04:11:37Z)
• Improving Isochronous Machine Translation with Target Factors and Auxiliary Counters [71.02335065794384]
  We introduce target factors in a transformer model to predict durations jointly with target language phoneme sequences. We show that our model improves translation quality and isochrony compared to previous work.
  arXiv  Detail & Related papers  (2023-05-22T16:36:04Z)
• Direct Speech-to-speech Translation without Textual Annotation using Bottleneck Features [13.44542301438426]
  We propose a direct speech-to-speech translation model which can be trained without any textual annotation or content information. Experiments on Mandarin-Cantonese speech translation demonstrate the feasibility of the proposed approach.
  arXiv  Detail & Related papers  (2022-12-12T10:03:10Z)
• SimulMT to SimulST: Adapting Simultaneous Text Translation to End-to-End Simultaneous Speech Translation [23.685648804345984]
  Simultaneous text translation and end-to-end speech translation have recently made great progress but little work has combined these tasks together. We investigate how to adapt simultaneous text translation methods such as wait-k and monotonic multihead attention to end-to-end simultaneous speech translation by introducing a pre-decision module. A detailed analysis is provided on the latency-quality trade-offs of combining fixed and flexible pre-decision with fixed and flexible policies.
  arXiv  Detail & Related papers  (2020-11-03T22:47:58Z)
• 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)
• Consecutive Decoding for Speech-to-text Translation [51.155661276936044]
  COnSecutive Transcription and Translation (COSTT) is an integral approach for speech-to-text translation. The key idea is to generate source transcript and target translation text with a single decoder. Our method is verified on three mainstream datasets.
  arXiv  Detail & Related papers  (2020-09-21T10:10:45Z)
• SimulEval: An Evaluation Toolkit for Simultaneous Translation [59.02724214432792]
  Simultaneous translation on both text and speech focuses on a real-time and low-latency scenario. SimulEval is an easy-to-use and general evaluation toolkit for both simultaneous text and speech translation.
  arXiv  Detail & Related papers  (2020-07-31T17:44:41Z)

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.