Yossi Adi


2022

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Direct Speech-to-Speech Translation With Discrete Units
Ann Lee | Peng-Jen Chen | Changhan Wang | Jiatao Gu | Sravya Popuri | Xutai Ma | Adam Polyak | Yossi Adi | Qing He | Yun Tang | Juan Pino | Wei-Ning Hsu
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present a direct speech-to-speech translation (S2ST) model that translates speech from one language to speech in another language without relying on intermediate text generation. We tackle the problem by first applying a self-supervised discrete speech encoder on the target speech and then training a sequence-to-sequence speech-to-unit translation (S2UT) model to predict the discrete representations of the target speech. When target text transcripts are available, we design a joint speech and text training framework that enables the model to generate dual modality output (speech and text) simultaneously in the same inference pass. Experiments on the Fisher Spanish-English dataset show that the proposed framework yields improvement of 6.7 BLEU compared with a baseline direct S2ST model that predicts spectrogram features. When trained without any text transcripts, our model performance is comparable to models that predict spectrograms and are trained with text supervision, showing the potential of our system for translation between unwritten languages.

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Text-Free Prosody-Aware Generative Spoken Language Modeling
Eugene Kharitonov | Ann Lee | Adam Polyak | Yossi Adi | Jade Copet | Kushal Lakhotia | Tu Anh Nguyen | Morgane Riviere | Abdelrahman Mohamed | Emmanuel Dupoux | Wei-Ning Hsu
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Speech pre-training has primarily demonstrated efficacy on classification tasks, while its capability of generating novel speech, similar to how GPT-2 can generate coherent paragraphs, has barely been explored. Generative Spoken Language Modeling (GSLM) (CITATION) is the only prior work addressing the generative aspect of speech pre-training, which builds a text-free language model using discovered units. Unfortunately, because the units used in GSLM discard most prosodic information, GSLM fails to leverage prosody for better comprehension and does not generate expressive speech. In this work, we present a prosody-aware generative spoken language model (pGSLM). It is composed of a multi-stream transformer language model (MS-TLM) of speech, represented as discovered unit and prosodic feature streams, and an adapted HiFi-GAN model converting MS-TLM outputs to waveforms. Experimental results show that the pGSLM can utilize prosody to improve both prosody and content modeling, and also generate natural, meaningful, and coherent speech given a spoken prompt. Audio samples can be found at https://speechbot.github.io/pgslm. Codes and models are available at https://github.com/pytorch/fairseq/tree/main/examples/textless_nlp/pgslm.

2021

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fairseq Sˆ2: A Scalable and Integrable Speech Synthesis Toolkit
Changhan Wang | Wei-Ning Hsu | Yossi Adi | Adam Polyak | Ann Lee | Peng-Jen Chen | Jiatao Gu | Juan Pino
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

This paper presents fairseq Sˆ2, a fairseq extension for speech synthesis. We implement a number of autoregressive (AR) and non-AR text-to-speech models, and their multi-speaker variants. To enable training speech synthesis models with less curated data, a number of preprocessing tools are built and their importance is shown empirically. To facilitate faster iteration of development and analysis, a suite of automatic metrics is included. Apart from the features added specifically for this extension, fairseq Sˆ2 also benefits from the scalability offered by fairseq and can be easily integrated with other state-of-the-art systems provided in this framework. The code, documentation, and pre-trained models will be made available at https://github.com/pytorch/fairseq/tree/master/examples/speech_synthesis.

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On Generative Spoken Language Modeling from Raw Audio
Kushal Lakhotia | Eugene Kharitonov | Wei-Ning Hsu | Yossi Adi | Adam Polyak | Benjamin Bolte | Tu-Anh Nguyen | Jade Copet | Alexei Baevski | Abdelrahman Mohamed | Emmanuel Dupoux
Transactions of the Association for Computational Linguistics, Volume 9

Abstract We introduce Generative Spoken Language Modeling, the task of learning the acoustic and linguistic characteristics of a language from raw audio (no text, no labels), and a set of metrics to automatically evaluate the learned representations at acoustic and linguistic levels for both encoding and generation. We set up baseline systems consisting of a discrete speech encoder (returning pseudo-text units), a generative language model (trained on pseudo- text), and a speech decoder (generating a waveform from pseudo-text) all trained without supervision and validate the proposed metrics with human evaluation. Across 3 speech encoders (CPC, wav2vec 2.0, HuBERT), we find that the number of discrete units (50, 100, or 200) matters in a task-dependent and encoder- dependent way, and that some combinations approach text-based systems.1