Table of Links Abstract and 1. Introduction Related Work
Model
Experiments
Ablation Study
Conclusion, Limitations, and Risks 3. Model This section introduces SEAMLESSEXPRESSIVELM, a decoder-only language model for style transferred speech-to-speech translation. 3.1 Speech Tokenizers Speech tokenizers convert continuous speech waveform into a sequence of discrete units. HuBERT is used to derive semantic units of speech which mainly keep the semantic information (Hsu et al., 2021). EnCodec extracts multi-codebook units to carry more fine-grained acoustic information in speech such as speaker vocal style and intonation(Défossez et al., 2022). Suppose that EnCodec has C codebooks, and C = 8 in our experiments. Both HuBERT and EnCodec units are leveraged in our modeling as described below. 3.2 Architecture As illustrated in Figure 1, the input sequence reflects the chain of thoughts in translation process. The model starts from semantic translation, proceeds to transfer the style from prompt acoustic units to the first acoustic stream and lastly learns the mapping to residual acoustic streams. Embedding layer. We construct embedding tables for semantic and acoustic units to vectorize the speech tokens before passing them to the speech language model. For semantic units and first-stream acoustic units, their embeddings can be retrieved directly from the embedding table Emb(·). As for prompt acoustic tokens from multiple streams, we sum up embeddings from all codebooks as the acoustic embedding in each position. 3.3 Training Acoustic prompt. Prompt acoustic units Aprompt provide all acoustic information to be preserved in the target speech. Ideally given source and target speech which both semantically and acoustically aligned, the model could simply use source units as the acoustic prompt. Now we want to relax the data requirements and use only semantically aligned speech. We carve a portion of target speech as the acoustic prompt. To prevent the model from naively copy-paste the acoustic prompt in target acoustic generation, the acoustic prompt is randomly selected at each train step and the prompt length has non-trivial effect on mdel performance. The designs of acoustic prompt is discussed in Section 5.2 AR loss. The autoregressive layers takes care of target semantic units and first-stream acoustic units. We use chain-of-thought to train the model to first translate speech semantically and then transfer speaker vocal style to the target speech. AR layers of SEAMLESSEXPRESSIVELM are trained with next token prediction similar to existing language models. It is noted that source semantic units and source acoustic units are given as the prompt, and these tokens do not count towards the training loss. Assume that P(·) is the ground truth probability (either 0 or 1) of target units. NAR loss. In each train step, we randomly select one stream c of acoustic units for NAR layers to predict. 3.4 Inference The model decodes target semantic units with beam search, and then generates acoustic units with temperature sampling. Target speech is synthesized by EnCodec decoder from predicted acoustic units. Authors:
(1) Hongyu Gong, Meta AI (hygong@meta.com);
(2) Bandhav Veluri, Meta AI (bandhav@meta.com). This paper is available on arxiv under CC BY 4.0 DEED license. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Related Work Model Experiments Ablation Study Conclusion, Limitations, and Risks Related Work Related Work Model Model Experiments Experiments Ablation Study Ablation Study Conclusion, Limitations, and Risks Conclusion, Limitations, and Risks 3. Model This section introduces SEAMLESSEXPRESSIVELM, a decoder-only language model for style transferred speech-to-speech translation. 3.1 Speech Tokenizers Speech tokenizers convert continuous speech waveform into a sequence of discrete units. HuBERT is used to derive semantic units of speech which mainly keep the semantic information (Hsu et al., 2021). EnCodec extracts multi-codebook units to carry more fine-grained acoustic information in speech such as speaker vocal style and intonation(Défossez et al., 2022). Suppose that EnCodec has C codebooks, and C = 8 in our experiments. Both HuBERT and EnCodec units are leveraged in our modeling as described below. 3.2 Architecture As illustrated in Figure 1, the input sequence reflects the chain of thoughts in translation process. The model starts from semantic translation, proceeds to transfer the style from prompt acoustic units to the first acoustic stream and lastly learns the mapping to residual acoustic streams. Embedding layer. We construct embedding tables for semantic and acoustic units to vectorize the speech tokens before passing them to the speech language model. For semantic units and first-stream acoustic units, their embeddings can be retrieved directly from the embedding table Emb(·). As for prompt acoustic tokens from multiple streams, we sum up embeddings from all codebooks as the acoustic embedding in each position. Embedding layer. 3.3 Training Acoustic prompt. Prompt acoustic units A prompt provide all acoustic information to be preserved in the target speech. Ideally given source and target speech which both semantically and acoustically aligned, the model could simply use source units as the acoustic prompt. Now we want to relax the data requirements and use only semantically aligned speech. We carve a portion of target speech as the acoustic prompt. To prevent the model from naively copy-paste the acoustic prompt in target acoustic generation, the acoustic prompt is randomly selected at each train step and the prompt length has non-trivial effect on mdel performance. The designs of acoustic prompt is discussed in Section 5.2 Acoustic prompt. A AR loss. The autoregressive layers takes care of target semantic units and first-stream acoustic units. We use chain-of-thought to train the model to first translate speech semantically and then transfer speaker vocal style to the target speech. AR layers of SEAMLESSEXPRESSIVELM are trained with next token prediction similar to existing language models. It is noted that source semantic units and source acoustic units are given as the prompt, and these tokens do not count towards the training loss. Assume that P(·) is the ground truth probability (either 0 or 1) of target units. AR loss. NAR loss. In each train step, we randomly select one stream c of acoustic units for NAR layers to predict. NAR loss. 3.4 Inference The model decodes target semantic units with beam search, and then generates acoustic units with temperature sampling. Target speech is synthesized by EnCodec decoder from predicted acoustic units. Authors: (1) Hongyu Gong, Meta AI (hygong@meta.com); (2) Bandhav Veluri, Meta AI (bandhav@meta.com). Authors : Authors (1) Hongyu Gong, Meta AI (hygong@meta.com); (2) Bandhav Veluri, Meta AI (bandhav@meta.com). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

SEAMLESSEXPRESSIVELM Unifies Semantic & Acoustic Modeling for Efficient Speech Translation

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Ablation Study Reveals the Role of Semantic & Acoustic Prompts in SEAMLESSEXPRESSIVELM's Performance

Future of Speech Translation with SEAMLESSEXPRESSIVELM

Advancements in Speech Translation Address Inefficiency and Errors in Cascaded Models

Getting the Most out of a Large Language Model

Deductive Verification of Chain-of-Thought Reasoning in LLMs

Solving the AI Hallucination Problem with Self-Verifying Natural Programs

Ablation Study Reveals the Role of Semantic & Acoustic Prompts in SEAMLESSEXPRESSIVELM's Performance

Future of Speech Translation with SEAMLESSEXPRESSIVELM

Advancements in Speech Translation Address Inefficiency and Errors in Cascaded Models

Getting the Most out of a Large Language Model

Deductive Verification of Chain-of-Thought Reasoning in LLMs

Solving the AI Hallucination Problem with Self-Verifying Natural Programs

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps