HALL-E: Hierarchical Neural Codec Language Model for Minute-Long Zero-Shot Text-to-Speech Synthesis
Keywords: Text-to-speech synthesis, LLM-based TTS, neural audio codec, long-form generation
TL;DR: We introduce MReQ and HALL-E, methods that reduce frame rates in TTS models to enable minute-long speech synthesis, and present the MinutesSpeech dataset.
Abstract: Recently, Text-to-speech (TTS) models based on large language models (LLMs)
that translate natural language text into sequences of discrete audio tokens have
gained great research attention, with advances in neural audio codec (NAC) mod-
els using residual vector quantization (RVQ). However, long-form speech synthe-
sis remains a significant challenge due to the high frame rate, which increases the
length of audio tokens and makes it difficult for autoregressive language models
to generate audio tokens for even a minute of speech. To address this challenge,
this paper introduces two novel post-training approaches: 1) Multi-Resolution Re-
quantization (MReQ) and 2) HALL-E. MReQ is a framework to reduce the frame
rate of pre-trained NAC models. Specifically, it incorporates multi-resolution
residual vector quantization (MRVQ) module that hierarchically reorganizes dis-
crete audio tokens through teacher-student distillation. HALL-E is an LLM-based
TTS model designed to predict hierarchical tokens of MReQ. Specifically, it incor-
porates the technique of using MRVQ sub-modules and continues training from a
pre-trained LLM-based TTS model. Furthermore, to promote TTS research, we
create MinutesSpeech, a new benchmark dataset consisting of 40k hours of filtered
speech data for training and evaluating speech synthesis ranging from 3s up to
180s. In experiments, we demonstrated the effectiveness of our approaches by ap-
plying our post-training framework to VALL-E. We achieved the frame rate down
to as low as 8 Hz, enabling the stable minitue-long speech synthesis in a single
inference step. Audio samples, dataset, codes and pre-trained models are available
at https://yutonishimura-v2.github.io/HALL-E_DEMO.
Supplementary Material: zip
Primary Area: generative models
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Submission Number: 4372
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