Habibi: Laying the Open-Source Foundation of Unified-Dialectal Arabic Speech Synthesis
Abstract
A notable gap persists in speech synthesis research and development for Arabic dialects, particularly from a unified modeling perspective. Despite its high practical value, the inherent linguistic complexity of Arabic dialects, further compounded by a lack of standardized data, benchmarks, and evaluation guidelines, steers researchers toward safer ground. To bridge this divide, we present Habibi, a suite of specialized and unified text-to-speech models that harnesses existing open-source ASR corpora to support a wide range of high- to low-resource Arabic dialects through linguistically-informed curriculum learning. Our approach outperforms the leading commercial service in generation quality, while maintaining extensibility through effective in-context learning, without requiring text diacritization. We are committed to open-sourcing the model, along with creating the first systematic benchmark for multi-dialect Arabic speech synthesis. Furthermore, by identifying the key challenges in and establishing evaluation standards for the process, we aim to provide a solid groundwork for subsequent research. Resources at https://SWivid.github.io/Habibi/ .
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of Habibi, an open-source framework for unified-dialectal Arabic speech synthesis that demonstrates high-quality zero-shot synthesis capabilities across diverse Arabic dialects. This work significantly advances the field of speech synthesis for Arabic by addressing the challenges posed by dialectal diversity and data scarcity through innovative methodologies and comprehensive evaluation strategies.
Methodology
The methodology is robust, leveraging linguistically-informed curriculum learning and a comprehensive data curation pipeline to address the unique challenges of Arabic dialectal speech synthesis. The authors provide a systematic approach to unify various dialects into a single model, which is innovative given the linguistic diversity and data scarcity in Arabic dialects. The use of existing ASR corpora and the implementation of a two-stage curriculum learning strategy are well-justified and effectively executed.
Results & evaluation
The experimental evaluation is thorough, utilizing a systematic benchmark for multi-dialect Arabic zero-shot TTS and comparing the performance of their models against leading commercial systems. The results are presented clearly, demonstrating the effectiveness of their approach across multiple dialects. The ablation studies provide additional insights into the impact of different training strategies and data quality on model performance.
Reproducibility
The paper outlines the training setup and evaluation metrics in detail, which aids in reproducibility. However, the reliance on specific datasets and the absence of a direct link to the datasets used may pose challenges for complete replication of results. The authors do commit to open-sourcing their models, which is a positive step towards reproducibility.
Limitations
The paper acknowledges limitations such as the lack of explicit support for code-switching scenarios and the need for more effective checkpoint fusion strategies. It also notes that data quality remains a significant factor affecting performance, which could be further explored in future work.
Broader impact
This work has significant implications for speech synthesis technology in Arabic, a language with substantial dialectal diversity. By providing an open-source framework and benchmark, it lays the groundwork for future research and development in this area, potentially enhancing accessibility and usability of TTS systems for Arabic speakers. The approach could also inspire similar methodologies for other low-resource languages. The main contribution of this paper is the introduction of Habibi, an open-source framework for unified-dialectal Arabic speech synthesis that demonstrates high-quality zero-shot synthesis capabilities across diverse Arabic dialects. This work significantly advances the field of speech synthesis for Arabic by addressing the challenges posed by dialectal diversity and data scarcity through innovative methodologies and comprehensive evaluation strategies.
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