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--- |
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license: apache-2.0 |
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task_categories: |
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- audio-to-audio |
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- text-to-speech |
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tags: |
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- audio-super-resolution |
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--- |
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# LJSpeech-1.1 High-Resolution Dataset (48,000 Hz) |
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This dataset was created using the method described in [HiFi-SR: A Unified Generative Transformer-Convolutional Adversarial Network for High-Fidelity Speech Super-Resolution](https://huggingface.co/papers/2501.10045) and is part of [ClearerVoice-Studio: Bridging Advanced Speech Processing Research and Practical Deployment](https://huggingface.co/papers/2506.19398) ([Github](https://github.com/modelscope/ClearerVoice-Studio)). |
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The LJSpeech-1.1 dataset, widely recognized for its utility in text-to-speech (TTS) and other speech processing tasks, has now been enhanced through a cutting-edge speech |
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super-resolution algorithm. The original dataset, which featured a sampling rate of 22,050 Hz, has been upscaled to 48,000 Hz using [**ClearerVoice-Studio**](https://github.com/modelscope/ClearerVoice-Studio), providing a high-fidelity version suitable |
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for advanced audio processing tasks [1]. |
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**Key Features** |
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- High-Resolution Audio: The dataset now offers audio files at a sampling rate of 48,000 Hz, delivering enhanced perceptual quality with richer high-frequency details. |
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- Original Content Integrity: The original linguistic content and annotation structure remain unchanged, ensuring compatibility with existing workflows. |
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- Broader Application Scope: Suitable for professional-grade audio synthesis, TTS systems, and other high-quality audio applications. |
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- Open Source: Freely available for academic and research purposes, fostering innovation in the speech and audio domains. |
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**Original Dataset** |
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- Source: The original LJSpeech-1.1 dataset contains 13,100 audio clips of a single female speaker reading passages from public domain books. |
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- Duration: Approximately 24 hours of speech data. |
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- Annotations: Each audio clip is paired with a corresponding text transcript. |
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**Super-Resolution Processing** |
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The original 22,050 Hz audio recordings were processed using a state-of-the-art MossFormer2-based speech super-resolution model. This model employs: |
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- Advanced Neural Architectures: A combination of transformer-based sequence modeling and convolutional networks. |
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- Perceptual Optimization: Loss functions designed to preserve the naturalness and clarity of speech. |
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- High-Frequency Reconstruction: Algorithms specifically tuned to recover lost high-frequency components, ensuring smooth and artifact-free enhancement. |
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**Output Format** |
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- Sampling Rate: 48,000 Hz |
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- Audio Format: WAV |
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- Bit Depth: 16-bit |
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- Channel Configuration: Mono |
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**Use Cases** |
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1. Text-to-Speech (TTS) Synthesis |
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○ Train high-fidelity TTS systems capable of generating human-like speech. |
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○ Enable expressive and emotionally nuanced TTS outputs. |
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2. Speech Super-Resolution Benchmarking |
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○ Serve as a reference dataset for evaluating speech super-resolution algorithms. |
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○ Provide a standardized benchmark for perceptual quality metrics. |
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3. Audio Enhancement and Restoration |
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○ Restore low-resolution or degraded speech signals for professional applications. |
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○ Create high-quality voiceovers and narration for multimedia projects. |
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**File Structure** |
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The dataset retains the original LJSpeech-1.1 structure, ensuring ease of use: |
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```sh |
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LJSpeech-1.1-48kHz/ |
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├── metadata.csv # Text transcriptions and audio file mappings |
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├── wavs/ # Directory containing 48,000 Hz WAV files |
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└── LICENSE.txt # License information |
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``` |
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**Licensing** |
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The LJSpeech-1.1 High-Resolution Dataset is released under the same open license as the original LJSpeech-1.1 dataset (https://keithito.com/LJ-Speech-Dataset/). Users are free to use, modify, and share the dataset for academic and non-commercial purposes, provided proper attribution is given. |
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[1] Shengkui Zhao, Kun Zhou, Zexu Pan, Yukun Ma, Chong Zhang, Bin Ma, "[HiFi-SR: A Unified Generative Transformer-Convolutional Adversarial Network for High-Fidelity Speech Super-Resolution](https://arxiv.org/abs/2501.10045)", ICASSP 2025. |
