| --- |
| license: mit |
| language: |
| - zh |
| - en |
| tags: |
| - audio |
| - speech-alignment |
| - wav2vec2 |
| - ctc |
| - forced-alignment |
| --- |
| |
| # 🌊 FlexAligner: Robust Speech-Text Alignment Framework |
|
|
| **FlexAligner** is a robust two-stage speech-text alignment framework designed specifically for "non-ideal" real-world acoustic data. |
| **FlexAligner** 是一种强鲁棒性的两阶段语音-文本对齐框架，专门为“非理想”的真实世界语音数据设计。 |
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| --- |
|
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| ## 🌟 Key Features / 核心功能 |
|
|
| ### English |
| - **Robustness to Mismatched Data**: Unlike traditional MFA (Montreal Forced Aligner), FlexAligner automatically identifies and skips mismatched segments (e.g., laughter, background noise, or un-transcribed words), preventing cumulative errors. |
| - **Two-Stage Architecture**: |
| 1. **Stage 1 (CTC Chunking)**: Macro-segmentation to locate reliable "speech islands." |
| 2. **Stage 2 (CE Alignment)**: Micro-alignment using dynamic hop calibration for sub-millisecond boundary regression. |
| - **Eliminating Temporal Drift**: Includes a self-calibrating decoding algorithm for long recordings, ensuring phoneme boundaries at the end of the file remain strictly aligned with samples. |
|
|
| ### 中文 |
| - **处理不匹配数据**：不同于传统的 MFA (Montreal Forced Aligner)，FlexAligner 能够自动识别并跳过音频与文本不匹配的部分（如笑声、长时间噪音或漏记的单词），而不会产生累积误差。 |
| - **两阶段对齐架构**： |
| 1. **Stage 1 (CTC Chunking)**: 宏观切分，定位可靠的语音“岛屿”。 |
| 2. **Stage 2 (CE Alignment)**: 微观对齐，利用动态步长校准实现亚毫秒级的边界回归。 |
| - **消除时间漂移**：针对长音频设计了自校准解码算法，确保音频末尾的音素边界依然能够与采样点严格对齐。 |
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| --- |
|
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| ## 📦 Model Components / 模型组成 |
|
|
| This repository contains the weights for the two core components of FlexAligner: |
| 本仓库包含 FlexAligner 运行所需的两套核心权重： |
|
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| - `hf_phs/`: Wav2Vec 2.0 based CTC chunking model. / 基于 Wav2Vec 2.0 训练的 CTC 切分模型。 |
| - `ce2/`: High-precision frame-level Cross-Entropy alignment model. / 高精度帧级别交叉熵对齐模型。 |
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| --- |
|
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| ## 🚀 Quick Start / 快速上手 |
|
|
| ### CLI Usage / 命令行使用 |
| After installation, you can call the cloud models directly via the CLI: |
| 安装后，你可以通过 CLI 命令行直接调用云端模型： |
|
|
| ```bash |
| flex-align input.wav transcript.txt --dynamic -o output.TextGrid |
| ``` |
|
|
| Python API |
| Or integrate it into your Python pipeline: / 或者在 Python 代码中集成： |
|
|
| ```Python |
| from flexaligner import FlexAligner |
| |
| # Use the Hugging Face Repo ID to automatically download and load weights |
| # 填入本仓库 ID，程序会自动处理模型下载与加载 |
| aligner = FlexAligner(config={ |
| "chunk_model_path": "USTCPhonetics/FlexAligner", |
| "use_dynamic_hop": True |
| }) |
| |
| aligner.align("test.wav", "test.txt", "result.TextGrid") |
| ``` |
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| --- |
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| 📜 License / 协议 |
| This project is licensed under the MIT License. Feel free to use it in academic research or commercial projects. 本项目遵循 MIT License。你可以自由地在学术研究或商业项目中使用。 |
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