| # NeMo Forced Aligner (NFA) |
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| <p align="center"> |
| Try it out: <a href="https://huggingface.co/spaces/erastorgueva-nv/NeMo-Forced-Aligner">HuggingFace Space 🎤</a> | Tutorial: <a href="https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/tools/NeMo_Forced_Aligner_Tutorial.ipynb">"How to use NFA?" 🚀</a> | Blog post: <a href="https://nvidia.github.io/NeMo/blogs/2023/2023-08-forced-alignment/">"How does forced alignment work?" 📚</a> |
| </p> |
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| <p align="center"> |
| <img width="80%" src="https://github.com/NVIDIA/NeMo/releases/download/v1.20.0/nfa_forced_alignment_pipeline.png"> |
| </p> |
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| NFA is a tool for generating token-, word- and segment-level timestamps of speech in audio using NeMo's CTC-based Automatic Speech Recognition models. You can provide your own reference text, or use ASR-generated transcription. You can use NeMo's ASR Model checkpoints out of the box in [14+ languages](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/results.html#speech-recognition-languages), or train your own model. NFA can be used on long audio files of 1+ hours duration (subject to your hardware and the ASR model used). |
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| ## Quickstart |
| 1. Install [NeMo](https://github.com/NVIDIA/NeMo#installation). |
| 2. Prepare a NeMo-style manifest containing the paths of audio files you would like to process, and (optionally) their text. |
| 3. Run NFA's `align.py` script with the desired config, e.g.: |
| ``` bash |
| python <path_to_NeMo>/tools/nemo_forced_aligner/align.py \ |
| pretrained_name="stt_en_fastconformer_hybrid_large_pc" \ |
| manifest_filepath=<path to manifest of utterances you want to align> \ |
| output_dir=<path to where your output files will be saved> |
| ``` |
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| <p align="center"> |
| <img src="https://github.com/NVIDIA/NeMo/releases/download/v1.20.0/nfa_run.png"> |
| </p> |
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| ## Documentation |
| More documentation is available [here](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/tools/nemo_forced_aligner.html). |
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