---
title: FALCON Forced Aligner
emoji: 🦅
colorFrom: indigo
colorTo: blue
sdk: gradio
sdk_version: 4.44.1
python_version: "3.8"
app_file: app.py
pinned: false
license: mit
short_description: Neural forced alignment via Soft Dynamic Programming
---

# FALCON — Forced Alignment through Contrastive Optimization Networks

Interactive demo of **FALCON**, a fully differentiable neural forced aligner that predicts
precise **phoneme- and word-level** boundary timestamps from a waveform + transcript, using a
Soft Dynamic Programming decoder.

Upload audio + a transcript (`.phn` / `.wrd` / `.txt`), choose the options, and get a boundary
table, a downloadable Praat `.TextGrid`, and a time-aligned visualization (waveform ·
spectrogram · phoneme posteriors · Soft-DP path · contrastive score).

- **Paper:** *Fully Differentiable Neural Forced Alignment via Soft Dynamic Programming* — [arXiv:2606.25460](https://arxiv.org/abs/2606.25460)
- **Code:** https://github.com/MLSpeech/FALCON
- **Weights:** https://huggingface.co/MLSpeech/FALCON-weights

Example inputs are in `assets/` — the TIMIT sentence *"Don't ask me to carry an oily rag like that."*
in every supported format. The checkpoints are downloaded automatically from the weights repo on
first use (this runs on a free CPU Space, so the first alignment takes a moment to fetch a model).