# monotonic_align

**Repository Path**: ruby11dog/monotonic_align

## Basic Information

- **Project Name**: monotonic_align
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-10-17
- **Last Updated**: 2023-10-17

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

Adapted from the MAS in [Glow-TTS](https://github.com/jaywalnut310/glow-tts/tree/master/monotonic_align). I made it installable and added variants.

# Installation
```
pip install git+https://github.com/resemble-ai/monotonic_align.git
```
Installing `monotonic_align` doesn't require torch, but using ``monotonic_align`` will.
Please install PyTorch yourself, as its installation differ from system to system.


# How to Use
```python
# Suppose you have:
# 1. a probability matrix of size (batch_size=B, symbol_len=S, mel_lens=T)
#    NOTE: a similarity matrix (a higher score means better) or negative cost will do
#          but may have issues.
# 2. an array of symbol lengths `symbol_lens` of size (batch_size=B)
# 3. an array of mel-spectrogram lengths `mel_lens` of size (batch_size=B)

from monotonic_align import mask_from_lens, maximum_path
mask_ST = mask_from_lens(similarity, symbol_lens, mel_lens)
alignment = maximum_path(similarity, mask_ST)  # (B, S, T)

# NOTE:
# - If `mask` is not specified, the default mask is `True` for all elements.
# - You can specify `topology` if you want to use other variants of alignment algorithms.
```