semvec/pypi-packages huggingface dataset:
boio 0.1.0
An opinionated way of doing AI - Task-oriented ai nodes for general-computing
License: MIT
Requires Python: >=3.8
Keywords: ai, transcription, audio, whisper, canary, speech-to-text

<div align="center">
  <h1>boio</h1>
  <img src="public/icon.png" alt="icon" width="80" height="80">
  <p><em>an opinionated way of doing ai</em></p>
</div>

## Installation

```bash
uv venv
source .venv/bin/activate
uv pip install -e .
```

## Usage

```python
import boio

# Basic transcription
text = boio.transcribe("audio.wav")

# control source and target langs
text = boio.transcribe("audio.wav", source_lang="es", target_lang="es")
```

### Environment Setup

Create a `.env` file in your project root:

```bash
# .env
SURUS_API_KEY=your_api_key_here
```

Or set the environment variable directly:
```bash
export SURUS_API_KEY="your_api_key"
```

## Development

```bash
# Setup environment
uv venv
source .venv/bin/activate
uv pip install -e ".[dev]"

# Run tests
pytest tests/
```
LRU-tensorflow 0.1.2
Unofficial TensorFlow implementation of a Linear Recurrent Unit, proposed by Google Deepmind.
License: MIT
Keywords: Artificial Intelligence, Deep Learning, Recurrent Neural Networks, Linear Recurrent Unit

# Linear Recurrent Units in Tensorflow: An Unofficial Implementation
This repository presents an unofficial implementation of Linear Recurrent Units (LRUs) proposed by Google DeepMind, utilizing Tensorflow. LRUs draw inspiration from Deep State-Space Machines, with a particular focus on S4 and S5 models.

# Installation:
```
$ pip install LRU-tensorflow
```
# Usage:
```python
import tensorflow as tf
from LRU_tensorflow import LRU

# Define model dimensions
state_features = 19  # N: state dimension
input_size     = 5   # H: model (input) dimension

# Create LRU layer
lru = LRU(N=state_features, H=input_size)

# Prepare sample input: (batch_size, seq_length, input_size)
batch_size = 32
seq_length = 50
test_input = tf.random.uniform((batch_size, seq_length, input_size))

# Forward pass
predictions = lru(test_input)

# Output shape: (batch_size, seq_length, input_size)
print(predictions.shape)
# Expected: (32, 50, 5)
```

# Paper:
<a href='https://arxiv.org/abs/2303.06349'>Resurrecting Recurrent Neural Networks for Long Sequences</a>


# Notes:
+ If you require an implementation that supports 3-dimensional input sequences, you may want to refer to <a href='https://github.com/Gothos/LRU-pytorch'>github.com/Gothos/LRU-pytorch</a>. However, please be aware that this alternative implementation might be slower due to the absence of associative scans.