semvec/pypi-packages huggingface dataset:
allvoicelabs-mcp 0.0.2
AllVoiceLabs MCP Server
Requires Python: >=3.11
Keywords: elevenlabs, mcp, text-to-speech, speech-to-text, voice-cloning

# AllVoiceLabs MCP

This is an AllVoiceLabs MCP (Model Control Protocol) service that provides voice synthesis and conversion functionality.

## Quickstart

1. Get your API key from [AllVoiceLab](https://www.allvoicelab.com/).
2. Install `uv` (Python package manager), install with `curl -LsSf https://astral.sh/uv/install.sh | sh`
3. Use Cline for example:

```json
{
  "mcpServers": {
    "AllVoceLabs": {
      "command": "uvx",
      "args": ["allvoicelabs-mcp"],
      "env": {
        "ALLVOICELABS_API_KEY": "<insert-your-api-key-here>",
        "ALLVOICELABS_API_DOMAIN": "<insert-api-domain-here>"
      }
    }
  }
}
```
electrocute 0.1.2
A Python library for electronic formulas and signal processing
Requires Python: >=3.7

# ⚡ Electrocute

**Electrocute** is a Python library built for electronics and signal processing enthusiasts. Whether you're an engineer, student, or hobbyist, this package offers intuitive functions for working with electrical formulas and analyzing signals with ease.

---

## 📦 Installation

```bash
pip install electrocute
```

---

## 🚀 Quick Start

### Basic Electronic Formulas

```python
from electrocute.formulas.basic import BasicFormulas

# Calculate voltage using Ohm's Law: V = I * R
voltage = BasicFormulas.ohms_law(current=2, resistance=5)
print(f"Voltage: {voltage} V")  # Output: Voltage: 10 V

# Calculate power: P = V * I
power = BasicFormulas.power(voltage=10, current=2)
print(f"Power: {power} W")  # Output: Power: 20 W
```

### Frequency Spectrum Analysis

```python
import numpy as np
from electrocute.signal_processing.transforms import Transforms

# Generate a simple sine wave signal
t = np.linspace(0, 1, 1000, endpoint=False)
signal = np.sin(2 * np.pi * 10 * t)

# Get the frequency spectrum
freqs, amps = Transforms.frequency_spectrum(signal, sampling_rate=1000)
```

### Filtering Signals

```python
from electrocute.signal_processing.filters import Filters

# Create a signal with multiple frequency components
t = np.linspace(0, 1, 1000, endpoint=False)
signal = np.sin(2 * np.pi * 10 * t) + 0.5 * np.sin(2 * np.pi * 20 * t)

# Apply a low-pass filter with a cutoff at 15 Hz
filtered_signal = Filters.low_pass_filter(signal, cutoff=15, sampling_rate=1000)
```

---

## 🔧 Features

- 📐 **Electronic Formulas**: Ohm's Law, power, and more.
- 🎚️ **Signal Processing**: FFT, DFT, filtering, and spectral analysis.
- 📚 **Extensible API**: Easy-to-use, modular design for seamless integration.

---

## 🤝 Contributing

Contributions are welcome!  
If you'd like to report a bug, request a feature, or contribute code, feel free to open an issue or submit a pull request on [GitHub](https://github.com/madhurthareja/electrocute).

---

## 👨‍💻 Maintainers

This library is actively developed and maintained by **Madhur Thareja**.

---

## 📄 License

Licensed under the [MIT License](https://opensource.org/licenses/MIT).  
Feel free to use, modify, and distribute this library.
simple-stories-train 0.0.1
Training of small LM models for SimpleStories
Requires Python: >=3.11

# simple_stories_train

Project for training small LMs. Designed for training on SimpleStories, an extension of
[TinyStories](https://arxiv.org/abs/2305.07759).


- Training script is based on the efficeint [train_gpt2.py](https://github.com/karpathy/llm.c/blob/master/train_gpt2.py) in [llm.c](https://github.com/karpathy/llm.c) (licensed
  under MIT ((c) 2024 Andrei Karpathy))
- Some model architecture implementations are based on
  [TransformerLens](https://github.com/TransformerLensOrg/TransformerLens) (licensed under
  MIT ((c) 2022 TransformerLensOrg)).

## Installation

From the root of the repository, run one of

```bash
make install-dev  # To install the package, dev requirements and pre-commit hooks
make install  # To just install the package (runs `pip install -e .`)
```

## Development

Suggested extensions and settings for VSCode are provided in `.vscode/`. To use the suggested
settings, copy `.vscode/settings-example.json` to `.vscode/settings.json`.

There are various `make` commands that may be helpful

```bash
make check  # Run pre-commit on all files (i.e. pyright, ruff linter, and ruff formatter)
make type  # Run pyright on all files
make format  # Run ruff linter and formatter on all files
make test  # Run tests that aren't marked `slow`
make test-all  # Run all tests
```

## Usage

### Training a model
```
python train_llama.py [PATH/TO/CONFIG.yaml] [--key1 value1 --key2 value2 ...]
```
where
- `PATH/TO/CONFIG.yaml` contains the training config. If no path is provided, a default config will be used.
- `--key1 value1 --key2 value2 ...` override values in the config. Note that if you wish to update a
  nested value, you must use dotted notation (e.g. `--train_dataset_config.name my_dataset`).

If running on CPU, you may need to set `--compile=False`.

To run on multiple GPUs, use
```
torchrun --standalone --nproc_per_node=N train_llama.py ...
```
where `N` is the number of GPUs to use.

### Logging with Weights & Biases
To track training with Weights & Biases, you can set the WANDB_PROJECT and WANDB_API_KEY variables in
`.env`. API keys can be obtained from your [Weights & Biases account settings](https://wandb.ai/settings).
F6Transform 0.1.0
F6 Transform: A minimal library for 3D rigid transformations.
Requires Python: >=3.7

# F6Transform

**F6 Transform**: A minimal library for 3D rigid transformations.

This library provides essential operations for 3D translations and rotations, including:
- Conversion between Euler angles and rotation matrices
- Conversion between 6D motion representations and homogeneous matrices
- Inversion and combination of motions

## Installation

You can install F6Transform via pip:

```bash
pip install F6Transform