semvec/pypi-packages huggingface dataset:
voicehelper.py 0.1.7
These are two neural networks connected by the same module. One for recognition, the other for voice generation.
License: MIT
Requires Python: >=3.9

# voicehelper.py
## РћРїРёСЃР°РЅРёРµ
Р­С‚Рѕ РјРѕРґСѓР»СЊ СЃРѕРµРґРёРЅРёРІС€РёР№ РІ СЃРµР±Рµ РґРІРµ РЅРµР№СЂРѕРЅРЅС‹Рµ СЃРµС‚Рё. Р”Р»СЏ СѓРїСЂРѕС‰РµРЅРёСЏ СЃРѕР·РґР°РЅРёСЏ РіРѕР»РѕСЃРѕРІРѕРіРѕ РїРѕРјРѕС€РЅРёРєР°.
## РЈСЃС‚Р°РЅРѕРІРєР°
```cmd
pip install --upgrade voicehelper.py
```

`!!! Р’РќРРњР•РќРР• !!!` РЈСЃС‚Р°РЅРѕРІРєР° **torch** РґРѕР»Р¶РЅР° РїСЂРѕРёСЃС…РѕРґРёС‚СЊ РѕС‚РґРµР»СЊРЅРѕ. [РЎР°Р№С‚ PyTorch Installation](https://pytorch.org/get-started/locally/#start-locally)

## РќР°Р·РІР°РЅРёРµ РјРѕРґРµР»РµР№ РґР»СЏ Р·Р°РіСЂСѓР·РєРё
- Р”Р»СЏ [SpeechSynthesizer](https://github.com/snakers4/silero-models#v3)
- Р”Р»СЏ [SpeechRecognition](https://github.com/romanin-rf/voicehelper.py-models#for-speechrecognition)


## РџСЂРёРјРµСЂ
- РџСЂРёРјРµСЂ СЃРѕР·РґР°РЅРёСЏ Р“РѕР»РѕСЃРѕРІРѕРіРѕ РђСЃСЃРёСЃС‚РµРЅС‚Р°
```python
import VoiceHelper
from rich.console import Console

# РљРѕРЅСЃРѕР»СЊ
c = Console()

# РљР»Р°СЃСЃ РЅРµР№СЂРѕСЃРµС‚Рё
c.clear()
c.rule("Р—Р°РїСѓСЃРє")

# РџСЂРѕС†РµСЃСЃ РёРЅРёС†Р°Р»РёР·Р°С†РёРё
try:
    ssynth = VoiceHelper.SpeechSynthesizer(
        device_type="cuda"
    )
except:
    ssynth = VoiceHelper.SpeechSynthesizer(
        device_type="cpu"
    )
vh = VoiceHelper.VoiceHelper(
    ssynth,
    VoiceHelper.SpeechRecognition()
)

# РљРѕРјР°РЅРґС‹
@vh.add_command("РїСЂРёРІРµС‚")
def Hello(event: VoiceHelper.Event):
    vh.say("РђРіР°, СЏ Р·РґРµСЃСЊ!")

@vh.add_command(["СЃРєР°Р¶Рё <text>"])
def cSay(event: VoiceHelper.Event, text: str):
    vh.say(text)

@vh.add_command("РїРѕРєР°")
def cGoodBye(event: VoiceHelper.Event):
    vh.say("Р”Рѕ СЃРІРёРґР°РЅРёСЏ!")
    vh.stop()

@vh.add_command("<text>")
def cLogger(event: VoiceHelper.Event, text: str) -> None:
    c.print(f"[red]You[/] [green]->[/] [yellow]{text}[/]")

# Р—Р°РїСѓСЃРє
if __name__ == "__main__":
    c.rule("Р›РѕРіРё")
    vh.start()
```
- РџСЂРёРјРµСЂ РіРµРЅРµСЂР°С†РёРё РіРѕР»РѕСЃР°
```python
from VoiceHelper.NueroNetworks import SpeechSynthesizer

ss = SpeechSynthesizer(sample_rate=48000, thread_count=1) # РїРѕ СѓРјРѕР»С‡Р°РЅРёСЋ СЃС‚РѕРёС‚ СЂСѓСЃСЃРєР°СЏ СЂРµС‡СЊ

with open("test.wav", "wb") as file:
    file.write(ss.get_audio_bytes("РџСЂРёРІРµС‚!", "WAV", "PCM_32"))
```
ShapeY 0.1.8
Benchmark that tests shape recognition
License: MIT
Keywords: tests shape recognition capacity

# ShapeY

ShapeY is a benchmark that tests a vision system's shape recognition capacity. ShapeY currently consists of ~68k images of 200 3D objects taken from ShapeNet. Note that this benchmark is not meant to be used as a training dataset, but rather serves to validate that the visual object recogntion / classification under inspection has developed a capacity to perform well on our benchmarking tasks, which are designed to be hard if the system does not understand shape.

## Installing ShapeY
Requirements: Python 3, Cuda version 10.2 (prerequisite for cupy)

To install ShapeY, run the following command:
```
pip3 install shapey==0.1.7
```

## Step0: Download ShapeY200 dataset
Run `download.sh` to download the dataset. The script automatically unzips the images under `data/ShapeY200/`.
Downloading uses gdown, which is google drive command line tool. If it does not work, please just follow the two links down below to download the ShapeY200 / ShapeY200CR datasets.

ShapeY200:
https://drive.google.com/uc?id=1arDu0c9hYLHVMiB52j_a-e0gVnyQfuQV

ShapeY200CR:
https://drive.google.com/uc?id=1WXpNUVRn6D0F9T3IHruml2DcDCFRsix-

After downloading the two datasets, move each of them to the `data/` directory. For example, all of the images for ShapeY200 should be under `data/ShapeY200/dataset/`.

## Step1: Extract the embedding vectors from your own vision model using our dataset
Implement the function `your_feature_output_code` in `step1_save_feature/your_feature_extraction_code.py`. The function should take in the path to the dataset as input and return two lists - one for the image names and another for the corresponding embedding vectors taken from whatever system.

## Step2: Run macro.py
After you have implemented the function, run `macro.py` to generate the results.
`macro.py` will automatically run the following steps:
1. Compute correlation between all embedding vectors (using `step2_compute_feature_correlation/compute_correlation.py`)

2. Run benchmark analysis (using `step3_benchmark_analysis/get_nn_classification_error_with_exclusion_distance.py`)

3. Graph results (top1 object matching error, top1 category matching error, etc.)