#python#deep_learning#inference#openai#quantization#speech_recognition#speech_to_text#transformer#whisper
Faster-Whisper is a fast version of OpenAI's Whisper that transcribes audio up to 4x quicker with the same accuracy, using less memory on CPU or GPU—benchmarks show it beats original Whisper (e.g., 1m03s vs 2m23s for 13-min audio on GPU). Install via `pip install faster-whisper`, no FFmpeg needed, and use simple Python code like `WhisperModel("large-v3").transcribe("audio.mp3")` for segments with timestamps. You benefit by getting quick, efficient speech-to-text for real-time apps, saving time and resources on long files or batches.
https://github.com/SYSTRAN/faster-whisper
# The standard string repr for dicts is hard to read:
»> my_mapping = {'a': 23, 'b': 42, 'c': 0xc0ffee}
»> my_mapping
{'b': 42, 'c': 12648430. 'a': 23} # 😞
# The "#json" module can do a much better job:
»> import json
»> print(json.dumps(my_mapping, indent=4, sort_keys=True))
{
"a": 23,
"b": 42,
"c": 12648430
}
# Note this only works with dicts containing
# primitive types (check out the "pprint" module):
»> json.dumps({all: 'yup'})
TypeError: keys must be a string
История(12м) как в Альфа-Банке сокращали размер JSON файла, который передается на устройство для работы SDUI. Решением стала шаблонизация для отказа от одинаковых блоков UI с разными данными
#оптимизация#json
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