Skip to main content

A python package for out-of-the-box ML solutions

Project description

Common ML Functionalities API

Python package which provides useful and common interactions with ML functionalities
such as detecting language, chatbots, link transcriptions, audio enhancements, and much more.

Powered by Sieve, Langchain


Features

  • Translation (sieve/seamless_text2text): Translates text from a source language to a target language.
  • Youtube Link Transcription (langchain/youtube_transcripts): Transcribes audio from a given URL.
  • Detect Language (sieve): Identifies the language of the given text.
  • Text to Speech (sieve): Converts text into spoken words.
  • Translate and Talk (sieve): Translates text from one language to another and then converts the translated text to speech.
  • Youtube Video Download (sieve): Downloads a Youtube video in MP4 format given its URL.
  • Detect Language and Translate: Detects the language of the text and translates it to a specified target language.
  • Audio Enhancement (sieve): Enhances the quality of an mp3 or wav file.
  • Visual question answering (sieve/llava-vl-13b): Visual question answering with GPT-4 level capabilities.

Setup

To set up the package, follow these steps:

  1. Install the Package: Install this package using pip: pip install minimalml

  2. Import in Your Project: Import the required functionalities in your Python project:

from minimalml.translation import translate
from minimalml.text_to_speech import convert_text_to_speech
# ... other imports as needed

Usage

The package offers a range of functionalities that can be easily integrated into Python projects. Here are some of the features and how to use them:


Translation: Use translate(original_text, source_language, target_language) to translate text.

from minimalml.language import translate

translated_text = translate("Hello, world!", "en", "es")
print(translated_text)

Youtube Link Transcription: Use yb_transcript(url) to get transcriptions of YouTube videos.

from minimalml.yb import yb_transcript

transcript = yb_transcript("https://www.youtube.com/watch?v=example")
print("Transcript:", transcript)

Detect Language: Use detect(text) to identify the language of a given text.

from minimalml.language import detect

language_code = detect("Ceci est un texte en français.")
print(f"The detected language code is: {language_code}")

Text to Speech: Use convert_text_to_speech(text) to convert text into speech.

from minimalml.text_to_speech import convert_text_to_speech

audio_file_path = convert_text_to_speech("Hello, this is a test.")
print(f"Audio file saved at: {audio_file_path}")

Translate and Talk: Use translate_and_talk(text, target_language) to translate and convert text to speech.

from minimalml.text_to_speech import translate_and_talk

audio_file_path = translate_and_talk("This is a test.", "es")
print(f"Translated and converted to speech, file saved at: {audio_file_path}")

Youtube Video Download: Use yb_download(url) to download YouTube videos.

from minimalml.yb import yb_download

video_path = yb_download("https://www.youtube.com/watch?v=example")
print(f"Video downloaded at: {video_path}")

Detect Language and Translate: Combine detect and translate for detecting and translating text.

from minimalml.language import detect_and_translate

translated_text = detect_and_translate("Bonjour le monde!", "en")
print(translated_text)

Audio Enhancement: Use enhance_audio(audio_url, speed_boost, steps) for audio enhancement.

from minimalml.audio import enhance_audio

enhanced_audio_path = enhance_audio("http://example.com/audio.mp3", True, 50)
print(f"Enhanced audio saved at: {enhanced_audio_path}")

Visual question answering: Use visual_question_answering(image_url, prompt) for visual Q&A.

from minimalml.visual import visual_question_answering

answer = visual_question_answering("http://example.com/image.jpg", "What is depicted in this image?")
print(answer)

Contributing!

Please consider contributing to this young and humble project! Email any questions at andere.emi@gmail.com

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

minimalml-0.1.2.tar.gz (3.3 kB view details)

Uploaded Source

Built Distribution

minimalml-0.1.2-py3-none-any.whl (2.9 kB view details)

Uploaded Python 3

File details

Details for the file minimalml-0.1.2.tar.gz.

File metadata

  • Download URL: minimalml-0.1.2.tar.gz
  • Upload date:
  • Size: 3.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.6

File hashes

Hashes for minimalml-0.1.2.tar.gz
Algorithm Hash digest
SHA256 13a3404b84db424f8df0723c1d791da871b1559a8c642a01958f8ed1c80ab13b
MD5 333a8ecf3e92969897d086bc674d5cdf
BLAKE2b-256 dd763a87a7f17b53a7e78aee5ca4ec2a0985d04914abe4a5b25a60ca2baf4c84

See more details on using hashes here.

File details

Details for the file minimalml-0.1.2-py3-none-any.whl.

File metadata

  • Download URL: minimalml-0.1.2-py3-none-any.whl
  • Upload date:
  • Size: 2.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.6

File hashes

Hashes for minimalml-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 14bbaed1d911e4e3b4e1e5a4b4aefa31a7c8291593f66d1c3682108abc715d71
MD5 b00f7bbbbf65a69e5adb226ae4f2be26
BLAKE2b-256 e0d139a74b3aade09ad2ee540abbc065454bd1144d7d307fd5ede9348bb5f110

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page