#multilingual models

Runtime Speech Recognizer provides efficient speech recognition utilizing OpenAI Whisper. It supports both English-only and multilingual models covering up to 100 languages. Offers the ability to choose model sizes from 75 Mb to 2.9 Gb with automatic language model download and optional speech translation to English. Customizable features come without static libraries or external dependencies, allowing cross-platform integration on Windows, Mac, Linux, Android, and iOS. Ideal for developers in need of reliable speech recognition across different applications.

DeepSeek Coder, trained on 2 trillion tokens, excels in multilingual code modeling. Supporting over 80 programming languages, it performs well on benchmarks such as HumanEval, MBPP, and DS-1000. Available in models from 1B to 33B, it meets different computational requirements. It is designed for effective code completion and infilling, making it suitable for both project-level applications and educational purposes. This open-source language model's efficiency and scalability offer distinct advantages over CodeLlama and GPT-35-turbo.

Discover the capabilities of a leading Text-to-Speech library supporting 16 languages and delivering efficient performance with latency below 200ms. The library includes models such as Tacotron, Glow-TTS, and VITS, with options for fine-tuning and multi-speaker TTS support. Utilize over 1100 Fairseq models for various linguistic needs and access numerous tools for training and refining speech models. Designed for a diverse range of applications, this library offers developers a flexible solution for generating high-quality speech.

The framework offers sentence, paragraph, and image embedding solutions via BERT and RoBERTa models across 100+ languages. It includes a wide range of pre-trained models suited for various applications and allows fine-tuning for specific tasks. Features include multilingual learning, ideal for semantic search and clustering. Fully compatible with PyTorch, easily installable via pip or conda.