#multilingual

Whisper, a speech recognition solution by OpenAI, utilizes a Transformer sequence-to-sequence approach for multilingual transcription and language identification. With models ranging from 'tiny' to 'turbo', it balances speed with accuracy and is compatible with multiple Python versions, supporting comprehensive audio processing tasks in Python as well as via command-line, catering to developers in need of robust pre-trained models across multiple languages.

The Multi-Tacotron Voice Cloning project is a multilingual phonemic implementation for Russian and English, built on a deep learning framework. The project, an extension of Real-Time-Voice-Cloning, facilitates the creation of numeric voice representations from brief audio samples. It includes pre-trained models and necessary datasets, providing efficient pathways for text-to-speech conversion. The diverse datasets and neural networks such as Tacotron 2 and WaveRNN enable seamless multilingual capabilities, suited for advanced TTS synthesis requirements.

OpenAI's Whisper facilitates efficient and accurate transcription of YouTube videos with multilingual speech recognition, translation, and language identification. Compatible with various GPUs on Google Colab, it ensures high processing speeds and effective performance, even on less powerful GPUs. Users can modify inference settings and save transcripts and audio to Google Drive. Whisper's capability to handle diverse audio datasets makes it relevant for precise transcriptions.

This open-source application leverages ChatGPT for automatic text translations, eliminating the need to specify source languages. Compatible with macOS, Windows, and Linux, it supports various languages and allows customization of shortcut keys and API domains. Ideal for developers and translators seeking a flexible text translation solution, contributions are welcome on GitHub.

The tool facilitates the creation of multilingual datasets for training text-to-speech and speech recognition models by transcribing and refining audio quality. It segments audio, identifies speaker gender, and utilizes pyannote embeddings for automatic speaker naming. Suitable for detecting multiple speakers, it enhances audio using deepfilternet, resembleai, or mayavoz. The tool supports input from local files, YouTube, LibriVox, and TED Talks, storing data efficiently in a Chroma database.

Seamless is an AI-driven multilingual and multimodal translation project that supports extensive language coverage and promotes authentic interactions. The SeamlessM4T model underpins the broader ecosystem, including SeamlessExpressive and SeamlessStreaming, allowing for real-time, expressive translations and simultaneous translation capabilities in around 100 languages. Seamless utilizes the innovative UnitY2 architecture to improve translation efficiency and reduce latency.

RealtimeTTS is a text-to-speech library designed for real-time applications, providing fast and high-quality audio conversion. It supports various TTS engines including OpenAI, Elevenlabs, and Azure, and offers multilingual capabilities. With a robust fallback mechanism, it ensures reliable performance. Custom installation options and high-quality speech generation make it suitable for professional environments. Its counterpart, RealtimeSTT, offers additional functionalities for comprehensive real-time audio solutions involving large language models.

SenseVoice is a speech foundation model providing capabilities in automatic speech recognition, speech emotion recognition, and audio event detection across 50+ languages. It delivers high precision in multilingual recognition, outperforming many leading models. The non-autoregressive framework offers significantly faster audio processing, up to 15 times quicker than comparable models. With flexible finetuning and versatile deployment options, the model meets varied business and technical requirements. Recent enhancements include ONNX and libtorch export features, improving integration and usability.

Crystal is a C++ Text-to-Speech engine tailored for multilingual synthesis, built on a unified framework using Speech Synthesis Markup Language (SSML) to ensure compatibility and extensibility. It features native support for SSML to simplify integration, offers dynamic module loading for cross-platform flexibility, and allows easy customization of TTS engines for varying languages and dialects without complex parsing burdens.

The WIT dataset offers a vast collection of 37.6 million image-text examples sourced from 108 languages on Wikipedia, optimized for pretraining multimodal machine learning models. Its strengths include broad multilingual support, detailed metadata, and demanding real-world evaluations. The dataset facilitates advancements in multilingual and multimodal research by using images as a universal medium to bridge language barriers, enhancing text comprehension across languages. WIT is widely recognized in research circles and is available for download.

Discover the capabilities of OpenAI's Whisper model for automatic speech recognition within Unity3D. Supporting approximately 60 languages, this package operates locally without an internet connection, offering language translation such as German to English. It provides various model sizes for a balance between speed and accuracy and is compatible with platforms like Windows, MacOS, Linux, iOS, Android, and VisionOS. Open-source under the MIT License, it can be used in commercial projects, with guidelines for optimizing CUDA and Metal for specific hardware.

XPhoneBERT, a multilingual phoneme model, optimizes text-to-speech (TTS) technology by refining phoneme representations. With its BERT-base architecture trained on 330 million phoneme-level sentences from about 100 languages, it enhances TTS systems' naturalness and prosody, even with limited training data. Seamlessly integrating with Python's 'transformers' package and 'text2phonemesequence' for phoneme conversion, XPhoneBERT supports efficient multilingual pre-training.

StableTTS is a state-of-the-art flow-matching TTS model that integrates DiT, supporting efficient speech generation across Chinese, English, and Japanese. This 31M parameter model enhances audio quality and supports CFG and FireflyGAN vocoders, with improvements in the Chinese text frontend. The newly released version 1.1 introduces features like U-Net-inspired skip connections and a cosine timestep scheduler, all within a single multilingual checkpoint. Designed for user-friendly training, it simplifies data preparation and finetuning, making it an adaptable solution for varied audio generation applications.

Bert-VITS2 employs a multilingual BERT-based VITS2 framework, advancing Text-to-Speech technologies with high-quality, autoregressive synthesis techniques inspired by MassTTS. The project suggests FishAudio's Fish-Speech for ongoing enhancements. Resources like demo videos and technical slides are available for better understanding. The project is only for lawful applications, with a prohibition on political use. Contributions from a diverse community continually enhance its capabilities.

The koishi-plugin-novelai is an image generator plugin based on NovelAI, featuring model and sampler customization, image resizing, and advanced request syntax. It offers functionalities like automatic Chinese keyword translation and message retraction post-send. Compatible with platforms such as SD-WebUI and Stable Horde, it can be extended with Koishi's plugin system to support multiple platforms (e.g., QQ, Discord, Telegram), enable rate limiting, manage user context, and provide multilingual responses. Experience seamless art generation and intelligent automation for diverse platforms.

RHVoice is an open-source speech synthesizer that employs statistical parametric methods, initially developed for Russian and expanded to include languages like American and Scottish English, and Brazilian Portuguese. It functions across Windows, GNU/Linux, and Android, ensuring smooth integration with existing text-to-speech interfaces. Voices are intelligible and derived from natural recordings, and compatibility extends to tools like NVDA. Comprehensive documentation and active community resources facilitate user interaction and project development.

BeikeShop is an open-source e-commerce platform crafted with Laravel. It's designed for foreign trade and supports multiple languages and currencies, allowing effortless global reach. Key features include a commission-free structure, plugin versatility, and a user-friendly interface, ideal for businesses scaling internationally without upfront costs. Comprehensive management of payments, logistics, and memberships enhances its functionality.

Enhancing multilingual topic coherence with Contextualized Topic Models using BERT. These models integrate contextual and traditional bag-of-words approaches, using CombinedTM for coherence and ZeroShotTM for language diversity. Adaptable to any pre-trained embedding, this framework supports cutting-edge topic modeling. Emphasizing multilingual applications, it predicts topics in unseen data efficiently. Detailed tutorials and documentation support both language-specific and cross-lingual tasks. Discover intuitive human-in-the-loop classification with Kitty, swiftly identifying document clusters. This open-source project benefits from community support and is available under the MIT license.

The REBEL project converts Relation Extraction into a seq2seq task, using BART-based autoregressive models for efficient extraction of relation triplets. By turning these triplets into linear sequences, it overcomes traditional pipeline limitations, and supports over 200 relation types. Integration possibilities with platforms like Hugging Face and spaCy enable easy adoption in various applications, achieving top performance in multiple benchmarks. The recent addition of mREBEL enhances multilingual extraction capabilities, covering a wide range of language datasets.

A comprehensive resource for tracking and showcasing open-access TTS models, it aids researchers, developers, and enthusiasts in staying updated with the latest advancements. This platform enhances the accessibility and awareness of various TTS models, featuring capabilities like multilingual options, emotional control, and longform synthesis. By compiling open-source TTS projects, it promotes contributions and provides insights into each model's licensing, documentation, and demonstrations, ensuring up-to-date engagement with cutting-edge developments.

Explore a comprehensive toolkit of embedding models aimed at enhancing retrieval-augmented language models. Includes models for inference, fine-tuning, and evaluation across multiple languages. Achieve high performance in text and image retrieval with models like OmniGen for image generation. Follow the latest innovations such as MemoRAG and lightweight rerankers for efficiency. Access community support for continuous updates. Improve your NLP projects with easy installation and integration via the FlagEmbedding platform.

With over 3 million word/pronunciation pairs, WikiPron provides an essential resource for linguists and developers. This versatile command-line tool and Python API enable detailed extraction of multilingual pronunciation data from Wiktionary. Users can specify language, dialect, and transcription level for precise and customized data collection. Advanced options enhance scraping capabilities, facilitating seamless data management and research integration. Unleash a variety of pronunciation resources to enrich linguistic models and analyses.

HanLP is a versatile, open-source multilingual natural language processing toolkit powered by PyTorch and TensorFlow 2.x. It is built for production-grade environments and supports a wide array of languages and tasks, including tokenization, part-of-speech tagging, named entity recognition, and dependency parsing. With both RESTful and native APIs, HanLP guarantees semantic consistency and is optimized for high accuracy and efficiency, backed by continuous updates from extensive multilingual corpora.

This library uses N-grams to accurately detect text language, supporting 110 languages with options for custom language addition and configuration. It requires PHP 7.4 and the Multibyte String extension, and provides guidance for upgrading from version 3 to 4.

Surya is a versatile document OCR toolkit offering high accuracy text recognition in over 90 languages, rivaling leading cloud services. Its features include text detection, layout analysis, reading order, and table recognition across diverse document types. The toolkit provides a straightforward API for processing formats like PDFs and images, ensuring consistent performance. Well-suited for research, personal use, and with specific provisions for commercial application, it integrates seamlessly with Python workflows.

CodeGeeX2 is a multilingual code generation model based on the ChatGLM2 framework, achieving enhanced performance with 6 billion parameters. It supports languages including Python, C++, and Java, offering improvements over its predecessor. Key features include faster inference, support for up to 8192 sequence length, and deployment requiring only 6GB GPU memory. The updated CodeGeeX plugin facilitates over 100 languages and offers contextual and cross-file completion. The model weights are available for academic research, with an option for commercial use.