#jupyter_notebook #asr #asr_benchmark #colab #english #enterprise_grade_stt #german #pretrained_models #pytorch #silero_models #spanish #speech_recognition #speech_to_text #stt #stt_benchmark

https://github.com/snakers4/silero-models

#python #callcenter #conformer #ctc_decode #deepspeech #fastspeech2 #language_model #mandarin_language #ngram #parallel_wavegan #punctuation_restoration #speech_alignment #speech_recognition #speech_to_text #speech_translation #streaming_asr #text_frontend #text_to_speech #transformer

https://github.com/PaddlePaddle/PaddleSpeech

#cplusplus #android #asr #deep_learning #deep_neural_networks #deepspeech #google_speech_to_text #ios #kaldi #offline #privacy #python #raspberry_pi #speaker_identification #speaker_verification #speech_recognition #speech_to_text #speech_to_text_android #stt #voice_recognition #vosk

https://github.com/alphacep/vosk-api

#c_lang #openai #speech_to_text #transformer #whisper

https://github.com/ggerganov/whisper.cpp

#python #automatic_speech_recognition #docker #openai_whisper #speech_recognition #speech_to_text

https://github.com/ahmetoner/whisper-asr-webservice

#python #asr #audio #audio_processing #deep_learning #huggingface #language_model #pytorch #speaker_diarization #speaker_recognition #speaker_verification #speech_enhancement #speech_processing #speech_recognition #speech_separation #speech_to_text #speech_toolkit #speechrecognition #spoken_language_understanding #transformers #voice_recognition

SpeechBrain is an open-source toolkit that helps you quickly develop Conversational AI technologies, such as speech assistants, chatbots, and language models. It uses PyTorch and offers many pre-trained models and tutorials to make it easy to get started. You can train models for various tasks like speech recognition, speaker recognition, and text processing with just a few lines of code. SpeechBrain also supports GPU training, dynamic batching, and integration with HuggingFace models, making it powerful and efficient. This toolkit is beneficial because it simplifies the development process, provides extensive documentation and tutorials, and is highly customizable, making it ideal for research, prototyping, and educational purposes.

https://github.com/speechbrain/speechbrain

#python #python #realtime #speech_to_text

RealtimeSTT is a library that converts speech to text in real-time. It listens to your microphone and transcribes what you say immediately. Here are the key benefits It uses advanced models like Faster-Whisper for quick and precise transcription.
- **Voice Activity Detection** You can set a specific word, like "Jarvis," to start the recording.
- **Realtime Transcription** Allows you to adjust settings like sensitivity, model size, and even use GPU for better performance.

Installing it is simple with `pip install RealtimeSTT`, and it includes examples to get you started quickly. This library is great for building voice-controlled applications or any project needing real-time speech-to-text functionality.

https://github.com/KoljaB/RealtimeSTT

#cplusplus #aarch64 #android #arm32 #asr #cpp #csharp #dotnet #ios #lazarus #linux #macos #mfc #object_pascal #onnx #raspberry_pi #risc_v #speech_to_text #text_to_speech #vits #windows

This tool supports various speech functions like speech recognition, text-to-speech, speaker identification, and more. It works on multiple platforms including Android, iOS, Windows, macOS, and Linux, and supports several programming languages such as C++, Python, JavaScript, and others. You can use it locally or through web assembly, making it versatile and convenient. This benefits you by allowing you to integrate advanced speech capabilities into your projects easily, regardless of the platform or programming language you use.

https://github.com/k2-fsa/sherpa-onnx

#python #artificial_intelligence #llm #python #real_time #speech_to_text #text_to_speech

FastRTC is a Python library that helps you create real-time audio and video streams using WebRTC or WebSockets. It allows you to turn any Python function into a live stream, making it useful for applications like voice chats or video conferencing. Key features include automatic voice detection, built-in UI support with Gradio, and integration with FastAPI for custom frontends. This library simplifies the process of handling real-time communication, allowing developers to focus on their application logic rather than complex streaming setups.

https://github.com/freddyaboulton/fastrtc

#python #apple_silicon #audio_processing #mlx #multimodal #speech_recognition #speech_synthesis #speech_to_text #text_to_speech #transformers

MLX-Audio is a powerful tool for converting text into speech and speech into new audio. It works well on Apple Silicon devices, like M-series chips, making it fast and efficient. You can choose from different languages and voices, and even adjust how fast the speech is. It also includes a web interface where you can see audio in 3D and play your own files. This tool is helpful for making audiobooks, interactive media, and personal projects because it's easy to use and provides high-quality audio quickly.

https://github.com/Blaizzy/mlx-audio