# Audio transcription engine

> AI-ranked search results for `transcribe wav` on awesome-repositories.com — ordered by an LLM for relevance, best match first. 111 total matches; showing the top 30.

Explore on the web: https://awesome-repositories.com/q/transcribe-wav

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## Results

- [m-bain/whisperx](https://awesome-repositories.com/repository/m-bain-whisperx.md) (20,228 ⭐) — WhisperX is an automated speech recognition toolkit designed to convert spoken audio into text while maintaining precise synchronization with the original media. It functions as an integrated pipeline that combines transcription, phoneme-based alignment, and speaker diarization to produce structured, attributed transcripts.

The project distinguishes itself through its use of forced alignment, which matches existing text to audio signals at the phoneme level to generate accurate word-level timestamps. It also incorporates speaker diarization to identify and label unique voices within a recordi
- [chidiwilliams/buzz](https://awesome-repositories.com/repository/chidiwilliams-buzz.md) (17,903 ⭐) — Buzz is a desktop application that provides a local speech-to-text engine for transcribing and translating audio and video files. By leveraging local machine inference, the software ensures data privacy and offline performance, removing the need for cloud connectivity during media processing.

The application distinguishes itself through a modular plugin architecture that allows for the integration of custom functionality, such as content summarization and automated text formatting, without modifying the core codebase. It also features a speaker diarization pipeline that identifies and labels
- [ggml-org/whisper.cpp](https://awesome-repositories.com/repository/ggml-org-whisper-cpp.md) (50,770 ⭐) — Whisper.cpp is a high-performance, local-first speech recognition engine designed to run large-scale machine learning models on consumer hardware. It functions as a portable library that converts audio into text, supporting both static file transcription and real-time stream processing. By utilizing a lightweight inference engine and weight quantization, the project minimizes memory and compute overhead, allowing for efficient execution without reliance on external cloud APIs or internet connectivity.

The project distinguishes itself through a hardware-agnostic compute abstraction that offloa
- [modelscope/funasr](https://awesome-repositories.com/repository/modelscope-funasr.md) (18,481 ⭐) — FunASR is an automatic speech recognition toolkit and multilingual speech-to-text engine designed to convert spoken audio into written text across more than fifty languages. It provides a framework for speaker diarization, an OpenAI-compatible transcription API for local server hosting, and speech models compatible with the ONNX format.

The project distinguishes itself by supporting high-performance inference on edge hardware via self-contained binaries and portable model exports. It incorporates specialized capabilities for natural speech generation with adjustable timbre and emotional expre
- [paddlepaddle/paddlespeech](https://awesome-repositories.com/repository/paddlepaddle-paddlespeech.md) (12,626 ⭐) — PaddleSpeech is a comprehensive toolkit of neural models for speech recognition, synthesis, and translation built on the PaddlePaddle deep learning framework. It provides a collection of frameworks and tools for converting spoken audio into written text, synthesizing natural audio from text, and performing direct speech translation.

The toolkit includes specialized capabilities for keyword spotting to detect trigger words and speaker verification systems that extract unique voiceprints to identify and distinguish between individuals. It also features end-to-end translation tools that map audi
- [ggerganov/whisper.cpp](https://awesome-repositories.com/repository/ggerganov-whisper-cpp.md) (50,791 ⭐) — whisper.cpp is a C++ implementation of the Whisper speech-to-text model, serving as a lightweight machine learning inference engine and quantized runtime. It provides high-performance automatic speech recognition and real-time audio transcription without requiring a Python environment.

The project utilizes model quantization to reduce memory usage and increase inference speed on local hardware. It incorporates hardware acceleration to optimize processing speed across different processors.

The system covers audio processing capabilities including voice activity detection, speaker diarization,
- [wenet-e2e/wenet](https://awesome-repositories.com/repository/wenet-e2e-wenet.md) (5,035 ⭐) — WeNet is an end-to-end automatic speech recognition (ASR) toolkit designed for both Chinese and English, built around transformer-based models. It supports streaming and non-streaming inference out of the box, and is structured to be production-ready, with model export and deployment paths for servers and mobile devices.

The toolkit distinguishes itself through a chunk-based streaming transformer architecture that processes audio in fixed-size segments for low latency while preserving context across chunks. It jointly trains models with both CTC and attention loss to combine alignment accurac
- [collabora/whisperlive](https://awesome-repositories.com/repository/collabora-whisperlive.md) (3,819 ⭐) — WhisperLive is a real-time speech-to-text server that converts live audio streams into text using Whisper models. It functions as a backend service that receives microphone input via WebSockets and provides incremental transcriptions with word-level timestamps.

The system utilizes a GPU-accelerated inference engine and a keyword-boosted transcription API to improve the recognition accuracy of domain-specific jargon, acronyms, and product names. It also includes a speaker diarization tool that clusters audio embeddings to identify and label different participants within a recording.

Additiona
- [alphacep/vosk-api](https://awesome-repositories.com/repository/alphacep-vosk-api.md) (14,853 ⭐) — Vosk is an offline speech-to-text engine and API that converts spoken audio into text locally on a device. It provides a cross-platform speech toolkit with language bindings for integrating voice recognition into server environments, Android, iOS, and Raspberry Pi.

The project includes a speaker identification tool to distinguish between different voices and an acoustic model trainer for building custom neural network models. These training tools enable speech feature extraction and model accuracy evaluation to improve recognition for specialized domains.

The system supports real-time audio
- [mozilla/deepspeech](https://awesome-repositories.com/repository/mozilla-deepspeech.md) (26,748 ⭐) — DeepSpeech is an open-source speech-to-text framework and machine learning engine designed to convert spoken audio into written text locally on a device. It provides on-device speech recognition that operates without requiring an internet connection to external servers.

The system supports real-time speech transcription across a variety of hardware platforms, ranging from single-board computers and edge devices to GPU servers. This allows for audio analysis and processing directly on the local hardware.
- [systran/faster-whisper](https://awesome-repositories.com/repository/systran-faster-whisper.md) (21,043 ⭐) — Faster-Whisper is a high-performance implementation of the Whisper speech-to-text model designed for efficient audio transcription. It provides an end-to-end processing pipeline that converts spoken audio into written text while maintaining lower memory consumption and faster execution speeds than standard implementations.

The project achieves its performance through a specialized inference engine that utilizes optimized kernels and weight quantization to reduce computational complexity. It supports large-scale operations by grouping audio segments into dynamic batches and filtering out non-s
- [mahmoudashraf97/whisper-diarization](https://awesome-repositories.com/repository/mahmoudashraf97-whisper-diarization.md) (5,576 ⭐) — Whisper-diarization is a system for identifying and separating different speakers in audio recordings by combining OpenAI Whisper for transcription with automated speaker attribution. It functions as a pipeline that isolates vocal tracks from background noise and assigns transcribed segments to specific individuals.

The project uses forced alignment to synchronize transcribed text timestamps with audio signals, improving the accuracy of speaker attribution. It employs voice activity detection to separate speech from silence and noise, ensuring precise boundaries for identification.

The syste
- [nvidia-nemo/nemo](https://awesome-repositories.com/repository/nvidia-nemo-nemo.md) (17,389 ⭐) — NeMo is a comprehensive framework designed for the development, training, and deployment of large-scale conversational and generative artificial intelligence models. It provides an integrated platform for building multimodal systems, encompassing speech processing, language modeling, and reinforcement learning alignment. The framework is built to handle the entire lifecycle of AI development, from data curation and model pretraining to production-ready service deployment.

The platform distinguishes itself through advanced distributed training capabilities, including tensor and pipeline parall
- [k2-fsa/sherpa-onnx](https://awesome-repositories.com/repository/k2-fsa-sherpa-onnx.md) (13,017 ⭐) — Sherpa-ONNX is an ONNX-based speech processing toolkit that provides a local speech recognition engine, an on-device voice synthesis tool, and a speaker identification framework. It is designed as a cross-platform speech API that enables speech-to-text, text-to-speech, and speaker verification tasks to be executed locally on a device without requiring network access.

The project is distinguished by its ability to perform zero-shot voice cloning and speaker diarization on-device. It supports a wide range of hardware accelerations, including GPU and various NPU architectures, and provides a Web
- [openai/whisper](https://awesome-repositories.com/repository/openai-whisper.md) (102,828 ⭐) — This project is a speech recognition and translation engine that utilizes a sequence-to-sequence transformer architecture to convert audio into text. It is built upon a weakly supervised learning framework, which leverages large-scale, unlabelled audio-transcript data to create generalized speech representations capable of performing simultaneous transcription, language identification, and translation.

The system distinguishes itself through a unified multi-task modeling approach that shares token sequences across different objectives, allowing it to handle diverse languages and vocabularies
- [kaldi-asr/kaldi](https://awesome-repositories.com/repository/kaldi-asr-kaldi.md) (15,415 ⭐) — Kaldi is an automatic speech recognition toolkit used to train and deploy models that convert spoken audio into text. It functions as a framework for designing and evaluating acoustic and language models through a structured pipeline of processing tools.

The system acts as a cross-platform speech engine, capable of compiling recognition logic for Android and WebAssembly to enable execution on mobile devices and web browsers. It also includes a dedicated converter for migrating speech recognition models from the HTK format into a compatible internal structure.

The toolkit covers a broad range
- [mozilla-ai/llamafile](https://awesome-repositories.com/repository/mozilla-ai-llamafile.md) (23,726 ⭐) — Llamafile is a machine learning model runner and packager that enables local inference by bundling model weights and runtime environments into a single, self-contained executable. It functions as a cross-platform engine, allowing users to execute large language models and perform speech-to-text tasks directly on their own hardware without requiring external software dependencies or complex installations.

The project distinguishes itself by utilizing a specialized binary format that allows the same executable to run natively across multiple operating systems and hardware architectures. It auto
- [blaizzy/mlx-audio](https://awesome-repositories.com/repository/blaizzy-mlx-audio.md) (5,994 ⭐) — mlx-audio is an audio processing toolkit built on Apple MLX that provides speech transcription, text-to-speech synthesis, voice cloning, and audio source separation using local models. It offers an OpenAI-compatible REST API and web interface for running audio generation and transcription tasks, enabling drop-in integration with existing tools that follow that endpoint structure.

The toolkit supports text-prompted audio source separation, allowing specific sounds to be isolated from mixed recordings based on natural language descriptions. It also provides voice cloning from a short reference
- [vaibhavs10/insanely-fast-whisper](https://awesome-repositories.com/repository/vaibhavs10-insanely-fast-whisper.md) (12,969 ⭐) — This project is a high-throughput transcription engine and PyTorch inference wrapper designed to convert spoken audio files into text using the OpenAI Whisper model. It functions as a hardware-accelerated speech-to-text transcriber that runs locally on a user's machine.

The system focuses on AI model performance tuning to maximize hardware throughput. It utilizes GPU acceleration, half-precision floating point tensors, and Flash-Attention to reduce processing time and memory overhead during transcription.

The implementation covers large-scale transcription workflows and local speech-to-text
- [aigc-audio/audiogpt](https://awesome-repositories.com/repository/aigc-audio-audiogpt.md) (10,174 ⭐) — AudioGPT is an LLM-driven audio framework and processing suite that uses large language models to orchestrate neural audio pipelines. It functions as a multimodal audio generator and processing system, integrating a collection of pretrained models to handle speech synthesis, sound generation, and audio manipulation.

The system is distinguished by its ability to generate audio from diverse inputs, including text and images, and its capacity to produce synchronized talking head videos. It also operates as a neural speech translator, converting spoken language between different tongues while pre
- [cjpais/handy](https://awesome-repositories.com/repository/cjpais-handy.md) (15,515 ⭐) — Handy is a local speech-to-text automation tool designed to convert spoken audio into text and inject it directly into active desktop applications. By running machine learning models entirely on the host hardware, it provides a private, offline-first environment for dictation and command execution. The system functions as a background service that manages microphone input, transcription state, and text output, enabling hands-free typing across various software environments.

The project distinguishes itself through a modular pipeline that integrates local language models for post-transcription
- [cmusphinx/pocketsphinx](https://awesome-repositories.com/repository/cmusphinx-pocketsphinx.md) (4,276 ⭐) — PocketSphinx is an offline speech recognition engine that converts raw audio from files or live microphone streams into written text without requiring a network connection. It functions as a speech-to-text library, a real-time transcription engine, and a voice command processor, capable of detecting and transcribing spoken commands from continuous audio streams with configurable acoustic and language models.

The engine uses weighted finite-state transducers to represent acoustic, phonetic, and language models as a single search graph for efficient decoding. It employs fixed-point acoustic mod
- [thewh1teagle/vibe](https://awesome-repositories.com/repository/thewh1teagle-vibe.md) (5,298 ⭐) — Vibe is a cross-platform transcription tool that converts spoken audio into text by running Whisper neural models directly on your device, with no cloud dependency. It can transcribe audio from files, microphones, system output, and network streams, and supports both batch processing of multiple files and real-time captioning from continuous input.

Beyond basic transcription, Vibe identifies and labels different speakers through speaker diarization, and offers a choice of Command-Line Interface or HTTP API for automated and remote workflows. It also includes plugins to export transcripts to c
- [chenyme/chenyme-aavt](https://awesome-repositories.com/repository/chenyme-chenyme-aavt.md) (2,928 ⭐) — Chenyme-AAVT is an AI-powered video transcription tool and translation platform. It converts speech from media files into editable text transcripts using speech recognition models and voice activity detection to ensure accurate phrasing and timing.

The system functions as a content generator that transforms video transcripts into structured blog posts and marketing graphics using large language models. It also includes a subtitle formatting editor that allows for the modification of subtitle styles with a real-time video preview.

The platform provides multilingual translation capabilities th
- [argmaxinc/whisperkit](https://awesome-repositories.com/repository/argmaxinc-whisperkit.md) (5,639 ⭐)
- [davabase/whisper_real_time](https://awesome-repositories.com/repository/davabase-whisper-real-time.md) (2,938 ⭐) — Whisper Real-Time is a speech-to-text engine designed to convert continuous microphone input into written transcripts. It functions as a real-time audio processor that leverages the OpenAI Whisper model to generate immediate textual output from live spoken language.

The system utilizes a transformer-based architecture to map audio sequences to text tokens. It manages incoming data through a sliding-window buffering mechanism and a circular buffer, which ensures a steady stream of audio for the inference engine. To maintain accuracy during continuous processing, the software employs a stateful
- [koljab/realtimestt](https://awesome-repositories.com/repository/koljab-realtimestt.md) (9,477 ⭐) — RealtimeSTT is a local speech-to-text engine and real-time automatic speech recognition server. It utilizes transformer-based recognition and omnilingual pipelines to convert live audio streams into text, providing a WebSocket-based streaming API for raw PCM audio transmission.

The project is distinguished by a dual-backend transcription pipeline that uses a lightweight engine for immediate partial suggestions and a heavier model for final high-accuracy results. It includes a wake word detection system to trigger recording and employs a shared-resource inference model to distribute heavy spee
- [const-me/whisper](https://awesome-repositories.com/repository/const-me-whisper.md) (10,489 ⭐) — Whisper is a high-performance speech-to-text inference engine that uses graphics hardware shaders to accelerate the transcription of spoken audio into written text. It implements a GPU-accelerated automatic speech recognition framework specifically designed to run Whisper models.

The system focuses on high-speed processing for both recorded audio files and live microphone streams. It utilizes voice activity detection to analyze raw audio in real time, triggering the inference engine only when human speech is detected.

The engine covers a broad range of capabilities including real-time audio
- [facebookresearch/omnilingual-asr](https://awesome-repositories.com/repository/facebookresearch-omnilingual-asr.md) (2,671 ⭐) — Omnilingual-ASR is a multilingual automatic speech recognition framework and toolkit designed to transcribe audio across 1,600 languages. It provides a complete pipeline for converting speech to text, including a toolkit for fine-tuning pre-trained speech models to specific languages or datasets using custom training recipes.

The system supports zero-shot speech recognition, allowing the model to predict text in unseen languages without extensive training data. It further enables few-shot language guidance through in-context examples and uses language codes to constrain transcription output t
- [snakers4/silero-models](https://awesome-repositories.com/repository/snakers4-silero-models.md) (5,977 ⭐) — This is a collection of pre-trained neural models for speech recognition, synthesis, and voice activity detection. It provides a library of assets designed for speech-to-text, text-to-speech, and the identification of human speech segments within audio.

The project features text-to-speech synthesis with support for multiple languages and the use of Speech Synthesis Markup Language to control prosody, pitch, and timing. For speech recognition, the system includes capabilities for transcribing audio to text with word-level timestamp extraction and an automated punctuation restorer to insert cap