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Whisper.cpp Alternatives
Similar projects and alternatives to whisper.cpp
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textgen
Open-source desktop app for local LLMs. Text, vision, tool-calling, OpenAI/Anthropic-compatible API. 100% private.
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tinygrad
Discontinued You like pytorch? You like micrograd? You love tinygrad! ❤️ [Moved to: https://github.com/tinygrad/tinygrad] (by geohot)
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Whisper
High-performance GPGPU inference of OpenAI's Whisper automatic speech recognition (ASR) model (by Const-me)
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char
Discontinued AI notepad for meetings [GET https://api.github.com/repos/fastrepl/char: 404 - Not Found // See: https://docs.github.com/rest/repos/repos#get-a-repository]
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whisper-playground
Build real time speech2text web apps using OpenAI's Whisper https://openai.com/blog/whisper/
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better whisper.cpp alternative or higher similarity.
whisper.cpp discussion
whisper.cpp reviews and mentions
Posts with mentions or reviews of whisper.cpp.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2026-04-27.
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I added voice messages to my self-hosted AI agent, for free
whisper.cpp is Georgi Gerganov's port of OpenAI's Whisper model to plain C++. It runs on CPU, no GPU needed, and the smallest model (ggml-tiny.bin) is only about 75MB.
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Claude Code + Telegram: How to Supercharge Your AI Assistant with Voice, Threading & More
That's it. The plugin handles format conversion, chunking, and transcription automatically. For fully offline transcription, install whisper.cpp instead.
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I Built a Voice Assistant That Runs Entirely in Your Browser
The first issue is that running Whisper eats up some serious computational resources, so it's not ideal to have it running constantly. In the Whisper WASM port, there is an example of a wake word detection implementation command.wasm, which partially addresses this with VAD, only running transcription once voice activity is detected.
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Show HN: OWhisper – Ollama for realtime speech-to-text
Thank you for taking the time to build something and share it. However what is the advantage of using this over whisper.cpp stream that can also do real time conversion?
https://github.com/ggml-org/whisper.cpp/tree/master/examples...
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Kitten TTS: 25MB CPU-Only, Open-Source Voice Model
Whisper and the many variants. Here's a good implementation.
https://github.com/ggml-org/whisper.cpp
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Ask HN: What API or software are people using for transcription?
Whisper large v3 from openai, but we host it ourselves on Modal.com. It's easy, fast, no rate limits, and cheap as well.
If you want to run it locally, I'd still go with whisper, then I'd look at something like whisper.cpp https://github.com/ggml-org/whisper.cpp. Runs quite well.
- Whispercpp – Local, Fast, and Private Audio Transcription for Ruby
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Build Your Own Siri. Locally. On-Device. No Cloud
not the gp but found this https://github.com/ggml-org/whisper.cpp/blob/master/models/c...
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Run LLMs on Apple Neural Engine (ANE)
Actually that's a really good question, I hadn't considered that the comparison here is just CPU vs using Metal (CPU+GPU).
To answer the question though - I think this would be used for cases where you are building an app that wants to utilize a small AI model while at the same time having the GPU free to do graphics related things, which I'm guessing is why Apple stuck these into their hardware in the first place.
Here is an interesting comparison between the two from a whisper.cpp thread - ignoring startup times - the CPU+ANE seems about on par with CPU+GPU: https://github.com/ggml-org/whisper.cpp/pull/566#issuecommen...
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Building a personal, private AI computer on a budget
A great thread with the type of info your looking for lives here: https://github.com/ggerganov/whisper.cpp/issues/89
But you can likely find similar threads for the llama.cpp benchmark here: https://github.com/ggerganov/llama.cpp/tree/master/examples/...
These are good examples because the llama.cpp and whisper.cpp benchmarks take full advantage of the Apple hardware but also take full advantage of non-Apple hardware with GPU support, AVX support etc.
It’s been true for a while now that the memory bandwidth of modern Apple systems in tandem with the neural cores and gpu has made them very competitive Nvidia for local inference and even training.
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A note from our sponsor - SaaSHub
www.saashub.com | 15 Jun 2026
Stats
Basic whisper.cpp repo stats
205
50,618
9.9
6 days ago
ggml-org/whisper.cpp is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of whisper.cpp is C++.
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