serving
whisper.cpp
serving | whisper.cpp | |
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12 | 187 | |
6,085 | 31,649 | |
0.2% | - | |
9.8 | 9.8 | |
3 days ago | 3 days ago | |
C++ | C | |
Apache License 2.0 | MIT License |
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serving
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Llama.cpp: Full CUDA GPU Acceleration
Yet another TEDIOUS BATTLE: Python vs. C++/C stack.
This project gained popularity due to the HIGH DEMAND for running large models with 1B+ parameters, like `llama`. Python dominates the interface and training ecosystem, but prior to llama.cpp, non-ML professionals showed little interest in a fast C++ interface library. While existing solutions like tensorflow-serving [1] in C++ were sufficiently fast with GPU support, llama.cpp took the initiative to optimize for CPU and trim unnecessary code, essentially code-golfing and sacrificing some algorithm correctness for improved performance, which isn't favored by "ML research".
NOTE: In my opinion, a true pioneer was DarkNet, which implemented the YOLO model series and significantly outperformed others [2]. Same trick basically like llama.cpp
[1] https://github.com/tensorflow/serving
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[D] How do OpenAI and other companies manage to have real-time inference on model with billions of parameters over an API?
I mean, probably - it's written in C++ https://github.com/tensorflow/serving
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Should I wait for the M2 Macbook Pro?
We’re looking into that solution at the moment, the issue I’m referring to is related to this https://github.com/tensorflow/serving/issues/1948 we’ll know if the plug-in approach works for our uses soon but haven’t started looking into implementing it yet
- TF Serving has been unavailable for 9 days so far due to outdated GPG key
- TF Serving has been unavailable for 8 days
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Would you use maturin for ML model serving?
Which ML framework do you use? Tensorflow has https://github.com/tensorflow/serving. You could also use the Rust bindings to load a saved model and expose it using one of the Rust HTTP servers. It doesn't matter whether you trained your model in Python as long as you export its saved model.
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Is LaMDA Sentient? – An Interview [pdf]
Most likely it's a model server running something like https://github.com/tensorflow/serving and if there isn't a lot of load, the resource could kill some of its tasks. I wouldn't imagine it's sitting around pondering deep thoughts.
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Ask HN: How to deploy a TensorFlow model for access through an HTTP endpoint?
https://github.com/tensorflow/serving
https://thenewstack.io/tutorial-deploying-tensorflow-models-...
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Popular Machine Learning Deployment Tools
GitHub
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If data science uses a lot of computational power, then why is python the most used programming language?
You serve models via https://www.tensorflow.org/tfx/guide/serving which is written entirely in C++ (https://github.com/tensorflow/serving/tree/master/tensorflow_serving/model_servers), no Python on the serving path or in the shipped product.
whisper.cpp
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Show HN: I created automatic subtitling app to boost short videos
whisper.cpp [1] has a karaoke example that uses ffmpeg's drawtext filter to display rudimentary karaoke-like captions. It also supports diarisation. Perhaps it could be a starting point to create a better script that does what you need.
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1: https://github.com/ggerganov/whisper.cpp/blob/master/README....
- LLaMA Now Goes Faster on CPUs
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LLMs on your local Computer (Part 1)
The ggml library is one of the first library for local LLM interference. It’s a pure C library that converts models to run on several devices, including desktops, laptops, and even mobile device - and therefore, it can also be considered as a tinkering tool, trying new optimizations, that will then be incorporated into other downstream projects. This tool is at the heart of several other projects, powering LLM interference on desktop or even mobile phones. Subprojects for running specific LLMs or LLM families exists, such as whisper.cpp.
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Voxos.ai – An Open-Source Desktop Voice Assistant
I'm not sure if it is _fully_ openai compatible, but whispercpp has a server bundled that says it is "OAI-like": https://github.com/ggerganov/whisper.cpp/tree/master/example...
I don't have any direct experience with it... I've only played around with whisper locally, using scripts.
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Jarvis: A Voice Virtual Assistant in Python (OpenAI, ElevenLabs, Deepgram)
unless i'm misunderstanding `whisper.cpp` seems to support streaming & the repository includes a native example[0] and a WASM example[1] with a demo site[2].
[0]: https://github.com/ggerganov/whisper.cpp/tree/master/example...
- Wchess
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I've open sourced my Flutter plugin to run on-device LLMs on any platform. TestFlight builds available now.
Usage 1: Good to transcribe audio. An example use case could be to summarize YouTube videos or long courses. Usage 2: You talk with voice to your AI that responds with text (later with audio too). - https://github.com/ggerganov/whisper.cpp
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Scrybble is the ReMarkable highlights to Obsidian exporter I have been looking for
🗣️🎙️ whisper.cpp (offline speech-to-text transcription, models trained by OpenAI, CLI based, browser based)
- Whisper.wasm
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Whisper C++ not working for me. Anyone else?
Has anyone played around with Whisper C++ for swift? I'm hitting a snag even on the demo. I've downloaded the github repo and everything matches up with this video [ https://youtu.be/b10OHCDHDQ4 ] but when he hits the transcribe button, it actually prints out the captioning. When I do it, it skips that part and just says "Done...". But it, does everything else - plays the audio, says it's transcribing.. just doesn't show me the transcription: and it's not in the debug window either. But the demo isn't throwing any errors, and I haven't messed with the code really so this is their example. https://github.com/ggerganov/whisper.cpp
What are some alternatives?
server - The Triton Inference Server provides an optimized cloud and edge inferencing solution.
faster-whisper - Faster Whisper transcription with CTranslate2
MNN - MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba
bark - 🔊 Text-Prompted Generative Audio Model
flashlight - A C++ standalone library for machine learning
Whisper - High-performance GPGPU inference of OpenAI's Whisper automatic speech recognition (ASR) model
XLA.jl - Julia on TPUs
whisper - Robust Speech Recognition via Large-Scale Weak Supervision
oneflow - OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient.
whisperX - WhisperX: Automatic Speech Recognition with Word-level Timestamps (& Diarization)
glow - Compiler for Neural Network hardware accelerators
llama.cpp - LLM inference in C/C++