rocBLAS
whisper.cpp
rocBLAS | whisper.cpp | |
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6 | 187 | |
317 | 31,426 | |
2.8% | - | |
9.7 | 9.8 | |
6 days ago | 1 day ago | |
C++ | C | |
GNU General Public License v3.0 or later | MIT License |
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rocBLAS
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Nvidia DGX GH200: The First 100 Terabyte GPU Memory System
The same is also true for https://github.com/ROCmSoftwarePlatform/rocBLAS and https://github.com/ROCmSoftwarePlatform/hipBLASLt although the build stack, distribution— leaves a lot to be desired, and otherwise quite unstable.
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Whisper.cpp v1.4.0
Full circle eh. I wonder how well it compares to just trying to use the actual Whisper models on a variety of existing Gpu capable bigger frameworks.
I don't know much practically about how hard it would be to take the Whisper PyTorch (1 or 2?) trained models & to make good use of them elsewhere. I expect Whisper.cpp probably better caters to users, is more readily consumable.
Fwiw, Whisper.cpp uses Nvidia's cuBLAS. There does appear to be an AMD rocm port. https://github.com/ROCmSoftwarePlatform/rocBLAS
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which CPU to choose?
It's not what you asked, but I felt I should point out that rocBLAS is no longer maintained for gfx803 (the architecture of the RX 570) and PyTorch depends on rocBLAS. PyTorch will work at least to some extent, but there are known bugs that may never be fixed. I've been trying to change this, but that's how things are right now.
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Trying to get Pytorch ROCm to work on Ubuntu 20.04 with Fiji cards
The last release that officially supported gfx803 was ROCm 3.5. All testing on that hardware ceased shortly after said release, and the code paths for that architecture have been unmaintained for nearly two years. For a specific example of a problem you may encounter, see: https://github.com/ROCmSoftwarePlatform/rocBLAS/issues/1218
- Compute Ecosystem of AMD GPUs
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PyTorch 1.8 adds AMD ROCm support
Although the code is still there, support for (slightly) older devices are already suffering from lack of maintainence and bugs. For instance there's a bug causing gfx803 devices to produce wrong outputs starting from mid-2020, and I'm pretty sure they're never gonna fix it.
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?
kokkos-kernels - Kokkos C++ Performance Portability Programming Ecosystem: Math Kernels - Provides BLAS, Sparse BLAS and Graph Kernels
faster-whisper - Faster Whisper transcription with CTranslate2
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
Whisper - High-performance GPGPU inference of OpenAI's Whisper automatic speech recognition (ASR) model
HIP-CPU - An implementation of HIP that works on CPUs, across OSes.
bark - 🔊 Text-Prompted Generative Audio Model
AdaptiveCpp - Implementation of SYCL and C++ standard parallelism for CPUs and GPUs from all vendors: The independent, community-driven compiler for C++-based heterogeneous programming models. Lets applications adapt themselves to all the hardware in the system - even at runtime!
whisper - Robust Speech Recognition via Large-Scale Weak Supervision
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
whisperX - WhisperX: Automatic Speech Recognition with Word-level Timestamps (& Diarization)
hipBLASLt - hipBLASLt is a library that provides general matrix-matrix operations with a flexible API and extends functionalities beyond a traditional BLAS library
llama.cpp - LLM inference in C/C++