whisper.cpp
whisperX
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whisper.cpp | whisperX | |
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187 | 24 | |
31,174 | 8,965 | |
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9.8 | 8.4 | |
2 days ago | 3 days ago | |
C | Python | |
MIT License | BSD 4-Clause "Original" or "Old" License |
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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
whisperX
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Easy video transcription and subtitling with Whisper, FFmpeg, and Python
It uses this, which does support diarization: https://github.com/m-bain/whisperX
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SOTA ASR Tooling: Long-Form Transcription
Author compared various whisper implementation
"We found that WhisperX is the best framework for transcribing long audio files efficiently and accurately. It’s much better than using the standard openai-whisper library."
https://github.com/m-bain/whisperX
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Deploying whisperX on AWS SageMaker as Asynchronous Endpoint
import os # Directory and file paths dir_path = './models-v1' inference_file_path = os.path.join(dir_path, 'code/inference.py') requirements_file_path = os.path.join(dir_path, 'code/requirements.txt') # Create the directory structure os.makedirs(os.path.dirname(inference_file_path), exist_ok=True) # Inference.py content inference_content = '''# inference.py # inference.py import io import json import logging import os import tempfile import time import boto3 import torch import whisperx DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' s3 = boto3.client('s3') def model_fn(model_dir, context=None): """ Load and return the WhisperX model necessary for audio transcription. """ print("Entering model_fn") logging.info("Loading WhisperX model") model = whisperx.load_model(whisper_arch=f"{model_dir}/guillaumekln/faster-whisper-large-v2", device=DEVICE, language="en", compute_type="float16", vad_options={'model_fp': f"{model_dir}/whisperx/vad/pytorch_model.bin"}) print("Loaded WhisperX model") print("Exiting model_fn with model loaded") return { 'model': model } def input_fn(request_body, request_content_type): """ Process and load audio from S3, given the request body containing S3 bucket and key. """ print("Entering input_fn") if request_content_type != 'application/json': raise ValueError("Invalid content type. Must be application/json") request = json.loads(request_body) s3_bucket = request['s3bucket'] s3_key = request['s3key'] # Download the file from S3 temp_file = tempfile.NamedTemporaryFile(delete=False) s3.download_file(Bucket=s3_bucket, Key=s3_key, Filename=temp_file.name) print(f"Downloaded audio from S3: {s3_bucket}/{s3_key}") print("Exiting input_fn") return temp_file.name def predict_fn(input_data, model, context=None): """ Perform transcription on the provided audio file and delete the file afterwards. """ print("Entering predict_fn") start_time = time.time() whisperx_model = model['model'] logging.info("Loading audio") audio = whisperx.load_audio(input_data) logging.info("Transcribing audio") transcription_result = whisperx_model.transcribe(audio, batch_size=16) try: os.remove(input_data) # input_data contains the path to the temp file print(f"Temporary file {input_data} deleted.") except OSError as e: print(f"Error: {input_data} : {e.strerror}") end_time = time.time() elapsed_time = end_time - start_time logging.info(f"Transcription took {int(elapsed_time)} seconds") print(f"Exiting predict_fn, processing took {int(elapsed_time)} seconds") return transcription_result def output_fn(prediction, accept, context=None): """ Prepare the prediction result for the response. """ print("Entering output_fn") if accept != "application/json": raise ValueError("Accept header must be application/json") response_body = json.dumps(prediction) print("Exiting output_fn with response prepared") return response_body, accept ''' # Write the inference.py file with open(inference_file_path, 'w') as file: file.write(inference_content) # Requirements.txt content requirements_content = '''speechbrain==0.5.16 faster-whisper==0.7.1 git+https://github.com/m-bain/whisperx.git@1b092de19a1878a8f138f665b1467ca21b076e7e ffmpeg-python ''' # Write the requirements.txt file with open(requirements_file_path, 'w') as file: file.write(requirements_content)
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OpenVoice: Versatile Instant Voice Cloning
Whisper doesn't, but WhisperX <https://github.com/m-bain/whisperX/> does. I am using it right now and it's perfectly serviceable.
For reference, I'm transcribing research-related podcasts, meaning speech doesn't overlap a lot, which would be a problem for WhisperX from what I understand. There's also a lot of accents, which are straining on Whisper (though it's also doing well), but surely help WhisperX. It did have issues with figuring number of speakers on it's own, but that wasn't a problem for my use case.
- FLaNK 15 Jan 2024
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Subtitle is now open-source
I've had good results with whisperx when I needed to generate captions. https://github.com/m-bain/whisperX
There is currently a problem with diarization, but otherwise, it is SOTA.
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Insanely Fast Whisper: Transcribe 300 minutes of audio in less than 98 seconds
https://github.com/m-bain/whisperX/issues/569
WhisperX with the new model. It's not fast.
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Distil-Whisper: distilled version of Whisper that is 6 times faster, 49% smaller
How much faster in real wall-clock time is this in batched data than https://github.com/m-bain/whisperX ?
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whisper self hosted what's the most cost-efficient way
Checkout whisperx
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Whisper Turbo: transcribe 20x faster than realtime using Rust and WebGPU
Neat to see a new implementation, although I'll note that for those looking for a drop-in replacement for the whisper library, I believe that both faster-whisper https://github.com/guillaumekln/faster-whisper and https://github.com/m-bain/whisperX are easier (PyTorch-based, doesn't require a web browser), and a lot faster (WhisperX is up to 70X realtime).
What are some alternatives?
faster-whisper - Faster Whisper transcription with CTranslate2
whisper - Robust Speech Recognition via Large-Scale Weak Supervision
Whisper - High-performance GPGPU inference of OpenAI's Whisper automatic speech recognition (ASR) model
bark - 🔊 Text-Prompted Generative Audio Model
insanely-fast-whisper - Incredibly fast Whisper-large-v3
openai-whisper-cpu - Improving transcription performance of OpenAI Whisper for CPU based deployment
llama.cpp - LLM inference in C/C++
ControlNet - Let us control diffusion models!
NeMo - A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)
subgen - Autogenerate subtitles using OpenAI Whisper Model via Jellyfin, Plex, Emby, Tautulli, or Bazarr