marimo
whisperX
marimo | whisperX | |
---|---|---|
13 | 24 | |
4,050 | 9,173 | |
6.2% | - | |
9.9 | 8.4 | |
5 days ago | 11 days ago | |
Python | Python | |
Apache License 2.0 | BSD 4-Clause "Original" or "Old" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
marimo
-
Show HN: Marimo – open-source reactive Python notebook – running in WASM
We do support GitHub Copilot in the pip/conda installable version that you can run locally on your computer. (https://github.com/marimo-team/marimo)
We have considered adding more copilot features for refactoring or text-to-cell.
-
Show HN: Privacy-first cross platform spreadsheet pipeline app
i use marimo for this sort of stuff. Its a jupyter alike, but can be used to make simple internal apps
https://github.com/marimo-team/marimo
- FLaNK 15 Jan 2024
-
Show HN: Marimo – an open-source reactive notebook for Python
He is an example: https://marimo.io/@public/signal-decomposition
- Marimo – a fresh take at reactive Python notebooks
-
HTML Web Components
We use web components in our project (a reactive Python notebook that, among other things, lets users build simple web apps [1]) to make it easy for the user to instantiate and compose our UI elements. Users can easily interpolate these elements into markdown, for example, since their representation is just HTML.
[1] https://github.com/marimo-team/marimo
-
Marimo: Next-Generation Python Notebook
Thanks for sharing! marimo is free and open source (Apache 2.0): https://github.com/marimo-team/marimo
It's been under development for over a year and is used in research, science, and education across a number of labs and companies. We'll have lots more to share soon!
whisperX
-
Easy video transcription and subtitling with Whisper, FFmpeg, and Python
It uses this, which does support diarization: https://github.com/m-bain/whisperX
-
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
-
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)
-
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
-
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.
-
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.
-
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 ?
-
whisper self hosted what's the most cost-efficient way
Checkout whisperx
-
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?
jupyter-vim-binding - Jupyter meets Vim. Vimmer will fall in love.
whisper.cpp - Port of OpenAI's Whisper model in C/C++
vscode-dvc - Machine learning experiment tracking and data versioning with DVC extension for VS Code
whisper - Robust Speech Recognition via Large-Scale Weak Supervision
hal9 - Hal9 — Create and Share Generative Apps
faster-whisper - Faster Whisper transcription with CTranslate2
htm - Hyperscript Tagged Markup: JSX alternative using standard tagged templates, with compiler support.
insanely-fast-whisper - Incredibly fast Whisper-large-v3
webcomponents - Web Components specifications
openai-whisper-cpu - Improving transcription performance of OpenAI Whisper for CPU based deployment
htmx - </> htmx - high power tools for HTML
ControlNet - Let us control diffusion models!