openWakeWord
whisperX
openWakeWord | whisperX | |
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5 | 24 | |
457 | 9,173 | |
- | - | |
8.4 | 8.4 | |
about 1 month ago | 8 days ago | |
Jupyter Notebook | 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.
openWakeWord
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OpenAI releases Whisper v3, new generation open source ASR model
https://github.com/dscripka/openWakeWord
Balancing wake reliability vs false wake activation is a tricky balance. OWW is decent but could certainly be better.
It's used with Home Assistant now so I expect the training data and implementation overall to get significantly better fairly soon.
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Distil-Whisper: distilled version of Whisper that is 6 times faster, 49% smaller
There's also OpenWakeWord[0]. The models are readily available in tflite and ONNX formats and are impressively "light" in terms of compute requirements and performance.
It should be possible.
[0] - https://github.com/dscripka/openWakeWord
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Real-Time Noise Suppression for PipeWire writen in Rust
hey, quick question. do you mind if I use your stft function in the speech preprocessing library I've been working on? we've been trying to add support for doing mel spectrograms to build a runner for openwakeword, but progress is pretty slow because I've been soloing something I really don't have the right background for(I've never directly studied or worked with signal processing)
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I'm new to Rust but want to contribute
potentially build another runner for open wakeword
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I want to contribute in a big project
here's what's on the pipeline next: - finish mel-spectrogram implementation - publish initial version on crates - move python caching rust side - finish implementing in the precise rust port - potentially build another runner for (open wakeword)[https://github.com/dscripka/openWakeWord] - build an android app that supports user-defined wakewords and has some popular defaults to load. ps not a voice assistant, just the thing that activates the voice assistant.
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?
WhisperInput - Offline voice input panel & keyboard with punctuation for Android.
whisper.cpp - Port of OpenAI's Whisper model in C/C++
mfcc-rust
whisper - Robust Speech Recognition via Large-Scale Weak Supervision
project-2501 - Project 2501 is an open-source AI assistant, written in C++.
faster-whisper - Faster Whisper transcription with CTranslate2
Clippy - A bunch of lints to catch common mistakes and improve your Rust code. Book: https://doc.rust-lang.org/clippy/
insanely-fast-whisper - Incredibly fast Whisper-large-v3
whisper-dictation - Dictation app based on the OpenAI speed to text models
openai-whisper-cpu - Improving transcription performance of OpenAI Whisper for CPU based deployment
TX-2-simulator - Simulator for the pioneering TX-2 computer
ControlNet - Let us control diffusion models!