syncabook
whisperX
syncabook | whisperX | |
---|---|---|
5 | 24 | |
236 | 9,064 | |
- | - | |
4.3 | 8.4 | |
4 months ago | 6 days ago | |
HTML | Python | |
MIT License | 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.
syncabook
-
I made an open-source, self-hostable synced narration platform for ebooks
Amazing! I've made a similar ebooks-audiobooks aligner years ago: https://github.com/r4victor/syncabook. At that time, I chose to synthesize the text and align two audio sequences because I found texts-alignment approaches (including ML-based ones) too compute-intensive and inadequate for long texts. I see Storyteller works by aligning the texts. Could you give some view on how long it takes to sync a book?
Also, my experience was that audio and text versions are often very different (e.g. the audio having an intro missing from the text). It'd very interesting to know how well Storyteller handles such cases. Does it require manual audio/text editing or handle the differences automatically?
-
I decided to spruce up the eBook I'm reading with additional functionality (more info in comments)
Unfortunaly, this audiobook isn't synchorized to the text on Kindle. So, in order to synchronize the epub and audio myself, initially, I tried using syncabook, but the results weren't great for some reason. I then tried using OpenAI's Whisper to generate the text from the audio. This might've given decent results, but ultimately I decided I wanted to keep the book's formatting (paragraphs, emphasis, etc.)
-
Anyone know of a tool to align (existing) subtitles to audio along sentence boundaries?
I've tried syncabook, but that didn't help. I've tried whisperX, to get word-level timings, but the results are pretty bad/unusable.
-
Have you found a way to get epub3 audio-ebooks?
From searching a bit I found https://github.com/r4victor/syncabook this might help anyone intrigued about the idea. I suspect this is the tool you're talking about? Your post is really a long form question, and could have been a lot shorter.
-
[Intermediate] Ultimate Guide to Making Japanese Audiobooks with Subtitles (and Where to Get Them)
This software can generate epubs with audio completely automatically for a lot of simpler languages. The problem is it doesn't support Japanese.
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. Itβs 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?
thorium-reader - A cross platform desktop reading app, based on the Readium Desktop toolkit
whisper.cpp - Port of OpenAI's Whisper model in C/C++
kobo-book-downloader - A tool to download and remove DRM from your purchased Kobo.com ebooks and audiobooks.
whisper - Robust Speech Recognition via Large-Scale Weak Supervision
syosetu2epub - Python package that takes a link to a Syosetu web novel and converts it to an e-reader friendly EPUB file
faster-whisper - Faster Whisper transcription with CTranslate2
tools - The Standard Ebooks toolset for producing our ebook files.
insanely-fast-whisper - Incredibly fast Whisper-large-v3
epubcheck - The conformance checker for EPUB publications
openai-whisper-cpu - Improving transcription performance of OpenAI Whisper for CPU based deployment
aeneas - aeneas is a Python/C library and a set of tools to automagically synchronize audio and text (aka forced alignment)
ControlNet - Let us control diffusion models!