Retrieval-based-Voice-Conversion-WebUI
whisperX
Retrieval-based-Voice-Conversion-WebUI | whisperX | |
---|---|---|
56 | 24 | |
19,255 | 9,064 | |
7.0% | - | |
9.6 | 8.4 | |
3 days ago | 9 days ago | |
Python | 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.
Retrieval-based-Voice-Conversion-WebUI
-
OpenVoice: Versatile Instant Voice Cloning
RVC does live voice changing with a little latency: https://github.com/RVC-Project/Retrieval-based-Voice-Convers...
The product isn't exactly spectacular, but most of the works seems to have bene done. Just needs someone to go over the UI and make it less unstable, really.
-
I made a theme song for Vito Loses
Retrieval-based-Voice-Conversion-WebUI. Nearly destroyed a hard drive in the process of getting the fucking thing to train on Vito's voice but it came together eventually.
-
Spider-Man: The Animated Series in REAL
Yeah, Man all the AI tools are fun RVC is fun to play with as well.
-
Ask HN: AI Voice Reverse
Would it be possible to reverse a AI generated voice if they spoke themselves[0] instead of using TTS[1]?
Since the AI voice is trained shouldn't a reversing AI also be able to seperate the trained data?
[0] https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI
-
RIAA Reports AI Vocal Cloning Site 'Voicify' to the U.S. Government
Well fortunately most people I see making AI cover they use open source tools to do that (https://github.com/RVC-Project/Retrieval-based-Voice-Convers...)
-
Open Source Libraries
RVC-Project/Retrieval-based-Voice-Conversion-WebUI: Singing Voice Conversion
- RVC WebUI and training on Intel ARC
-
Lyrebird the Linux voice changer now supports PipeWire
At least that's what https://github.com/RVC-Project/Retrieval-based-Voice-Convers... links to
Realtime Voice Conversion Software using RVC : w-okada/voice-changer
-
The next FF record, will have different versions with Burton's vocals...
I don't know how to use it yet, but the program is here. https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/blob/main/docs/README.en.md
- Retrieval Based Voice Conversion (WebUI)
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?
RVC-GUI - Just a fork of RVC for easy audio file voice conversion locally
whisper.cpp - Port of OpenAI's Whisper model in C/C++
Mangio-RVC-Fork - *CREPE+HYBRID TRAINING* A very experimental fork of the Retrieval-based-Voice-Conversion-WebUI repo that incorporates a variety of other f0 methods, along with a hybrid f0 nanmedian method.
whisper - Robust Speech Recognition via Large-Scale Weak Supervision
bark - 🔊 Text-Prompted Generative Audio Model
faster-whisper - Faster Whisper transcription with CTranslate2
ultimatevocalremovergui - GUI for a Vocal Remover that uses Deep Neural Networks.
insanely-fast-whisper - Incredibly fast Whisper-large-v3
voice-changer - リアルタイムボイスチェンジャー Realtime Voice Changer
openai-whisper-cpu - Improving transcription performance of OpenAI Whisper for CPU based deployment
so-vits-svc-fork - so-vits-svc fork with realtime support, improved interface and more features. [Moved to: https://github.com/voicepaw/so-vits-svc-fork]
ControlNet - Let us control diffusion models!