whisperX Alternatives

whisperX reviews and mentions

• Easy video transcription and subtitling with Whisper, FFmpeg, and Python
  1 project | news.ycombinator.com | 6 Apr 2024

  It uses this, which does support diarization: https://github.com/m-bain/whisperX

• SOTA ASR Tooling: Long-Form Transcription
  1 project | news.ycombinator.com | 31 Mar 2024

  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
  2 projects | dev.to | 31 Mar 2024

  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
  7 projects | news.ycombinator.com | 29 Mar 2024

  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
  21 projects | dev.to | 15 Jan 2024
• Subtitle is now open-source
  3 projects | news.ycombinator.com | 24 Nov 2023

  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
  8 projects | news.ycombinator.com | 14 Nov 2023

  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
  14 projects | news.ycombinator.com | 31 Oct 2023

  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
  1 project | /r/selfhosted | 17 Oct 2023

  Checkout whisperx

• Whisper Turbo: transcribe 20x faster than realtime using Rust and WebGPU
  3 projects | news.ycombinator.com | 12 Sep 2023

  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).

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