whisper-realtime-transcriber 0.1.3

Let whisper models transcribe in realtime.

Whisper Realtime Transcriber

Overview

This repository contains the source code of a realtime transcriber for various whisper models, published on huggingface.

Prerequisites

Before you begin, make sure you meet the following prerequisites:

• Python 3.10.12 installed on your machine.
• Microphone connected to your machine.

Installation

1. Install torch with CUDA support (optional)

• Follow the instructions here and install version >=2.4.0

2. Install the package:

   pip install --upgrade whisper-realtime-transcriber
   

Usage

After completing the installation, you can now use the transcriber:

• Necessary imports

import asyncio

from whisper_realtime_transcriber.InputStreamGenerator import InputStreamGenerator
from whisper_realtime_transcriber.WhisperModel import WhisperModel
from whisper_realtime_transcriber.RealtimeTranscriber import RealtimeTranscriber

• Standard way - model and generator are initialized by the RealtimeTranscriber and all outputs get printed directly to the console.

transcriber = RealtimeTranscriber()

asyncio.run(transcriber.execute_event_loop())

• Loading a different model than the ones provided.

asr_model = WhisperModel(inputstream_generator, model_id="openai/whisper-tiny")

transcriber = RealtimeTranscriber(asr_model=asr_model)

asyncio.run(transcriber.execute_event_loop())

• Executing a custom function inside the RealtimeTranscriber.

def print_transcription(some_transcription):
  print(some_transcription)

# Specifying a function will set continuous to False - this will allow one
# to execute a custom function during the coroutine, that is doing something with the transcriptions.
# After the function finished it's work the coroutine will restart.
transcriber = RealtimeTranscriber(func=print_transcription)
  
asyncio.run(transcriber.execute_event_loop())

• Loading the InputStreamGenerator and/or Whisper Model with custom values.

inputstream_generator = InputStreamGenerator(samplerate=8000, blocksize=2000, min_chunks=2)
asr_model = WhisperModel(inputstream_generator, model_size="large-v3", device="cuda")

transcriber = RealtimeTranscriber(inputstream_generator, asr_model)

asyncio.run(transcriber.execute_event_loop())

Feel free to reach out if you encounter any issues or have questions!

How it works

• The transcriber consists of two modules: a Inputstream Generator and a Whisper Model.
• The implementation of the Inputstream Generator is based on this implemantation.
• The Inputstream Generator reads the microphone input and passes it to the Whisper Model. The Whisper Model then generates the transcription.
• This is happening in an async event loop so that the Whsiper Model can continuously generate transcriptions from the provided audio input, generated and processed by the Inputstream Generator.
• On a machine with a 12GB Nvidia RTX 3060 the distilled large-v3 model runs at a realtime-factor of about 0.4, this means 10s of audio input get transcribed in 4s - the longer the input the bigger is the realtime-factor.

ToDos

• Add functionality to transcribe from audio files.
• Get somehow rid of the hallucinations of the whisper models when no voice is active.