How to build a realtime ASR with opensource Whisper

Building a real-time Automatic Speech Recognition (ASR) system using OpenAI's Whisper involves several steps, including setting up your environment, downloading Whisper models, and creating a real-time audio processing pipeline. Here's a general approach to get you started:

Step 1: Install Whisper

First, you need to ensure that Python is installed on your system. Whisper can be installed via pip. In your terminal, run:

bash
pip install git+https://github.com/openai/whisper.git

Make sure your Python environment has the necessary dependencies for Whisper.

Step 2: Real-Time Audio Processing Setup

For real-time ASR, you need to capture audio from your microphone, process it in chunks, and then feed these chunks to Whisper for transcription. Python libraries such as sounddevice and numpy can be helpful for capturing and processing audio in real-time.

bash
pip install sounddevice numpy

Step 3: Implementing Real-Time Transcription

The following Python script provides a basic structure for implementing real-time ASR with Whisper. This script captures audio from the microphone, processes it in real-time, and prints the transcriptions:

```

import sounddevice as sd

import numpy as np

import whisper

# Load Whisper model

model = whisper.load_model("base")  # You can choose different model sizes

def audio_callback(indata, frames, time, status):

    # This function will be called for each audio block captured by the microphone

    audio_np = np.array(indata[:, 0], dtype=np.float32)  # Convert to numpy array

    # Here, you could buffer the audio and then process it with Whisper

    

    # Example: Passing the audio to Whisper directly for real-time transcription

    # might not be efficient or feasible due to the model's expectations for input length and format.

    # Instead, consider buffering the audio and processing it in larger chunks.

    

    # Print the status for debugging

    print("Capturing audio...", status)

# Start streaming from microphone

with sd.InputStream(callback=audio_callback):

    sd.sleep(10000)  # Keep capturing for a certain time or implement a loop for continuous capture

```

Notes:

• This script sets up a basic framework. You need to adapt the audio_callback function to buffer audio data and then periodically send this data to the Whisper model for transcription. Whisper typically requires a longer audio segment (several seconds) to produce accurate transcriptions, so real-time performance may vary based on how you implement buffering and processing.
• Consider the limitations and performance of the Whisper model you choose (base, small, medium, large). Larger models offer better accuracy but require more computational resources.

Step 4: Post-Processing and Displaying Transcriptions

After receiving the transcription from Whisper, you can display it in real-time or use it as needed in your application. Be mindful of the latency involved in processing and transcription when designing your real-time ASR system.

Final Thoughts:

• Real-time ASR with Whisper involves handling streaming audio data, which can be complex. Performance and accuracy will depend on your implementation details and the computational resources available.
• Test with different Whisper models and audio processing strategies to find the best balance between latency and transcription accuracy for your use case.