Chapter 4.1: Voice to Text with Whisper

The ultimate goal of robotics is to create machines that can collaborate with humans naturally. The most natural human interface is language. To build a robot that can understand spoken commands, the first step is to convert the sound of a human voice into written text. This process is called Automatic Speech Recognition (ASR).

This chapter introduces OpenAI's Whisper, a state-of-the-art, open-source ASR system. We'll discuss how it works, how to run it, and how to integrate it into a ROS 2 system to create a node that listens for commands.

What is Whisper?

Whisper is a family of pre-trained ASR models released by OpenAI in 2022. It was trained on a massive and diverse dataset of 680,000 hours of multilingual and multitask supervised data collected from the web. This vast training data makes Whisper incredibly robust and accurate across a wide range of languages, accents, and acoustic conditions.

Key Features of Whisper:

• Multilingual: It supports transcription in dozens of languages and can even translate from those languages into English.
• Robustness: It performs well even with background noise, accents, and technical jargon, which is a significant advantage over many older ASR systems.
• Open Source: The models and the inference code are open source, allowing us to run them locally on our own hardware without relying on a cloud service. This is critical for robotics, where latency and internet connectivity can be issues.
• Multiple Model Sizes: Whisper comes in various sizes, from tiny (39 million parameters) to large (1.55 billion parameters). This allows us to choose a model that fits our hardware constraints.
  • Smaller models are faster but less accurate.
  • Larger models are more accurate but require more computational resources (ideally a GPU).

How to Use Whisper

Using Whisper in Python is remarkably simple thanks to the official openai-whisper package.

1. Installation:

   pip install -U openai-whisper
   # For GPU-accelerated transcription (highly recommended)
   pip install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

   You will also need ffmpeg installed on your system: sudo apt update && sudo apt install ffmpeg.

2. Basic Transcription: The core of the library is the whisper.transcribe() function.

   import whisper
   
   # Load a model (e.g., 'base', 'medium', 'large')
   model = whisper.load_model("base.en") # '.en' for English-only model
   
   # Transcribe an audio file
   result = model.transcribe("path/to/my/audio_file.wav")
   
   # Print the result
   print(result["text"])

The transcribe function handles all the complexity: it loads the audio, splits it into 30-second chunks, runs the neural network, and returns the transcribed text.

Creating a Voice-to-Text ROS 2 Node

To use Whisper in our robotics project, we need to create a ROS 2 node that can:

1. Access the microphone audio stream.
2. Listen for a user to speak.
3. Capture the spoken audio.
4. Use Whisper to transcribe the audio into text.
5. Publish the resulting text to a ROS 2 topic for other nodes (like our planner) to use.

Capturing Microphone Audio

Accessing microphone data in Python can be done with libraries like sounddevice or pyaudio. The process generally involves:

1. Opening an audio stream from the default microphone device.
2. Continuously reading chunks of audio data (as NumPy arrays).
3. Implementing a simple voice activity detection (VAD) algorithm. A basic VAD can work by monitoring the energy (root mean square) of the audio chunks. When the energy surpasses a certain threshold, we start recording. When it drops below the threshold for a certain duration, we stop recording.

The ROS 2 Node Structure

Here is a conceptual outline of our voice_commander node:

import rclpy
from rclpy.node import Node
from std_msgs.msg import String
import whisper
import sounddevice as sd
import numpy as np
# Other necessary imports for audio handling...

class VoiceCommanderNode(Node):
    def __init__(self):
        super().__init__('voice_commander_node')
        
        # Publisher for the transcribed command
        self.command_publisher_ = self.create_publisher(String, 'recognized_command', 10)
        
        # Load the Whisper model
        self.model = whisper.load_model("base.en")
        
        # Audio stream parameters
        self.samplerate = 16000 # Whisper was trained on 16kHz audio
        self.channels = 1
        
        # VAD parameters
        self.energy_threshold = 1000
        self.silence_duration = 1.0 # seconds
        
        # Start listening
        self.start_listening()
        self.get_logger().info("Voice commander node started. Say a command!")

    def start_listening(self):
        # This is a simplified representation.
        # A real implementation would use a callback-based audio stream.
        # It would continuously listen, detect speech, record it,
        # and then call self.process_audio() on the recording.
        pass

    def process_audio(self, audio_data_np):
        """
        Transcribe the captured audio and publish the command.
        """
        self.get_logger().info("Processing speech...")
        
        # Transcribe using Whisper
        # Note: audio_data_np needs to be in the correct format (float32)
        result = self.model.transcribe(audio_data_np, fp16=False) # fp16=False for CPU
        
        transcribed_text = result["text"].strip()
        
        if transcribed_text:
            self.get_logger().info(f'Whisper transcribed: "{transcribed_text}"')
            
            # Publish the command
            msg = String()
            msg.data = transcribed_text
            self.command_publisher_.publish(msg)
        else:
            self.get_logger().info("Whisper could not transcribe any text.")

# main() function as before

When this node is running, it will constantly listen to the microphone. When you speak a command, it will record the audio, send it to Whisper for transcription, and the resulting text (e.g., "robot, pick up the red block") will be published on the /recognized_command topic.

Summary

OpenAI's Whisper provides a powerful, robust, and open-source solution for converting speech to text. By integrating it into a ROS 2 node that listens to a microphone, we can create the first link in our Vision-Language-Action chain: a voice interface for our robot. The text commands published by this node will be the input for the next stage of our system: the planning and reasoning LLM.