Overcoming Challenges in Building a Voice-to-Voice Chatbot: A Journey with Hugging Face, GitHub, and API Integration

In this article, I will share the journey of creating a Voice-to-Voice Chatbot using OpenAI's Whisper, Groq API, and Google's Text-to-Speech (gTTS). The project aimed to develop an interactive real-time chatbot that takes voice input, processes it, and responds with a voice output. Here's a breakdown of the approach, challenges, and solutions involved in building the chatbot, and how I integrated it into a simple Gradio interface for deployment on Hugging Face Spaces.

The Project Overview

The goal was simple yet ambitious: Create a real-time voice interaction system that:

1. Uses Whisper to transcribe spoken input into text.

2. Sends the transcription to Groq's API to interact with a language model (LLM).

3. Converts the LLM’s response into speech using gTTS.

I used Google Colab for development and Gradio for easy interface creation and deployment on Hugging Face Spaces. Throughout this project, I faced some challenges, but with the help of Python libraries and thoughtful error handling, I was able to overcome them.

The Tech Stack

• Whisper (OpenAI): Used for speech-to-text.

• Groq API: Served as the LLM backend, where I sent the transcribed text for a response.

• gTTS: Converted the text response from the LLM to speech.

• Gradio: Used for creating a user-friendly interface to interact with the chatbot.

Key Components of the Chatbot

1. Speech-to-Text with Whisper: Whisper is a powerful model for converting speech to text, and it works directly with audio files in various formats. I used Whisper to transcribe the user’s voice input.

2. Querying the Groq LLM: Once the transcription was complete, the text was sent to Groq's API to interact with the Llama model and generate a relevant response. The model I used is “llama-3.3-70b-versatile,” chosen for its ability to handle diverse conversational contexts.

3. Text-to-Speech with gTTS: The response from Groq was in text format, which I used gTTS to convert back to speech, creating a complete voice-to-voice interaction.

4. Gradio Interface: I leveraged Gradio to create a clean and intuitive interface where users can speak into their microphone, and the chatbot responds with audio. This made it easy to deploy and share the project.

Challenges Faced

Challenge #1: Handling the API Key Securely

One of the first obstacles I faced was how to securely manage the Groq API key without exposing it in the code, especially when deploying on Hugging Face Spaces.

Solution:

I used environment variables to keep the API key secure. In Google Colab, I set it directly using:

pythonCopy codeimport os
os.environ["GROQ_API_KEY"] = "your_groq_api_key_here"

On Hugging Face Spaces, I stored the key as a Secret in the Space’s settings, ensuring it stayed hidden from the public.

Challenge #2: Audio Input Handling

The second challenge was correctly handling the audio input from Gradio's microphone source. Initially, I had trouble with the input format, leading to errors.

Solution:

I resolved this by carefully managing the audio data format. I used soundfile to read the audio in the correct format and transcribe it via Whisper. I ensured the audio file was saved in WAV format for proper compatibility with Whisper's transcription model.

Challenge #3: Debugging Groq API Responses

Once the transcription was done, the next step was sending the text to the Groq API. However, at first, I didn’t get any response from the API, leaving the interface unresponsive.

Solution:

I added error handling in the code to catch any issues with the Groq API request. This way, if the transcription was empty or the API call failed, I could display a meaningful error message.

Here’s an example of the error handling for querying Groq:

pythonCopy codedef query_groq_llm(transcription):
    try:
        if not transcription.strip():
            return "No transcription available to query the LLM."

        chat_completion = client.chat.completions.create(
            messages=[{"role": "user", "content": transcription}],
            model="llama-3.3-70b-versatile",
            stream=False,
        )
        return chat_completion.choices[0].message.content
    except Exception as e:
        return f"Error querying the LLM: {e}"

Challenge #4: Voice Output Handling

Once the response came back from the Groq API, the next step was to convert it back to audio using gTTS. The issue here was ensuring the audio played correctly after conversion.

Solution:

I used temporary audio files to store the generated speech and returned the file path to Gradio, which then played the response audio.

Final Solution and Code

Here is the complete solution, which integrates Whisper, Groq, and gTTS seamlessly:

pythonCopy codeimport os
import tempfile
import gradio as gr
from groq import Groq
from gtts import gTTS
import whisper
import soundfile as sf
import io

# Initialize Groq client
client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
whisper_model = whisper.load_model("base")

def transcribe_audio(audio):
    audio_data, samplerate = sf.read(io.BytesIO(audio))
    sf.write("input.wav", audio_data, samplerate)
    result = whisper_model.transcribe("input.wav")
    return result["text"]

def query_groq_llm(transcription):
    chat_completion = client.chat.completions.create(
        messages=[{"role": "user", "content": transcription}],
        model="llama-3.3-70b-versatile",
        stream=False,
    )
    return chat_completion.choices[0].message.content

def text_to_speech(response):
    tts = gTTS(text=response, lang="en")
    temp_audio = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
    tts.save(temp_audio.name)
    return temp_audio.name

def chatbot_pipeline(audio):
    transcription = transcribe_audio(audio)
    response = query_groq_llm(transcription)
    audio_response = text_to_speech(response)
    return transcription, response, audio_response

def interface(audio):
    transcription, response, audio_response = chatbot_pipeline(audio)
    return transcription, response, (audio_response,)

iface = gr.Interface(
    fn=interface,
    inputs=gr.Audio(type="numpy", label="Speak into the microphone"),
    outputs=[gr.Textbox(label="Transcription"), gr.Textbox(label="LLM Response"), gr.Audio(label="Response Audio")],
    title="Real-Time Voice-to-Voice Chatbot",
    description="Speak into the microphone, and the chatbot will respond with audio!"
)

iface.launch(debug=True)

Deployment

To deploy the project on Hugging Face Spaces, I followed these steps:

1. Save the code in a file named app.py.

2. Create a requirements.txt with the necessary dependencies:

    txtCopy codegradio
    gtts
    whisper
    openai-groq-api
    soundfile
   

3. Push the code and files to a Hugging Face Space.

Conclusion

Building the Voice-to-Voice Chatbot was a challenging yet rewarding process. I had to integrate several technologies, handle multiple APIs, and troubleshoot various issues. However, by carefully handling errors, securing API keys, and ensuring compatibility between the different components, I was able to create a chatbot that delivers a seamless voice interaction experience.

Check out the Voice-to-Voice Chatbot on Hugging Face and the GitHub Repository for the source code.

I hope this article helps others who are looking to build similar applications. Let me know if you have any questions or feedback!

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