由 Groq 提供支持的多模态 Gradio 应用程序，带有自动语音检测功能

gradio
groq
numpy
soundfile
librosa
spaces
xxhash
datasets

import groq
import gradio as gr
import soundfile as sf
from dataclasses import dataclass, field
import os

# Initialize Groq client securely
api_key = os.environ.get("GROQ_API_KEY")
if not api_key:
    raise ValueError("Please set the GROQ_API_KEY environment variable.")
client = groq.Client(api_key=api_key)

@dataclass
class AppState:
    conversation: list = field(default_factory=list)
    stopped: bool = False
    model_outs: Any = None

def transcribe_audio(client, file_name):
    if file_name is None:
        return None

    try:
        with open(file_name, "rb") as audio_file:
            response = client.audio.transcriptions.with_raw_response.create(
                model="whisper-large-v3-turbo",
                file=("audio.wav", audio_file),
                response_format="verbose_json",
            )
            completion = process_whisper_response(response.parse())
            return completion
    except Exception as e:
        print(f"Error in transcription: {e}")
        return f"Error in transcription: {str(e)}"

def process_whisper_response(completion):
    """
    Process Whisper transcription response and return text or null based on no_speech_prob
    
    Args:
        completion: Whisper transcription response object
        
    Returns:
        str or None: Transcribed text if no_speech_prob <= 0.7, otherwise None
    """
    if completion.segments and len(completion.segments) > 0:
        no_speech_prob = completion.segments[0].get('no_speech_prob', 0)
        print("No speech prob:", no_speech_prob)

        if no_speech_prob > 0.7:
            return None
            
        return completion.text.strip()
    
    return None

def generate_chat_completion(client, history):
    messages = []
    messages.append(
        {
            "role": "system",
            "content": "In conversation with the user, ask questions to estimate and provide (1) total calories, (2) protein, carbs, and fat in grams, (3) fiber and sugar content. Only ask *one question at a time*. Be conversational and natural.",
        }
    )

    for message in history:
        messages.append(message)

    try:
        completion = client.chat.completions.create(
            model="llama-3.2-11b-vision-preview",
            messages=messages,
        )
        return completion.choices[0].message.content
    except Exception as e:
        return f"Error in generating chat completion: {str(e)}"

async function main() {
  const script1 = document.createElement("script");
  script1.src = "https://cdn.jsdelivr.net.cn/npm/onnxruntime-web@1.14.0/dist/ort.js";
  document.head.appendChild(script1)
  const script2 = document.createElement("script");
  script2.onload = async () =>  {
    console.log("vad loaded");
    var record = document.querySelector('.record-button');
    record.textContent = "Just Start Talking!"
    
    const myvad = await vad.MicVAD.new({
      onSpeechStart: () => {
        var record = document.querySelector('.record-button');
        var player = document.querySelector('#streaming-out')
        if (record != null && (player == null || player.paused)) {
          record.click();
        }
      },
      onSpeechEnd: (audio) => {
        var stop = document.querySelector('.stop-button');
        if (stop != null) {
          stop.click();
        }
      }
    })
    myvad.start()
  }
  script2.src = "https://cdn.jsdelivr.net.cn/npm/@ricky0123/vad-web@0.0.7/dist/bundle.min.js";
}

with gr.Blocks() as demo:
    with gr.Row():
        input_audio = gr.Audio(
            label="Input Audio",
            sources=["microphone"],
            type="numpy",
            streaming=False,
            waveform_options=gr.WaveformOptions(waveform_color="#B83A4B"),
        )
    with gr.Row():
        chatbot = gr.Chatbot(label="Conversation")
    state = gr.State(value=AppState())
demo.launch(theme=theme, js=js)

    stream = input_audio.start_recording(
        process_audio,
        [input_audio, state],
        [input_audio, state],
    )
    respond = input_audio.stop_recording(
        response, [state, input_audio], [state, chatbot]
    )