1. 环境安装

pip install aiortc numpy sounddevice vosk transformers gtts

2. 模块构成：ASR+LLM+TTS

3. ASR

from vosk import Model, KaldiRecognizer
import sounddevice as sd
import json

model = Model("path_to_vosk_model")
recognizer = KaldiRecognizer(model, 16000)

def asr_callback(indata, frames, time, status):
    if recognizer.AcceptWaveform(indata):
        result = recognizer.Result()
        text = json.loads(result).get("text", "")
        if text:
            handle_text(text)

def start_asr_stream():
    with sd.RawInputStream(samplerate=16000, blocksize=8000, dtype='int16',
                           channels=1, callback=asr_callback):
        print("Listening...")
        sd.sleep(1000000)

4. LLM

from transformers import pipeline

llm = pipeline("text-generation", model="gpt-2")

def handle_text(text):
    print(f"Recognized: {text}")
    response = llm(text)[0]["generated_text"]
    print(f"Response: {response}")
    speak(response)

5. TTS

from gtts import gTTS
import os
import sounddevice as sd
import soundfile as sf

def speak(text):
    tts = gTTS(text)
    tts.save("response.mp3")
    data, fs = sf.read("response.mp3", dtype='float32')
    sd.play(data, fs)
    sd.wait()
    os.remove("response.mp3")

6. 集成 aiortc 进行 WebRTC 实现

from aiortc import RTCPeerConnection, RTCSessionDescription, MediaStreamTrack
from aiortc.contrib.signaling import TcpSocketSignaling
from aiortc.contrib.media import MediaPlayer, MediaRecorder

pc = RTCPeerConnection()

@pc.on("track")
async def on_track(track):
    if track.kind == "audio":
        # 使用 track 音频数据进行 ASR
        pass

# 使用 signaling 实现信令交换
signaling = TcpSocketSignaling("localhost", 12345)
await pc.setRemoteDescription(await signaling.receive())

await start_asr_stream()