1.首先下载模型，加载模型
import torch
import numpy as np
import webrtcvad
import pyaudio
import queue
import threading
from datetime import datetime
from faster_whisper import WhisperModel
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM,pipeline
from transformers import T5ForConditionalGeneration, T5Tokenizer
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16
save_directory = "./faster-distil-whiper-large-v3-local" # 替换为你希望保存的本地路径
# en_zh_directory = "./opus-mt-en-zh-local" # 替换为你希望保存的本地路径
en_zh_directory = "./t5-translate-en-ru-zh-base-200-sent-local" # 替换为你希望保存的本地路径
whisperModel = WhisperModel(save_directory, device="cuda", compute_type="float32")model = T5ForConditionalGeneration.from_pretrained(en_zh_directory)
model.eval()
model.to(device)
tokenizer = T5Tokenizer.from_pretrained(en_zh_directory)
vad = webrtcvad.Vad(3) # 设置 VAD 灵敏度（0-3，3 最敏感）
prefix = 'translate to zh: '
2.配置麦克风
# 初始化 PyAudio
p = pyaudio.PyAudio()
# 设置音频流参数
FORMAT = pyaudio.paInt16 # 16-bit 音频格式
CHANNELS = 1 # 单声道
RATE = 16000 # 采样率（Whisper 需要 16kHz）
FRAME_DURATION = 20 # 每帧的时长（ms）
CHUNK = int(RATE * FRAME_DURATION / 1000) # 每帧的帧数
MIN_SILENCE_DURATION = 0.2 # 最小静音时长（秒）
3.队列构建，构建录音基本参数
# 共享队列，用于录音和推理线程之间的数据交换
audio_queue = queue.Queue()silence_frames = 0
silence_frames_lock = threading.Lock()
4.构建录音函数
# 录音线程
def record_audio():global silence_framesstream = p.open(format=FORMAT,channels=CHANNELS,rate=RATE,input=True,frames_per_buffer=CHUNK,)print("开始录音...按 Ctrl+C 停止")try:while True:# 从麦克风读取音频数据data = stream.read(CHUNK)audio_data = np.frombuffer(data, dtype=np.int16).astype(np.float32) / 32768.0# 使用 VAD 检测语音活动if vad.is_speech(data, RATE):audio_queue.put(audio_data)with silence_frames_lock:silence_frames = 0 # 重置静音计数器else:with silence_frames_lock:silence_frames += 1 # 重置静音计数器except KeyboardInterrupt:print("录音停止")finally:stream.stop_stream()stream.close()p.terminate()
5.构建翻译函数
def process_audio():global silence_framesaudio_buffer = np.array([], dtype=np.float32)silence_frames = 0while True:try:# 从队列中获取音频数据audio_data = audio_queue.get(timeout=1) # 超时 1 秒audio_buffer = np.concatenate((audio_buffer, audio_data))except Exception as e:passcurrent_silence_frames = 0# 检查静音计数器with silence_frames_lock:current_silence_frames = silence_frames# 如果检测到静音时间超过阈值，处理累积的音频if (current_silence_frames > MIN_SILENCE_DURATION * (RATE / CHUNK)) or len(audio_buffer) > 320 * 200:if(len(audio_buffer) > 0):#, language="en"segments, _ = whisperModel.transcribe(audio_buffer,vad_filter=True,vad_parameters=dict(min_silence_duration_ms=200), language="en", condition_on_previous_text=True)for segment in segments:if(segment.text == ""):continueelif(segment.text == "Thank you."):print("[%s] %s (%s)" % (str(datetime.now()), "感谢", segment.text))else: src_text = prefix + segment.textinput_ids = tokenizer(src_text, return_tensors="pt")generated_tokens = model.generate(**input_ids.to(device))result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)print("[%s] %s (%s)" % (str(datetime.now()), result[0], segment.text))# result = pipeline(segment.text)# print("[%s] %s (%s)" % (str(datetime.now()), result[0]['translation_text'], segment.text))audio_buffer = np.array([], dtype=np.float16)silence_frames = 0
6.启动线程，启动程序
# 启动录音线程和推理线程
record_thread = threading.Thread(target=record_audio)
process_thread = threading.Thread(target=process_audio)record_thread.start()
process_thread.start()
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