159 lines
5.1 KiB
Python
159 lines
5.1 KiB
Python
from transformers import AutoModelForCausalLM, AutoTokenizer
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from cosyvoice.cli.cosyvoice import CosyVoice
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from cosyvoice.utils.file_utils import load_wav
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from funasr import AutoModel
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import torchaudio
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import pygame
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import time
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import sys
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import sounddevice as sd
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from scipy.io.wavfile import write
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import numpy as np
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def record_audio(filename="output.wav", sample_rate=44100):
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print("按下 Enter 开始录音...")
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input() # 等待用户按下 Enter 键开始录音
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print("录音中... 按下 Enter 键结束录音")
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# 开始录音
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recording = []
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try:
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def callback(indata, frames, time, status):
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recording.append(indata.copy())
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with sd.InputStream(samplerate=sample_rate, channels=1, callback=callback):
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input() # 等待用户再次按下 Enter 键结束录音
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except Exception as e:
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print(f"录音出现错误: {e}")
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return
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# 将录音数据合并并保存为 WAV 文件
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audio_data = np.concatenate(recording, axis=0)
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write(filename, sample_rate, (audio_data * 32767).astype(np.int16))
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print(f"录音已保存为 {filename}")
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# --- 播放音频 -
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def play_audio(file_path):
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try:
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pygame.mixer.init()
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pygame.mixer.music.load(file_path)
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pygame.mixer.music.play()
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while pygame.mixer.music.get_busy():
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time.sleep(1) # 等待音频播放结束
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print("播放完成！")
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except Exception as e:
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print(f"播放失败: {e}")
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finally:
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pygame.mixer.quit()
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import os
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import shutil
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def clear_folder(folder_path):
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# 检查文件夹是否存在
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if not os.path.exists(folder_path):
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os.makedirs(folder_path, exist_ok=True)
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print(f"文件夹 '{folder_path}' 不存在，已创建")
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return
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# 获取文件夹中的所有文件和子文件夹
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items = os.listdir(folder_path)
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# 如果文件夹为空，直接返回
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if not items:
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print(f"文件夹 '{folder_path}' 已经为空")
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return
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# 遍历文件和文件夹并删除
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for item in items:
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item_path = os.path.join(folder_path, item)
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# 判断是否是文件夹或文件
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if os.path.isfile(item_path):
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os.remove(item_path) # 删除文件
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print(f"删除文件: {item_path}")
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elif os.path.isdir(item_path):
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shutil.rmtree(item_path) # 删除文件夹及其内容
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print(f"删除文件夹: {item_path}")
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print(f"文件夹 '{folder_path}' 已清空")
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# ------------------- 模型初始化 ---------------
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# --- SenceVoice-语音识别模型
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model_dir = r"E:\2_PYTHON\Project\GPT\QWen\pretrained_models\SenseVoiceSmall"
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model_senceVoice = AutoModel( model=model_dir, trust_remote_code=True, )
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# --- QWen2.5大语言模型 ---
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# model_name = r":\2_PYTHON\Project\GPT\QWen\Qwen2.5-0.5B-Instruct"
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model_name = r"E:\2_PYTHON\Project\GPT\QWen\Qwen2.5-1.5B-Instruct"
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# model_name = r':\2_PYTHON\Project\GPT\QWen\Qwen2.5-7B-Instruct-GPTQ-Int4'
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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import pyttsx3
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# 初始化 TTS 引擎
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engine = pyttsx3.init()
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# 设置语音属性
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engine.setProperty('rate', 200) # 语速engine.setProperty('volume', 0.9) # 音量（0.0 到 1.0）
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# 选择语音
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voices = engine.getProperty('voices')
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# print(voices)
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engine.setProperty('voice', voices[0].id) # 使用第一个语音
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while(1):
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# 使用函数录音，作为输入
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record_audio("my_recording.wav")
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# input_file = ( "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav" )
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input_file = ("my_recording.wav")
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res = model_senceVoice.generate(
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input=input_file,
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cache={},
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language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech"
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use_itn=False,
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)
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# -------- 模型推理阶段，将语音识别结果作为大模型Prompt ------
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prompt = res[0]['text'].split(">")[-1] + "，回答简短一些，保持50字以内！"
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messages = [
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{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
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{"role": "user", "content": prompt},
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512,
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print("Input:", prompt)
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print("Answer:", response)
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# 答复输出文件夹
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folder_path = "./out_answer/"
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clear_folder(folder_path)
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# 输入文本
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text = response
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# 朗读文本
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# engine.say(text)
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# # 等待朗读完成
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# engine.runAndWait()
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engine.save_to_file(text, os.path.join(folder_path,"sft_0.wav"))
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engine.runAndWait()
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play_audio(f'{folder_path}/sft_0.wav')
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