锦中人工智能助手

# 导入必要的库

import os

from google.cloud import speech_v1p1beta1 as speech

# 设置环境变量

os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "path/to/your/credentials.json"

# 初始化客户端

client = speech.SpeechClient()

# 读取音频文件

with open("video_audio.wav", "rb") as audio_file:

content = audio_file.read()

# 创建配置

config = speech.RecognitionConfig(

encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,

sample_rate_hertz=16000,

language_code="zh-CN"

)

# 调用API

response = client.recognize(config=config, audio={"content": content})

# 输出结果

for result in response.results:

print("Transcript: {}".format(result.alternatives[0].transcript))

# 导入pydub库

from pydub import AudioSegment

# 加载视频文件

video = AudioSegment.from_mp3("video.mp3")

# 提取音频

audio = video[:5000] # 只提取前5秒

# 保存为wav格式

audio.export("video_audio.wav", format="wav")

# 导入必要的库

from transformers import AutoTokenizer, AutoModelForQuestionAnswering

import torch

# 加载预训练模型和分词器

model_name = "deepset/roberta-base-squad2"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForQuestionAnswering.from_pretrained(model_name)

# 输入问题和上下文

question = "什么是人工智能？"

context = "人工智能（AI）是指由人创造的能够执行通常需要人类智能的任务的系统。"

# 分词和编码

inputs = tokenizer.encode_plus(question, context, return_tensors="pt")

# 获取模型输出

outputs = model(**inputs)

# 找出起始和结束位置

start_scores = outputs.start_logits

end_scores = outputs.end_logits

# 获取最可能的答案位置

answer_start = torch.argmax(start_scores)

answer_end = torch.argmax(end_scores) + 1

# 解码得到答案

answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs["input_ids"][0][answer_start:answer_end]))

print("答案：", answer)