西昌网站开发公司,国外最开放的浏览器是哪个,如何建设网站子页,产品市场调研怎么做Wenet是一个流行的语音处理工具#xff0c;它专注于长音频的处理#xff0c;具备分割、降噪和识别功能。它的长音频分割降噪识别功能允许对长时间录制的音频进行分段处理#xff0c;首先对音频进行分割#xff0c;将其分解成更小的段落或语音片段。接着进行降噪处理#x… Wenet是一个流行的语音处理工具它专注于长音频的处理具备分割、降噪和识别功能。它的长音频分割降噪识别功能允许对长时间录制的音频进行分段处理首先对音频进行分割将其分解成更小的段落或语音片段。接着进行降噪处理消除可能存在的噪音、杂音或干扰提高语音质量和清晰度。最后Wenet利用先进的语音识别技术对经过处理的音频段落进行识别将其转换为文字或语音内容从而实现对长音频内容的准确识别和转录。这种功能可以应用于许多领域如语音识别、语音转文字、语音翻译以及音频内容分析等为长音频数据的处理提供了高效而准确的解决方案。 支持上传WAV、MP3、M4A、FLAC、AAC
体验一下 import streamlit as st
import jieba
from wordcloud import WordCloud
import matplotlib.pyplot as plt
from pydub import AudioSegment
from noisereduce import reduce_noise
import wenet
import base64
import os
import numpy as np# 载入模型
chs_model wenet.load_model(chinese)
en_model wenet.load_model(english)# 执行语音识别的函数
def recognition(audio, langCN):if audio is None:return 输入错误请上传音频文件if lang CN:ans chs_model.transcribe(audio)elif lang EN:ans en_model.transcribe(audio)else:return 错误请选择语言if ans is None:return 错误没有文本输出请重试txt ans[text]return txtdef reduce_noise_segmented(input_file,chunk_duration_ms,frame_rate):try:audio AudioSegment.from_file(input_file,formatinput_file.name.split(.)[-1])# 将双声道音频转换为单声道audio audio.set_channels(1)# 压缩音频的帧率为 16000audio audio.set_frame_rate(frame_rate)duration len(audio)# 分段处理音频chunked_audio []start 0while start duration:end min(start chunk_duration_ms, duration)chunk audio[start:end]chunked_audio.append(chunk)start endreturn chunked_audioexcept Exception as e:st.error(f发生错误{str(e)})return Nonedef extract_keywords(result):word_list jieba.lcut(result)return word_listdef get_base64_link(file_path, link_text):with open(file_path, rb) as file:audio_content file.read()encoded base64.b64encode(audio_content).decode(utf-8)href fa hrefdata:audio/wav;base64,{encoded} downloadprocessed_audio.wav{link_text}/areturn hrefdef main():st.title(语音识别与词云生成)uploaded_file st.file_uploader(上传音乐文件, type[wav,mp3,m4a,flac,aac])if uploaded_file:st.audio(uploaded_file, formataudio/wav)segment_duration st.slider(分段处理时长毫秒, min_value1000, max_value10000, value5000, step1000)frame_rate st.slider(压缩帧率, min_value8000, max_value48000, value16000, step1000)language_choice st.selectbox(选择语言, (中文, 英文))bust.button(识别语音)if bu:if uploaded_file:st.success(正在识别中请稍等...)output_audio_path os.path.basename(uploaded_file.name)chunked_audio reduce_noise_segmented(uploaded_file, segment_duration, frame_rate)# 计算总的音频段数total_chunks len(chunked_audio)if total_chunks0:# 创建进度条progress_bar st.progress(0)# 对每个音频段进行降噪并合并reduced_noise_chunks []result_array []for i, chunk in enumerate(chunked_audio):audio_array chunk.get_array_of_samples()reduced_noise reduce_noise(np.array(audio_array), chunk.frame_rate)reduced_chunk AudioSegment(reduced_noise.tobytes(),frame_ratechunk.frame_rate,sample_widthchunk.sample_width,channelschunk.channels)reduced_noise_chunks.append(reduced_chunk)languageif language_choice中文:languageCNelse:languageENpath第str(i1)段音频.wavreduced_chunk.export(path,formatwav)while os.path.exists(path):result recognition(path, language)if result:st.write(f第{i1}段音频识别结果 result)result_array.append(result)break# 更新进度条的值progress int((i 1) / total_chunks * 100)progress_bar.progress(progress)st.write(识别的结果为,.join(result_array))keywords extract_keywords(.join(result_array))st.write(提取的关键词, keywords)text .join(keywords)wc WordCloud(font_pathSimSun.ttf,collocationsFalse, width800, height400, margin2, background_colorwhite).generate(text.lower())st.image(wc.to_array(), caption词云)# 合并降噪后的音频段reduced_audio reduced_noise_chunks[0]for i in range(1, len(reduced_noise_chunks)):reduced_audio reduced_noise_chunks[i]# 导出处理后的音频文件reduced_audio.export(output_audio_path,formatwav)while os.path.exists(output_audio_path):# 提供处理后音频的下载链接st.markdown(get_base64_link(output_audio_path, 下载降噪音频), unsafe_allow_htmlTrue) breakelse:st.warning(请上传文件)if __name__ __main__:main()依赖
wenet githttps://github.com/wenet-e2e/wenet
streamlit
wordcloud
pydub
jieba
noisereduce
numpy1.23.5服务器部署
