使用Python和百度语音识别生成视频字幕的实现
从视频中提取音频
安装 moviepy
pip install moviepy
相关代码:
audio_file = work_path + '\\out.wav' video = VideoFileClip(video_file) video.audio.write_audiofile(audio_file,ffmpeg_params=['-ar','16000','-ac','1'])
根据静音对音频分段
使用音频库 pydub,安装:
pip install pydub
第一种方法:
# 这里silence_thresh是认定小于-70dBFS以下的为silence,发现小于 sound.dBFS * 1.3 部分超过 700毫秒,就进行拆分。这样子分割成一段一段的。 sounds = split_on_silence(sound, min_silence_len = 500, silence_thresh= sound.dBFS * 1.3) sec = 0 for i in range(len(sounds)): s = len(sounds[i]) sec += s print('split duration is ', sec) print('dBFS: {0}, max_dBFS: {1}, duration: {2}, split: {3}'.format(round(sound.dBFS,2),round(sound.max_dBFS,2),sound.duration_seconds,len(sounds)))
感觉分割的时间不对,不好定位,我们换一种方法:
# 通过搜索静音的方法将音频分段 # 参考:https://wqian.net/blog/2018/1128-python-pydub-split-mp3-index.html timestamp_list = detect_nonsilent(sound,500,sound.dBFS*1.3,1) for i in range(len(timestamp_list)): d = timestamp_list[i][1] - timestamp_list[i][0] print("Section is :", timestamp_list[i], "duration is:", d) print('dBFS: {0}, max_dBFS: {1}, duration: {2}, split: {3}'.format(round(sound.dBFS,2),round(sound.max_dBFS,2),sound.duration_seconds,len(timestamp_list)))
输出结果如下:
感觉这样好处理一些
使用百度语音识别
现在百度智能云平台创建一个应用,获取 API Key 和 Secret Key:
获取 Access Token
使用百度 AI 产品需要授权,一定量是免费的,生成字幕够用了。
''' 百度智能云获取 Access Token ''' def fetch_token(): params = {'grant_type': 'client_credentials', 'client_id': API_KEY, 'client_secret': SECRET_KEY} post_data = urlencode(params) if (IS_PY3): post_data = post_data.encode( 'utf-8') req = Request(TOKEN_URL, post_data) try: f = urlopen(req) result_str = f.read() except URLError as err: print('token http response http code : ' + str(err.errno)) result_str = err.reason if (IS_PY3): result_str = result_str.decode() print(result_str) result = json.loads(result_str) print(result) if ('access_token' in result.keys() and 'scope' in result.keys()): print(SCOPE) if SCOPE and (not SCOPE in result['scope'].split(' ')): # SCOPE = False 忽略检查 raise DemoError('scope is not correct') print('SUCCESS WITH TOKEN: %s EXPIRES IN SECONDS: %s' % (result['access_token'], result['expires_in'])) return result['access_token'] else: raise DemoError('MAYBE API_KEY or SECRET_KEY not correct: access_token or scope not found in token response')
使用 Raw 数据进行合成
这里使用百度语音极速版来合成文字,因为官方介绍专有GPU服务集群,识别响应速度较标准版API提升2倍及识别准确率提升15%。适用于近场短语音交互,如手机语音搜索、聊天输入等场景。 支持上传完整的录音文件,录音文件时长不超过60秒。实时返回识别结果
def asr_raw(speech_data, token): length = len(speech_data) if length == 0: # raise DemoError('file %s length read 0 bytes' % AUDIO_FILE) raise DemoError('file length read 0 bytes') params = {'cuid': CUID, 'token': token, 'dev_pid': DEV_PID} #测试自训练平台需要打开以下信息 #params = {'cuid': CUID, 'token': token, 'dev_pid': DEV_PID, 'lm_id' : LM_ID} params_query = urlencode(params) headers = { 'Content-Type': 'audio/' + FORMAT + '; rate=' + str(RATE), 'Content-Length': length } url = ASR_URL + "?" + params_query # print post_data req = Request(ASR_URL + "?" + params_query, speech_data, headers) try: begin = timer() f = urlopen(req) result_str = f.read() # print("Request time cost %f" % (timer() - begin)) except URLError as err: # print('asr http response http code : ' + str(err.errno)) result_str = err.reason if (IS_PY3): result_str = str(result_str, 'utf-8') return result_str
生成字幕
字幕格式: https://www.cnblogs.com/tocy/p/subtitle-format-srt.html
生成字幕其实就是语音识别的应用,将识别后的内容按照 srt 字幕格式组装起来就 OK 了。具体字幕格式的内容可以参考上面的文章,代码如下:
idx = 0 for i in range(len(timestamp_list)): d = timestamp_list[i][1] - timestamp_list[i][0] data = sound[timestamp_list[i][0]:timestamp_list[i][1]].raw_data str_rst = asr_raw(data, token) result = json.loads(str_rst) # print("rst is ", result) # print("rst is ", rst['err_no'][0]) if result['err_no'] == 0: text.append('{0}\n{1} --> {2}\n'.format(idx, format_time(timestamp_list[i][0]/ 1000), format_time(timestamp_list[i][1]/ 1000))) text.append( result['result'][0]) text.append('\n') idx = idx + 1 print(format_time(timestamp_list[i][0]/ 1000), "txt is ", result['result'][0]) with open(srt_file,"r+") as f: f.writelines(text)
总结
我在视频网站下载了一个视频来作测试,极速模式从速度和识别率来说都是最好的,感觉比网易见外平台还好用。
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