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。
使用百度语音识别生成字幕
转载：https://blog.csdn.net/rocshaw/article/details/104040466
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