Question

我觉得这是一个相当普遍的问题，但我还没有找到合适的答案。我有许多人类语音的音频文件，我想在单词上打破，这可以通过查看波形中的暂停来启发式地完成，但是有人能指向我自动执行此操作的python中的函数/库吗？

Answer 1

更简单的方法是使用pydub模块。最近添加的silent utilities完成了所有繁重工作，例如setting up silence threahold，setting up silence length。与其他提到的方法相比，显着简化了代码。

这是一个演示实现，灵感来自here

<强>设定：

我在文件＆＃34; a-z.wav＆＃34;中有一个带有A到Z的英语口语的音频文件。在当前工作目录中创建了子目录splitAudio。在执行演示代码时，文件被分成26个单独的文件，每个音频文件存储每个音节。

<强>观察：一些音节被切断，可能需要修改以下参数，
min_silence_len=500
silence_thresh=-16

有人可能希望根据自己的要求调整这些。

演示代码：

from pydub import AudioSegment
from pydub.silence import split_on_silence

sound_file = AudioSegment.from_wav("a-z.wav")
audio_chunks = split_on_silence(sound_file, 
    # must be silent for at least half a second
    min_silence_len=500,

    # consider it silent if quieter than -16 dBFS
    silence_thresh=-16
)

for i, chunk in enumerate(audio_chunks):

    out_file = ".//splitAudio//chunk{0}.wav".format(i)
    print "exporting", out_file
    chunk.export(out_file, format="wav")

<强>输出：

Python 2.7.9 (default, Dec 10 2014, 12:24:55) [MSC v.1500 32 bit (Intel)] on win32
Type "copyright", "credits" or "license()" for more information.
>>> ================================ RESTART ================================
>>> 
exporting .//splitAudio//chunk0.wav
exporting .//splitAudio//chunk1.wav
exporting .//splitAudio//chunk2.wav
exporting .//splitAudio//chunk3.wav
exporting .//splitAudio//chunk4.wav
exporting .//splitAudio//chunk5.wav
exporting .//splitAudio//chunk6.wav
exporting .//splitAudio//chunk7.wav
exporting .//splitAudio//chunk8.wav
exporting .//splitAudio//chunk9.wav
exporting .//splitAudio//chunk10.wav
exporting .//splitAudio//chunk11.wav
exporting .//splitAudio//chunk12.wav
exporting .//splitAudio//chunk13.wav
exporting .//splitAudio//chunk14.wav
exporting .//splitAudio//chunk15.wav
exporting .//splitAudio//chunk16.wav
exporting .//splitAudio//chunk17.wav
exporting .//splitAudio//chunk18.wav
exporting .//splitAudio//chunk19.wav
exporting .//splitAudio//chunk20.wav
exporting .//splitAudio//chunk21.wav
exporting .//splitAudio//chunk22.wav
exporting .//splitAudio//chunk23.wav
exporting .//splitAudio//chunk24.wav
exporting .//splitAudio//chunk25.wav
exporting .//splitAudio//chunk26.wav
>>>

Answer 2

Use IBM STT. Using timestamps=true you will get the word break up along with when the system detects them to have been spoken.

There are a lot of other cool features like word_alternatives_threshold to get other possibilities of words and word_confidence to get the confidence with which the system predicts the word. Set word_alternatives_threshold to between (0.1 and 0.01) to get a real idea.

This needs sign on, following which you can use the username and password generated.

The IBM STT is already a part of the speechrecognition module mentioned, but to get the word timestamp, you will need to modify the function.

An extracted and modified form looks like:

def extracted_from_sr_recognize_ibm(audio_data, username=IBM_USERNAME, password=IBM_PASSWORD, language="en-US", show_all=False, timestamps=False,
                                word_confidence=False, word_alternatives_threshold=0.1):
    assert isinstance(username, str), "``username`` must be a string"
    assert isinstance(password, str), "``password`` must be a string"

    flac_data = audio_data.get_flac_data(
        convert_rate=None if audio_data.sample_rate >= 16000 else 16000,  # audio samples should be at least 16 kHz
        convert_width=None if audio_data.sample_width >= 2 else 2  # audio samples should be at least 16-bit
    )
    url = "https://stream-fra.watsonplatform.net/speech-to-text/api/v1/recognize?{}".format(urlencode({
        "profanity_filter": "false",
        "continuous": "true",
        "model": "{}_BroadbandModel".format(language),
        "timestamps": "{}".format(str(timestamps).lower()),
        "word_confidence": "{}".format(str(word_confidence).lower()),
        "word_alternatives_threshold": "{}".format(word_alternatives_threshold)
    }))
    request = Request(url, data=flac_data, headers={
        "Content-Type": "audio/x-flac",
        "X-Watson-Learning-Opt-Out": "true",  # prevent requests from being logged, for improved privacy
    })
    authorization_value = base64.standard_b64encode("{}:{}".format(username, password).encode("utf-8")).decode("utf-8")
    request.add_header("Authorization", "Basic {}".format(authorization_value))

    try:
        response = urlopen(request, timeout=None)
    except HTTPError as e:
        raise sr.RequestError("recognition request failed: {}".format(e.reason))
    except URLError as e:
        raise sr.RequestError("recognition connection failed: {}".format(e.reason))
    response_text = response.read().decode("utf-8")
    result = json.loads(response_text)

    # return results
    if show_all: return result
    if "results" not in result or len(result["results"]) < 1 or "alternatives" not in result["results"][0]:
        raise Exception("Unknown Value Exception")

    transcription = []
    for utterance in result["results"]:
        if "alternatives" not in utterance:
            raise Exception("Unknown Value Exception. No Alternatives returned")
        for hypothesis in utterance["alternatives"]:
            if "transcript" in hypothesis:
                transcription.append(hypothesis["transcript"])
    return "\n".join(transcription)

Answer 3

你可以看一下Audiolab它提供了一个很好的API来将语音样本转换为numpy数组。 Audiolab模块使用libsndfile C ++库来完成繁重的工作。

然后，您可以解析数组以查找较低的值以查找暂停。

Answer 4

如果单词清楚分开，

pyAudioAnalysis可以对音频文件进行分段（自然语言中很少出现这种情况）。该软件包相对易于使用：

python pyAudioAnalysis/pyAudioAnalysis/audioAnalysis.py silenceRemoval -i SPEECH_AUDIO_FILE_TO_SPLIT.mp3 --smoothing 1.0 --weight 0.3

有关我的blog的更多详细信息。

在python中的单词上拆分语音音频文件

4 个答案: