在 python 中的单词上拆分语音音频文件

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。 我有很多人类语音的音频文件，我想打破单词，这可以通过查看波形中的暂停来试探性地完成，但是任何人都可以指向我 python 中的一个函数/库，它会自动执行此操作吗？

Answer 1

更简单的方法是使用pydub模块。 最近添加的静音实用程序可以完成所有繁重的操作，例如setting up silence threahold ， setting up silence length 。 与提到的其他方法相比，显着简化了代码。

这是一个演示实现，灵感来自这里

建立：

我在“az.wav”文件中有一个带有A到Z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。

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

人们可能想要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")

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

使用IBM STT 。 使用timestamps=true您将获得单词分解以及系统检测到它们的时间。

还有许多其他很酷的功能，如word_alternatives_threshold以获得单词的其他可能性和word_confidence以获得系统预测单词的信心。 将word_alternatives_threshold设置为介于（0.1和0.01）之间以获得真实的想法。

这需要登录，然后您可以使用生成的用户名和密码。

IBM STT已经是所提到的语音识别模块的一部分，但是要获得单词timestamp，您需要修改该功能。

提取和修改的表单如下所示：

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

您可以查看Audiolab它提供了一个不错的API，可以将语音样本转换为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

如果单词明确分开， pyAudioAnalysis可以对音频文件进行分段（在自然语音中很少这种情况）。 该包装相对易于使用：

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

我博客上的更多细节。

Answer 5

我的变体 function，可能会更容易根据您的需要进行修改：

from scipy.io.wavfile import write as write_wav
import numpy as np
import librosa

def zero_runs(a):
    iszero = np.concatenate(([0], np.equal(a, 0).view(np.int8), [0]))
    absdiff = np.abs(np.diff(iszero))
    ranges = np.where(absdiff == 1)[0].reshape(-1, 2)
    return ranges

def split_in_parts(audio_path, out_dir):
    # Some constants
    min_length_for_silence = 0.01 # seconds
    percentage_for_silence = 0.01 # eps value for silence
    required_length_of_chunk_in_seconds = 60 # Chunk will be around this value not exact
    sample_rate = 16000 # Set to None to use default

    # Load audio
    waveform, sampling_rate = librosa.load(audio_path, sr=sample_rate)

    # Create mask of silence
    eps = waveform.max() * percentage_for_silence
    silence_mask = (np.abs(waveform) < eps).astype(np.uint8)

    # Find where silence start and end
    runs = zero_runs(silence_mask)
    lengths = runs[:, 1] - runs[:, 0]

    # Left only large silence ranges
    min_length_for_silence = min_length_for_silence * sampling_rate
    large_runs = runs[lengths > min_length_for_silence]
    lengths = lengths[lengths > min_length_for_silence]

    # Mark only center of silence
    silence_mask[...] = 0
    for start, end in large_runs:
        center = (start + end) // 2
        silence_mask[center] = 1

    min_required_length = required_length_of_chunk_in_seconds * sampling_rate
    chunks = []
    prev_pos = 0
    for i in range(min_required_length, len(waveform), min_required_length):
        start = i
        end = i + min_required_length
        next_pos = start + silence_mask[start:end].argmax()
        part = waveform[prev_pos:next_pos].copy()
        prev_pos = next_pos
        if len(part) > 0:
            chunks.append(part)

    # Add last part of waveform
    part = waveform[prev_pos:].copy()
    chunks.append(part)
    print('Total chunks: {}'.format(len(chunks)))

    new_files = []
    for i, chunk in enumerate(chunks):
        out_file = out_dir + "chunk_{}.wav".format(i)
        print("exporting", out_file)
        write_wav(out_file, sampling_rate, chunk)
        new_files.append(out_file)

    return new_files