我觉得这是一个比较常见的问题，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，我想将它们分成单个单词，可以通过查看波形中的暂停来启发式地完成，但是有没有人能指向Python中自动执行此操作的函数/库？
5个回答
46
一个更简单的方法是使用pydub模块。最近添加的静音工具承担了所有繁重的工作，例如设置静音阈值，设置静音长度等，相比于其他提到的方法大大简化了代码。
以下是演示实现，灵感来自这里。
设置：
我有一个包含从A到Z的英文字母的音频文件，“a-z.wav”。在当前工作目录中创建了一个子目录splitAudio。执行演示代码后，这些文件被分成26个单独的文件，每个音频文件存储每个音节。
观察结果：
有些音节被切断了，可能需要修改以下参数：
min_silence_len=500
silence_thresh=-16
您可能需要根据自己的要求进行调整。
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
>>>
- Anil_M
2
使用这种方法的单词之间应该有明显的间隔。 - pouya
是的，我自己也一直在研究这个问题，但正如“pouya”所提到的，pydub或pyaudioanalysis只有在单词之间存在巨大的间隔时才能起作用，而在任何实际情况下都不会出现这种情况！问题还存在于相反的方向，如果说话者不是母语人士并且需要时间来发音某些单词，则某些单词可能会被分成音节。 - Deepak Agarwal
4
使用IBM STT。使用timestamps=true，您将获得单词拆分以及系统检测到其被发音的时间。
还有很多其他很酷的功能，比如word_alternatives_threshold可以获得其他可能的单词，word_confidence可以获得系统预测单词的置信度。将word_alternatives_threshold设置为0.1到0.01之间以获得实际想法。
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speechrecognition模块的一部分，但要获取单词时间戳，您需要修改函数。
一个提取并修改过的形式如下：
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)
- MonsieurBeilto
2
pyAudioAnalysis可以对音频文件进行分段，但前提是单词必须清晰分离（在自然语言中很少出现这种情况）。该软件包使用相对简单：
python pyAudioAnalysis/pyAudioAnalysis/audioAnalysis.py silenceRemoval -i SPEECH_AUDIO_FILE_TO_SPLIT.mp3 --smoothing 1.0 --weight 0.3
更多详细信息请查看我的博客。原始答案翻译成为“最初的回答”。
- epo3
1
这是我的函数变体，可能更容易根据您的需求进行修改：
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
- ZFTurbo
网页内容由stack overflow 提供, 点击上面的可以查看英文原文，
原文链接
原文链接
SpeechRecognition，该库专门提供了一个将音频文件转录成文字的示例。下次可以先使用谷歌搜索 :) - Akshat Mahajan