如何在输出识别词的同时不断进行语音识别

Question

我正在做一个使用SpeechRecognition模块进行语音识别的项目。为了提高我的语音识别能力，我想做的一件事就是能够尽快输出已经被识别的单词。我希望它类似于每当你说谷歌翻译，一旦你说了一个字，它输出它在屏幕上，让你知道你已经说了。

我尝试过的一些事情是有一个数组来存储单独的音频记录，让语音识别遍历数组，识别每个音频记录，然后输出它。这是行不通的，因为不同的单词需要不同的时间来表达。

我进一步研究了SpeechRecognition模块提供给我的用于语音识别的Google，并希望了解如何通过在某些地方添加打印语句来调整实际库以实现目标。我不知道该放在哪里，因为我是一个语言识别的初学者，而且我对谷歌语音识别API不太了解。

这里是google代码，它访问云来做sr。

def recognize_google(self, audio_data, key=None, language="en-US", show_all=False):
        """
        Performs speech recognition on ``audio_data`` (an ``AudioData`` instance), using the Google Speech Recognition API.

        The Google Speech Recognition API key is specified by ``key``. If not specified, it uses a generic key that works out of the box. This should generally be used for personal or testing purposes only, as it **may be revoked by Google at any time**.

        To obtain your own API key, simply following the steps on the `API Keys <http://www.chromium.org/developers/how-tos/api-keys>`__ page at the Chromium Developers site. In the Google Developers Console, Google Speech Recognition is listed as "Speech API".

        The recognition language is determined by ``language``, an RFC5646 language tag like ``"en-US"`` (US English) or ``"fr-FR"`` (International French), defaulting to US English. A list of supported language tags can be found in this `StackOverflow answer <http://stackoverflow.com/a/14302134>`__.

        Returns the most likely transcription if ``show_all`` is false (the default). Otherwise, returns the raw API response as a JSON dictionary.

        Raises a ``speech_recognition.UnknownValueError`` exception if the speech is unintelligible. Raises a ``speech_recognition.RequestError`` exception if the speech recognition operation failed, if the key isn't valid, or if there is no internet connection.
        """
        assert isinstance(audio_data, AudioData), "``audio_data`` must be audio data"
        assert key is None or isinstance(key, str), "``key`` must be ``None`` or a string"
        assert isinstance(language, str), "``language`` must be a string"

        flac_data = audio_data.get_flac_data(
            convert_rate=None if audio_data.sample_rate >= 8000 else 8000,  # audio samples must be at least 8 kHz
            convert_width=2  # audio samples must be 16-bit
        )
        if key is None: key = "AIzaSyBOti4mM-6x9WDnZIjIeyEU21OpBXqWBgw"
        url = "http://www.google.com/speech-api/v2/recognize?{}".format(urlencode({
            "client": "chromium",
            "lang": language,
            "key": key,
        }))
        request = Request(url, data=flac_data, headers={"Content-Type": "audio/x-flac; rate={}".format(audio_data.sample_rate)})

        # obtain audio transcription results
        try:
            response = urlopen(request, timeout=self.operation_timeout)
        except HTTPError as e:
            raise RequestError("recognition request failed: {}".format(e.reason))
        except URLError as e:
            raise RequestError("recognition connection failed: {}".format(e.reason))
        response_text = response.read().decode("utf-8")

        # ignore any blank blocks
        actual_result = []
        for line in response_text.split("\n"):
            if not line: continue
            result = json.loads(line)["result"]
            if len(result) != 0:
                actual_result = result[0]
                print(actual_result)
                sleep(1000)
                break

        # return results
        if show_all: return actual_result
        if not isinstance(actual_result, dict) or len(actual_result.get("alternative", [])) == 0: raise UnknownValueError()

        if "confidence" in actual_result["alternative"]:
            # return alternative with highest confidence score
            best_hypothesis = max(actual_result["alternative"], key=lambda alternative: alternative["confidence"])
        else:
            # when there is no confidence available, we arbitrarily choose the first hypothesis.
            best_hypothesis = actual_result["alternative"][0]
        if "transcript" not in best_hypothesis: raise UnknownValueError()
        return best_hypothesis["transcript"]

下面是我的基本代码(这里没有显示我以前尝试过的内容)：它能够成功地进行语音识别。

r = sr.Recognizer()
m = sr.Microphone(); 

r = sr.Recognizer()
on = True 
while on :
    with sr.Microphone() as source:
        audio = r.listen(source)

        try:
            text = r.recognize_google(audio)
            print("You said: {}".format(text))
        except:
            print("Sorry, we did not recognize your voice")

您应该知道的最后一种方法是制作音频文件或对象的录制函数：

def listen(self, source, timeout=None, phrase_time_limit=None, snowboy_configuration=None):
        """
        Records a single phrase from ``source`` (an ``AudioSource`` instance) into an ``AudioData`` instance, which it returns.

        This is done by waiting until the audio has an energy above ``recognizer_instance.energy_threshold`` (the user has started speaking), and then recording until it encounters ``recognizer_instance.pause_threshold`` seconds of non-speaking or there is no more audio input. The ending silence is not included.

        The ``timeout`` parameter is the maximum number of seconds that this will wait for a phrase to start before giving up and throwing an ``speech_recognition.WaitTimeoutError`` exception. If ``timeout`` is ``None``, there will be no wait timeout.

        The ``phrase_time_limit`` parameter is the maximum number of seconds that this will allow a phrase to continue before stopping and returning the part of the phrase processed before the time limit was reached. The resulting audio will be the phrase cut off at the time limit. If ``phrase_timeout`` is ``None``, there will be no phrase time limit.

        The ``snowboy_configuration`` parameter allows integration with `Snowboy <https://snowboy.kitt.ai/>`__, an offline, high-accuracy, power-efficient hotword recognition engine. When used, this function will pause until Snowboy detects a hotword, after which it will unpause. This parameter should either be ``None`` to turn off Snowboy support, or a tuple of the form ``(SNOWBOY_LOCATION, LIST_OF_HOT_WORD_FILES)``, where ``SNOWBOY_LOCATION`` is the path to the Snowboy root directory, and ``LIST_OF_HOT_WORD_FILES`` is a list of paths to Snowboy hotword configuration files (`*.pmdl` or `*.umdl` format).

        This operation will always complete within ``timeout + phrase_timeout`` seconds if both are numbers, either by returning the audio data, or by raising a ``speech_recognition.WaitTimeoutError`` exception.
        """
        assert isinstance(source, AudioSource), "Source must be an audio source"
        assert source.stream is not None, "Audio source must be entered before listening, see documentation for ``AudioSource``; are you using ``source`` outside of a ``with`` statement?"
        assert self.pause_threshold >= self.non_speaking_duration >= 0
        if snowboy_configuration is not None:
            assert os.path.isfile(os.path.join(snowboy_configuration[0], "snowboydetect.py")), "``snowboy_configuration[0]`` must be a Snowboy root directory containing ``snowboydetect.py``"
            for hot_word_file in snowboy_configuration[1]:
                assert os.path.isfile(hot_word_file), "``snowboy_configuration[1]`` must be a list of Snowboy hot word configuration files"

        seconds_per_buffer = float(source.CHUNK) / source.SAMPLE_RATE
        pause_buffer_count = int(math.ceil(self.pause_threshold / seconds_per_buffer))  # number of buffers of non-speaking audio during a phrase, before the phrase should be considered complete
        phrase_buffer_count = int(math.ceil(self.phrase_threshold / seconds_per_buffer))  # minimum number of buffers of speaking audio before we consider the speaking audio a phrase
        non_speaking_buffer_count = int(math.ceil(self.non_speaking_duration / seconds_per_buffer))  # maximum number of buffers of non-speaking audio to retain before and after a phrase

        # read audio input for phrases until there is a phrase that is long enough
        elapsed_time = 0  # number of seconds of audio read
        buffer = b""  # an empty buffer means that the stream has ended and there is no data left to read
        while True:
            frames = collections.deque()

            if snowboy_configuration is None:
                # store audio input until the phrase starts
                while True:
                    # handle waiting too long for phrase by raising an exception
                    elapsed_time += seconds_per_buffer
                    if timeout and elapsed_time > timeout:
                        raise WaitTimeoutError("listening timed out while waiting for phrase to start")

                    buffer = source.stream.read(source.CHUNK)
                    if len(buffer) == 0: break  # reached end of the stream
                    frames.append(buffer)
                    if len(frames) > non_speaking_buffer_count:  # ensure we only keep the needed amount of non-speaking buffers
                        frames.popleft()

                    # detect whether speaking has started on audio input
                    energy = audioop.rms(buffer, source.SAMPLE_WIDTH)  # energy of the audio signal
                    if energy > self.energy_threshold: break

                    # dynamically adjust the energy threshold using asymmetric weighted average
                    if self.dynamic_energy_threshold:
                        damping = self.dynamic_energy_adjustment_damping ** seconds_per_buffer  # account for different chunk sizes and rates
                        target_energy = energy * self.dynamic_energy_ratio
                        self.energy_threshold = self.energy_threshold * damping + target_energy * (1 - damping)
            else:
                # read audio input until the hotword is said
                snowboy_location, snowboy_hot_word_files = snowboy_configuration
                buffer, delta_time = self.snowboy_wait_for_hot_word(snowboy_location, snowboy_hot_word_files, source, timeout)
                elapsed_time += delta_time
                if len(buffer) == 0: break  # reached end of the stream
                frames.append(buffer)

            # read audio input until the phrase ends
            pause_count, phrase_count = 0, 0
            phrase_start_time = elapsed_time
            while True:
                # handle phrase being too long by cutting off the audio
                elapsed_time += seconds_per_buffer
                if phrase_time_limit and elapsed_time - phrase_start_time > phrase_time_limit:
                    break

                buffer = source.stream.read(source.CHUNK)
                if len(buffer) == 0: break  # reached end of the stream
                frames.append(buffer)
                phrase_count += 1

                # check if speaking has stopped for longer than the pause threshold on the audio input
                energy = audioop.rms(buffer, source.SAMPLE_WIDTH)  # unit energy of the audio signal within the buffer
                if energy > self.energy_threshold:
                    pause_count = 0
                else:
                    pause_count += 1
                if pause_count > pause_buffer_count:  # end of the phrase
                    break

            # check how long the detected phrase is, and retry listening if the phrase is too short
            phrase_count -= pause_count  # exclude the buffers for the pause before the phrase
            if phrase_count >= phrase_buffer_count or len(buffer) == 0: break  # phrase is long enough or we've reached the end of the stream, so stop listening

        # obtain frame data
        for i in range(pause_count - non_speaking_buffer_count): frames.pop()  # remove extra non-speaking frames at the end
        frame_data = b"".join(frames)

        return AudioData(frame_data, source.SAMPLE_RATE, source.SAMPLE_WIDTH)

我想要实现的软件，能够采取当前的代码，并以这样一种方式，它输出认可的词，尽快。这将类似于你在谷歌翻译时的讲话。

Answer 1

你看过这个吗？https://speech-to-text-demo.ng.bluemix.net只要点击“录制音频”，你就会在你说话的时候看到屏幕上的假设。该演示程序是开源的，您只需在GitHub中分叉代码即可。可以通过使用您喜欢的编程语言使用WebSocket API调用服务来实现连续的语音识别效果。

Answer 2

你想要的是一个连续的语音识别器。它们是存在的，但是需要为这个应用程序专门构建一个语音识别器，因为它需要非常快的响应，并且能够正确地处理尚未完成的话语。

您正在使用的Google (https://www.google.com/speech-api/v2/recognize)不是一个连续的语音识别器。不能通过修改调用它的代码将其转换为连续识别器。

Answer 3

你可以使用Pocketsphinx的另一个语音引擎，他们不使用互联网连接

https://pypi.org/project/pocketsphinx/

from pocketsphinx import LiveSpeech

speech = LiveSpeech(lm=False, keyphrase='forward', kws_threshold=1e-20)
for phrase in speech:
    print(phrase.segments(detailed=True))