Я работаю над проектом, который предусматривает распознавание речи с использованием модуля SpeechRecognition. Единственное, что я хочу сделать, чтобы улучшить распознавание речи, - это вывести слова, которые были распознаны, как можно скорее. Я хочу, чтобы это было так, как если бы вы говорили в Google Translate, как только вы произносите слово, оно выводит его на экран, чтобы вы знали, что вы это сказали.
Некоторые из вещей, которые я попробовал, - это иметь массив, в котором хранятся отдельные аудиозаписи, и иметь распознавание речи, проходить через массив, распознавая каждую аудиозапись и затем выводя ее. Это не сработало, потому что разные слова говорят по-разному.
Я заглянул в Google API для распознавания речи, предоставленной мне модулем SpeechRecognition, и хотел посмотреть, как можно настроить фактическую библиотеку, добавив операторы печати в некоторых местах для достижения цели. Я не знал, где это сделать, так как я новичок в распознавании речи и мало что знаю об API распознавания речи Google.
Вот код Google API, он обращается к облаку, чтобы сделать 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"]
Вот мой базовый код (вещи, которые я ранее пробовал, здесь не показаны): он способен успешно распознавать речь.
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")
Последний метод, который вам следует знать, - это функция записи для создания аудиофайлов или объектов:
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)
Я хотел бы получить программное обеспечение, способное принимать текущий код и реализовывать его так, чтобы оно выводило распознанное слово как можно скорее. Это было бы похоже на то, когда вы говорите в Google Translate.