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")
This is the final method, that's recording function to make audio files or objects:
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)
