This section demonstrates how to transcribe streaming audio, like the
input from a microphone, to text.
Streaming speech recognition allows you to stream audio to
Speech-to-Text and receive a stream speech recognition results
in real time as the audio is processed. See also the
audio limits for streaming speech recognition requests.
Streaming speech recognition is available via gRPC only.
Performing streaming speech recognition on a local file
Below is an example of performing streaming speech recognition on a local audio
file. There is a 10 MB limit on all streaming requests sent to the API. This
limit applies to to both the initial StreamingRecognize request
and the size of each individual message in the stream. Exceeding this limit will
throw an error.
/**
* Performs streaming speech recognition on raw PCM audio data.
*
* @param fileName the path to a PCM audio file to transcribe.
*/
public static void streamingRecognizeFile(String fileName) throws Exception, IOException {
Path path = Paths.get(fileName);
byte[] data = Files.readAllBytes(path);
// Instantiates a client with GOOGLE_APPLICATION_CREDENTIALS
try (SpeechClient speech = SpeechClient.create()) {
// Configure request with local raw PCM audio
RecognitionConfig recConfig =
RecognitionConfig.newBuilder()
.setEncoding(AudioEncoding.LINEAR16)
.setLanguageCode("en-US")
.setSampleRateHertz(16000)
.setModel("default")
.build();
StreamingRecognitionConfig config =
StreamingRecognitionConfig.newBuilder().setConfig(recConfig).build();
class ResponseApiStreamingObserver<T> implements ApiStreamObserver<T> {
private final SettableFuture<List<T>> future = SettableFuture.create();
private final List<T> messages = new java.util.ArrayList<T>();
@Override
public void onNext(T message) {
messages.add(message);
}
@Override
public void onError(Throwable t) {
future.setException(t);
}
@Override
public void onCompleted() {
future.set(messages);
}
// Returns the SettableFuture object to get received messages / exceptions.
public SettableFuture<List<T>> future() {
return future;
}
}
ResponseApiStreamingObserver<StreamingRecognizeResponse> responseObserver =
new ResponseApiStreamingObserver<>();
BidiStreamingCallable<StreamingRecognizeRequest, StreamingRecognizeResponse> callable =
speech.streamingRecognizeCallable();
ApiStreamObserver<StreamingRecognizeRequest> requestObserver =
callable.bidiStreamingCall(responseObserver);
// The first request must **only** contain the audio configuration:
requestObserver.onNext(
StreamingRecognizeRequest.newBuilder().setStreamingConfig(config).build());
// Subsequent requests must **only** contain the audio data.
requestObserver.onNext(
StreamingRecognizeRequest.newBuilder()
.setAudioContent(ByteString.copyFrom(data))
.build());
// Mark transmission as completed after sending the data.
requestObserver.onCompleted();
List<StreamingRecognizeResponse> responses = responseObserver.future().get();
for (StreamingRecognizeResponse response : responses) {
// For streaming recognize, the results list has one is_final result (if available) followed
// by a number of in-progress results (if iterim_results is true) for subsequent utterances.
// Just print the first result here.
StreamingRecognitionResult result = response.getResultsList().get(0);
// There can be several alternative transcripts for a given chunk of speech. Just use the
// first (most likely) one here.
SpeechRecognitionAlternative alternative = result.getAlternativesList().get(0);
System.out.printf("Transcript : %s\n", alternative.getTranscript());
}
}
}
const fs = require('fs');
// Imports the Google Cloud client library
const speech = require('@google-cloud/speech');
// Creates a client
const client = new speech.SpeechClient();
/**
* TODO(developer): Uncomment the following lines before running the sample.
*/
// const filename = 'Local path to audio file, e.g. /path/to/audio.raw';
// const encoding = 'Encoding of the audio file, e.g. LINEAR16';
// const sampleRateHertz = 16000;
// const languageCode = 'BCP-47 language code, e.g. en-US';
const request = {
config: {
encoding: encoding,
sampleRateHertz: sampleRateHertz,
languageCode: languageCode,
},
interimResults: false, // If you want interim results, set this to true
};
// Stream the audio to the Google Cloud Speech API
const recognizeStream = client
.streamingRecognize(request)
.on('error', console.error)
.on('data', data => {
console.log(
`Transcription: ${data.results[0].alternatives[0].transcript}`
);
});
// Stream an audio file from disk to the Speech API, e.g. "./resources/audio.raw"
fs.createReadStream(filename).pipe(recognizeStream);
def transcribe_streaming(stream_file):
"""Streams transcription of the given audio file."""
import io
from google.cloud import speech
client = speech.SpeechClient()
with io.open(stream_file, "rb") as audio_file:
content = audio_file.read()
# In practice, stream should be a generator yielding chunks of audio data.
stream = [content]
requests = (
speech.StreamingRecognizeRequest(audio_content=chunk) for chunk in stream
)
config = speech.RecognitionConfig(
encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=16000,
language_code="en-US",
)
streaming_config = speech.StreamingRecognitionConfig(config=config)
# streaming_recognize returns a generator.
responses = client.streaming_recognize(
config=streaming_config,
requests=requests,
)
for response in responses:
# Once the transcription has settled, the first result will contain the
# is_final result. The other results will be for subsequent portions of
# the audio.
for result in response.results:
print("Finished: {}".format(result.is_final))
print("Stability: {}".format(result.stability))
alternatives = result.alternatives
# The alternatives are ordered from most likely to least.
for alternative in alternatives:
print("Confidence: {}".format(alternative.confidence))
print(u"Transcript: {}".format(alternative.transcript))
While you can stream a local audio file to the Speech-to-Text API,
it is recommended that you perform synchronous or
asynchronous audio recognition for batch mode results.
Performing streaming speech recognition on an audio stream
Speech-to-Text can also perform recognition on streaming, real-time
audio.
Here is an example of performing streaming speech recognition on an audio stream
received from a microphone:
from __future__ import division
import re
import sys
from google.cloud import speech
import pyaudio
from six.moves import queue
# Audio recording parameters
RATE = 16000
CHUNK = int(RATE / 10) # 100ms
class MicrophoneStream(object):
"""Opens a recording stream as a generator yielding the audio chunks."""
def __init__(self, rate, chunk):
self._rate = rate
self._chunk = chunk
# Create a thread-safe buffer of audio data
self._buff = queue.Queue()
self.closed = True
def __enter__(self):
self._audio_interface = pyaudio.PyAudio()
self._audio_stream = self._audio_interface.open(
format=pyaudio.paInt16,
# The API currently only supports 1-channel (mono) audio
# https://goo.gl/z757pE
channels=1,
rate=self._rate,
input=True,
frames_per_buffer=self._chunk,
# Run the audio stream asynchronously to fill the buffer object.
# This is necessary so that the input device's buffer doesn't
# overflow while the calling thread makes network requests, etc.
stream_callback=self._fill_buffer,
)
self.closed = False
return self
def __exit__(self, type, value, traceback):
self._audio_stream.stop_stream()
self._audio_stream.close()
self.closed = True
# Signal the generator to terminate so that the client's
# streaming_recognize method will not block the process termination.
self._buff.put(None)
self._audio_interface.terminate()
def _fill_buffer(self, in_data, frame_count, time_info, status_flags):
"""Continuously collect data from the audio stream, into the buffer."""
self._buff.put(in_data)
return None, pyaudio.paContinue
def generator(self):
while not self.closed:
# Use a blocking get() to ensure there's at least one chunk of
# data, and stop iteration if the chunk is None, indicating the
# end of the audio stream.
chunk = self._buff.get()
if chunk is None:
return
data = [chunk]
# Now consume whatever other data's still buffered.
while True:
try:
chunk = self._buff.get(block=False)
if chunk is None:
return
data.append(chunk)
except queue.Empty:
break
yield b"".join(data)
def listen_print_loop(responses):
"""Iterates through server responses and prints them.
The responses passed is a generator that will block until a response
is provided by the server.
Each response may contain multiple results, and each result may contain
multiple alternatives; for details, see https://goo.gl/tjCPAU. Here we
print only the transcription for the top alternative of the top result.
In this case, responses are provided for interim results as well. If the
response is an interim one, print a line feed at the end of it, to allow
the next result to overwrite it, until the response is a final one. For the
final one, print a newline to preserve the finalized transcription.
"""
num_chars_printed = 0
for response in responses:
if not response.results:
continue
# The `results` list is consecutive. For streaming, we only care about
# the first result being considered, since once it's `is_final`, it
# moves on to considering the next utterance.
result = response.results[0]
if not result.alternatives:
continue
# Display the transcription of the top alternative.
transcript = result.alternatives[0].transcript
# Display interim results, but with a carriage return at the end of the
# line, so subsequent lines will overwrite them.
#
# If the previous result was longer than this one, we need to print
# some extra spaces to overwrite the previous result
overwrite_chars = " " * (num_chars_printed - len(transcript))
if not result.is_final:
sys.stdout.write(transcript + overwrite_chars + "\r")
sys.stdout.flush()
num_chars_printed = len(transcript)
else:
print(transcript + overwrite_chars)
# Exit recognition if any of the transcribed phrases could be
# one of our keywords.
if re.search(r"\b(exit|quit)\b", transcript, re.I):
print("Exiting..")
break
num_chars_printed = 0
def main():
# See http://g.co/cloud/speech/docs/languages
# for a list of supported languages.
language_code = "en-US" # a BCP-47 language tag
client = speech.SpeechClient()
config = speech.RecognitionConfig(
encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code,
)
streaming_config = speech.StreamingRecognitionConfig(
config=config, interim_results=True
)
with MicrophoneStream(RATE, CHUNK) as stream:
audio_generator = stream.generator()
requests = (
speech.StreamingRecognizeRequest(audio_content=content)
for content in audio_generator
)
responses = client.streaming_recognize(streaming_config, requests)
# Now, put the transcription responses to use.
listen_print_loop(responses)
if __name__ == "__main__":
main()
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