從麥克風轉錄串流音訊。
深入探索
如需包含這個程式碼範例的詳細說明文件，請參閱下列文章：
程式碼範例
Go
如要瞭解如何安裝及使用 Cloud STT 的用戶端程式庫，請參閱「Cloud STT 用戶端程式庫」。詳情請參閱「Cloud STT Go API 參考文件」。
如要向 Cloud STT 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。
import (
"context"
"fmt"
"io"
"log"
"os"
speech "cloud.google.com/go/speech/apiv1"
"cloud.google.com/go/speech/apiv1/speechpb"
)
func main() {
ctx := context.Background()
client, err := speech.NewClient(ctx)
if err != nil {
log.Fatal(err)
}
stream, err := client.StreamingRecognize(ctx)
if err != nil {
log.Fatal(err)
}
// Send the initial configuration message.
if err := stream.Send(&speechpb.StreamingRecognizeRequest{
StreamingRequest: &speechpb.StreamingRecognizeRequest_StreamingConfig{
StreamingConfig: &speechpb.StreamingRecognitionConfig{
Config: &speechpb.RecognitionConfig{
Encoding: speechpb.RecognitionConfig_LINEAR16,
SampleRateHertz: 16000,
LanguageCode: "en-US",
},
},
},
}); err != nil {
log.Fatal(err)
}
go func() {
// Pipe stdin to the API.
buf := make([]byte, 1024)
for {
n, err := os.Stdin.Read(buf)
if n > 0 {
if err := stream.Send(&speechpb.StreamingRecognizeRequest{
StreamingRequest: &speechpb.StreamingRecognizeRequest_AudioContent{
AudioContent: buf[:n],
},
}); err != nil {
log.Printf("Could not send audio: %v", err)
}
}
if err == io.EOF {
// Nothing else to pipe, close the stream.
if err := stream.CloseSend(); err != nil {
log.Fatalf("Could not close stream: %v", err)
}
return
}
if err != nil {
log.Printf("Could not read from stdin: %v", err)
continue
}
}
}()
for {
resp, err := stream.Recv()
if err == io.EOF {
break
}
if err != nil {
log.Fatalf("Cannot stream results: %v", err)
}
if err := resp.Error; err != nil {
// Workaround while the API doesn't give a more informative error.
if err.Code == 3 || err.Code == 11 {
log.Print("WARNING: Speech recognition request exceeded limit of 60 seconds.")
}
log.Fatalf("Could not recognize: %v", err)
}
for _, result := range resp.Results {
fmt.Printf("Result: %+v\n", result)
}
}
}
Java
如要瞭解如何安裝及使用 Cloud STT 的用戶端程式庫，請參閱「Cloud STT 用戶端程式庫」。詳情請參閱「Cloud STT Java API 參考文件」。
如要向 Cloud STT 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。
/** Performs microphone streaming speech recognition with a duration of 1 minute. */
public static void streamingMicRecognize() throws Exception {
ResponseObserver<StreamingRecognizeResponse> responseObserver = null;
try (SpeechClient client = SpeechClient.create()) {
responseObserver =
new ResponseObserver<StreamingRecognizeResponse>() {
ArrayList<StreamingRecognizeResponse> responses = new ArrayList<>();
public void onStart(StreamController controller) {}
public void onResponse(StreamingRecognizeResponse response) {
responses.add(response);
}
public void onComplete() {
for (StreamingRecognizeResponse response : responses) {
StreamingRecognitionResult result = response.getResultsList().get(0);
SpeechRecognitionAlternative alternative = result.getAlternativesList().get(0);
System.out.printf("Transcript : %s\n", alternative.getTranscript());
}
}
public void onError(Throwable t) {
System.out.println(t);
}
};
ClientStream<StreamingRecognizeRequest> clientStream =
client.streamingRecognizeCallable().splitCall(responseObserver);
RecognitionConfig recognitionConfig =
RecognitionConfig.newBuilder()
.setEncoding(RecognitionConfig.AudioEncoding.LINEAR16)
.setLanguageCode("en-US")
.setSampleRateHertz(16000)
.build();
StreamingRecognitionConfig streamingRecognitionConfig =
StreamingRecognitionConfig.newBuilder().setConfig(recognitionConfig).build();
StreamingRecognizeRequest request =
StreamingRecognizeRequest.newBuilder()
.setStreamingConfig(streamingRecognitionConfig)
.build(); // The first request in a streaming call has to be a config
clientStream.send(request);
// SampleRate:16000Hz, SampleSizeInBits: 16, Number of channels: 1, Signed: true,
// bigEndian: false
AudioFormat audioFormat = new AudioFormat(16000, 16, 1, true, false);
DataLine.Info targetInfo =
new Info(
TargetDataLine.class,
audioFormat); // Set the system information to read from the microphone audio stream
if (!AudioSystem.isLineSupported(targetInfo)) {
System.out.println("Microphone not supported");
System.exit(0);
}
// Target data line captures the audio stream the microphone produces.
TargetDataLine targetDataLine = (TargetDataLine) AudioSystem.getLine(targetInfo);
targetDataLine.open(audioFormat);
targetDataLine.start();
System.out.println("Start speaking");
long startTime = System.currentTimeMillis();
// Audio Input Stream
AudioInputStream audio = new AudioInputStream(targetDataLine);
while (true) {
long estimatedTime = System.currentTimeMillis() - startTime;
byte[] data = new byte[6400];
audio.read(data);
if (estimatedTime > 60000) { // 60 seconds
System.out.println("Stop speaking.");
targetDataLine.stop();
targetDataLine.close();
break;
}
request =
StreamingRecognizeRequest.newBuilder()
.setAudioContent(ByteString.copyFrom(data))
.build();
clientStream.send(request);
}
} catch (Exception e) {
System.out.println(e);
}
responseObserver.onComplete();
}Node.js
如要瞭解如何安裝及使用 Cloud STT 的用戶端程式庫，請參閱「Cloud STT 用戶端程式庫」。詳情請參閱「Cloud STT Node.js API 參考文件」。
如要向 Cloud STT 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。
const recorder = require('node-record-lpcm16');
// 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 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
};
// Create a recognize stream
const recognizeStream = client
.streamingRecognize(request)
.on('error', console.error)
.on('data', data =>
process.stdout.write(
data.results[0] && data.results[0].alternatives[0]
? `Transcription: ${data.results[0].alternatives[0].transcript}\n`
: '\n\nReached transcription time limit, press Ctrl+C\n'
)
);
// Start recording and send the microphone input to the Speech API.
// Ensure SoX is installed, see https://www.npmjs.com/package/node-record-lpcm16#dependencies
recorder
.record({
sampleRateHertz: sampleRateHertz,
threshold: 0,
// Other options, see https://www.npmjs.com/package/node-record-lpcm16#options
verbose: false,
recordProgram: 'rec', // Try also "arecord" or "sox"
silence: '10.0',
})
.stream()
.on('error', console.error)
.pipe(recognizeStream);
console.log('Listening, press Ctrl+C to stop.');Python
如要瞭解如何安裝及使用 Cloud STT 的用戶端程式庫，請參閱「Cloud STT 用戶端程式庫」。詳情請參閱「Cloud STT Python API 參考文件」。
如要向 Cloud STT 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。
import queue
import re
import sys
from google.cloud import speech
import pyaudio
# Audio recording parameters
RATE = 16000
CHUNK = int(RATE / 10) # 100ms
class MicrophoneStream:
"""Opens a recording stream as a generator yielding the audio chunks."""
def __init__(self: object, rate: int = RATE, chunk: int = CHUNK) -> None:
"""The audio -- and generator -- is guaranteed to be on the main thread."""
self._rate = rate
self._chunk = chunk
# Create a thread-safe buffer of audio data
self._buff = queue.Queue()
self.closed = True
def __enter__(self: object) -> object:
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: object,
type: object,
value: object,
traceback: object,
) -> None:
"""Closes the stream, regardless of whether the connection was lost or not."""
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: object,
in_data: object,
frame_count: int,
time_info: object,
status_flags: object,
) -> object:
"""Continuously collect data from the audio stream, into the buffer.
Args:
in_data: The audio data as a bytes object
frame_count: The number of frames captured
time_info: The time information
status_flags: The status flags
Returns:
The audio data as a bytes object
"""
self._buff.put(in_data)
return None, pyaudio.paContinue
def generator(self: object) -> object:
"""Generates audio chunks from the stream of audio data in chunks.
Args:
self: The MicrophoneStream object
Returns:
A generator that outputs audio chunks.
"""
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: object) -> str:
"""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.
Args:
responses: List of server responses
Returns:
The transcribed text.
"""
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
return transcript
def main() -> None:
"""Transcribe speech from audio file."""
# 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()後續步驟
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