本部分演示了如何将流式音频（如麦克风输入）转录为文字。
流式语音识别允许您将音频流式传输到 Speech-to-Text，并在音频处理的过程中实时接收流式语音识别的结果。另请参阅流式语音识别请求的音频限制。流式语音识别只能通过 gRPC 实现。
对本地文件执行流式语音识别
以下是对本地音频文件执行流式语音识别的示例。发送至 API 的所有流式传输请求不能超过 10 MB。此限制适用于初始 StreamingRecognize 请求和数据流中每一条消息的大小。超出此限制时，系统会抛出错误。
Go
如需了解如何安装和使用 Speech-to-Text 客户端库，请参阅 Speech-to-Text 客户端库。 如需了解详情，请参阅 Speech-to-Text Go API 参考文档。
如需向 Speech-to-Text 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
import (
"context"
"flag"
"fmt"
"io"
"log"
"os"
"path/filepath"
speech "cloud.google.com/go/speech/apiv1"
"cloud.google.com/go/speech/apiv1/speechpb"
)
func main() {
flag.Usage = func() {
fmt.Fprintf(os.Stderr, "Usage: %s <AUDIOFILE>\n", filepath.Base(os.Args[0]))
fmt.Fprintf(os.Stderr, "<AUDIOFILE> must be a path to a local audio file. Audio file must be a 16-bit signed little-endian encoded with a sample rate of 16000.\n")
}
flag.Parse()
if len(flag.Args()) != 1 {
log.Fatal("Please pass path to your local audio file as a command line argument")
}
audioFile := flag.Arg(0)
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)
}
f, err := os.Open(audioFile)
if err != nil {
log.Fatal(err)
}
defer f.Close()
go func() {
buf := make([]byte, 1024)
for {
n, err := f.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 %s: %v", audioFile, 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 {
log.Fatalf("Could not recognize: %v", err)
}
for _, result := range resp.Results {
fmt.Printf("Result: %+v\n", result)
}
}
}
Java
如需了解如何安装和使用 Speech-to-Text 客户端库，请参阅 Speech-to-Text 客户端库。 如需了解详情，请参阅 Speech-to-Text Java API 参考文档。
如需向 Speech-to-Text 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
/**
* 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());
}
}
}Node.js
如需了解如何安装和使用 Speech-to-Text 客户端库，请参阅 Speech-to-Text 客户端库。 如需了解详情，请参阅 Speech-to-Text Node.js API 参考文档。
如需向 Speech-to-Text 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
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);Python
如需了解如何安装和使用 Speech-to-Text 客户端库，请参阅 Speech-to-Text 客户端库。 如需了解详情，请参阅 Speech-to-Text Python API 参考文档。
如需向 Speech-to-Text 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
def transcribe_streaming(stream_file: str) -> speech.RecognitionConfig:
"""Streams transcription of the given audio file using Google Cloud Speech-to-Text API.
Args:
stream_file (str): Path to the local audio file to be transcribed.
Example: "resources/audio.raw"
"""
client = speech.SpeechClient()
with open(stream_file, "rb") as audio_file:
audio_content = audio_file.read()
# In practice, stream should be a generator yielding chunks of audio data.
stream = [audio_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(f"Finished: {result.is_final}")
print(f"Stability: {result.stability}")
alternatives = result.alternatives
# The alternatives are ordered from most likely to least.
for alternative in alternatives:
print(f"Confidence: {alternative.confidence}")
print(f"Transcript: {alternative.transcript}")
其他语言
C#：请按照客户端库页面上的 C# 设置说明操作，然后访问 .NET 版 Speech-to-Text 参考文档。
PHP：请按照客户端库页面上的 PHP 设置说明操作，然后访问 PHP 版 Speech-to-Text 参考文档。
Ruby：请按照客户端库页面上的 Ruby 设置说明操作，然后访问 Ruby 版 Speech-to-Text 参考文档。
虽然您可以将本地音频文件流式传输到 Speech-to-Text API，但建议您执行同步或异步音频识别以便批量获得处理结果。
对音频流执行流式语音识别
Speech-to-Text 也可以对实时流式音频执行识别。
以下示例对从麦克风接收到的音频流执行流式语音识别：
Go
如需了解如何安装和使用 Speech-to-Text 客户端库，请参阅 Speech-to-Text 客户端库。 如需了解详情，请参阅 Speech-to-Text Go API 参考文档。
如需向 Speech-to-Text 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
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)
}
}
}
Python
如需了解如何安装和使用 Speech-to-Text 客户端库，请参阅 Speech-to-Text 客户端库。 如需了解详情，请参阅 Speech-to-Text Python API 参考文档。
如需向 Speech-to-Text 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
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()Java
如需了解如何安装和使用 Speech-to-Text 客户端库，请参阅 Speech-to-Text 客户端库。 如需了解详情，请参阅 Speech-to-Text Java API 参考文档。
如需向 Speech-to-Text 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
/** 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
此示例要求您安装 SoX，并确保其在 $PATH 中可用。
- 对于 Mac OS：
brew install sox。 - 对于大多数 Linux 发行版：
sudo apt-get install sox libsox-fmt-all。 - 对于 Windows：请下载二进制文件。
如需详细了解如何安装和创建 Speech-to-Text 客户端，请参阅 Speech-to-Text 客户端库。
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.');其他语言
C#：请按照客户端库页面上的 C# 设置说明操作，然后访问 .NET 版 Speech-to-Text 参考文档。
PHP：请按照客户端库页面上的 PHP 设置说明操作，然后访问 PHP 版 Speech-to-Text 参考文档。
Ruby：请按照客户端库页面上的 Ruby 设置说明操作，然后访问 Ruby 版 Speech-to-Text 参考文档。
执行无限流式语音识别
以下示例对从麦克风接收到的无限音频串流执行流式语音识别：
Python
如需了解如何安装和使用 Speech-to-Text 客户端库，请参阅 Speech-to-Text 客户端库。 如需了解详情，请参阅 Speech-to-Text Python API 参考文档。
如需向 Speech-to-Text 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
import queue
import re
import sys
import time
from google.cloud import speech
import pyaudio
# Audio recording parameters
STREAMING_LIMIT = 240000 # 4 minutes
SAMPLE_RATE = 16000
CHUNK_SIZE = int(SAMPLE_RATE / 10) # 100ms
RED = "\033[0;31m"
GREEN = "\033[0;32m"
YELLOW = "\033[0;33m"
def get_current_time() -> int:
"""Return Current Time in MS.
Returns:
int: Current Time in MS.
"""
return int(round(time.time() * 1000))
class ResumableMicrophoneStream:
"""Opens a recording stream as a generator yielding the audio chunks."""
def __init__(
self: object,
rate: int,
chunk_size: int,
) -> None:
"""Creates a resumable microphone stream.
Args:
self: The class instance.
rate: The audio file's sampling rate.
chunk_size: The audio file's chunk size.
returns: None
"""
self._rate = rate
self.chunk_size = chunk_size
self._num_channels = 1
self._buff = queue.Queue()
self.closed = True
self.start_time = get_current_time()
self.restart_counter = 0
self.audio_input = []
self.last_audio_input = []
self.result_end_time = 0
self.is_final_end_time = 0
self.final_request_end_time = 0
self.bridging_offset = 0
self.last_transcript_was_final = False
self.new_stream = True
self._audio_interface = pyaudio.PyAudio()
self._audio_stream = self._audio_interface.open(
format=pyaudio.paInt16,
channels=self._num_channels,
rate=self._rate,
input=True,
frames_per_buffer=self.chunk_size,
# 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,
)
def __enter__(self: object) -> object:
"""Opens the stream.
Args:
self: The class instance.
returns: None
"""
self.closed = False
return self
def __exit__(
self: object,
type: object,
value: object,
traceback: object,
) -> object:
"""Closes the stream and releases resources.
Args:
self: The class instance.
type: The exception type.
value: The exception value.
traceback: The exception traceback.
returns: None
"""
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,
*args: object,
**kwargs: object,
) -> object:
"""Continuously collect data from the audio stream, into the buffer.
Args:
self: The class instance.
in_data: The audio data as a bytes object.
args: Additional arguments.
kwargs: Additional arguments.
returns: None
"""
self._buff.put(in_data)
return None, pyaudio.paContinue
def generator(self: object) -> object:
"""Stream Audio from microphone to API and to local buffer
Args:
self: The class instance.
returns:
The data from the audio stream.
"""
while not self.closed:
data = []
if self.new_stream and self.last_audio_input:
chunk_time = STREAMING_LIMIT / len(self.last_audio_input)
if chunk_time != 0:
if self.bridging_offset < 0:
self.bridging_offset = 0
if self.bridging_offset > self.final_request_end_time:
self.bridging_offset = self.final_request_end_time
chunks_from_ms = round(
(self.final_request_end_time - self.bridging_offset)
/ chunk_time
)
self.bridging_offset = round(
(len(self.last_audio_input) - chunks_from_ms) * chunk_time
)
for i in range(chunks_from_ms, len(self.last_audio_input)):
data.append(self.last_audio_input[i])
self.new_stream = False
# 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()
self.audio_input.append(chunk)
if chunk is None:
return
data.append(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)
self.audio_input.append(chunk)
except queue.Empty:
break
yield b"".join(data)
def listen_print_loop(responses: object, stream: object) -> None:
"""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.
Arg:
responses: The responses returned from the API.
stream: The audio stream to be processed.
"""
for response in responses:
if get_current_time() - stream.start_time > STREAMING_LIMIT:
stream.start_time = get_current_time()
break
if not response.results:
continue
result = response.results[0]
if not result.alternatives:
continue
transcript = result.alternatives[0].transcript
result_seconds = 0
result_micros = 0
if result.result_end_time.seconds:
result_seconds = result.result_end_time.seconds
if result.result_end_time.microseconds:
result_micros = result.result_end_time.microseconds
stream.result_end_time = int((result_seconds * 1000) + (result_micros / 1000))
corrected_time = (
stream.result_end_time
- stream.bridging_offset
+ (STREAMING_LIMIT * stream.restart_counter)
)
# Display interim results, but with a carriage return at the end of the
# line, so subsequent lines will overwrite them.
if result.is_final:
sys.stdout.write(GREEN)
sys.stdout.write("\033[K")
sys.stdout.write(str(corrected_time) + ": " + transcript + "\n")
stream.is_final_end_time = stream.result_end_time
stream.last_transcript_was_final = True
# 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):
sys.stdout.write(YELLOW)
sys.stdout.write("Exiting...\n")
stream.closed = True
break
else:
sys.stdout.write(RED)
sys.stdout.write("\033[K")
sys.stdout.write(str(corrected_time) + ": " + transcript + "\r")
stream.last_transcript_was_final = False
def main() -> None:
"""start bidirectional streaming from microphone input to speech API"""
client = speech.SpeechClient()
config = speech.RecognitionConfig(
encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=SAMPLE_RATE,
language_code="en-US",
max_alternatives=1,
)
streaming_config = speech.StreamingRecognitionConfig(
config=config, interim_results=True
)
mic_manager = ResumableMicrophoneStream(SAMPLE_RATE, CHUNK_SIZE)
print(mic_manager.chunk_size)
sys.stdout.write(YELLOW)
sys.stdout.write('\nListening, say "Quit" or "Exit" to stop.\n\n')
sys.stdout.write("End (ms) Transcript Results/Status\n")
sys.stdout.write("=====================================================\n")
with mic_manager as stream:
while not stream.closed:
sys.stdout.write(YELLOW)
sys.stdout.write(
"\n" + str(STREAMING_LIMIT * stream.restart_counter) + ": NEW REQUEST\n"
)
stream.audio_input = []
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, stream)
if stream.result_end_time > 0:
stream.final_request_end_time = stream.is_final_end_time
stream.result_end_time = 0
stream.last_audio_input = []
stream.last_audio_input = stream.audio_input
stream.audio_input = []
stream.restart_counter = stream.restart_counter + 1
if not stream.last_transcript_was_final:
sys.stdout.write("\n")
stream.new_stream = True
if __name__ == "__main__":
main()
Java
如需了解如何安装和使用 Speech-to-Text 客户端库，请参阅 Speech-to-Text 客户端库。 如需了解详情，请参阅 Speech-to-Text Java API 参考文档。
如需向 Speech-to-Text 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
import com.google.api.gax.rpc.ClientStream;
import com.google.api.gax.rpc.ResponseObserver;
import com.google.api.gax.rpc.StreamController;
import com.google.cloud.speech.v1p1beta1.RecognitionConfig;
import com.google.cloud.speech.v1p1beta1.SpeechClient;
import com.google.cloud.speech.v1p1beta1.SpeechRecognitionAlternative;
import com.google.cloud.speech.v1p1beta1.StreamingRecognitionConfig;
import com.google.cloud.speech.v1p1beta1.StreamingRecognitionResult;
import com.google.cloud.speech.v1p1beta1.StreamingRecognizeRequest;
import com.google.cloud.speech.v1p1beta1.StreamingRecognizeResponse;
import com.google.protobuf.ByteString;
import com.google.protobuf.Duration;
import java.text.DecimalFormat;
import java.util.ArrayList;
import java.util.concurrent.BlockingQueue;
import java.util.concurrent.LinkedBlockingQueue;
import java.util.concurrent.TimeUnit;
import javax.sound.sampled.AudioFormat;
import javax.sound.sampled.AudioSystem;
import javax.sound.sampled.DataLine;
import javax.sound.sampled.DataLine.Info;
import javax.sound.sampled.TargetDataLine;
public class InfiniteStreamRecognize {
private static final int STREAMING_LIMIT = 290000; // ~5 minutes
public static final String RED = "\033[0;31m";
public static final String GREEN = "\033[0;32m";
public static final String YELLOW = "\033[0;33m";
// Creating shared object
private static volatile BlockingQueue<byte[]> sharedQueue = new LinkedBlockingQueue<byte[]>();
private static TargetDataLine targetDataLine;
private static int BYTES_PER_BUFFER = 6400; // buffer size in bytes
private static int restartCounter = 0;
private static ArrayList<ByteString> audioInput = new ArrayList<ByteString>();
private static ArrayList<ByteString> lastAudioInput = new ArrayList<ByteString>();
private static int resultEndTimeInMS = 0;
private static int isFinalEndTime = 0;
private static int finalRequestEndTime = 0;
private static boolean newStream = true;
private static double bridgingOffset = 0;
private static boolean lastTranscriptWasFinal = false;
private static StreamController referenceToStreamController;
private static ByteString tempByteString;
public static void main(String... args) {
InfiniteStreamRecognizeOptions options = InfiniteStreamRecognizeOptions.fromFlags(args);
if (options == null) {
// Could not parse.
System.out.println("Failed to parse options.");
System.exit(1);
}
try {
infiniteStreamingRecognize(options.langCode);
} catch (Exception e) {
System.out.println("Exception caught: " + e);
}
}
public static String convertMillisToDate(double milliSeconds) {
long millis = (long) milliSeconds;
DecimalFormat format = new DecimalFormat();
format.setMinimumIntegerDigits(2);
return String.format(
"%s:%s /",
format.format(TimeUnit.MILLISECONDS.toMinutes(millis)),
format.format(
TimeUnit.MILLISECONDS.toSeconds(millis)
- TimeUnit.MINUTES.toSeconds(TimeUnit.MILLISECONDS.toMinutes(millis))));
}
/** Performs infinite streaming speech recognition */
public static void infiniteStreamingRecognize(String languageCode) throws Exception {
// Microphone Input buffering
class MicBuffer implements Runnable {
@Override
public void run() {
System.out.println(YELLOW);
System.out.println("Start speaking...Press Ctrl-C to stop");
targetDataLine.start();
byte[] data = new byte[BYTES_PER_BUFFER];
while (targetDataLine.isOpen()) {
try {
int numBytesRead = targetDataLine.read(data, 0, data.length);
if ((numBytesRead <= 0) && (targetDataLine.isOpen())) {
continue;
}
sharedQueue.put(data.clone());
} catch (InterruptedException e) {
System.out.println("Microphone input buffering interrupted : " + e.getMessage());
}
}
}
}
// Creating microphone input buffer thread
MicBuffer micrunnable = new MicBuffer();
Thread micThread = new Thread(micrunnable);
ResponseObserver<StreamingRecognizeResponse> responseObserver = null;
try (SpeechClient client = SpeechClient.create()) {
ClientStream<StreamingRecognizeRequest> clientStream;
responseObserver =
new ResponseObserver<StreamingRecognizeResponse>() {
ArrayList<StreamingRecognizeResponse> responses = new ArrayList<>();
public void onStart(StreamController controller) {
referenceToStreamController = controller;
}
public void onResponse(StreamingRecognizeResponse response) {
responses.add(response);
StreamingRecognitionResult result = response.getResultsList().get(0);
Duration resultEndTime = result.getResultEndTime();
resultEndTimeInMS =
(int)
((resultEndTime.getSeconds() * 1000) + (resultEndTime.getNanos() / 1000000));
double correctedTime =
resultEndTimeInMS - bridgingOffset + (STREAMING_LIMIT * restartCounter);
SpeechRecognitionAlternative alternative = result.getAlternativesList().get(0);
if (result.getIsFinal()) {
System.out.print(GREEN);
System.out.print("\033[2K\r");
System.out.printf(
"%s: %s [confidence: %.2f]\n",
convertMillisToDate(correctedTime),
alternative.getTranscript(),
alternative.getConfidence());
isFinalEndTime = resultEndTimeInMS;
lastTranscriptWasFinal = true;
} else {
System.out.print(RED);
System.out.print("\033[2K\r");
System.out.printf(
"%s: %s", convertMillisToDate(correctedTime), alternative.getTranscript());
lastTranscriptWasFinal = false;
}
}
public void onComplete() {}
public void onError(Throwable t) {}
};
clientStream = client.streamingRecognizeCallable().splitCall(responseObserver);
RecognitionConfig recognitionConfig =
RecognitionConfig.newBuilder()
.setEncoding(RecognitionConfig.AudioEncoding.LINEAR16)
.setLanguageCode(languageCode)
.setSampleRateHertz(16000)
.build();
StreamingRecognitionConfig streamingRecognitionConfig =
StreamingRecognitionConfig.newBuilder()
.setConfig(recognitionConfig)
.setInterimResults(true)
.build();
StreamingRecognizeRequest request =
StreamingRecognizeRequest.newBuilder()
.setStreamingConfig(streamingRecognitionConfig)
.build(); // The first request in a streaming call has to be a config
clientStream.send(request);
try {
// 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) AudioSystem.getLine(targetInfo);
targetDataLine.open(audioFormat);
micThread.start();
long startTime = System.currentTimeMillis();
while (true) {
long estimatedTime = System.currentTimeMillis() - startTime;
if (estimatedTime >= STREAMING_LIMIT) {
clientStream.closeSend();
referenceToStreamController.cancel(); // remove Observer
if (resultEndTimeInMS > 0) {
finalRequestEndTime = isFinalEndTime;
}
resultEndTimeInMS = 0;
lastAudioInput = null;
lastAudioInput = audioInput;
audioInput = new ArrayList<ByteString>();
restartCounter++;
if (!lastTranscriptWasFinal) {
System.out.print('\n');
}
newStream = true;
clientStream = client.streamingRecognizeCallable().splitCall(responseObserver);
request =
StreamingRecognizeRequest.newBuilder()
.setStreamingConfig(streamingRecognitionConfig)
.build();
System.out.println(YELLOW);
System.out.printf("%d: RESTARTING REQUEST\n", restartCounter * STREAMING_LIMIT);
startTime = System.currentTimeMillis();
} else {
if ((newStream) && (lastAudioInput.size() > 0)) {
// if this is the first audio from a new request
// calculate amount of unfinalized audio from last request
// resend the audio to the speech client before incoming audio
double chunkTime = STREAMING_LIMIT / lastAudioInput.size();
// ms length of each chunk in previous request audio arrayList
if (chunkTime != 0) {
if (bridgingOffset < 0) {
// bridging Offset accounts for time of resent audio
// calculated from last request
bridgingOffset = 0;
}
if (bridgingOffset > finalRequestEndTime) {
bridgingOffset = finalRequestEndTime;
}
int chunksFromMs =
(int) Math.floor((finalRequestEndTime - bridgingOffset) / chunkTime);
// chunks from MS is number of chunks to resend
bridgingOffset =
(int) Math.floor((lastAudioInput.size() - chunksFromMs) * chunkTime);
// set bridging offset for next request
for (int i = chunksFromMs; i < lastAudioInput.size(); i++) {
request =
StreamingRecognizeRequest.newBuilder()
.setAudioContent(lastAudioInput.get(i))
.build();
clientStream.send(request);
}
}
newStream = false;
}
tempByteString = ByteString.copyFrom(sharedQueue.take());
request =
StreamingRecognizeRequest.newBuilder().setAudioContent(tempByteString).build();
audioInput.add(tempByteString);
}
clientStream.send(request);
}
} catch (Exception e) {
System.out.println(e);
}
}
}
}Node.js
此示例要求您安装 SoX，并确保其在 $PATH 中可用。
- 对于 Mac OS：
brew install sox。 - 对于大多数 Linux 发行版：
sudo apt-get install sox libsox-fmt-all。 - 对于 Windows：请下载二进制文件。
如需详细了解如何安装和创建 Speech-to-Text 客户端，请参阅 Speech-to-Text 客户端库。
// const encoding = 'LINEAR16';
// const sampleRateHertz = 16000;
// const languageCode = 'en-US';
// const streamingLimit = 10000; // ms - set to low number for demo purposes
const chalk = require('chalk');
const {Writable} = require('stream');
const recorder = require('node-record-lpcm16');
// Imports the Google Cloud client library
// Currently, only v1p1beta1 contains result-end-time
const speech = require('@google-cloud/speech').v1p1beta1;
const client = new speech.SpeechClient();
const config = {
encoding: encoding,
sampleRateHertz: sampleRateHertz,
languageCode: languageCode,
};
const request = {
config,
interimResults: true,
};
let recognizeStream = null;
let restartCounter = 0;
let audioInput = [];
let lastAudioInput = [];
let resultEndTime = 0;
let isFinalEndTime = 0;
let finalRequestEndTime = 0;
let newStream = true;
let bridgingOffset = 0;
let lastTranscriptWasFinal = false;
function startStream() {
// Clear current audioInput
audioInput = [];
// Initiate (Reinitiate) a recognize stream
recognizeStream = client
.streamingRecognize(request)
.on('error', err => {
if (err.code === 11) {
// restartStream();
} else {
console.error('API request error ' + err);
}
})
.on('data', speechCallback);
// Restart stream when streamingLimit expires
setTimeout(restartStream, streamingLimit);
}
const speechCallback = stream => {
// Convert API result end time from seconds + nanoseconds to milliseconds
resultEndTime =
stream.results[0].resultEndTime.seconds * 1000 +
Math.round(stream.results[0].resultEndTime.nanos / 1000000);
// Calculate correct time based on offset from audio sent twice
const correctedTime =
resultEndTime - bridgingOffset + streamingLimit * restartCounter;
process.stdout.clearLine();
process.stdout.cursorTo(0);
let stdoutText = '';
if (stream.results[0] && stream.results[0].alternatives[0]) {
stdoutText =
correctedTime + ': ' + stream.results[0].alternatives[0].transcript;
}
if (stream.results[0].isFinal) {
process.stdout.write(chalk.green(`${stdoutText}\n`));
isFinalEndTime = resultEndTime;
lastTranscriptWasFinal = true;
} else {
// Make sure transcript does not exceed console character length
if (stdoutText.length > process.stdout.columns) {
stdoutText =
stdoutText.substring(0, process.stdout.columns - 4) + '...';
}
process.stdout.write(chalk.red(`${stdoutText}`));
lastTranscriptWasFinal = false;
}
};
const audioInputStreamTransform = new Writable({
write(chunk, encoding, next) {
if (newStream && lastAudioInput.length !== 0) {
// Approximate math to calculate time of chunks
const chunkTime = streamingLimit / lastAudioInput.length;
if (chunkTime !== 0) {
if (bridgingOffset < 0) {
bridgingOffset = 0;
}
if (bridgingOffset > finalRequestEndTime) {
bridgingOffset = finalRequestEndTime;
}
const chunksFromMS = Math.floor(
(finalRequestEndTime - bridgingOffset) / chunkTime
);
bridgingOffset = Math.floor(
(lastAudioInput.length - chunksFromMS) * chunkTime
);
for (let i = chunksFromMS; i < lastAudioInput.length; i++) {
recognizeStream.write(lastAudioInput[i]);
}
}
newStream = false;
}
audioInput.push(chunk);
if (recognizeStream) {
recognizeStream.write(chunk);
}
next();
},
final() {
if (recognizeStream) {
recognizeStream.end();
}
},
});
function restartStream() {
if (recognizeStream) {
recognizeStream.end();
recognizeStream.removeListener('data', speechCallback);
recognizeStream = null;
}
if (resultEndTime > 0) {
finalRequestEndTime = isFinalEndTime;
}
resultEndTime = 0;
lastAudioInput = [];
lastAudioInput = audioInput;
restartCounter++;
if (!lastTranscriptWasFinal) {
process.stdout.write('\n');
}
process.stdout.write(
chalk.yellow(`${streamingLimit * restartCounter}: RESTARTING REQUEST\n`)
);
newStream = true;
startStream();
}
// Start recording and send the microphone input to the Speech API
recorder
.record({
sampleRateHertz: sampleRateHertz,
threshold: 0, // Silence threshold
silence: 1000,
keepSilence: true,
recordProgram: 'rec', // Try also "arecord" or "sox"
})
.stream()
.on('error', err => {
console.error('Audio recording error ' + err);
})
.pipe(audioInputStreamTransform);
console.log('');
console.log('Listening, press Ctrl+C to stop.');
console.log('');
console.log('End (ms) Transcript Results/Status');
console.log('=========================================================');
startStream();后续步骤
- 了解如何衡量和提高准确率
自行试用
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