WebSocket 接口
WebSocket 连接建立后，您可以通过文本消息发送语音识别请求，并逐步接收响应。您可以逐步发送实时录音的语音流等音频数据，并逐步获取识别结果。
处理步骤如下:
备注
我们提供了客户端库，隐藏了 WebSocket 接口的细节，可以轻松创建实时语音识别应用程序。使用方法请参阅实时语音识别库 Wrp 的使用方法。
使用语音流的应用程序在建立 WebSocket 连接后，会接收命令和命令响应(s、p、e)，以及根据服务器处理发送的事件(S、E、C、U、A)，并进行实现。一般流程如下:
下面将逐步说明实现方法。关于命令和命令响应(s、p、e)、根据服务器处理发送的事件(S、E、C、U、A)的详细信息以及响应，请参阅命令和响应。
使用方法
1. 建立 WebSocket 连接
通过 WebSocket 连接到语音识别服务器。此时，您可以选择以下两个 endpoint 之一来选择是否允许保存日志:
wss://acp-api.amivoice.com/v1/ (保存日志)
wss://acp-api.amivoice.com/v1/nolog/ (不保存日志)
关于日志保存，请参阅日志保存。
注意
如果不保存日志的端点末尾的「/（斜杠）」丢失，将变成保存日志。请特别注意是否忘记添加「/」。
（正确） wss://acp-api.amivoice.com/v1/nolog/ （不保存日志）
（错误） wss://acp-api.amivoice.com/v1/nolog （保存日志）
与服务器的通信通过文本消息进行。这里我们使用 Python 代码来说明，但使用其他语言也可以在建立 WebSocket 连接后通过发送和接收文本消息来进行实时语音识别。
这里我们使用 Python 的 websocket-client 库来简化 WebSocket 操作。我们使用 AmiVoice API 的 WebSocket 接口的保存日志 endpoint 通过 WebSocket 进行连接。当与服务器建立 WebSocket 连接时，会调用 on_open，当从服务器接收到消息时，会调用 on_message。我们将在说明与语音识别服务器通信的过程中向这些方法添加处理。
import websocket
import logging
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.DEBUG, format="%(asctime)s %(threadName)s %(message)s")
def on_open(ws):
logger.info("open")
def on_message(ws, message):
logger.info(f"message: {message}")
def on_close(ws):
logger.info("close")
ws = websocket.WebSocketApp('wss://acp-api.amivoice.com/v1/',
on_open=on_open,
on_message=on_message,
on_close=on_close)
ws.run_forever()
警告
在基础系统非常繁忙的情况下，WebSocket 连接可能会极少数失败。在这种情况下，请尝试多次重试，直到连接成功。
2. 发送语音识别请求
WebSocket 连接成功后，发送 s 命令。s 命令的格式如下:
s <audio_format> <grammar_file_names> <key>=<value> ...
在 audio_format 中指定该会话中要发送的音频的音频格式。在 grammar_file_name 中指定请求参数中的连接引擎名 称。然后以 <key>=<value> 的形式设置认证信息 (authorization)，如 authorization={APPKEY}。在 <key>=<value> 中可以设置其他请求参数。
考虑使用通用引擎(-a-general)转录随样本提供的音频文件(test.wav)。由于该音频文件的采样率为 16kHz，且为 wav 容器文件，因此在 audio_format 中指定 16K。详细信息请参阅带 header 的音频文件的情况。在 grammar_file_name 中设置最通用的 -a-general。在 WebSocket 连接时的处理程序 on_open 中添加以下代码:
APPKEY='XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'
def on_open(ws):
logger.info("open")
command = f"s 16K -a-general authorization={APPKEY}"
logger.info(f"send> {command}")
ws.send(command)
要设置其他请求参数，请以 <key1>=<value1> <key2>=<value2>... 的形式添加到 s 命令中。这里，我们添加了两个参数到 s 命令行中，以显示不必要的词语如"あのー"和"えーっと"的 keepFillerToken，并将更新事件的发送时间 resultUpdatedInterval 更改为 1 秒。
def on_open(ws):
logger.info("open")
command = f"s 16K -a-general authorization={APPKEY} keepFillerToken=1 resultUpdatedInterval=1000"
logger.info(f"send> {command}")
ws.send(command)
如果要在请求参数中设置的值包含空格，请用双引号将 value 括起来，如 "value"。例如，在 segmenterProperties 中设置多个参数时，可以如下所示:
segmenterProperties="useDiarizer=1 diarizerAlpha=1e-20 diarizerTransitionBias=1e-10"
on_open 如下所示:
def on_open(ws):
logger.info("open")
command = f"s 16K -a-general authorization={APPKEY} segmenterProperties=\"useDiarizer=1 diarizerAlpha=1e-20 diarizerTransitionBias=1e-10\""
logger.info(f"send> {command}")
ws.send(command)
详细信息请参阅参考文档中的 s 命令数据包。
响应
当接受语音识别请求时，服务器会返回文本消息，即 s 命令响应数据包。
成功时
s
失败时
在 s 后面加上半角空格， 然后接收错误消息。关于错误类型，请参阅参考文档中的 s 命令响应数据包。
s 错误消息
例:
s received unsupported audio format
警告
为了处理 s 命令的响应，在 on_message 中添加以下代码:
def on_message(ws, message):
event = message[0]
content = message[2:].rstrip()
logger.info(f"message: {event} {content}")
if event == 's':
if content != "":
logger.error(content)
return
# s 命令成功
3. 发送音频数据
s 命令成功后，可以发送音频文件。使用 p 命令发送二进制的音频数据。p 命令的格式如下:
p<audio_data>
audio_data 是二进制的音频数据。请设置符合会话开始时 s 命令中指定的音频格式的音频数据。
信息
请发送符合 s 命令中指定的音频格式的音频数据。即使格式不同，也不会出现错误，但响应可能会非常耗时，或无法获得正确的识别结果。
备注
- 一次
p命令可以发送的音频数据大小最大为 16MB。如果数据大小超过此值，请进行分割。 - 音频数据可以在任意位置分割，无需考虑 wav 的块或 mp3/ogg/opus 等的帧边界。
- 中途无法更改要发送的数据格式。如果要更改音频格式，请使用
e命令结束，然后从s开始重新发送语音识别请求。对于 带 header 的音频文件，同样需要对每个文件使用e命令结束，然后从s开始重新发送语音识别请求。
在 s 命令响应的处理程序中，发送随样本提供的音频文件(test.wav)。在 on_message 中添加以下内容。这里，我们模拟从麦克风录音的情况，以实时相同的时间间隔发送数据，所以使用了 sleep。
import time
import threading
def on_message(ws, message):
event = message[0]
content = message[2:].rstrip()
logger.info(f"message: {event} {content}")
if event == 's':
if content != "":
logger.error(content)
return
# s 命令成功
# 如果请求成功，则向服务器发送音频文件数据
def send_audio(*args):
with open(filename, mode='rb') as file:
buf = file.read(audio_block_size)
while buf:
logger.info("send> p [..({} bytes)..]".format(len(buf)))
ws.send(b'p' + buf,
opcode=websocket.ABNF.OPCODE_BINARY)
buf = file.read(audio_block_size)
# test.wav 为 16 位，16kHz，所以每秒 32,000 字节。每次发送 16,000 字节，等待 0.5 秒，就与实时相同
time.sleep(0.5)
logger.info("send> e")
# 音频发送完毕后，发送 e 命令
ws.send('e')
threading.Thread(target=send_audio).start()
4. 获取状态事件
发送音频后，会收到 G、发话开始(S)、发话结束(E)、语音识别处理开始(C)的事件。G 是通知服务器端生成的信息的事件，请忽略。随 S 和 E 事件，可以获得以音频开头为 0 的相对时间(以毫秒为单位)。
例如，发送音频文件(test.wav)时从服务器收到的事件如下:
G
S 250
C
E 8800
为了处理状态事件，在 on_message 中添加以下代码。这里添加的代码不会产生任何作用，但如果要使用发话开始和发话结束的时间，请添加适当的处理。
def on_message(ws, message):
event = message[0]
content = message[2:].rstrip()
logger.info(f"message: {event} {content}")
if event == 's':
# ...省略...
elif event == 'G':
pass
elif event == 'S':
starttime = int(content)
elif event == 'E':
endtime = int(content)
elif event == 'C':
pass
备注
5. 获取语音识别结果
语音识别服务器会将处理过程中的结果作为U事件通知。这里我们使用s命令设置了resultUpdatedInterval=1000，所以大约每1秒钟会收到一次U事件。当处理完成并确定结果时，会收到A事件。关于结果的详细信息，请参考语音识别结果。对于test.wav音频的一系列结果，请参考示例代码的结果。
def on_message(ws, message):
event = message[0]
content = message[2:].rstrip()
logger.info(f"message: {event} {content}")
if event == 's':
# ...省略...
elif event == 'G':
pass
elif event == 'S':
starttime = int(content)
elif event == 'E':
endtime = int(content)
elif event == 'C':
pass
elif event == 'U':
raw = json.loads(content) if content else ''
elif event == 'A' or event == 'R':
raw = json.loads(content) if content else ''
6. 通知音频数据发送结束
当所有音频都发送完毕后，可以发送e命令来结束语音识别会话。
e
在以下代码中，发送完所有音频文件数据后，会发送e命令：
def on_message(ws, message):
event = message[0]
content = message[2:].rstrip()
logger.info(f"message: {event} {content}")
if event == 's':
if content != "":
logger.error(content)
return
# s命令成功
# 如果请求成功，则向服务器发送音频文件数据
def send_audio(*args):
with open(filename, mode='rb') as file:
buf = file.read(audio_block_size)
while buf:
logger.info("send> p [..({} bytes)..]".format(len(buf)))
ws.send(b'p' + buf,
opcode=websocket.ABNF.OPCODE_BINARY)
buf = file.read(audio_block_size)
# test.wav是16位、16kHz的音频，所以每秒32,000字节。每次发送16,000字节，等待0.5秒，就能与实时发送速度相匹配
time.sleep(0.5)
# 音频发送完毕后，发送e命令
logger.info("send> e")
ws.send('e')
threading.Thread(target=send_audio).start()
发送e命令后，语音识别服务器会处理所有接收到的音频，返回所有结果，然后返回e命令的响应。根据发送的音频长度，完成可能需要一些时间，所以请等待e命令的响应以获得所有结果。
警告
为了应对通信障碍或服务器延迟等意外情况，请设置适当的通信超时，确保即使没有收到语音识别服务器的响应，应用程序也能正常运行。
7. WebSocket的断开连接
在这里，当收到e命令的响应时，我们关闭WebSocket连接。
def on_message(ws, message):
event = message[0]
content = message[2:].rstrip()
logger.info(f"message: {event} {content}")
if event == 's':
# ...省略...
elif event == 'G':
pass
elif event == 'S':
starttime = int(content)
elif event == 'E':
endtime = int(content)
elif event == 'C':
pass
elif event == 'U':
raw = json.loads(content) if content else ''
elif event == 'A' or event == 'R':
raw = json.loads(content) if content else ''
elif event == 'e':
logger.info("close>")
ws.close()
示例代码
以下是完整的Python代码。
websocket-sample.py
import time
import websocket
import json
import threading
import logging
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.DEBUG, format="%(asctime)s %(threadName)s %(message)s")
server = 'wss://acp-api.amivoice.com/v1/'
filename = 'test.wav'
codec = "16K"
audio_block_size = 16000
grammar_file_names = "-a-general"
options = {
"profileId" : "",
"profileWords" : "",
"keepFillerToken": "",
"resultUpdatedInterval" : "1000",
"authorization" : 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX',
}
def on_open(ws):
logger.info("open")
def start(*args):
command = "s {} {}".format(codec, grammar_file_names)
for k, v in options.items():
if v != "":
if k == 'profileWords':
v = '"' + v.replace('"', '""') + '"'
command += f" {k}={v}"
logger.info("send> {command}")
ws.send(command)
threading.Thread(target=start).start()
def on_message(ws, message):
event = message[0]
content = message[2:].rstrip()
logger.info(f"message: {event} {content}")
if event == 's':
if content == "can't connect to recognizer server":
logger.error(content)
return
def send_audio(*args):
with open(filename, mode='rb') as file:
buf = file.read(audio_block_size)
while buf:
logger.debug("send> p [..({} bytes)..]".format(len(buf)))
ws.send(b'p' + buf,
opcode=websocket.ABNF.OPCODE_BINARY)
buf = file.read(audio_block_size)
time.sleep(0.5)
logger.info("send> e")
ws.send('e')
threading.Thread(target=send_audio).start()
elif event == 'G':
pass
elif event == 'S':
starttime = int(content)
elif event == 'E':
endtime = int(content)
elif event == 'C':
pass
elif event == 'U':
raw = json.loads(content) if content else ''
elif event == 'A' or event == 'R':
raw = json.loads(content) if content else ''
elif event == 'e':
logger.info("close>")
ws.close()
def on_close(ws):
logger.info("close")
logger.info("open> {}".format(server))
ws = websocket.WebSocketApp(server,
on_open=on_open,
on_message=on_message,
on_close=on_close)
ws.run_forever()
执行
可以按以下方式执行：
$ python websocket-sample.py
结果
发送音频文件(test.wav)时的操作日志如下所示。显示了从程序开始的毫秒级时间、线程名称和消息。
Thread-1线程显示正在发送音频(send> p [..(16000 bytes)..])，并且可以确认大约每1秒钟获得一次中间结果(message: U)。最后还获得了最终确定的结果(message: A)。
4 MainThread open> wss://acp-api.amivoice.com/v1/
94 MainThread open
94 MainThread send> s LSB16K -a-general resultUpdatedInterval=1000 authorization={APPKEY}
133 MainThread message: s
134 Thread-1 send> p [..(16000 bytes)..]
174 MainThread message: G
637 Thread-1 send> p [..(16000 bytes)..]
668 MainThread message: S 250
668 MainThread message: C
1139 Thread-1 send> p [..(16000 bytes)..]
1639 Thread-1 send> p [..(16000 bytes)..]
2144 Thread-1 send> p [..(16000 bytes)..]
2647 Thread-1 send> p [..(16000 bytes)..]
3148 Thread-1 send> p [..(16000 bytes)..]
3174 MainThread message: U {"results":[{"tokens":[{"written":"\u30a2\u30c9\u30d0\u30f3\u30b9\u30c8\u30fb\u30e1\u30c7\u30a3\u30a2"},{"written":"..."}],"text":"\u30a2\u30c9\u30d0\u30f3\u30b9\u30c8\u30fb\u30e1\u30c7\u30a3\u30a2..."}],"text":"\u30a2\u30c9\u30d0\u30f3\u30b9\u30c8\u30fb\u30e1\u30c7\u30a3\u30a2..."}
3649 Thread-1 send> p [..(16000 bytes)..]
4153 Thread-1 send> p [..(16000 bytes)..]
4179 MainThread message: U {"results":[{"tokens":[{"written":"\u30a2\u30c9\u30d0\u30f3\u30b9\u30c8\u30fb\u30e1\u30c7\u30a3\u30a2"},{"written":"\u306f"},{"written":"\u3001"},{"written":"\u3072\u3068\u3068\u304d"},{"written":"..."}],"text":"\u30a2\u30c9\u30d0\u30f3\u30b9\u30c8\u30fb\u30e1\u30c7\u30a3\u30a2\u306f\u3001\u3072\u3068\u3068\u304d..."}],"text":"\u30a2\u30c9\u30d0\u30f3\u30b9\u30c8\u30fb\u30e1\u30c7\u30a3\u30a2\u306f\u3001\u3072\u3068\u3068\u304d..."}
4656 Thread-1 send> p [..(16000 bytes)..]
5157 Thread-1 send> p [..(16000 bytes)..]
5184 MainThread message: U {"results":[{"tokens":[{"written":"\u30a2\u30c9\u30d0\u30f3\u30b9\u30c8\u30fb\u30e1\u30c7\u30a3\u30a2"},{"written":"\u306f"},{"written":"\u3001"},{"written":"\u4eba"},{"written":"\u3068"},{"written":"\u6a5f\u68b0"},{"written":"\u3068"},{"written":"\u306e"},{"written":"\u81ea\u7136"},{"written":"\u306a"},{"written":"..."}],"text":"\u30a2\u30c9\u30d0\u30f3\u30b9\u30c8\u30fb\u30e1\u30c7\u30a3\u30a2\u306f\u3001\u4eba\u3068\u6a5f\u68b0\u3068\u306e\u81ea\u7136\u306a..."}],"text":"\u30a2\u30c9\u30d0\u30f3\u30b9\u30c8\u30fb\u30e1\u30c7\u30a3\u30a2\u306f\u3001\u4eba\u3068\u6a5f\u68b0\u3068\u306e\u81ea\u7136\u306a..."}
5658 Thread-1 send> p [..(16000 bytes)..]
6159 Thread-1 send> p [..(16000 bytes)..]
6187 MainThread message: U {"results":[{"tokens":[{"written":"\u30a2\u30c9\u30d0\u30f3\u30b9\u30c8\u30fb\u30e1\u30c7\u30a3\u30a2"},{"written":"\u306f"},{"written":"\u3001"},{"written":"\u4eba"},{"written":"\u3068"},{"written":"\u6a5f\u68b0"},{"written":"\u3068"},{"written":"\u306e"},{"written":"\u81ea\u7136"},{"written":"\u306a"},{"written":"\u30b3\u30df\u30e5\u30cb\u30b1\u30fc\u30b7\u30e7\u30f3"},{"written":"\u3092"},{"written":"\u6301"},{"written":"..."}],"text":"\u30a2\u30c9\u30d0\u30f3\u30b9\u30c8\u30fb\u30e1\u30c7\u30a3\u30a2\u306f\u3001\u4eba\u3068\u6a5f\u68b0\u3068\u306e\u81ea\u7136\u306a\u30b3\u30df\u30e5\u30cb\u30b1\u30fc\u30b7\u30e7\u30f3\u3092\u6301..."}],"text":"\u30a2\u30c9\u30d0\u30f3\u30b9\u30c8\u30fb\u30e1\u30c7\u30a3\u30a2\u306f\u3001\u4eba\u3068\u6a5f\u68b0\u3068\u306e\u81ea\u7136\u306a\u30b3\u30df\u30e5\u30cb\u30b1\u30fc\u30b7\u30e7\u30f3\u3092\u6301..."}
6660 Thread-1 send> p [..(16000 bytes)..]
7161 Thread-1 send> p [..(16000 bytes)..]
7185 MainThread message: U {"results":[{"tokens":[{"written":"\u30a2\u30c9\u30d0\u30f3\u30b9\u30c8\u30fb\u30e1\u30c7\u30a3\u30a2"},{"written":"\u306f"},{"written":"\u3001"},{"written":"\u4eba"},{"written":"\u3068"},{"written":"\u6a5f\u68b0"},{"written":"\u3068"},{"written":"\u306e"},{"written":"\u81ea\u7136"},{"written":"\u306a"},{"written":"\u30b3\u30df\u30e5\u30cb\u30b1\u30fc\u30b7\u30e7\u30f3"},{"written":"\u3092"},{"written":"\u5b9f\u73fe"},{"written":"\u3057"},{"written":"\u3001"},{"written":"..."}],"text":"\u30a2\u30c9\u30d0\u30f3\u30b9\u30c8\u30fb\u30e1\u30c7\u30a3\u30a2\u306f\u3001\u4eba\u3068\u6a5f\u68b0\u3068\u306e\u81ea\u7136\u306a\u30b3\u30df\u30e5\u30cb\u30b1\u30fc\u30b7\u30e7\u30f3\u3092\u5b9f\u73fe\u3057\u3001..."}],"text":"\u30a2\u30c9\u30d0\u30f3\u30b9\u30c8\u30fb\u30e1\u30c7\u30a3\u30a2\u306f\u3001\u4eba\u3068\u6a5f\u68b0\u3068\u306e\u81ea\u7136\u306a\u30b3\u30df\u30e5\u30cb\u30b1\u30fc\u30b7\u30e7\u30f3\u3092\u5b9f\u73fe\u3057\u3001..."}
7662 Thread-1 send> p [..(16000 bytes)..]
8164 Thread-1 send> p [..(16000 bytes)..]
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8668 Thread-1 send> p [..(16000 bytes)..]
9171 Thread-1 send> p [..(16000 bytes)..]
9188 MainThread message: E 8800
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9495 MainThread message: G
9672 Thread-1 send> p [..(2980 bytes)..]
10174 Thread-1 send> e
10225 MainThread message: e
10225 MainThread close>
会话的维持
在实时发送音频的情况下，如果600秒内未检测到音频(持续发送无声音频)，服务器将断开会话。此时，您将收到如下的p命令响应数据包：
p can't feed audio data to recognizer server
此外，如果60秒内没有通信，服务器端将关闭会话。此时，您将收到如下的e命令响应数据包：
e timeout occurred while recognizing audio data from client
收到这些响应后，请重新建立连接，然后再次发送音频。
客户端程序的状态转换
客户端程序的状态根据命令的发送和响应按以下方式转换。
其他文档
- 关于命令和响应的序列以及响应的详细信息，请参阅流式响应。
- API参考，请参阅WebSocket接口。
- 我们提供了一个客户端库(
Wrp)，它将WebSocket接口使用时的通信处理和步骤类库化，只需实现语音识别应用程序所需的接口，就可以轻松创建语音识别应用程序。首先请参阅实时语音识别库Wrp的使用方法。