[Serving]add an serving example of tts (#384)

* add tts example

* update example

Co-authored-by: Zeyu Chen <chenzeyu01@baidu.com>

examples/audio/pp-tts/serving/streaming_pp_tts/stream_client.py

@@ -0,0 +1,130 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import queue
+import sys
+from functools import partial
+
+import numpy as np
+import tritonclient.grpc as grpcclient
+from tritonclient.utils import *
+
+FLAGS = None
+
+
+class UserData:
+    def __init__(self):
+        self._completed_requests = queue.Queue()
+
+
+# Define the callback function. Note the last two parameters should be
+# result and error. InferenceServerClient would povide the results of an
+# inference as grpcclient.InferResult in result. For successful
+# inference, error will be None, otherwise it will be an object of
+# tritonclientutils.InferenceServerException holding the error details
+def callback(user_data, result, error):
+    if error:
+        user_data._completed_requests.put(error)
+    else:
+        user_data._completed_requests.put(result)
+
+
+def async_stream_send(triton_client, values, request_id, model_name):
+
+    infer_inputs = []
+    outputs = []
+    for idx, data in enumerate(values):
+        data = np.array([data.encode('utf-8')], dtype=np.object_)
+        infer_input = grpcclient.InferInput('INPUT_0', [len(data)], "BYTES")
+        infer_input.set_data_from_numpy(data)
+        infer_inputs.append(infer_input)
+
+        outputs.append(grpcclient.InferRequestedOutput('OUTPUT_0'))
+        # Issue the asynchronous sequence inference.
+        triton_client.async_stream_infer(
+            model_name=model_name,
+            inputs=infer_inputs,
+            outputs=outputs,
+            request_id=request_id)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '-v',
+        '--verbose',
+        action="store_true",
+        required=False,
+        default=False,
+        help='Enable verbose output')
+    parser.add_argument(
+        '-u',
+        '--url',
+        type=str,
+        required=False,
+        default='localhost:8001',
+        help='Inference server URL and it gRPC port. Default is localhost:8001.')
+
+    FLAGS = parser.parse_args()
+
+    # We use custom "sequence" models which take 1 input
+    # value. The output is the accumulated value of the inputs. See
+    # src/custom/sequence.
+    model_name = "streaming_pp_tts"
+
+    values = ["哈哈哈哈"]
+
+    request_id = "0"
+
+    string_result0_list = []
+
+    user_data = UserData()
+
+    # It is advisable to use client object within with..as clause
+    # when sending streaming requests. This ensures the client
+    # is closed when the block inside with exits.
+    with grpcclient.InferenceServerClient(
+            url=FLAGS.url, verbose=FLAGS.verbose) as triton_client:
+        try:
+            # Establish stream
+            triton_client.start_stream(callback=partial(callback, user_data))
+            # Now send the inference sequences...
+            async_stream_send(triton_client, values, request_id, model_name)
+        except InferenceServerException as error:
+            print(error)
+            sys.exit(1)
+
+        # Retrieve results...
+        recv_count = 0
+        result_dict = {}
+        status = True
+        while True:
+            data_item = user_data._completed_requests.get()
+            if type(data_item) == InferenceServerException:
+                raise data_item
+            else:
+                this_id = data_item.get_response().id
+                if this_id not in result_dict.keys():
+                    result_dict[this_id] = []
+                result_dict[this_id].append((recv_count, data_item))
+                sub_wav = data_item.as_numpy('OUTPUT_0')
+                status = data_item.as_numpy('status')
+                print('sub_wav = ', sub_wav, "subwav.shape = ", sub_wav.shape)
+                print('status = ', status)
+                if status[0] == 1:
+                    break
+            recv_count += 1
+
+    print("PASS: stream_client")