[server] add stream tts server

paddlespeech/server/bin/paddlespeech_server.py

@@ -23,8 +23,9 @@
 from ..util import stats_wrapper
 from paddlespeech.cli.log import logger
 from paddlespeech.server.engine.engine_pool import init_engine_pool
-from paddlespeech.server.restful.api import setup_router
+from paddlespeech.server.restful.api import setup_router as setup_http_router
 from paddlespeech.server.utils.config import get_config
+from paddlespeech.server.ws.api import setup_router as setup_ws_router
 
 __all__ = ['ServerExecutor', 'ServerStatsExecutor']
 
@@ -63,7 +64,12 @@ def init(self, config) -> bool:
         """
         # init api
         api_list = list(engine.split("_")[0] for engine in config.engine_list)
-        api_router = setup_router(api_list)
+        if config.protocol == "websocket":
+            api_router = setup_ws_router(api_list)
+        elif config.protocol == "http":
+            api_router = setup_http_router(api_list)
+        else:
+            raise Exception("unsupported protocol")
         app.include_router(api_router)
 
         if not init_engine_pool(config):

paddlespeech/server/conf/tts_online_application.yaml

@@ -0,0 +1,46 @@
+# This is the parameter configuration file for PaddleSpeech Serving.
+
+#################################################################################
+#                             SERVER SETTING                                    #
+#################################################################################
+host: 127.0.0.1
+port: 8092
+
+# The task format in the engin_list is: <speech task>_<engine type>
+# task choices = ['asr_online', 'tts_online']
+# protocol = ['websocket', 'http'] (only one can be selected).
+protocol: 'http'
+engine_list: ['tts_online']
+
+
+#################################################################################
+#                                ENGINE CONFIG                                  #
+#################################################################################
+
+################################### TTS #########################################
+################### speech task: tts; engine_type: online #######################
+tts_online: 
+    # am (acoustic model) choices=['fastspeech2_csmsc']        
+    am: 'fastspeech2_csmsc'   
+    am_config: 
+    am_ckpt: 
+    am_stat: 
+    phones_dict: 
+    tones_dict: 
+    speaker_dict: 
+    spk_id: 0
+
+    # voc (vocoder) choices=['mb_melgan_csmsc']
+    voc: 'mb_melgan_csmsc'
+    voc_config: 
+    voc_ckpt: 
+    voc_stat: 
+
+    # others
+    lang: 'zh'
+    device:  # set 'gpu:id' or 'cpu'
+    am_block: 42
+    am_pad: 12
+    voc_block: 14
+    voc_pad: 14
+

paddlespeech/server/engine/asr/online/asr_engine.py

@@ -27,6 +27,7 @@
 from paddlespeech.s2t.modules.ctc import CTCDecoder
 from paddlespeech.s2t.utils.utility import UpdateConfig
 from paddlespeech.server.engine.base_engine import BaseEngine
+from paddlespeech.server.utils.audio_process import pcm2float
 from paddlespeech.server.utils.paddle_predictor import init_predictor
 
 __all__ = ['ASREngine']
@@ -222,21 +223,6 @@ def decode_one_chunk(self, x_chunk, x_chunk_lens, model_type: str):
         else:
             raise Exception("invalid model name")
 
-    def _pcm16to32(self, audio):
-        """pcm int16 to float32
-
-        Args:
-            audio(numpy.array): numpy.int16
-
-        Returns:
-            audio(numpy.array): numpy.float32
-        """
-        if audio.dtype == np.int16:
-            audio = audio.astype("float32")
-            bits = np.iinfo(np.int16).bits
-            audio = audio / (2**(bits - 1))
-        return audio
-
     def extract_feat(self, samples, sample_rate):
         """extract feat
 
@@ -249,7 +235,7 @@ def extract_feat(self, samples, sample_rate):
             x_chunk_lens (numpy.array): shape[B]
         """
         # pcm16 -> pcm 32
-        samples = self._pcm16to32(samples)
+        samples = pcm2float(samples)
 
         # read audio
         speech_segment = SpeechSegment.from_pcm(

paddlespeech/server/engine/engine_factory.py

@@ -34,6 +34,9 @@ def get_engine(engine_name: Text, engine_type: Text):
         elif engine_name == 'tts' and engine_type == 'python':
             from paddlespeech.server.engine.tts.python.tts_engine import TTSEngine
             return TTSEngine()
+        elif engine_name == 'tts' and engine_type == 'online':
+            from paddlespeech.server.engine.tts.online.tts_engine import TTSEngine
+            return TTSEngine()
         elif engine_name == 'cls' and engine_type == 'inference':
             from paddlespeech.server.engine.cls.paddleinference.cls_engine import CLSEngine
             return CLSEngine()

paddlespeech/server/engine/tts/online/__init__.py

@@ -0,0 +1,13 @@
+# 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.

paddlespeech/server/engine/tts/online/tts_engine.py

@@ -0,0 +1,220 @@
+# Copyright (c) 2021 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 base64
+import time
+
+import numpy as np
+import paddle
+
+from paddlespeech.cli.log import logger
+from paddlespeech.cli.tts.infer import TTSExecutor
+from paddlespeech.server.engine.base_engine import BaseEngine
+from paddlespeech.server.utils.audio_process import float2pcm
+from paddlespeech.server.utils.util import get_chunks
+
+__all__ = ['TTSEngine']
+
+
+class TTSServerExecutor(TTSExecutor):
+    def __init__(self):
+        super().__init__()
+        pass
+
+    @paddle.no_grad()
+    def infer(
+            self,
+            text: str,
+            lang: str='zh',
+            am: str='fastspeech2_csmsc',
+            spk_id: int=0,
+            am_block: int=42,
+            am_pad: int=12,
+            voc_block: int=14,
+            voc_pad: int=14, ):
+        """
+        Model inference and result stored in self.output.
+        """
+        am_name = am[:am.rindex('_')]
+        am_dataset = am[am.rindex('_') + 1:]
+        get_tone_ids = False
+        merge_sentences = False
+        frontend_st = time.time()
+        if lang == 'zh':
+            input_ids = self.frontend.get_input_ids(
+                text,
+                merge_sentences=merge_sentences,
+                get_tone_ids=get_tone_ids)
+            phone_ids = input_ids["phone_ids"]
+            if get_tone_ids:
+                tone_ids = input_ids["tone_ids"]
+        elif lang == 'en':
+            input_ids = self.frontend.get_input_ids(
+                text, merge_sentences=merge_sentences)
+            phone_ids = input_ids["phone_ids"]
+        else:
+            print("lang should in {'zh', 'en'}!")
+        self.frontend_time = time.time() - frontend_st
+
+        for i in range(len(phone_ids)):
+            am_st = time.time()
+            part_phone_ids = phone_ids[i]
+            # am
+            if am_name == 'speedyspeech':
+                part_tone_ids = tone_ids[i]
+                mel = self.am_inference(part_phone_ids, part_tone_ids)
+            # fastspeech2
+            else:
+                # multi speaker
+                if am_dataset in {"aishell3", "vctk"}:
+                    mel = self.am_inference(
+                        part_phone_ids, spk_id=paddle.to_tensor(spk_id))
+                else:
+                    mel = self.am_inference(part_phone_ids)
+            am_et = time.time()
+
+            # voc streaming
+            voc_upsample = self.voc_config.n_shift
+            mel_chunks = get_chunks(mel, voc_block, voc_pad, "voc")
+            chunk_num = len(mel_chunks)
+            voc_st = time.time()
+            for i, mel_chunk in enumerate(mel_chunks):
+                sub_wav = self.voc_inference(mel_chunk)
+                front_pad = min(i * voc_block, voc_pad)
+
+                if i == 0:
+                    sub_wav = sub_wav[:voc_block * voc_upsample]
+                elif i == chunk_num - 1:
+                    sub_wav = sub_wav[front_pad * voc_upsample:]
+                else:
+                    sub_wav = sub_wav[front_pad * voc_upsample:(
+                        front_pad + voc_block) * voc_upsample]
+
+                yield sub_wav
+
+
+class TTSEngine(BaseEngine):
+    """TTS server engine
+
+    Args:
+        metaclass: Defaults to Singleton.
+    """
+
+    def __init__(self, name=None):
+        """Initialize TTS server engine
+        """
+        super(TTSEngine, self).__init__()
+
+    def init(self, config: dict) -> bool:
+        self.executor = TTSServerExecutor()
+        self.config = config
+        assert "fastspeech2_csmsc" in config.am and (
+            config.voc == "hifigan_csmsc-zh" or config.voc == "mb_melgan_csmsc"
+        ), 'Please check config, am support: fastspeech2, voc support: hifigan_csmsc-zh or mb_melgan_csmsc.'
+        try:
+            if self.config.device:
+                self.device = self.config.device
+            else:
+                self.device = paddle.get_device()
+            paddle.set_device(self.device)
+        except Exception as e:
+            logger.error(
+                "Set device failed, please check if device is already used and the parameter 'device' in the yaml file"
+            )
+            logger.error("Initialize TTS server engine Failed on device: %s." %
+                         (self.device))
+            return False
+
+        try:
+            self.executor._init_from_path(
+                am=self.config.am,
+                am_config=self.config.am_config,
+                am_ckpt=self.config.am_ckpt,
+                am_stat=self.config.am_stat,
+                phones_dict=self.config.phones_dict,
+                tones_dict=self.config.tones_dict,
+                speaker_dict=self.config.speaker_dict,
+                voc=self.config.voc,
+                voc_config=self.config.voc_config,
+                voc_ckpt=self.config.voc_ckpt,
+                voc_stat=self.config.voc_stat,
+                lang=self.config.lang)
+        except Exception as e:
+            logger.error("Failed to get model related files.")
+            logger.error("Initialize TTS server engine Failed on device: %s." %
+                         (self.device))
+            return False
+
+        self.am_block = self.config.am_block
+        self.am_pad = self.config.am_pad
+        self.voc_block = self.config.voc_block
+        self.voc_pad = self.config.voc_pad
+
+        logger.info("Initialize TTS server engine successfully on device: %s." %
+                    (self.device))
+        return True
+
+    def preprocess(self, text_bese64: str=None, text_bytes: bytes=None):
+        # Convert byte to text
+        if text_bese64:
+            text_bytes = base64.b64decode(text_bese64)  # base64 to bytes
+        text = text_bytes.decode('utf-8')  # bytes to text
+
+        return text
+
+    def run(self,
+            sentence: str,
+            spk_id: int=0,
+            speed: float=1.0,
+            volume: float=1.0,
+            sample_rate: int=0,
+            save_path: str=None):
+        """ run include inference and postprocess.
+
+        Args:
+            sentence (str): text to be synthesized
+            spk_id (int, optional): speaker id for multi-speaker speech synthesis. Defaults to 0.
+            speed (float, optional): speed. Defaults to 1.0.
+            volume (float, optional): volume. Defaults to 1.0.
+            sample_rate (int, optional): target sample rate for synthesized audio, 
+            0 means the same as the model sampling rate. Defaults to 0.
+            save_path (str, optional): The save path of the synthesized audio. 
+            None means do not save audio. Defaults to None.
+
+        Returns:
+            wav_base64: The base64 format of the synthesized audio.
+        """
+
+        lang = self.config.lang
+        wav_list = []
+
+        for wav in self.executor.infer(
+                text=sentence,
+                lang=lang,
+                am=self.config.am,
+                spk_id=spk_id,
+                am_block=self.am_block,
+                am_pad=self.am_pad,
+                voc_block=self.voc_block,
+                voc_pad=self.voc_pad):
+            # wav type: <class 'numpy.ndarray'>  float32, convert to pcm (base64)
+            wav = float2pcm(wav)  # float32 to int16
+            wav_bytes = wav.tobytes()  # to bytes
+            wav_base64 = base64.b64encode(wav_bytes).decode('utf8')  # to base64
+            wav_list.append(wav)
+
+            yield wav_base64
+
+        wav_all = np.concatenate(wav_list, axis=0)
+        logger.info("The durations of audio is: {} s".format(
+            len(wav_all) / self.executor.am_config.fs))

paddlespeech/server/restful/tts_api.py

@@ -15,6 +15,7 @@
 from typing import Union
 
 from fastapi import APIRouter
+from fastapi.responses import StreamingResponse
 
 from paddlespeech.cli.log import logger
 from paddlespeech.server.engine.engine_pool import get_engine_pool
@@ -125,3 +126,14 @@ def tts(request_body: TTSRequest):
         traceback.print_exc()
 
     return response
+
+
+@router.post("/paddlespeech/streaming/tts")
+async def stream_tts(request_body: TTSRequest):
+    text = request_body.text
+
+    engine_pool = get_engine_pool()
+    tts_engine = engine_pool['tts']
+    logger.info("Get tts engine successfully.")
+
+    return StreamingResponse(tts_engine.run(sentence=text))