format code

paddlespeech/s2t/frontend/speech.py

@@ -108,7 +108,12 @@ class SpeechSegment(AudioSegment):
                    token_ids)
 
     @classmethod
-    def from_pcm(cls, samples, sample_rate, transcript, tokens=None, token_ids=None):
+    def from_pcm(cls,
+                 samples,
+                 sample_rate,
+                 transcript,
+                 tokens=None,
+                 token_ids=None):
         """Create speech segment from pcm on online mode 
         Args:
             samples (numpy.ndarray): Audio samples [num_samples x num_channels].

paddlespeech/server/bin/main.py

@@ -18,8 +18,8 @@ from fastapi import FastAPI
 
 from paddlespeech.server.engine.engine_pool import init_engine_pool
 from paddlespeech.server.restful.api import setup_router as setup_http_router
-from paddlespeech.server.ws.api import setup_router as setup_ws_router
 from paddlespeech.server.utils.config import get_config
+from paddlespeech.server.ws.api import setup_router as setup_ws_router
 
 app = FastAPI(
     title="PaddleSpeech Serving API", description="Api", version="0.0.1")

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

@@ -11,29 +11,23 @@
 # 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 io
 import os
-import time
 from typing import Optional
-import pickle
-import numpy as np
-from numpy import float32
-import soundfile
 
+import numpy as np
 import paddle
+from numpy import float32
 from yacs.config import CfgNode
 
-from paddlespeech.s2t.frontend.speech import SpeechSegment
 from paddlespeech.cli.asr.infer import ASRExecutor
 from paddlespeech.cli.log import logger
 from paddlespeech.cli.utils import MODEL_HOME
 from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
+from paddlespeech.s2t.frontend.speech import SpeechSegment
 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.config import get_config
 from paddlespeech.server.utils.paddle_predictor import init_predictor
-from paddlespeech.server.utils.paddle_predictor import run_model
 
 __all__ = ['ASREngine']
 
@@ -141,10 +135,10 @@ class ASRServerExecutor(ASRExecutor):
             reduction=True,  # sum
             batch_average=True,  # sum / batch_size
             grad_norm_type=self.config.get('ctc_grad_norm_type', None))
-        
+
         # init decoder
         cfg = self.config.decode
-        decode_batch_size = 1      # for online
+        decode_batch_size = 1  # for online
         self.decoder.init_decoder(
             decode_batch_size, self.text_feature.vocab_list,
             cfg.decoding_method, cfg.lang_model_path, cfg.alpha, cfg.beta,
@@ -182,10 +176,11 @@ class ASRServerExecutor(ASRExecutor):
         Returns:
             [type]: [description]
         """
-        if "deepspeech2online" in model_type :
+        if "deepspeech2online" in model_type:
             input_names = self.am_predictor.get_input_names()
             audio_handle = self.am_predictor.get_input_handle(input_names[0])
-            audio_len_handle = self.am_predictor.get_input_handle(input_names[1])
+            audio_len_handle = self.am_predictor.get_input_handle(
+                input_names[1])
             h_box_handle = self.am_predictor.get_input_handle(input_names[2])
             c_box_handle = self.am_predictor.get_input_handle(input_names[3])
 
@@ -203,7 +198,8 @@ class ASRServerExecutor(ASRExecutor):
 
             output_names = self.am_predictor.get_output_names()
             output_handle = self.am_predictor.get_output_handle(output_names[0])
-            output_lens_handle = self.am_predictor.get_output_handle(output_names[1])
+            output_lens_handle = self.am_predictor.get_output_handle(
+                output_names[1])
             output_state_h_handle = self.am_predictor.get_output_handle(
                 output_names[2])
             output_state_c_handle = self.am_predictor.get_output_handle(
@@ -341,7 +337,8 @@ class ASREngine(BaseEngine):
             x_chunk_lens (numpy.array): shape[B]
             decoder_chunk_size(int)
         """
-        self.output = self.executor.decode_one_chunk(x_chunk, x_chunk_lens, self.config.model_type)
+        self.output = self.executor.decode_one_chunk(x_chunk, x_chunk_lens,
+                                                     self.config.model_type)
 
     def postprocess(self):
         """postprocess

paddlespeech/server/utils/buffer.py

@@ -43,10 +43,10 @@ class ChunkBuffer(object):
         audio = self.remained_audio + audio
         self.remained_audio = b''
 
-        n = int(self.sample_rate *
-                (self.frame_duration_ms / 1000.0) * self.sample_width)
-        shift_n = int(self.sample_rate *
-                      (self.shift_ms / 1000.0) * self.sample_width)
+        n = int(self.sample_rate * (self.frame_duration_ms / 1000.0) *
+                self.sample_width)
+        shift_n = int(self.sample_rate * (self.shift_ms / 1000.0) *
+                      self.sample_width)
         offset = 0
         timestamp = 0.0
         duration = (float(n) / self.sample_rate) / self.sample_width