Support batch input in cls task.

demos/audio_tagging/README.md

@@ -65,7 +65,7 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/cat.wav https://paddlespe
       config=None,  # Set `config` and `ckpt_path` to None to use pretrained model.
       label_file=None,
       ckpt_path=None,
-      audio_file='./cat.wav',
+      input_file='./cat.wav',
       topk=10,
       device=paddle.get_device())
   print('CLS Result: \n{}'.format(result))

demos/audio_tagging/README_cn.md

@@ -65,7 +65,7 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/cat.wav https://paddlespe
       config=None,  # Set `config` and `ckpt_path` to None to use pretrained model.
       label_file=None,
       ckpt_path=None,
-      audio_file='./cat.wav',
+      input_file='./cat.wav',
       topk=10,
       device=paddle.get_device())
   print('CLS Result: \n{}'.format(result))

paddlespeech/cli/cls/infer.py

@@ -12,7 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import argparse
+import ast
 import os
+import sys
 from typing import List
 from typing import Optional
 from typing import Union
@@ -109,6 +111,11 @@ class CLSExecutor(BaseExecutor):
             type=str,
             default=paddle.get_device(),
             help='Choose device to execute model inference.')
+        self.parser.add_argument(
+            '--job_dump_result',
+            type=ast.literal_eval,
+            default=False,
+            help='Save job result into file.')
 
     def _get_pretrained_path(self, tag: str) -> os.PathLike:
         """
@@ -214,7 +221,7 @@ class CLSExecutor(BaseExecutor):
         ret = ''
         for idx in topk_idx:
             label, score = self._label_list[idx], result[idx]
-            ret += f'{label}: {score}\n'
+            ret += f'{label} {score}\n'
         return ret
 
     def postprocess(self, topk: int) -> Union[str, os.PathLike]:
@@ -234,22 +241,36 @@ class CLSExecutor(BaseExecutor):
         label_file = parser_args.label_file
         cfg_path = parser_args.config
         ckpt_path = parser_args.ckpt_path
-        audio_file = parser_args.input
+        input_file = parser_args.input
         topk = parser_args.topk
         device = parser_args.device
+        job_dump_result = parser_args.job_dump_result
 
         try:
-            res = self(audio_file, model_type, cfg_path, ckpt_path, label_file,
-                       topk, device)
-            logger.info('CLS Result:\n{}'.format(res))
+            if job_dump_result:
+                assert self._is_job_input(
+                    input_file
+                ), 'Input file should be a job file(*.job) when `job_dump_result` is True.'
+                job_output_file = os.path.abspath(input_file) + '.done'
+                sys.stdout = open(job_output_file, 'w')
+
+            print(
+                self(input_file, model_type, cfg_path, ckpt_path, label_file,
+                     topk, device))
+
+            if job_dump_result:
+                logger.info(f'Results had been saved to: {job_output_file}')
+
             return True
         except Exception as e:
             logger.exception(e)
             return False
+        finally:
+            sys.stdout.close()
 
     @stats_wrapper
     def __call__(self,
-                 audio_file: os.PathLike,
+                 input_file: os.PathLike,
                  model: str='panns_cnn14',
                  config: Optional[os.PathLike]=None,
                  ckpt_path: Optional[os.PathLike]=None,
@@ -259,11 +280,24 @@ class CLSExecutor(BaseExecutor):
         """
             Python API to call an executor.
         """
-        audio_file = os.path.abspath(audio_file)
+        input_file = os.path.abspath(input_file)
         paddle.set_device(device)
         self._init_from_path(model, config, ckpt_path, label_file)
-        self.preprocess(audio_file)
-        self.infer()
-        res = self.postprocess(topk)  # Retrieve result of cls.
+
+        if self._is_job_input(input_file):  # *.job
+            job_outputs = {}
+            job_contents = self._job_preprocess(input_file)
+            for id_, file in job_contents.items():
+                try:
+                    self.preprocess(file)
+                    self.infer()
+                    job_outputs[id_] = self.postprocess(topk).strip()
+                except Exception as e:
+                    job_outputs[id_] = f'{e.__class__.__name__}: {e}'
+            res = self._job_postprecess(job_outputs)
+        else:
+            self.preprocess(input_file)
+            self.infer()
+            res = self.postprocess(topk)  # Retrieve result of cls.
 
         return res

paddlespeech/cli/executor.py

@@ -15,6 +15,7 @@ import os
 from abc import ABC
 from abc import abstractmethod
 from typing import Any
+from typing import Dict
 from typing import List
 from typing import Union
 
@@ -100,3 +101,50 @@ class BaseExecutor(ABC):
         Python API to call an executor.
         """
         pass
+
+    def _is_job_input(self, input_: Union[str, os.PathLike]) -> bool:
+        """
+        Check if current input file is a job input or not.
+
+        Args:
+            input_ (Union[str, os.PathLike]): Input file of current task.
+
+        Returns:
+            bool: return `True` for job input, `False` otherwise.
+        """
+        return os.path.isfile(input_) and input_.endswith('.job')
+
+    def _job_preprocess(self, job_input: os.PathLike) -> Dict[str, str]:
+        """
+        Read a job input file and return its contents in a dictionary.
+
+        Args:
+            job_input (os.PathLike): The job input file.
+
+        Returns:
+            Dict[str, str]: Contents of job input.
+        """
+        job_contents = {}
+        with open(job_input) as f:
+            for line in f:
+                line = line.strip()
+                if not line:
+                    continue
+                k, v = line.split(' ')
+                job_contents[k] = v
+        return job_contents
+
+    def _job_postprecess(self, job_outputs: Dict[str, str]) -> str:
+        """
+        Convert job results to string.
+
+        Args:
+            job_outputs (Dict[str, str]): A dictionary with job ids and results.
+
+        Returns:
+            str: A string object contains job outputs.
+        """
+        ret = ''
+        for k, v in job_outputs.items():
+            ret += f'{k} {v}\n'
+        return ret