[to #42322933] Update tts task inputs

Refactor tts task inputs
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9412937

modelscope/pipelines/audio/text_to_speech_pipeline.py

@@ -25,22 +25,19 @@ class TextToSpeechSambertHifiganPipeline(Pipeline):
         """
         super().__init__(model=model, **kwargs)
 
-    def forward(self, inputs: Dict[str, str]) -> Dict[str, np.ndarray]:
+    def forward(self, input: str, **forward_params) -> Dict[str, np.ndarray]:
         """synthesis text from inputs with pipeline
         Args:
-            inputs (Dict[str, str]): a dictionary that key is the name of
-            certain testcase and value is the text to synthesis.
+            input (str): text to synthesis
+            forward_params: valid param is 'voice' used to setting speaker vocie
         Returns:
-            Dict[str, np.ndarray]: a dictionary with key and value. The key
-            is the same as inputs' key which is the label of the testcase
-            and the value is the pcm audio data.
+            Dict[str, np.ndarray]: {OutputKeys.OUTPUT_PCM : np.ndarray(16bit pcm data)}
         """
-        output_wav = {}
-        for label, text in inputs.items():
-            output_wav[label] = self.model.forward(text, inputs.get('voice'))
+        output_wav = self.model.forward(input, forward_params.get('voice'))
         return {OutputKeys.OUTPUT_PCM: output_wav}
 
-    def postprocess(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
+    def postprocess(self, inputs: Dict[str, Any],
+                    **postprocess_params) -> Dict[str, Any]:
         return inputs
 
     def preprocess(self, inputs: Input, **preprocess_params) -> Dict[str, Any]:

requirements/audio.txt

@@ -10,7 +10,7 @@ nara_wpe
 numpy<=1.18
 protobuf>3,<=3.20
 ptflops
-pytorch_wavelets==1.3.0
+pytorch_wavelets
 PyWavelets>=1.0.0
 scikit-learn
 SoundFile>0.10

tests/pipelines/test_text_to_speech.py

@@ -24,7 +24,6 @@ class TextToSpeechSambertHifigan16kPipelineTest(unittest.TestCase):
 
     @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
     def test_pipeline(self):
-        single_test_case_label = 'test_case_label_0'
         text = '今天北京天气怎么样？'
         model_id = 'damo/speech_sambert-hifigan_tts_zhcn_16k'
         voice = 'zhitian_emo'
@@ -32,10 +31,9 @@ class TextToSpeechSambertHifigan16kPipelineTest(unittest.TestCase):
         sambert_hifigan_tts = pipeline(
             task=Tasks.text_to_speech, model=model_id)
         self.assertTrue(sambert_hifigan_tts is not None)
-        inputs = {single_test_case_label: text, 'voice': voice}
-        output = sambert_hifigan_tts(inputs)
+        output = sambert_hifigan_tts(input=text, voice=voice)
         self.assertIsNotNone(output[OutputKeys.OUTPUT_PCM])
-        pcm = output[OutputKeys.OUTPUT_PCM][single_test_case_label]
+        pcm = output[OutputKeys.OUTPUT_PCM]
         write('output.wav', 16000, pcm)