From a17f29ce54e247eb622b44e6fe0c3589ae8d1ac9 Mon Sep 17 00:00:00 2001 From: "jiaqi.sjq" Date: Mon, 18 Jul 2022 17:50:59 +0800 Subject: [PATCH] [to #42322933] Update tts task inputs Refactor tts task inputs Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9412937 --- .../pipelines/audio/text_to_speech_pipeline.py | 17 +++++++---------- requirements/audio.txt | 2 +- tests/pipelines/test_text_to_speech.py | 6 ++---- 3 files changed, 10 insertions(+), 15 deletions(-) diff --git a/modelscope/pipelines/audio/text_to_speech_pipeline.py b/modelscope/pipelines/audio/text_to_speech_pipeline.py index d8aefd57..f9e7d80a 100644 --- a/modelscope/pipelines/audio/text_to_speech_pipeline.py +++ b/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]: diff --git a/requirements/audio.txt b/requirements/audio.txt index 255b478a..9077f4c4 100644 --- a/requirements/audio.txt +++ b/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 diff --git a/tests/pipelines/test_text_to_speech.py b/tests/pipelines/test_text_to_speech.py index d28d85d9..552098c0 100644 --- a/tests/pipelines/test_text_to_speech.py +++ b/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)