merge with master

modelscope/models/audio/tts/am/sambert_hifi_16k.py

@@ -18,7 +18,7 @@ __all__ = ['SambertNetHifi16k']
 
 
 def multi_label_symbol_to_sequence(my_classes, my_symbol):
-    one_hot = MultiLabelBinarizer(my_classes)
+    one_hot = MultiLabelBinarizer(classes=my_classes)
     tokens = my_symbol.strip().split(' ')
     sequences = []
     for token in tokens:

modelscope/models/audio/tts/frontend/generic_text_to_speech_frontend.py

@@ -20,7 +20,6 @@ class GenericTtsFrontend(Model):
     def __init__(self, model_dir='.', lang_type='pinyin', *args, **kwargs):
         super().__init__(model_dir, *args, **kwargs)
         import ttsfrd
-
         frontend = ttsfrd.TtsFrontendEngine()
         zip_file = os.path.join(model_dir, 'resource.zip')
         self._res_path = os.path.join(model_dir, 'resource')

modelscope/pipelines/nlp/fill_mask_pipeline.py

@@ -39,6 +39,7 @@ class FillMaskPipeline(Pipeline):
         fill_mask_model.eval()
         super().__init__(
             model=fill_mask_model, preprocessor=preprocessor, **kwargs)
+
         self.preprocessor = preprocessor
         self.tokenizer = preprocessor.tokenizer
         self.mask_id = {'veco': 250001, 'sbert': 103}
@@ -94,6 +95,7 @@ class FillMaskPipeline(Pipeline):
         pred_strings = []
         for ids in rst_ids:  # batch
             # TODO vocab size is not stable
+
             if self.model.config.vocab_size == 21128:  # zh bert
                 pred_string = self.tokenizer.convert_ids_to_tokens(ids)
                 pred_string = ''.join(pred_string)

requirements/audio.txt

@@ -1,25 +1,25 @@
 #tts
-h5py==2.10.0
-https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/TTS/requirements/ttsfrd-0.0.1-cp36-cp36m-linux_x86_64.whl; python_version=='3.6'
-https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/TTS/requirements/ttsfrd-0.0.1-cp37-cp37m-linux_x86_64.whl; python_version=='3.7'
-https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/TTS/requirements/ttsfrd-0.0.1-cp38-cp38-linux_x86_64.whl; python_version=='3.8'
-https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/TTS/requirements/ttsfrd-0.0.1-cp39-cp39-linux_x86_64.whl; python_version=='3.9'
-https://swap.oss-cn-hangzhou.aliyuncs.com/Jiaqi%2Fmaas%2Ftts%2Frequirements%2Fpytorch_wavelets-1.3.0-py3-none-any.whl?Expires=1685688388&OSSAccessKeyId=LTAI4Ffebq4d9jTVDwiSbY4L&Signature=jcQbg5EZ%2Bdys3%2F4BRn3srrKLdIg%3D
+h5py
+https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/TTS/requirements/pytorch_wavelets-1.3.0-py3-none-any.whl
+https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/TTS/requirements/ttsfrd-0.0.2-cp36-cp36m-linux_x86_64.whl; python_version=='3.6'
+https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/TTS/requirements/ttsfrd-0.0.2-cp37-cp37m-linux_x86_64.whl; python_version=='3.7'
+https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/TTS/requirements/ttsfrd-0.0.2-cp38-cp38-linux_x86_64.whl; python_version=='3.8'
+https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/TTS/requirements/ttsfrd-0.0.2-cp39-cp39-linux_x86_64.whl; python_version=='3.9'
 inflect
-keras==2.2.4
+keras
 librosa
 lxml
 matplotlib
 nara_wpe
-numpy==1.18.*
+numpy
 protobuf>3,<=3.20
 ptflops
 PyWavelets>=1.0.0
-scikit-learn==0.23.2
+scikit-learn
 sox
 tensorboard
 tensorflow==1.15.*
-torch==1.10.*
+torch
 torchaudio
 torchvision
 tqdm

tests/pipelines/test_image_matting.py

@@ -60,7 +60,7 @@ class ImageMattingTest(unittest.TestCase):
         cv2.imwrite('result.png', result['output_png'])
         print(f'Output written to {osp.abspath("result.png")}')
 
-    @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
+    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_run_with_modelscope_dataset(self):
         dataset = PyDataset.load('beans', split='train', target='image')
         img_matting = pipeline(Tasks.image_matting, model=self.model_id)

tests/pipelines/test_text_classification.py

@@ -87,7 +87,7 @@ class SequenceClassificationTest(unittest.TestCase):
         result = text_classification(dataset)
         self.printDataset(result)
 
-    @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
+    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_run_with_modelscope_dataset(self):
         text_classification = pipeline(task=Tasks.text_classification)
         # loaded from modelscope dataset

tests/pydatasets/test_py_dataset.py

@@ -33,7 +33,7 @@ class ImgPreprocessor(Preprocessor):
 
 class PyDatasetTest(unittest.TestCase):
 
-    @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
+    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_ds_basic(self):
         ms_ds_full = PyDataset.load('squad')
         ms_ds_full_hf = hfdata.load_dataset('squad')
@@ -49,7 +49,7 @@ class PyDatasetTest(unittest.TestCase):
         print(next(iter(ms_ds_train)))
         print(next(iter(ms_image_train)))
 
-    @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
+    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     @require_torch
     def test_to_torch_dataset_text(self):
         model_id = 'damo/bert-base-sst2'
@@ -64,7 +64,7 @@ class PyDatasetTest(unittest.TestCase):
         dataloader = torch.utils.data.DataLoader(pt_dataset, batch_size=5)
         print(next(iter(dataloader)))
 
-    @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
+    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     @require_tf
     def test_to_tf_dataset_text(self):
         import tensorflow as tf