[to #42322933] audio pipelines accept url as input

modelscope/pipelines/audio/ans_pipeline.py

@@ -6,6 +6,7 @@ import numpy as np
 import soundfile as sf
 import torch
 
+from modelscope.fileio import File
 from modelscope.metainfo import Pipelines
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Input, Pipeline
@@ -34,11 +35,12 @@ class ANSPipeline(Pipeline):
         super().__init__(model=model, **kwargs)
         self.model.eval()
 
-    def preprocess(self, inputs: Input) -> Dict[str, Any]:
+    def preprocess(self, inputs: Input, **preprocess_params) -> Dict[str, Any]:
         if isinstance(inputs, bytes):
             data1, fs = sf.read(io.BytesIO(inputs))
         elif isinstance(inputs, str):
-            data1, fs = sf.read(inputs)
+            file_bytes = File.read(inputs)
+            data1, fs = sf.read(io.BytesIO(file_bytes))
         else:
             raise TypeError(f'Unsupported type {type(inputs)}.')
         if len(data1.shape) > 1:
@@ -50,7 +52,8 @@ class ANSPipeline(Pipeline):
         inputs = np.reshape(data, [1, data.shape[0]])
         return {'ndarray': inputs, 'nsamples': data.shape[0]}
 
-    def forward(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
+    def forward(self, inputs: Dict[str, Any],
+                **forward_params) -> Dict[str, Any]:
         ndarray = inputs['ndarray']
         if isinstance(ndarray, torch.Tensor):
             ndarray = ndarray.cpu().numpy()

modelscope/pipelines/audio/kws_farfield_pipeline.py

@@ -2,6 +2,7 @@ import io
 import wave
 from typing import Any, Dict
 
+from modelscope.fileio import File
 from modelscope.metainfo import Pipelines
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Input, Pipeline
@@ -39,6 +40,8 @@ class KWSFarfieldPipeline(Pipeline):
     def preprocess(self, inputs: Input, **preprocess_params) -> Dict[str, Any]:
         if isinstance(inputs, bytes):
             return dict(input_file=inputs)
+        elif isinstance(inputs, str):
+            return dict(input_file=inputs)
         elif isinstance(inputs, Dict):
             return inputs
         else:
@@ -47,6 +50,8 @@ class KWSFarfieldPipeline(Pipeline):
     def forward(self, inputs: Dict[str, Any],
                 **forward_params) -> Dict[str, Any]:
         input_file = inputs['input_file']
+        if isinstance(input_file, str):
+            input_file = File.read(input_file)
         if isinstance(input_file, bytes):
             input_file = io.BytesIO(input_file)
         self.frame_count = 0

modelscope/preprocessors/audio.py

@@ -6,9 +6,10 @@ import numpy as np
 import scipy.io.wavfile as wav
 import torch
 
+from modelscope.fileio import File
+from modelscope.preprocessors import Preprocessor
+from modelscope.preprocessors.builder import PREPROCESSORS
 from modelscope.utils.constant import Fields
-from . import Preprocessor
-from .builder import PREPROCESSORS
 
 
 def load_kaldi_feature_transform(filename):
@@ -201,7 +202,8 @@ class LinearAECAndFbank(Preprocessor):
         if isinstance(inputs, bytes):
             inputs = io.BytesIO(inputs)
         elif isinstance(inputs, str):
-            pass
+            file_bytes = File.read(inputs)
+            inputs = io.BytesIO(file_bytes)
         else:
             raise TypeError(f'Unsupported input type: {type(inputs)}.')
         sample_rate, data = wav.read(inputs)

tests/pipelines/test_key_word_spotting_farfield.py

@@ -6,6 +6,9 @@ from modelscope.utils.constant import Tasks
 from modelscope.utils.test_utils import test_level
 
 TEST_SPEECH_FILE = 'data/test/audios/3ch_nihaomiya.wav'
+TEST_SPEECH_URL = 'https://modelscope.cn/api/v1/models/damo/' \
+                  'speech_dfsmn_kws_char_farfield_16k_nihaomiya/repo' \
+                  '?Revision=master&FilePath=examples/3ch_nihaomiya.wav'
 
 
 class KWSFarfieldTest(unittest.TestCase):
@@ -13,7 +16,7 @@ class KWSFarfieldTest(unittest.TestCase):
     def setUp(self) -> None:
         self.model_id = 'damo/speech_dfsmn_kws_char_farfield_16k_nihaomiya'
 
-    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
     def test_normal(self):
         kws = pipeline(Tasks.keyword_spotting, model=self.model_id)
         inputs = {'input_file': os.path.join(os.getcwd(), TEST_SPEECH_FILE)}
@@ -21,6 +24,13 @@ class KWSFarfieldTest(unittest.TestCase):
         self.assertEqual(len(result['kws_list']), 5)
         print(result['kws_list'][-1])
 
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_url(self):
+        kws = pipeline(Tasks.keyword_spotting, model=self.model_id)
+        result = kws(TEST_SPEECH_URL)
+        self.assertEqual(len(result['kws_list']), 5)
+        print(result['kws_list'][-1])
+
     @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
     def test_output(self):
         kws = pipeline(Tasks.keyword_spotting, model=self.model_id)

tests/pipelines/test_speech_signal_process.py

@@ -9,8 +9,17 @@ from modelscope.utils.test_utils import test_level
 
 NEAREND_MIC_FILE = 'data/test/audios/nearend_mic.wav'
 FAREND_SPEECH_FILE = 'data/test/audios/farend_speech.wav'
+NEAREND_MIC_URL = 'https://modelscope.cn/api/v1/models/damo/' \
+                  'speech_dfsmn_aec_psm_16k/repo?Revision=master' \
+                  '&FilePath=examples/nearend_mic.wav'
+FAREND_SPEECH_URL = 'https://modelscope.cn/api/v1/models/damo/' \
+                    'speech_dfsmn_aec_psm_16k/repo?Revision=master' \
+                    '&FilePath=examples/farend_speech.wav'
 
 NOISE_SPEECH_FILE = 'data/test/audios/speech_with_noise.wav'
+NOISE_SPEECH_URL = 'https://modelscope.cn/api/v1/models/damo/' \
+                   'speech_frcrn_ans_cirm_16k/repo?Revision=master' \
+                   '&FilePath=examples/speech_with_noise.wav'
 
 
 class SpeechSignalProcessTest(unittest.TestCase, DemoCompatibilityCheck):
@@ -18,7 +27,7 @@ class SpeechSignalProcessTest(unittest.TestCase, DemoCompatibilityCheck):
     def setUp(self) -> None:
         pass
 
-    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
     def test_aec(self):
         model_id = 'damo/speech_dfsmn_aec_psm_16k'
         input = {
@@ -30,6 +39,18 @@ class SpeechSignalProcessTest(unittest.TestCase, DemoCompatibilityCheck):
         aec(input, output_path=output_path)
         print(f'Processed audio saved to {output_path}')
 
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_aec_url(self):
+        model_id = 'damo/speech_dfsmn_aec_psm_16k'
+        input = {
+            'nearend_mic': NEAREND_MIC_URL,
+            'farend_speech': FAREND_SPEECH_URL
+        }
+        aec = pipeline(Tasks.acoustic_echo_cancellation, model=model_id)
+        output_path = os.path.abspath('output.wav')
+        aec(input, output_path=output_path)
+        print(f'Processed audio saved to {output_path}')
+
     @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
     def test_aec_bytes(self):
         model_id = 'damo/speech_dfsmn_aec_psm_16k'
@@ -62,7 +83,7 @@ class SpeechSignalProcessTest(unittest.TestCase, DemoCompatibilityCheck):
         aec(inputs, output_path=output_path)
         print(f'Processed audio saved to {output_path}')
 
-    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
     def test_ans(self):
         model_id = 'damo/speech_frcrn_ans_cirm_16k'
         ans = pipeline(Tasks.acoustic_noise_suppression, model=model_id)
@@ -71,6 +92,14 @@ class SpeechSignalProcessTest(unittest.TestCase, DemoCompatibilityCheck):
             output_path=output_path)
         print(f'Processed audio saved to {output_path}')
 
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_ans_url(self):
+        model_id = 'damo/speech_frcrn_ans_cirm_16k'
+        ans = pipeline(Tasks.acoustic_noise_suppression, model=model_id)
+        output_path = os.path.abspath('output.wav')
+        ans(NOISE_SPEECH_URL, output_path=output_path)
+        print(f'Processed audio saved to {output_path}')
+
     @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
     def test_ans_bytes(self):
         model_id = 'damo/speech_frcrn_ans_cirm_16k'