[to #42322933]feat: split speech-signal-process task to subtasks

modelscope/models/audio/ans/frcrn.py

@@ -59,7 +59,8 @@ class FTB(nn.Module):
 
 
 @MODELS.register_module(
-    Tasks.speech_signal_process, module_name=Models.speech_frcrn_ans_cirm_16k)
+    Tasks.acoustic_noise_suppression,
+    module_name=Models.speech_frcrn_ans_cirm_16k)
 class FRCRNModel(TorchModel):
     r""" A decorator of FRCRN for integrating into modelscope framework """
 

modelscope/outputs.py

@@ -300,6 +300,8 @@ TASK_OUTPUTS = {
     #   "output_pcm": np.array with shape(samples,) and dtype float32
     # }
     Tasks.speech_signal_process: [OutputKeys.OUTPUT_PCM],
+    Tasks.acoustic_echo_cancellation: [OutputKeys.OUTPUT_PCM],
+    Tasks.acoustic_noise_suppression: [OutputKeys.OUTPUT_PCM],
 
     # ============ multi-modal tasks ===================
 

modelscope/pipelines/audio/ans_pipeline.py

@@ -27,7 +27,7 @@ def audio_norm(x):
 
 
 @PIPELINES.register_module(
-    Tasks.speech_signal_process,
+    Tasks.acoustic_noise_suppression,
     module_name=Pipelines.speech_frcrn_ans_cirm_16k)
 class ANSPipeline(Pipeline):
     r"""ANS (Acoustic Noise Suppression) Inference Pipeline .

modelscope/pipelines/audio/linear_aec_pipeline.py

@@ -48,7 +48,7 @@ def initialize_config(module_cfg):
 
 
 @PIPELINES.register_module(
-    Tasks.speech_signal_process,
+    Tasks.acoustic_echo_cancellation,
     module_name=Pipelines.speech_dfsmn_aec_psm_16k)
 class LinearAECPipeline(Pipeline):
     r"""AEC Inference Pipeline only support 16000 sample rate.

modelscope/utils/constant.py

@@ -77,6 +77,8 @@ class AudioTasks(object):
     auto_speech_recognition = 'auto-speech-recognition'
     text_to_speech = 'text-to-speech'
     speech_signal_process = 'speech-signal-process'
+    acoustic_echo_cancellation = 'acoustic-echo-cancellation'
+    acoustic_noise_suppression = 'acoustic-noise-suppression'
 
 
 class MultiModalTasks(object):

tests/pipelines/test_speech_signal_process.py

@@ -31,7 +31,7 @@ class SpeechSignalProcessTest(unittest.TestCase):
     def setUp(self) -> None:
         pass
 
-    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
+    @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
     def test_aec(self):
         # Download audio files
         download(NEAREND_MIC_URL, NEAREND_MIC_FILE)
@@ -42,33 +42,52 @@ class SpeechSignalProcessTest(unittest.TestCase):
             'farend_speech': FAREND_SPEECH_FILE
         }
         aec = pipeline(
-            Tasks.speech_signal_process,
+            Tasks.acoustic_echo_cancellation,
             model=model_id,
             pipeline_name=Pipelines.speech_dfsmn_aec_psm_16k)
         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() >= 2, 'skip test in current test level')
+    @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
+    def test_aec_bytes(self):
+        # Download audio files
+        download(NEAREND_MIC_URL, NEAREND_MIC_FILE)
+        download(FAREND_SPEECH_URL, FAREND_SPEECH_FILE)
+        model_id = 'damo/speech_dfsmn_aec_psm_16k'
+        input = {}
+        with open(NEAREND_MIC_FILE, 'rb') as f:
+            input['nearend_mic'] = f.read()
+        with open(FAREND_SPEECH_FILE, 'rb') as f:
+            input['farend_speech'] = f.read()
+        aec = pipeline(
+            Tasks.acoustic_echo_cancellation,
+            model=model_id,
+            pipeline_name=Pipelines.speech_dfsmn_aec_psm_16k)
+        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_ans(self):
         # Download audio files
         download(NOISE_SPEECH_URL, NOISE_SPEECH_FILE)
         model_id = 'damo/speech_frcrn_ans_cirm_16k'
         ans = pipeline(
-            Tasks.speech_signal_process,
+            Tasks.acoustic_noise_suppression,
             model=model_id,
             pipeline_name=Pipelines.speech_frcrn_ans_cirm_16k)
         output_path = os.path.abspath('output.wav')
         ans(NOISE_SPEECH_FILE, output_path=output_path)
         print(f'Processed audio saved to {output_path}')
 
-    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
+    @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
     def test_ans_bytes(self):
         # Download audio files
         download(NOISE_SPEECH_URL, NOISE_SPEECH_FILE)
         model_id = 'damo/speech_frcrn_ans_cirm_16k'
         ans = pipeline(
-            Tasks.speech_signal_process,
+            Tasks.acoustic_noise_suppression,
             model=model_id,
             pipeline_name=Pipelines.speech_frcrn_ans_cirm_16k)
         output_path = os.path.abspath('output.wav')