Add param model_name

Signed-off-by: Jael Gu <mengjia.gu@zilliz.com>

configs.py

@@ -34,3 +34,9 @@ default_params = {
     "mel_log": "log",
     "spec_norm": "l2"
 }
+
+hop25_params = default_params.copy()
+hop25_params.update(hop_size=0.25)
+
+distill_params = default_params.copy()
+distill_params.update(h=1024)

nn_fingerprint.py

@@ -30,7 +30,7 @@ from towhee.types.audio_frame import AudioFrame
 from towhee.models.nnfp import NNFp
 from towhee.models.utils.audio_preprocess import preprocess_wav, MelSpec
 
-from .configs import default_params
+from .configs import default_params, hop25_params, distill_params
 
 warnings.filterwarnings('ignore')
 log = logging.getLogger()
@@ -43,7 +43,7 @@ class NNFingerprint(NNOperator):
     """
 
     def __init__(self,
-                 params: dict = None,
+                 model_name: str = 'nnfp_default',
                  model_path: str = None,
                  framework: str = 'pytorch',
                  device: str = None
@@ -52,10 +52,14 @@ class NNFingerprint(NNOperator):
         if device is None:
             device = 'cuda' if torch.cuda.is_available() else 'cpu'
         self.device = device
-        if params is None:
+        if model_name == 'nnfp_default':
             self.params = default_params
+        elif model_name == 'nnfp_hop25':
+            self.params = hop25_params
+        elif model_name == 'nnfp_distill':
+            self.params == distill_params
         else:
-            self.params = params
+            raise ValueError('Invalid model name. Accept value from ["nnfp_default", "nnfp_hop25", "nnfp_distill"]')
 
         log.info('Loading model...')
         if model_path is None:
@@ -127,6 +131,9 @@ class NNFingerprint(NNOperator):
         if sr != self.params['sample_rate']:
             resampler = torchaudio.transforms.Resample(sr, self.params['sample_rate'], dtype=audio.dtype)
             audio = resampler(audio)
+            # import resampy
+            # audio = audio.detach().cpu().numpy()
+            # audio = resampy.resample(audio, sr, self.params['sample_rate'])
 
         wav = preprocess_wav(audio,
                              segment_size=int(self.params['sample_rate'] * self.params['segment_size']),