refacotring

egs/ljspeech/TTS/local/compute_fbank_ljspeech.py

@@ -38,7 +38,7 @@ from lhotse.audio import RecordingSet
 from lhotse.features.base import FeatureExtractor, register_extractor
 from lhotse.supervision import SupervisionSet
 from lhotse.utils import Seconds, compute_num_frames
-from matcha.utils.audio import mel_spectrogram
+from matcha.audio import mel_spectrogram
 
 from icefall.utils import get_executor
 

egs/ljspeech/TTS/matcha/export_onnx.py

@@ -73,8 +73,6 @@ class ModelWrapper(torch.nn.Module):
         )["mel"]
         # mel: (batch_size, feat_dim, num_frames)
 
-        #  audio = self.vocoder(mel).clamp(-1, 1).squeeze(1)
-
         return mel
 
 

egs/ljspeech/TTS/matcha/inference.py

@@ -28,7 +28,7 @@ def get_parser():
     parser.add_argument(
         "--epoch",
         type=int,
-        default=2810,
+        default=4000,
         help="""It specifies the checkpoint to use for decoding.
         Note: Epoch counts from 1.
         """,
@@ -44,6 +44,13 @@ def get_parser():
         """,
     )
 
+    parser.add_argument(
+        "--vocoder",
+        type=Path,
+        default="./generator_v1",
+        help="Path to the vocoder",
+    )
+
     parser.add_argument(
         "--tokens",
         type=Path,
@@ -61,6 +68,7 @@ def get_parser():
 
 
 def load_vocoder(checkpoint_path):
+    checkpoint_path = str(checkpoint_path)
     if checkpoint_path.endswith("v1"):
         h = AttributeDict(v1)
     elif checkpoint_path.endswith("v2"):
@@ -142,10 +150,17 @@ def main():
 
     logging.info("About to create model")
     model = get_model(params)
+
+    if not Path(f"{params.exp_dir}/epoch-{params.epoch}.pt").is_file():
+        raise ValueError("{params.exp_dir}/epoch-{params.epoch}.pt does not exist")
+
     load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
     model.eval()
 
-    vocoder = load_vocoder("/star-fj/fangjun/open-source/Matcha-TTS/generator_v1")
+    if not Path(params.vocoder).is_file():
+        raise ValueError(f"{params.vocoder} does not exist")
+
+    vocoder = load_vocoder(params.vocoder)
     denoiser = Denoiser(vocoder, mode="zeros")
 
     texts = [

icefall/checkpoint.py

@@ -90,7 +90,7 @@ def save_checkpoint(
 
     if params:
         for k, v in params.items():
-            assert k not in checkpoint
+            assert k not in checkpoint, k
             checkpoint[k] = v
 
     torch.save(checkpoint, filename)