add dataset

egs/ljspeech/TTS/matcha/train.py

@@ -7,20 +7,46 @@ import torch
 
 from icefall.utils import AttributeDict
 from matcha.models.matcha_tts import MatchaTTS
+from matcha.data.text_mel_datamodule import TextMelDataModule
 
 
-def get_model(params):
+def _get_data_params() -> AttributeDict:
-    m = MatchaTTS(**params.model)
+    params = AttributeDict(
-    return m
+        {
+            "name": "ljspeech",
+            "train_filelist_path": "./filelists/ljs_audio_text_train_filelist.txt",
+            "valid_filelist_path": "./filelists/ljs_audio_text_val_filelist.txt",
+            "batch_size": 32,
+            "num_workers": 3,
+            "pin_memory": False,
+            "cleaners": ["english_cleaners2"],
+            "add_blank": True,
+            "n_spks": 1,
+            "n_fft": 1024,
+            "n_feats": 80,
+            "sample_rate": 22050,
+            "hop_length": 256,
+            "win_length": 1024,
+            "f_min": 0,
+            "f_max": 8000,
+            "seed": 1234,
+            "load_durations": False,
+            "data_statistics": AttributeDict(
+                {
+                    "mel_mean": -5.517028331756592,
+                    "mel_std": 2.0643954277038574,
+                }
+            ),
+        }
+    )
+    return params
 
 
-def main():
+def _get_model_params() -> AttributeDict:
     n_feats = 80
     filter_channels_dp = 256
     encoder_params_p_dropout = 0.1
     params = AttributeDict(
-        {
-            "model": AttributeDict(
         {
             "n_vocab": 178,
             "n_spks": 1,  # for ljspeech.
@@ -82,8 +108,43 @@ def main():
             ),
         }
     )
+
+    return params
+
+
+def get_params():
+    params = AttributeDict(
+        {
+            "model": _get_model_params(),
+            "data": _get_data_params(),
         }
     )
+    return params
+
+
+def get_model(params):
+    m = MatchaTTS(**params.model)
+    return m
+
+
+def main():
+    params = get_params()
+
+    data_module = TextMelDataModule(hparams=params.data)
+    if False:
+        for b in data_module.train_dataloader():
+            assert isinstance(b, dict)
+            # b.keys()
+            # ['x', 'x_lengths', 'y', 'y_lengths', 'spks', 'filepaths', 'x_texts', 'durations']
+            # x: [batch_size, 289], torch.int64
+            # x_lengths: [batch_size], torch.int64
+            # y: [batch_size, n_feats, num_frames], torch.float32
+            # y_lengths: [batch_size], torch.int64
+            # spks: None
+            # filepaths: list, (batch_size,)
+            # x_texts: list, (batch_size,)
+            # durations: None
+
     m = get_model(params)
     print(m)
 

egs/ljspeech/TTS/matcha/utils/utils.py

@@ -6,19 +6,17 @@ from math import ceil
 from pathlib import Path
 from typing import Any, Callable, Dict, Tuple
 
-import gdown
 import matplotlib.pyplot as plt
 import numpy as np
 import torch
-import wget
+#  from omegaconf import DictConfig
-from omegaconf import DictConfig
 
-from matcha.utils import pylogger, rich_utils
+#  from matcha.utils import pylogger, rich_utils
 
-log = pylogger.get_pylogger(__name__)
+#  log = pylogger.get_pylogger(__name__)
 
 
-def extras(cfg: DictConfig) -> None:
+def extras(cfg: 'DictConfig') -> None:
     """Applies optional utilities before the task is started.
 
     Utilities:
@@ -207,6 +205,8 @@ def get_user_data_dir(appname="matcha_tts"):
 
 
 def assert_model_downloaded(checkpoint_path, url, use_wget=True):
+    import gdown
+    import wget
     if Path(checkpoint_path).exists():
         log.debug(f"[+] Model already present at {checkpoint_path}!")
         print(f"[+] Model already present at {checkpoint_path}!")