Auto download update, optimize dataloader num_workers (#195)

* update bert

* auto download emo

* fix typo

* fix typo

* fix bert download

* optimize code format

* remove unsued import

* fix a bug

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

bert/bert_models.json

@@ -1,6 +1,6 @@
 {
-    "deberta-v2-large-japanese": {
-        "repo_id": "ku-nlp/deberta-v2-large-japanese",
+    "deberta-v2-large-japanese-char-wwm": {
+        "repo_id": "ku-nlp/deberta-v2-large-japanese-char-wwm",
         "files": ["pytorch_model.bin"]
     },
     "chinese-roberta-wwm-ext-large": {

bert_gen.py

@@ -3,7 +3,7 @@
 import commons
 import utils
 from tqdm import tqdm
-from text import check_bert_models, cleaned_text_to_sequence, get_bert
+from text import cleaned_text_to_sequence, get_bert
 import argparse
 import torch.multiprocessing as mp
 from config import config
@@ -57,7 +57,6 @@ def process_line(line):
     args, _ = parser.parse_known_args()
     config_path = args.config
     hps = utils.get_hparams_from_file(config_path)
-    check_bert_models()
     lines = []
     with open(hps.data.training_files, encoding="utf-8") as f:
         lines.extend(f.readlines())

emo_gen.py

@@ -1,19 +1,21 @@
+import argparse
+import os
+from pathlib import Path
+
+import librosa
+import numpy as np
 import torch
 import torch.nn as nn
-from torch.utils.data import Dataset
-from torch.utils.data import DataLoader
+from torch.utils.data import DataLoader, Dataset
+from tqdm import tqdm
 from transformers import Wav2Vec2Processor
 from transformers.models.wav2vec2.modeling_wav2vec2 import (
     Wav2Vec2Model,
     Wav2Vec2PreTrainedModel,
 )
-import librosa
-import numpy as np
-import argparse
-from config import config
+
 import utils
-import os
-from tqdm import tqdm
+from config import config
 
 
 class RegressionHead(nn.Module):
@@ -78,11 +80,6 @@ def __getitem__(self, idx):
         return torch.from_numpy(processed_data)
 
 
-model_name = "./emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim"
-processor = Wav2Vec2Processor.from_pretrained(model_name)
-model = EmotionModel.from_pretrained(model_name)
-
-
 def process_func(
     x: np.ndarray,
     sampling_rate: int,
@@ -135,16 +132,12 @@ def get_emo(path):
     device = config.bert_gen_config.device
 
     model_name = "./emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim"
-    processor = (
-        Wav2Vec2Processor.from_pretrained(model_name)
-        if processor is None
-        else processor
-    )
-    model = (
-        EmotionModel.from_pretrained(model_name).to(device)
-        if model is None
-        else model.to(device)
-    )
+    REPO_ID = "audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim"
+    if not Path(model_name).joinpath("pytorch_model.bin").exists():
+        utils.download_emo_models(config.mirror, model_name, REPO_ID)
+
+    processor = Wav2Vec2Processor.from_pretrained(model_name)
+    model = EmotionModel.from_pretrained(model_name).to(device)
 
     lines = []
     with open(hps.data.training_files, encoding="utf-8") as f:

text/__init__.py

@@ -46,3 +46,6 @@ def check_bert_models():
         for k, v in models.items():
             local_path = Path("./bert").joinpath(k)
             _check_bert(v["repo_id"], v["files"], local_path)
+
+
+check_bert_models()

train_ms.py

@@ -130,7 +130,7 @@ def run():
     collate_fn = TextAudioSpeakerCollate()
     train_loader = DataLoader(
         train_dataset,
-        num_workers=config.train_ms_config.num_workers,
+        num_workers=min(config.train_ms_config.num_workers, os.cpu_count() - 1),
         shuffle=False,
         pin_memory=True,
         collate_fn=collate_fn,

utils.py

@@ -16,6 +16,24 @@
 logger = logging.getLogger(__name__)
 
 
+def download_emo_models(mirror, repo_id, model_name):
+    if mirror == "openi":
+        import openi
+
+        openi.model.download_model(
+            "Stardust_minus/Bert-VITS2",
+            repo_id.split("/")[-1],
+            "./emotional",
+        )
+    else:
+        hf_hub_download(
+            repo_id,
+            "pytorch_model.bin",
+            local_dir=model_name,
+            local_dir_use_symlinks=False,
+        )
+
+
 def download_checkpoint(
     dir_path, repo_config, token=None, regex="G_*.pth", mirror="openi"
 ):