不同版本Onnx模型导出适配 (#221)

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onnx_modules/V200/text/japanese_bert.py

@@ -0,0 +1,58 @@
+import sys
+
+import torch
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+
+from config import config
+from .japanese import text2sep_kata
+
+LOCAL_PATH = "./bert/deberta-v2-large-japanese"
+
+tokenizer = AutoTokenizer.from_pretrained(LOCAL_PATH)
+
+models = dict()
+
+
+def get_bert_feature(text, word2ph, device=config.bert_gen_config.device):
+    sep_text, _, _ = text2sep_kata(text)
+    sep_tokens = [tokenizer.tokenize(t) for t in sep_text]
+    sep_ids = [tokenizer.convert_tokens_to_ids(t) for t in sep_tokens]
+    sep_ids = [2] + [item for sublist in sep_ids for item in sublist] + [3]
+    return get_bert_feature_with_token(sep_ids, word2ph, device)
+
+
+def get_bert_feature_with_token(tokens, word2ph, device=config.bert_gen_config.device):
+    if (
+        sys.platform == "darwin"
+        and torch.backends.mps.is_available()
+        and device == "cpu"
+    ):
+        device = "mps"
+    if not device:
+        device = "cuda"
+    if device not in models.keys():
+        models[device] = AutoModelForMaskedLM.from_pretrained(LOCAL_PATH).to(device)
+    with torch.no_grad():
+        inputs = torch.tensor(tokens).to(device).unsqueeze(0)
+        token_type_ids = torch.zeros_like(inputs).to(device)
+        attention_mask = torch.ones_like(inputs).to(device)
+        inputs = {
+            "input_ids": inputs,
+            "token_type_ids": token_type_ids,
+            "attention_mask": attention_mask,
+        }
+
+        # for i in inputs:
+        #     inputs[i] = inputs[i].to(device)
+        res = models[device](**inputs, output_hidden_states=True)
+        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
+    assert inputs["input_ids"].shape[-1] == len(word2ph)
+    word2phone = word2ph
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        repeat_feature = res[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+
+    return phone_level_feature.T