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infer.py

@@ -0,0 +1,206 @@
+"""
+版本管理、兼容推理及模型加载实现。
+版本说明：
+    1. 版本号与github的release版本号对应，使用哪个release版本训练的模型即对应其版本号
+    2. 请在模型的config.json中显示声明版本号，添加一个字段"version" : "你的版本号"
+特殊版本说明：
+    1.1.1-fix： 1.1.1版本训练的模型，但是在推理时使用dev的日语修复
+    1.1.1-dev： dev开发
+    2.0：当前版本
+"""
+import torch
+import commons
+from text import cleaned_text_to_sequence, get_bert
+from text.cleaner import clean_text
+import utils
+
+from models import SynthesizerTrn
+from text.symbols import symbols
+from oldVersion.V111.models import SynthesizerTrn as V111SynthesizerTrn
+from oldVersion.V111.text import symbols as V111symbols
+from oldVersion.V110.models import SynthesizerTrn as V110SynthesizerTrn
+from oldVersion.V110.text import symbols as V110symbols
+from oldVersion.V101.models import SynthesizerTrn as V101SynthesizerTrn
+from oldVersion.V101.text import symbols as V101symbols
+
+from oldVersion import V111, V110, V101
+
+# 当前版本信息
+latest_version = "2.0"
+
+# 版本兼容
+SynthesizerTrnMap = {
+    "1.1.1-fix": V111SynthesizerTrn,
+    "1.1.1": V111SynthesizerTrn,
+    "1.1": V110SynthesizerTrn,
+    "1.1.0": V110SynthesizerTrn,
+    "1.0.1": V101SynthesizerTrn,
+    "1.0": V101SynthesizerTrn,
+    "1.0.0": V101SynthesizerTrn,
+}
+
+symbolsMap = {
+    "1.1.1-fix": V111symbols,
+    "1.1.1": V111symbols,
+    "1.1": V110symbols,
+    "1.1.0": V110symbols,
+    "1.0.1": V101symbols,
+    "1.0": V101symbols,
+    "1.0.0": V101symbols,
+}
+
+
+def get_net_g(model_path: str, version: str, device: str, hps):
+    if version != latest_version:
+        net_g = SynthesizerTrnMap[version](
+            len(symbolsMap[version]),
+            hps.data.filter_length // 2 + 1,
+            hps.train.segment_size // hps.data.hop_length,
+            n_speakers=hps.data.n_speakers,
+            **hps.model,
+        ).to(device)
+    else:
+        # 当前版本模型 net_g
+        net_g = SynthesizerTrn(
+            len(symbols),
+            hps.data.filter_length // 2 + 1,
+            hps.train.segment_size // hps.data.hop_length,
+            n_speakers=hps.data.n_speakers,
+            **hps.model,
+        ).to(device)
+    _ = net_g.eval()
+    _ = utils.load_checkpoint(model_path, net_g, None, skip_optimizer=True)
+    return net_g
+
+
+def get_text(text, language_str, hps, device):
+    # 在此处实现当前版本的get_text
+    norm_text, phone, tone, word2ph = clean_text(text, language_str)
+    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
+
+    if hps.data.add_blank:
+        phone = commons.intersperse(phone, 0)
+        tone = commons.intersperse(tone, 0)
+        language = commons.intersperse(language, 0)
+        for i in range(len(word2ph)):
+            word2ph[i] = word2ph[i] * 2
+        word2ph[0] += 1
+    bert = get_bert(norm_text, word2ph, language_str, device)
+    del word2ph
+    assert bert.shape[-1] == len(phone), phone
+
+    if language_str == "ZH":
+        bert = bert
+        ja_bert = torch.zeros(1024, len(phone))
+        en_bert = torch.zeros(1024, len(phone))
+    elif language_str == "JP":
+        bert = torch.zeros(1024, len(phone))
+        ja_bert = bert
+        en_bert = torch.zeros(1024, len(phone))
+    elif language_str == "EN":
+        bert = torch.zeros(1024, len(phone))
+        ja_bert = torch.zeros(1024, len(phone))
+        en_bert = bert
+    else:
+        raise ValueError("language_str should be ZH, JP or EN")
+
+    assert bert.shape[-1] == len(
+        phone
+    ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
+
+    phone = torch.LongTensor(phone)
+    tone = torch.LongTensor(tone)
+    language = torch.LongTensor(language)
+    return bert, ja_bert, en_bert, phone, tone, language
+
+
+def infer(
+    text,
+    sdp_ratio,
+    noise_scale,
+    noise_scale_w,
+    length_scale,
+    sid,
+    language,
+    hps,
+    net_g,
+    device,
+):
+    # 支持中日双语版本
+    inferMap_V2 = {
+        "1.1.1-fix": V111.infer_fix,
+        "1.1.1": V111.infer,
+        "1.1": V110.infer,
+        "1.1.0": V110.infer,
+    }
+    # 仅支持中文版本
+    # 在测试中，并未发现两个版本的模型不能互相通用
+    inferMap_V1 = {
+        "1.0.1": V101.infer,
+        "1.0": V101.infer,
+        "1.0.0": V101.infer,
+    }
+    version = hps.version if hasattr(hps, "version") else latest_version
+    # 非当前版本，根据版本号选择合适的infer
+    if version != latest_version:
+        if version in inferMap_V2.keys():
+            return inferMap_V2[version](
+                text,
+                sdp_ratio,
+                noise_scale,
+                noise_scale_w,
+                length_scale,
+                sid,
+                language,
+                hps,
+                net_g,
+                device,
+            )
+        if version in inferMap_V1.keys():
+            return inferMap_V1[version](
+                text,
+                sdp_ratio,
+                noise_scale,
+                noise_scale_w,
+                length_scale,
+                sid,
+                hps,
+                net_g,
+                device,
+            )
+    # 在此处实现当前版本的推理
+    bert, ja_bert, en_bert, phones, tones, lang_ids = get_text(
+        text, language, hps, device
+    )
+    with torch.no_grad():
+        x_tst = phones.to(device).unsqueeze(0)
+        tones = tones.to(device).unsqueeze(0)
+        lang_ids = lang_ids.to(device).unsqueeze(0)
+        bert = bert.to(device).unsqueeze(0)
+        ja_bert = ja_bert.to(device).unsqueeze(0)
+        en_bert = en_bert.to(device).unsqueeze(0)
+        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
+        del phones
+        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
+        audio = (
+            net_g.infer(
+                x_tst,
+                x_tst_lengths,
+                speakers,
+                tones,
+                lang_ids,
+                bert,
+                ja_bert,
+                en_bert,
+                sdp_ratio=sdp_ratio,
+                noise_scale=noise_scale,
+                noise_scale_w=noise_scale_w,
+                length_scale=length_scale,
+            )[0][0, 0]
+            .data.cpu()
+            .float()
+            .numpy()
+        )
+        del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers
+        torch.cuda.empty_cache()
+        return audio