Update infer.py

infer.py

@@ -1,261 +1,264 @@
-"""
-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
-    2.2：当前版本
-"""
-import torch
-import commons
-from text import cleaned_text_to_sequence, get_bert
-
-from clap_wrapper import get_clap_audio_feature, get_clap_text_feature
-from text.cleaner import clean_text
-import utils
-import numpy as np
-
-from models import SynthesizerTrn
-from text.symbols import symbols
-
-# 当前版本信息
-latest_version = "2.4"
-
-
-# def get_emo_(reference_audio, emotion, sid):
-#     emo = (
-#         torch.from_numpy(get_emo(reference_audio))
-#         if reference_audio and emotion == -1
-#         else torch.FloatTensor(
-#             np.load(f"emo_clustering/{sid}/cluster_center_{emotion}.npy")
-#         )
-#     )
-#     return emo
-
-
-def get_net_g(model_path: str, version: str, device: str, hps):
-    # 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, style_text=None, style_weight=0.7):
-    style_text = None if style_text == "" else style_text
-    # 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, style_text, style_weight)
-    del word2ph
-
-    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, phone, tone, language
-
-
-def infer(
-    text,
-    sdp_ratio,
-    noise_scale,
-    noise_scale_w,
-    length_scale,
-    sid,
-    language,
-    hps,
-    net_g,
-    device,
-    emotion,
-    reference_audio=None,
-    skip_start=False,
-    skip_end=False,
-    style_text=None,
-    style_weight=0.7,
-    text_mode="Text",
-):
-    # 2.2版本参数位置变了
-    # 2.1 参数新增 emotion reference_audio skip_start skip_end
-    version = hps.version if hasattr(hps, "version") else latest_version
-    language = "JP"
-    if isinstance(reference_audio, np.ndarray):
-        emo = get_clap_audio_feature(reference_audio, device)
-    else:
-        emo = get_clap_text_feature(emotion, device)
-    emo = torch.squeeze(emo, dim=1)
-
-    bert, phones, tones, lang_ids = get_text(
-        text,
-        language,
-        hps,
-        device,
-        style_text=style_text,
-        style_weight=style_weight,
-    )
-    if skip_start:
-        phones = phones[3:]
-        tones = tones[3:]
-        lang_ids = lang_ids[3:]
-        bert = bert[:, 3:]
-    if skip_end:
-        phones = phones[:-2]
-        tones = tones[:-2]
-        lang_ids = lang_ids[:-2]
-        bert = bert[:, :-2]
-    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)
-        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
-        emo = emo.to(device).unsqueeze(0)
-        del phones
-        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
-        print(text)
-        audio = (
-            net_g.infer(
-                x_tst,
-                x_tst_lengths,
-                speakers,
-                tones,
-                lang_ids,
-                bert,
-                emo,
-                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,
-            emo,
-        )  # , emo
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        return audio
-
-
-def infer_multilang(
-    text,
-    sdp_ratio,
-    noise_scale,
-    noise_scale_w,
-    length_scale,
-    sid,
-    language,
-    hps,
-    net_g,
-    device,
-    reference_audio=None,
-    emotion=None,
-    skip_start=False,
-    skip_end=False,
-    style_text=None,
-    style_weight=0.7,
-):
-    bert, ja_bert, en_bert, phones, tones, lang_ids = [], [], [], [], [], []
-    if isinstance(reference_audio, np.ndarray):
-        emo = get_clap_audio_feature(reference_audio, device)
-    else:
-        emo = get_clap_text_feature(emotion, device)
-    emo = torch.squeeze(emo, dim=1)
-    for idx, (txt, lang) in enumerate(zip(text, language)):
-        _skip_start = (idx != 0) or (skip_start and idx == 0)
-        _skip_end = (idx != len(language) - 1) or skip_end
-        (
-            temp_bert,
-            temp_phones,
-            temp_tones,
-            temp_lang_ids,
-        ) = get_text(
-            txt,
-            lang,
-            hps,
-            device,
-            style_text=style_text,
-            style_weight=style_weight,
-        )
-        if _skip_start:
-            temp_bert = temp_bert[:, 3:]
-            temp_phones = temp_phones[3:]
-            temp_tones = temp_tones[3:]
-            temp_lang_ids = temp_lang_ids[3:]
-        if _skip_end:
-            temp_bert = temp_bert[:, :-2]
-            temp_phones = temp_phones[:-2]
-            temp_tones = temp_tones[:-2]
-            temp_lang_ids = temp_lang_ids[:-2]
-        bert.append(temp_bert)
-        phones.append(temp_phones)
-        tones.append(temp_tones)
-        lang_ids.append(temp_lang_ids)
-    bert = torch.concatenate(bert, dim=1)
-    phones = torch.concatenate(phones, dim=0)
-    tones = torch.concatenate(tones, dim=0)
-    lang_ids = torch.concatenate(lang_ids, dim=0)
-    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)
-        emo = emo.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,
-                emo,
-                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,
-            emo,
-        )  # , emo
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        return audio
+"""
+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
+    2.2：当前版本
+"""
+import torch
+import commons
+from text import cleaned_text_to_sequence, get_bert
+
+from clap_wrapper import get_clap_audio_feature, get_clap_text_feature
+from text.cleaner import clean_text
+import utils
+import numpy as np
+
+from models import SynthesizerTrn
+from text.symbols import symbols
+
+# 当前版本信息
+latest_version = "2.4"
+
+
+# def get_emo_(reference_audio, emotion, sid):
+#     emo = (
+#         torch.from_numpy(get_emo(reference_audio))
+#         if reference_audio and emotion == -1
+#         else torch.FloatTensor(
+#             np.load(f"emo_clustering/{sid}/cluster_center_{emotion}.npy")
+#         )
+#     )
+#     return emo
+
+
+def get_net_g(model_path: str, version: str, device: str, hps):
+    # 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, style_text=None, style_weight=0.7):
+    style_text = None if style_text == "" else style_text
+    # 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, style_text, style_weight)
+    del word2ph
+
+    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, phone, tone, language
+
+
+def infer(
+    text,
+    sdp_ratio,
+    noise_scale,
+    noise_scale_w,
+    length_scale,
+    sid,
+    language,
+    hps,
+    net_g,
+    device,
+    emotion,
+    reference_audio=None,
+    skip_start=False,
+    skip_end=False,
+    style_text=None,
+    style_weight=0.7,
+    text_mode="Text",
+):
+    # 2.2版本参数位置变了
+    # 2.1 参数新增 emotion reference_audio skip_start skip_end
+    version = hps.version if hasattr(hps, "version") else latest_version
+    language = "JP"
+    if isinstance(reference_audio, np.ndarray):
+        emo = get_clap_audio_feature(reference_audio, device)
+    else:
+        emo = get_clap_text_feature(emotion, device)
+    emo = torch.squeeze(emo, dim=1)
+
+    bert, phones, tones, lang_ids = get_text(
+        text,
+        language,
+        hps,
+        device,
+        style_text=style_text,
+        style_weight=style_weight,
+    )
+    if skip_start:
+        phones = phones[3:]
+        tones = tones[3:]
+        lang_ids = lang_ids[3:]
+        bert = bert[:, 3:]
+    if skip_end:
+        phones = phones[:-2]
+        tones = tones[:-2]
+        lang_ids = lang_ids[:-2]
+        bert = bert[:, :-2]
+    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)
+        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
+        emo = emo.to(device).unsqueeze(0)
+        del phones
+
+        print([hps.data.spk2id[sid]]);
+        
+        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
+        print(text)
+        audio = (
+            net_g.infer(
+                x_tst,
+                x_tst_lengths,
+                speakers,
+                tones,
+                lang_ids,
+                bert,
+                emo,
+                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,
+            emo,
+        )  # , emo
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        return audio
+
+
+def infer_multilang(
+    text,
+    sdp_ratio,
+    noise_scale,
+    noise_scale_w,
+    length_scale,
+    sid,
+    language,
+    hps,
+    net_g,
+    device,
+    reference_audio=None,
+    emotion=None,
+    skip_start=False,
+    skip_end=False,
+    style_text=None,
+    style_weight=0.7,
+):
+    bert, ja_bert, en_bert, phones, tones, lang_ids = [], [], [], [], [], []
+    if isinstance(reference_audio, np.ndarray):
+        emo = get_clap_audio_feature(reference_audio, device)
+    else:
+        emo = get_clap_text_feature(emotion, device)
+    emo = torch.squeeze(emo, dim=1)
+    for idx, (txt, lang) in enumerate(zip(text, language)):
+        _skip_start = (idx != 0) or (skip_start and idx == 0)
+        _skip_end = (idx != len(language) - 1) or skip_end
+        (
+            temp_bert,
+            temp_phones,
+            temp_tones,
+            temp_lang_ids,
+        ) = get_text(
+            txt,
+            lang,
+            hps,
+            device,
+            style_text=style_text,
+            style_weight=style_weight,
+        )
+        if _skip_start:
+            temp_bert = temp_bert[:, 3:]
+            temp_phones = temp_phones[3:]
+            temp_tones = temp_tones[3:]
+            temp_lang_ids = temp_lang_ids[3:]
+        if _skip_end:
+            temp_bert = temp_bert[:, :-2]
+            temp_phones = temp_phones[:-2]
+            temp_tones = temp_tones[:-2]
+            temp_lang_ids = temp_lang_ids[:-2]
+        bert.append(temp_bert)
+        phones.append(temp_phones)
+        tones.append(temp_tones)
+        lang_ids.append(temp_lang_ids)
+    bert = torch.concatenate(bert, dim=1)
+    phones = torch.concatenate(phones, dim=0)
+    tones = torch.concatenate(tones, dim=0)
+    lang_ids = torch.concatenate(lang_ids, dim=0)
+    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)
+        emo = emo.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,
+                emo,
+                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,
+            emo,
+        )  # , emo
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        return audio