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GPT_SoVITS/feature_extractor/__init__.py

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+from . import cnhubert, whisper_enc
+
+content_module_map = {
+    'cnhubert': cnhubert,
+    'whisper': whisper_enc
+}

GPT_SoVITS/feature_extractor/cnhubert.py

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+import time
+
+import librosa
+import torch
+import torch.nn.functional as F
+import soundfile as sf
+import logging
+
+logging.getLogger("numba").setLevel(logging.WARNING)
+
+from transformers import (
+    Wav2Vec2FeatureExtractor,
+    HubertModel,
+    Wav2Vec2Model,
+)
+
+import utils
+import torch.nn as nn
+
+cnhubert_base_path=None
+class CNHubert(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.model = HubertModel.from_pretrained(cnhubert_base_path)
+        self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(cnhubert_base_path)
+    def forward(self, x):
+        input_values = self.feature_extractor(x, return_tensors="pt", sampling_rate=16000).input_values.to(x.device)
+        feats = self.model(input_values)["last_hidden_state"]
+        return feats
+
+# class CNHubertLarge(nn.Module):
+#     def __init__(self):
+#         super().__init__()
+#         self.model = HubertModel.from_pretrained("/data/docker/liujing04/gpt-vits/chinese-hubert-large")
+#         self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("/data/docker/liujing04/gpt-vits/chinese-hubert-large")
+#     def forward(self, x):
+#         input_values = self.feature_extractor(x, return_tensors="pt", sampling_rate=16000).input_values.to(x.device)
+#         feats = self.model(input_values)["last_hidden_state"]
+#         return feats
+#
+# class CVec(nn.Module):
+#     def __init__(self):
+#         super().__init__()
+#         self.model = HubertModel.from_pretrained("/data/docker/liujing04/vc-webui-big/hubert_base")
+#         self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("/data/docker/liujing04/vc-webui-big/hubert_base")
+#     def forward(self, x):
+#         input_values = self.feature_extractor(x, return_tensors="pt", sampling_rate=16000).input_values.to(x.device)
+#         feats = self.model(input_values)["last_hidden_state"]
+#         return feats
+#
+# class cnw2v2base(nn.Module):
+#     def __init__(self):
+#         super().__init__()
+#         self.model = Wav2Vec2Model.from_pretrained("/data/docker/liujing04/gpt-vits/chinese-wav2vec2-base")
+#         self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("/data/docker/liujing04/gpt-vits/chinese-wav2vec2-base")
+#     def forward(self, x):
+#         input_values = self.feature_extractor(x, return_tensors="pt", sampling_rate=16000).input_values.to(x.device)
+#         feats = self.model(input_values)["last_hidden_state"]
+#         return feats
+
+
+
+def get_model():
+    model = CNHubert()
+    model.eval()
+    return model
+
+# def get_large_model():
+#     model = CNHubertLarge()
+#     model.eval()
+#     return model
+#
+# def get_model_cvec():
+#     model = CVec()
+#     model.eval()
+#     return model
+#
+# def get_model_cnw2v2base():
+#     model = cnw2v2base()
+#     model.eval()
+#     return model
+
+def get_content(hmodel, wav_16k_tensor):
+    with torch.no_grad():
+        feats = hmodel(wav_16k_tensor)
+    return feats.transpose(1,2)
+
+
+if __name__ == '__main__':
+    model = get_model()
+    src_path = "/Users/Shared/原音频2.wav"
+    wav_16k_tensor = utils.load_wav_to_torch_and_resample(src_path, 16000)
+    model = model
+    wav_16k_tensor = wav_16k_tensor
+    feats = get_content(model,wav_16k_tensor)
+    print(feats.shape)
+

GPT_SoVITS/feature_extractor/whisper_enc.py

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+import torch
+
+
+def get_model():
+    import whisper
+    model = whisper.load_model("small", device='cpu')
+
+    return model.encoder
+
+
+def get_content(model=None, wav_16k_tensor=None):
+    from whisper import log_mel_spectrogram, pad_or_trim
+    dev = next(model.parameters()).device
+    mel = log_mel_spectrogram(wav_16k_tensor).to(dev)[:, :3000]
+    # if torch.cuda.is_available():
+    #     mel = mel.to(torch.float16)
+    feature_len = mel.shape[-1] // 2
+    assert  mel.shape[-1] < 3000, "输入音频过长，只允许输入30以内音频"
+    with torch.no_grad():
+        feature = model(pad_or_trim(mel, 3000).unsqueeze(0))[:1, :feature_len, :].transpose(1,2)
+    return feature
+