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Merge pull request #2460 from L-jasmine/export_v2pro
优化 torch_script 导出模型
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@ -103,7 +103,7 @@ def logits_to_probs(
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@torch.jit.script
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def multinomial_sample_one_no_sync(probs_sort):
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# Does multinomial sampling without a cuda synchronization
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q = torch.randn_like(probs_sort)
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q = torch.empty_like(probs_sort).exponential_(1.0)
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return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int)
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@ -114,7 +114,7 @@ def sample(
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temperature: float = 1.0,
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top_k: Optional[int] = None,
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top_p: Optional[int] = None,
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repetition_penalty: float = 1.0,
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repetition_penalty: float = 1.35,
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):
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probs = logits_to_probs(
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logits=logits,
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@ -129,8 +129,8 @@ def sample(
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@torch.jit.script
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def spectrogram_torch(y: Tensor, n_fft: int, sampling_rate: int, hop_size: int, win_size: int, center: bool = False):
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hann_window = torch.hann_window(win_size, device=y.device, dtype=y.dtype)
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def spectrogram_torch(hann_window:Tensor, y: Tensor, n_fft: int, sampling_rate: int, hop_size: int, win_size: int, center: bool = False):
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# hann_window = torch.hann_window(win_size, device=y.device, dtype=y.dtype)
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y = torch.nn.functional.pad(
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y.unsqueeze(1),
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(int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
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@ -309,8 +309,9 @@ class T2SBlock:
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attn = F.scaled_dot_product_attention(q, k, v)
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attn = attn.permute(2, 0, 1, 3).reshape(batch_size * q_len, self.hidden_dim)
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attn = attn.view(q_len, batch_size, self.hidden_dim).transpose(1, 0)
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# attn = attn.permute(2, 0, 1, 3).reshape(batch_size * q_len, self.hidden_dim)
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# attn = attn.view(q_len, batch_size, self.hidden_dim).transpose(1, 0)
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attn = attn.transpose(1, 2).reshape(batch_size, q_len, -1)
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attn = F.linear(attn, self.out_w, self.out_b)
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x = x + attn
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@ -348,7 +349,7 @@ class T2STransformer:
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class VitsModel(nn.Module):
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def __init__(self, vits_path, version=None):
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def __init__(self, vits_path, version=None, is_half=True, device="cpu"):
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super().__init__()
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# dict_s2 = torch.load(vits_path,map_location="cpu")
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dict_s2 = load_sovits_new(vits_path)
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@ -373,11 +374,18 @@ class VitsModel(nn.Module):
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n_speakers=self.hps.data.n_speakers,
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**self.hps.model,
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)
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self.vq_model.eval()
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self.vq_model.load_state_dict(dict_s2["weight"], strict=False)
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self.vq_model.dec.remove_weight_norm()
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if is_half:
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self.vq_model = self.vq_model.half()
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self.vq_model = self.vq_model.to(device)
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self.vq_model.eval()
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self.hann_window = torch.hann_window(self.hps.data.win_length, device=device, dtype= torch.float16 if is_half else torch.float32)
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def forward(self, text_seq, pred_semantic, ref_audio, speed=1.0, sv_emb=None):
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refer = spectrogram_torch(
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self.hann_window,
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ref_audio,
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self.hps.data.filter_length,
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self.hps.data.sampling_rate,
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@ -667,7 +675,7 @@ def export(gpt_path, vits_path, ref_audio_path, ref_text, output_path, export_be
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ssl_content = ssl(ref_audio).to(device)
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# vits_path = "SoVITS_weights_v2/xw_e8_s216.pth"
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vits = VitsModel(vits_path).to(device)
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vits = VitsModel(vits_path,device=device,is_half=False)
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vits.eval()
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# gpt_path = "GPT_weights_v2/xw-e15.ckpt"
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@ -765,10 +773,7 @@ def export_prov2(
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sv_model = ExportERes2NetV2(sv_cn_model)
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# vits_path = "SoVITS_weights_v2/xw_e8_s216.pth"
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vits = VitsModel(vits_path, version)
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if is_half:
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vits.vq_model = vits.vq_model.half()
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vits.to(device)
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vits = VitsModel(vits_path, version,is_half=is_half,device=device)
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vits.eval()
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# gpt_path = "GPT_weights_v2/xw-e15.ckpt"
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@ -243,6 +243,7 @@ class ExportGPTSovitsHalf(torch.nn.Module):
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self.sampling_rate: int = hps.data.sampling_rate
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self.hop_length: int = hps.data.hop_length
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self.win_length: int = hps.data.win_length
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self.hann_window = torch.hann_window(self.win_length, device=device, dtype=torch.float32)
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def forward(
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self,
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@ -255,6 +256,7 @@ class ExportGPTSovitsHalf(torch.nn.Module):
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top_k,
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):
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refer = spectrogram_torch(
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self.hann_window,
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ref_audio_32k,
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self.filter_length,
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self.sampling_rate,
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@ -321,6 +323,7 @@ class ExportGPTSovitsV4Half(torch.nn.Module):
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self.sampling_rate: int = hps.data.sampling_rate
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self.hop_length: int = hps.data.hop_length
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self.win_length: int = hps.data.win_length
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self.hann_window = torch.hann_window(self.win_length, device=device, dtype=torch.float32)
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def forward(
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self,
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@ -333,6 +336,7 @@ class ExportGPTSovitsV4Half(torch.nn.Module):
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top_k,
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):
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refer = spectrogram_torch(
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self.hann_window,
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ref_audio_32k,
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self.filter_length,
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self.sampling_rate,
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@ -1149,7 +1153,7 @@ def export_2(version="v3"):
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raw_t2s = raw_t2s.half().to(device)
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t2s_m = T2SModel(raw_t2s).half().to(device)
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t2s_m.eval()
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t2s_m = torch.jit.script(t2s_m)
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t2s_m = torch.jit.script(t2s_m).to(device)
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t2s_m.eval()
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# t2s_m.top_k = 15
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logger.info("t2s_m ok")
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@ -1251,6 +1255,6 @@ def test_export_gpt_sovits_v3():
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with torch.no_grad():
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export_1("onnx/ad/ref.wav","你这老坏蛋，我找了你这么久，真没想到在这里找到你。他说。","v4")
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# export_2("v4")
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# export_1("onnx/ad/ref.wav","你这老坏蛋，我找了你这么久，真没想到在这里找到你。他说。","v4")
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export_2("v4")
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# test_export_gpt_sovits_v3()
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