添加导出成 TorchScript 的脚本用于支持python以外的语言 (#1640)

* Fix onnx_export to support v2

* delete some useless code & add some args type for export torch-script

* Add export_torch_script.py

* (export_torch_script.py) 整合 vits 和 t2s 成一个 model 导出

* 恢复 `t2s_model.py` 把改动移到 `export_torch_script.py`

GPT_SoVITS/export_torch_script.py

@@ -0,0 +1,737 @@
+# modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_model.py
+# reference: https://github.com/lifeiteng/vall-e
+from typing import Optional
+from my_utils import load_audio
+from text import cleaned_text_to_sequence
+import torch
+import torchaudio
+
+from torch import IntTensor, LongTensor, Tensor, nn
+from torch.nn import functional as F
+
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+from feature_extractor import cnhubert
+
+from AR.models.t2s_lightning_module import Text2SemanticLightningModule
+from module.models_onnx import SynthesizerTrn
+
+
+
+import os
+import soundfile
+
+default_config = {
+    "embedding_dim": 512,
+    "hidden_dim": 512,
+    "num_head": 8,
+    "num_layers": 12,
+    "num_codebook": 8,
+    "p_dropout": 0.0,
+    "vocab_size": 1024 + 1,
+    "phoneme_vocab_size": 512,
+    "EOS": 1024,
+}
+
+def get_raw_t2s_model(dict_s1) -> Text2SemanticLightningModule: 
+    config = dict_s1["config"]
+    config["model"]["dropout"] = float(config["model"]["dropout"])
+    t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
+    t2s_model.load_state_dict(dict_s1["weight"])
+    t2s_model = t2s_model.eval()
+    return t2s_model
+
+@torch.jit.script
+def logits_to_probs(
+    logits,
+    previous_tokens: Optional[torch.Tensor] = None,
+    temperature: float = 1.0,
+    top_k: Optional[int] = None,
+    top_p: Optional[int] = None,
+    repetition_penalty: float = 1.0,
+):
+    # if previous_tokens is not None:
+    #     previous_tokens = previous_tokens.squeeze()
+    # print(logits.shape,previous_tokens.shape)
+    # pdb.set_trace()
+    if previous_tokens is not None and repetition_penalty != 1.0:
+        previous_tokens = previous_tokens.long()
+        score = torch.gather(logits, dim=1, index=previous_tokens)
+        score = torch.where(
+            score < 0, score * repetition_penalty, score / repetition_penalty
+        )
+        logits.scatter_(dim=1, index=previous_tokens, src=score)
+
+    if top_p is not None and top_p < 1.0:
+        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+        cum_probs = torch.cumsum(
+            torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1
+        )
+        sorted_indices_to_remove = cum_probs > top_p
+        sorted_indices_to_remove[:, 0] = False  # keep at least one option
+        indices_to_remove = sorted_indices_to_remove.scatter(
+            dim=1, index=sorted_indices, src=sorted_indices_to_remove
+        )
+        logits = logits.masked_fill(indices_to_remove, -float("Inf"))
+
+    logits = logits / max(temperature, 1e-5)
+
+    if top_k is not None:
+        v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
+        pivot = v[: , -1].unsqueeze(-1)
+        logits = torch.where(logits < pivot, -float("Inf"), logits)
+
+    probs = torch.nn.functional.softmax(logits, dim=-1)
+    return probs
+
+@torch.jit.script
+def multinomial_sample_one_no_sync(probs_sort):  
+    # Does multinomial sampling without a cuda synchronization
+    q = torch.randn_like(probs_sort)
+    return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int)
+
+@torch.jit.script
+def sample(
+    logits,
+    previous_tokens,
+    temperature: float = 1.0,
+    top_k: Optional[int] = None,
+    top_p: Optional[int] = None,
+    repetition_penalty: float = 1.0,
+):
+    probs = logits_to_probs(
+        logits=logits, previous_tokens=previous_tokens, temperature=temperature, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty
+    )
+    idx_next = multinomial_sample_one_no_sync(probs)
+    return idx_next, probs
+
+
+@torch.jit.script
+def spectrogram_torch(y:Tensor, n_fft:int, sampling_rate:int, hop_size:int, win_size:int, center:bool=False):
+    hann_window = torch.hann_window(win_size,device=y.device,dtype=y.dtype)
+    y = torch.nn.functional.pad(
+        y.unsqueeze(1),
+        (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
+        mode="reflect",
+    )
+    y = y.squeeze(1)
+    spec = torch.stft(
+        y,
+        n_fft,
+        hop_length=hop_size,
+        win_length=win_size,
+        window=hann_window,
+        center=center,
+        pad_mode="reflect",
+        normalized=False,
+        onesided=True,
+        return_complex=False,
+    )
+    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+    return spec
+
+
+class DictToAttrRecursive(dict):
+    def __init__(self, input_dict):
+        super().__init__(input_dict)
+        for key, value in input_dict.items():
+            if isinstance(value, dict):
+                value = DictToAttrRecursive(value)
+            self[key] = value
+            setattr(self, key, value)
+
+    def __getattr__(self, item):
+        try:
+            return self[item]
+        except KeyError:
+            raise AttributeError(f"Attribute {item} not found")
+
+    def __setattr__(self, key, value):
+        if isinstance(value, dict):
+            value = DictToAttrRecursive(value)
+        super(DictToAttrRecursive, self).__setitem__(key, value)
+        super().__setattr__(key, value)
+
+    def __delattr__(self, item):
+        try:
+            del self[item]
+        except KeyError:
+            raise AttributeError(f"Attribute {item} not found")
+
+@torch.jit.script
+class T2SMLP:
+    def __init__(self, w1, b1, w2, b2):
+        self.w1 = w1
+        self.b1 = b1
+        self.w2 = w2
+        self.b2 = b2
+
+    def forward(self, x):
+        x = F.relu(F.linear(x, self.w1, self.b1))
+        x = F.linear(x, self.w2, self.b2)
+        return x
+
+@torch.jit.script
+class T2SBlock:
+    def __init__(
+            self,
+            num_heads: int,
+            hidden_dim: int,
+            mlp: T2SMLP,
+            qkv_w,
+            qkv_b,
+            out_w,
+            out_b,
+            norm_w1,
+            norm_b1,
+            norm_eps1: float,
+            norm_w2,
+            norm_b2,
+            norm_eps2: float,
+    ):
+        self.num_heads = num_heads
+        self.mlp = mlp
+        self.hidden_dim: int = hidden_dim
+        self.qkv_w = qkv_w
+        self.qkv_b = qkv_b
+        self.out_w = out_w
+        self.out_b = out_b
+        self.norm_w1 = norm_w1
+        self.norm_b1 = norm_b1
+        self.norm_eps1 = norm_eps1
+        self.norm_w2 = norm_w2
+        self.norm_b2 = norm_b2
+        self.norm_eps2 = norm_eps2
+
+        self.false = torch.tensor(False, dtype=torch.bool)
+
+    @torch.jit.ignore
+    def to_mask(self, x:torch.Tensor, padding_mask:Optional[torch.Tensor]):
+        if padding_mask is None:
+            return x
+        
+        if padding_mask.dtype == torch.bool:
+            return x.masked_fill(padding_mask, 0)
+        else:
+            return x * padding_mask
+        
+    def process_prompt(self, x:torch.Tensor, attn_mask : torch.Tensor, padding_mask:Optional[torch.Tensor]=None):
+        q, k, v = F.linear(self.to_mask(x, padding_mask), self.qkv_w, self.qkv_b).chunk(3, dim=-1)
+
+        batch_size = q.shape[0]
+        q_len = q.shape[1]
+        kv_len = k.shape[1]
+        
+        q = self.to_mask(q, padding_mask)
+        k_cache = self.to_mask(k, padding_mask)
+        v_cache = self.to_mask(v, padding_mask)
+
+        q = q.view(batch_size, q_len, self.num_heads, -1).transpose(1, 2)
+        k = k_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
+        v = v_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
+
+        attn = F.scaled_dot_product_attention(q, k, v, ~attn_mask)
+
+        attn = attn.permute(2, 0, 1, 3).reshape(batch_size*q_len, self.hidden_dim)
+        attn = attn.view(q_len, batch_size, self.hidden_dim).transpose(1, 0)
+        attn = F.linear(self.to_mask(attn, padding_mask), self.out_w, self.out_b)
+
+        if padding_mask is not None:
+            for i in range(batch_size):
+                # mask = padding_mask[i,:,0]
+                if self.false.device!= padding_mask.device:
+                    self.false = self.false.to(padding_mask.device)
+                idx = torch.where(padding_mask[i,:,0]==self.false)[0]
+                x_item = x[i,idx,:].unsqueeze(0)
+                attn_item = attn[i,idx,:].unsqueeze(0)
+                x_item = x_item + attn_item
+                x_item = F.layer_norm(
+                    x_item, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1
+                )
+                x_item = x_item + self.mlp.forward(x_item)
+                x_item = F.layer_norm(
+                    x_item,
+                    [self.hidden_dim],
+                    self.norm_w2,
+                    self.norm_b2,
+                    self.norm_eps2,
+                )
+                x[i,idx,:] = x_item.squeeze(0)
+            x = self.to_mask(x, padding_mask)
+        else:
+            x = x + attn
+            x = F.layer_norm(
+                x, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1
+            )
+            x = x + self.mlp.forward(x)
+            x = F.layer_norm(
+                x,
+                [self.hidden_dim],
+                self.norm_w2,
+                self.norm_b2,
+                self.norm_eps2,
+            )
+        return x, k_cache, v_cache
+    
+    def decode_next_token(self, x:torch.Tensor, k_cache:torch.Tensor, v_cache:torch.Tensor):
+        q, k, v = F.linear(x, self.qkv_w, self.qkv_b).chunk(3, dim=-1)
+
+        k_cache = torch.cat([k_cache, k], dim=1)
+        v_cache = torch.cat([v_cache, v], dim=1)
+        
+        batch_size = q.shape[0]
+        q_len = q.shape[1]
+        kv_len = k_cache.shape[1]
+
+        q = q.view(batch_size, q_len, self.num_heads, -1).transpose(1, 2)
+        k = k_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
+        v = v_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
+
+        attn = F.scaled_dot_product_attention(q, k, v)
+
+        attn = attn.permute(2, 0, 1, 3).reshape(batch_size*q_len, self.hidden_dim)
+        attn = attn.view(q_len, batch_size, self.hidden_dim).transpose(1, 0)
+        attn = F.linear(attn, self.out_w, self.out_b)
+
+        x = x + attn
+        x = F.layer_norm(
+            x, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1
+        )
+        x = x + self.mlp.forward(x)
+        x = F.layer_norm(
+            x,
+            [self.hidden_dim],
+            self.norm_w2,
+            self.norm_b2,
+            self.norm_eps2,
+        )
+        return x, k_cache, v_cache
+
+@torch.jit.script
+class T2STransformer:
+    def __init__(self, num_blocks : int, blocks: list[T2SBlock]):
+        self.num_blocks : int = num_blocks
+        self.blocks = blocks
+
+    def process_prompt(
+        self, x:torch.Tensor, attn_mask : torch.Tensor,padding_mask : Optional[torch.Tensor]=None):
+        k_cache : list[torch.Tensor] = []
+        v_cache : list[torch.Tensor] = []
+        for i in range(self.num_blocks):
+            x, k_cache_, v_cache_ = self.blocks[i].process_prompt(x, attn_mask, padding_mask)
+            k_cache.append(k_cache_)
+            v_cache.append(v_cache_)
+        return x, k_cache, v_cache
+
+    def decode_next_token(
+        self, x:torch.Tensor, 
+        k_cache: list[torch.Tensor], 
+        v_cache: list[torch.Tensor]):
+        for i in range(self.num_blocks):
+            x, k_cache[i], v_cache[i] = self.blocks[i].decode_next_token(x, k_cache[i], v_cache[i])
+        return x, k_cache, v_cache
+       
+class VitsModel(nn.Module):
+    def __init__(self, vits_path):
+        super().__init__()
+        dict_s2 = torch.load(vits_path,map_location="cpu")
+        self.hps = dict_s2["config"]
+        if dict_s2['weight']['enc_p.text_embedding.weight'].shape[0] == 322:
+            self.hps["model"]["version"] = "v1"
+        else:
+            self.hps["model"]["version"] = "v2"
+        
+        self.hps = DictToAttrRecursive(self.hps)
+        self.hps.model.semantic_frame_rate = "25hz"
+        self.vq_model = SynthesizerTrn(
+            self.hps.data.filter_length // 2 + 1,
+            self.hps.train.segment_size // self.hps.data.hop_length,
+            n_speakers=self.hps.data.n_speakers,
+            **self.hps.model
+        )
+        self.vq_model.eval()
+        self.vq_model.load_state_dict(dict_s2["weight"], strict=False)
+
+    def forward(self, text_seq, pred_semantic, ref_audio):
+        refer = spectrogram_torch(
+            ref_audio,
+            self.hps.data.filter_length,
+            self.hps.data.sampling_rate,
+            self.hps.data.hop_length,
+            self.hps.data.win_length,
+            center=False
+        )
+        return self.vq_model(pred_semantic, text_seq, refer)[0, 0]
+
+class T2SModel(nn.Module):
+    def __init__(self,raw_t2s:Text2SemanticLightningModule):
+        super(T2SModel, self).__init__()
+        self.model_dim = raw_t2s.model.model_dim
+        self.embedding_dim = raw_t2s.model.embedding_dim
+        self.num_head = raw_t2s.model.num_head
+        self.num_layers = raw_t2s.model.num_layers
+        self.vocab_size = raw_t2s.model.vocab_size
+        self.phoneme_vocab_size = raw_t2s.model.phoneme_vocab_size
+        # self.p_dropout = float(raw_t2s.model.p_dropout)
+        self.EOS:int = int(raw_t2s.model.EOS)
+        self.norm_first = raw_t2s.model.norm_first
+        assert self.EOS == self.vocab_size - 1
+        self.hz = 50
+
+        self.bert_proj = raw_t2s.model.bert_proj
+        self.ar_text_embedding = raw_t2s.model.ar_text_embedding
+        self.ar_text_position = raw_t2s.model.ar_text_position
+        self.ar_audio_embedding = raw_t2s.model.ar_audio_embedding
+        self.ar_audio_position = raw_t2s.model.ar_audio_position
+        
+        # self.t2s_transformer = T2STransformer(self.num_layers, blocks)
+        # self.t2s_transformer = raw_t2s.model.t2s_transformer
+
+        blocks = []
+        h = raw_t2s.model.h
+
+        for i in range(self.num_layers):
+            layer = h.layers[i]
+            t2smlp = T2SMLP(
+                layer.linear1.weight,
+                layer.linear1.bias,
+                layer.linear2.weight,
+                layer.linear2.bias
+            )
+
+            block = T2SBlock(
+                self.num_head,
+                self.model_dim,
+                t2smlp,
+                layer.self_attn.in_proj_weight,
+                layer.self_attn.in_proj_bias,
+                layer.self_attn.out_proj.weight,
+                layer.self_attn.out_proj.bias,
+                layer.norm1.weight,
+                layer.norm1.bias,
+                layer.norm1.eps,
+                layer.norm2.weight,
+                layer.norm2.bias,
+                layer.norm2.eps
+            )
+
+            blocks.append(block)
+        
+        self.t2s_transformer = T2STransformer(self.num_layers, blocks)
+
+        # self.ar_predict_layer = nn.Linear(self.model_dim, self.vocab_size, bias=False)
+        self.ar_predict_layer = raw_t2s.model.ar_predict_layer
+        # self.loss_fct = nn.CrossEntropyLoss(reduction="sum")
+        self.max_sec = raw_t2s.config["data"]["max_sec"]
+        self.top_k = int(raw_t2s.config["inference"]["top_k"])
+        self.early_stop_num = torch.LongTensor([self.hz * self.max_sec])
+    
+    def forward(self,prompts:LongTensor, ref_seq:LongTensor, text_seq:LongTensor, ref_bert:torch.Tensor, text_bert:torch.Tensor):
+        bert = torch.cat([ref_bert.T, text_bert.T], 1)
+        all_phoneme_ids = torch.cat([ref_seq, text_seq], 1)
+        bert = bert.unsqueeze(0)
+
+        x = self.ar_text_embedding(all_phoneme_ids)
+        x = x + self.bert_proj(bert.transpose(1, 2))
+        x:torch.Tensor = self.ar_text_position(x)
+
+        early_stop_num = self.early_stop_num
+
+
+        #[1,N,512] [1,N]
+        # y, k, v, y_emb, x_example = self.first_stage_decoder(x, prompts)
+        y = prompts
+        # x_example = x[:,:,0] * 0.0
+
+        x_len = x.shape[1]
+        x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool)
+
+        y_emb = self.ar_audio_embedding(y)
+        y_len = y_emb.shape[1]
+        prefix_len = y.shape[1]
+        y_pos = self.ar_audio_position(y_emb)
+        xy_pos = torch.concat([x, y_pos], dim=1)
+
+        bsz = x.shape[0]
+        src_len = x_len + y_len
+        x_attn_mask_pad = F.pad(
+            x_attn_mask,
+            (0, y_len),  ###xx的纯0扩展到xx纯0+xy纯1，(x,x+y)
+            value=True,
+        )
+        y_attn_mask = F.pad(  ###yy的右上1扩展到左边xy的0,(y,x+y)
+            torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
+            (x_len, 0),
+            value=False,
+        )
+        xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0)\
+                                                .unsqueeze(0)\
+                                                .expand(bsz*self.num_head, -1, -1)\
+                                                .view(bsz, self.num_head, src_len, src_len)\
+                                                .to(device=x.device, dtype=torch.bool)
+        
+        idx = 0
+        
+        xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, xy_attn_mask, None)
+
+        logits = self.ar_predict_layer(xy_dec[:, -1])
+        logits = logits[:, :-1]
+        samples = sample(logits, y, top_k=self.top_k, top_p=1, repetition_penalty=1.35, temperature=1.0)[0]
+        y = torch.concat([y, samples], dim=1)
+        y_emb = self.ar_audio_embedding(y[:, -1:])
+        xy_pos = y_emb * self.ar_audio_position.x_scale + self.ar_audio_position.alpha * self.ar_audio_position.pe[:, y_len + idx].to(dtype=y_emb.dtype,device=y_emb.device)
+
+        stop = False
+        # for idx in range(1, 50):
+        for idx in range(1, 1500):
+            #[1, N] [N_layer, N, 1, 512] [N_layer, N, 1, 512] [1, N, 512] [1] [1, N, 512] [1, N]
+            # y, k, v, y_emb, logits, samples = self.stage_decoder(y, k, v, y_emb, x_example)
+            xy_dec, k_cache, v_cache = self.t2s_transformer.decode_next_token(xy_pos, k_cache, v_cache)
+            logits = self.ar_predict_layer(xy_dec[:, -1])
+
+            if(idx<11):###至少预测出10个token不然不给停止（0.4s）
+                logits = logits[:, :-1]
+            
+            samples = sample(logits, y, top_k=self.top_k, top_p=1, repetition_penalty=1.35, temperature=1.0)[0]
+
+            y = torch.concat([y, samples], dim=1)
+            
+            if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
+                stop = True
+            if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS:
+                stop = True
+            if stop:
+                if y.shape[1] == 0:
+                    y = torch.concat([y, torch.zeros_like(samples)], dim=1)
+                break
+
+            y_emb = self.ar_audio_embedding(y[:, -1:])
+            xy_pos = y_emb * self.ar_audio_position.x_scale + self.ar_audio_position.alpha * self.ar_audio_position.pe[:, y_len + idx].to(dtype=y_emb.dtype,device=y_emb.device)
+        
+        return y[:, -idx:].unsqueeze(0)
+
+bert_path = os.environ.get(
+    "bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
+)
+cnhubert_base_path = "GPT_SoVITS/pretrained_models/chinese-hubert-base"
+cnhubert.cnhubert_base_path = cnhubert_base_path
+
+@torch.jit.script
+def build_phone_level_feature(res:Tensor, word2ph:IntTensor):
+    phone_level_feature = []
+    for i in range(word2ph.shape[0]):
+        repeat_feature = res[i].repeat(word2ph[i].item(), 1)
+        phone_level_feature.append(repeat_feature)
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+    # [sum(word2ph), 1024]
+    return phone_level_feature
+    
+class MyBertModel(torch.nn.Module):
+    def __init__(self, bert_model):
+        super(MyBertModel, self).__init__()
+        self.bert = bert_model
+
+    def forward(self, input_ids:torch.Tensor, attention_mask:torch.Tensor, token_type_ids:torch.Tensor, word2ph:IntTensor):
+        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
+        res = torch.cat(outputs["hidden_states"][-3:-2], -1)[0][1:-1]
+        return build_phone_level_feature(res, word2ph)
+
+class SSLModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.ssl = cnhubert.get_model().model
+
+    def forward(self, ref_audio_16k)-> torch.Tensor:
+        ssl_content = self.ssl(ref_audio_16k)["last_hidden_state"].transpose(1, 2)
+        return ssl_content
+
+class ExportSSLModel(torch.nn.Module):
+    def __init__(self,ssl:SSLModel):
+        super().__init__()
+        self.ssl = ssl
+
+    def forward(self, ref_audio:torch.Tensor):
+        return self.ssl(ref_audio)
+    
+    @torch.jit.export
+    def resample(self,ref_audio:torch.Tensor,src_sr:int,dst_sr:int)->torch.Tensor:
+        audio = resamplex(ref_audio,src_sr,dst_sr).float()
+        return audio
+
+def export_bert(ref_bert_inputs):
+    ref_bert_inputs = {
+        'input_ids': ref_bert_inputs['input_ids'],
+        'attention_mask': ref_bert_inputs['attention_mask'],
+        'token_type_ids': ref_bert_inputs['token_type_ids'],
+        'word2ph': ref_bert_inputs['word2ph']
+    }
+    bert_model = AutoModelForMaskedLM.from_pretrained(bert_path,output_hidden_states=True)
+    my_bert_model = MyBertModel(bert_model)
+
+    my_bert_model = torch.jit.trace(my_bert_model,example_kwarg_inputs=ref_bert_inputs)
+    my_bert_model.save("onnx/bert_model.pt")
+    print('#### exported bert ####')
+    
+def export(gpt_path, vits_path):
+    tokenizer = AutoTokenizer.from_pretrained(bert_path)
+    
+    ref_bert_inputs = tokenizer("声音,是有温度的.夜晚的声音,会发光", return_tensors="pt")
+    ref_seq = torch.LongTensor([cleaned_text_to_sequence(['sh','eng1','y','in1',',','sh','i4','y','ou3','w','en1','d','u4','d','e','.','y','e4','w','an3','d','e','sh','eng1','y','in1',',','h','ui4','f','a1','g','uang1'],version='v2')])
+    ref_bert_inputs['word2ph'] = torch.Tensor([2,2,1,2,2,2,2,2,1,2,2,2,2,2,1,2,2,2]).int()
+
+    text_berf_inputs = tokenizer("大家好,我有一个春晚问题.", return_tensors="pt")
+    text_seq = torch.LongTensor([cleaned_text_to_sequence(["d", "a4", "j", "ia1", "h", "ao3",",","w","o3","y", "ou3","y","i2","g","e4","q","i2","g","uai4","w","en4","t","i2","."],version='v2')])
+    text_berf_inputs['word2ph'] = torch.Tensor([2,2,2,1,2,2,2,2,2,2,2,2,1]).int()
+
+    bert_model = AutoModelForMaskedLM.from_pretrained(bert_path,output_hidden_states=True)
+
+    bert = MyBertModel(bert_model)
+
+    # export_bert(ref_bert_inputs)
+    
+    ref_audio = torch.tensor([load_audio("output/denoise_opt/chen1.mp4_0000033600_0000192000.wav", 16000)]).float()
+    ssl = SSLModel()
+    s = ExportSSLModel(torch.jit.trace(ssl,example_inputs=(ref_audio)))
+    torch.jit.script(s).save("onnx/xw/ssl_model.pt")
+    print('#### exported ssl ####')
+
+    ref_bert = bert(**ref_bert_inputs)
+    text_bert = bert(**text_berf_inputs)
+    ssl_content = ssl(ref_audio)
+
+     # vits_path = "SoVITS_weights_v2/xw_e8_s216.pth"
+    vits = VitsModel(vits_path)
+    vits.eval()
+
+    # gpt_path = "GPT_weights_v2/xw-e15.ckpt"
+    dict_s1 = torch.load(gpt_path, map_location="cpu")
+    raw_t2s = get_raw_t2s_model(dict_s1)
+    t2s_m = T2SModel(raw_t2s)
+    t2s_m.eval()
+    t2s = torch.jit.script(t2s_m)
+    print('#### script t2s_m ####')
+    
+    print("vits.hps.data.sampling_rate:",vits.hps.data.sampling_rate)
+    gpt_sovits = GPT_SoVITS(t2s,vits)
+    gpt_sovits.eval()
+    ref_audio_sr = s.resample(ref_audio,16000,32000)
+    print('ref_audio_sr:',ref_audio_sr.shape)
+    
+    gpt_sovits_export = torch.jit.trace(
+        gpt_sovits,
+        example_inputs=(
+            ssl_content,
+            ref_audio_sr,
+            ref_seq,
+            text_seq,
+            ref_bert,
+            text_bert),
+        check_trace=False) # 默认是True 但是 check 的时候可能是随机生成的一个奇怪维度的值，导致报错
+    
+    gpt_sovits_export.save("onnx/xw/gpt_sovits_model.pt")
+    print('#### exported gpt_sovits ####')
+
+@torch.jit.script
+def parse_audio(ref_audio):
+    ref_audio_16k = torchaudio.functional.resample(ref_audio,48000,16000).float()#.to(ref_audio.device)
+    ref_audio_sr = torchaudio.functional.resample(ref_audio,48000,32000).float()#.to(ref_audio.device)
+    return ref_audio_16k,ref_audio_sr
+
+@torch.jit.script
+def resamplex(ref_audio:torch.Tensor,src_sr:int,dst_sr:int)->torch.Tensor:
+    return torchaudio.functional.resample(ref_audio,src_sr,dst_sr).float()
+
+class GPT_SoVITS(nn.Module):
+    def __init__(self, t2s:T2SModel,vits:VitsModel):
+        super().__init__()
+        self.t2s = t2s
+        self.vits = vits
+
+    def forward(self, ssl_content:torch.Tensor, ref_audio_sr:torch.Tensor, ref_seq:Tensor, text_seq:Tensor, ref_bert:Tensor, text_bert:Tensor):
+        codes = self.vits.vq_model.extract_latent(ssl_content.float())
+        prompt_semantic = codes[0, 0]
+        prompts = prompt_semantic.unsqueeze(0)
+
+        pred_semantic = self.t2s(prompts, ref_seq, text_seq, ref_bert, text_bert)
+        audio = self.vits(text_seq, pred_semantic, ref_audio_sr)
+        return audio
+
+def test(gpt_path, vits_path):
+    tokenizer = AutoTokenizer.from_pretrained(bert_path)
+    bert_model = AutoModelForMaskedLM.from_pretrained(bert_path,output_hidden_states=True)
+    bert = MyBertModel(bert_model)
+    # bert = torch.jit.load("onnx/bert_model.pt",map_location='cuda')
+
+    # gpt_path = "GPT_weights_v2/xw-e15.ckpt"
+    dict_s1 = torch.load(gpt_path, map_location="cpu")
+    raw_t2s = get_raw_t2s_model(dict_s1)
+    t2s = T2SModel(raw_t2s)
+    t2s.eval()
+    # t2s = torch.jit.load("onnx/xw/t2s_model.pt",map_location='cuda')
+
+    # vits_path = "SoVITS_weights_v2/xw_e8_s216.pth"
+    vits = VitsModel(vits_path)
+    vits.eval()
+
+    ssl = ExportSSLModel(SSLModel())
+    ssl.eval()
+
+    gpt_sovits = GPT_SoVITS(t2s,vits)
+
+    # vits = torch.jit.load("onnx/xw/vits_model.pt",map_location='cuda')
+    # ssl = torch.jit.load("onnx/xw/ssl_model.pt",map_location='cuda')
+
+     
+    ref_bert_inputs = tokenizer("声音,是有温度的.夜晚的声音,会发光", return_tensors="pt")
+    ref_seq = torch.LongTensor([cleaned_text_to_sequence(['sh','eng1','y','in1',',','sh','i4','y','ou3','w','en1','d','u4','d','e','.','y','e4','w','an3','d','e','sh','eng1','y','in1',',','h','ui4','f','a1','g','uang1'],version='v2')])
+    ref_bert_inputs['word2ph'] = torch.Tensor([2,2,1,2,2,2,2,2,1,2,2,2,2,2,1,2,2,2]).int()
+
+    text_berf_inputs = tokenizer("大家好,我有一个春晚问题.", return_tensors="pt")
+    text_seq = torch.LongTensor([cleaned_text_to_sequence(["d", "a4", "j", "ia1", "h", "ao3",",","w","o3","y", "ou3","y","i2","g","e4","q","i2","g","uai4","w","en4","t","i2","."],version='v2')])
+    text_berf_inputs['word2ph'] = torch.Tensor([2,2,2,1,2,2,2,2,2,2,2,2,1]).int()
+
+    ref_bert = bert(
+        ref_bert_inputs['input_ids'], 
+        ref_bert_inputs['attention_mask'], 
+        ref_bert_inputs['token_type_ids'], 
+        ref_bert_inputs['word2ph']
+    )
+    
+    text_bert = bert(text_berf_inputs['input_ids'], 
+                     text_berf_inputs['attention_mask'], 
+                     text_berf_inputs['token_type_ids'], 
+                     text_berf_inputs['word2ph'])
+
+    #[1,N]
+    ref_audio = torch.tensor([load_audio("output/denoise_opt/chen1.mp4_0000033600_0000192000.wav", 16000)]).float()
+    print('ref_audio:',ref_audio.shape)
+    
+    ref_audio_sr = ssl.resample(ref_audio,16000,32000)
+    print('start ssl')
+    ssl_content = ssl(ref_audio)
+
+    print('start gpt_sovits:')
+    with torch.no_grad():
+        audio = gpt_sovits(ssl_content, ref_audio_sr, ref_seq, text_seq, ref_bert, text_bert)
+    print('start write wav')
+    soundfile.write("out.wav", audio.detach().cpu().numpy(), 32000)
+
+    # audio = vits(text_seq, pred_semantic1, ref_audio)
+    # soundfile.write("out.wav", audio, 32000)
+
+import text
+import json
+
+def export_symbel(version='v2'):
+    if version=='v1':
+        symbols = text._symbol_to_id_v1
+        with open(f"onnx/symbols_v1.json", "w") as file:
+            json.dump(symbols, file, indent=4)
+    else:
+        symbols = text._symbol_to_id_v2
+        with open(f"onnx/symbols_v2.json", "w") as file:
+            json.dump(symbols, file, indent=4)
+
+if __name__ == "__main__":
+    export(gpt_path="GPT_weights_v2/chen1-e15.ckpt", vits_path="SoVITS_weights_v2/chen1_e8_s208.pth")
+    # test(gpt_path="GPT_weights_v2/chen1-e15.ckpt", vits_path="SoVITS_weights_v2/chen1_e8_s208.pth")
+    # export_symbel()

GPT_SoVITS/module/attentions_onnx.py

@@ -4,8 +4,8 @@ from torch import nn
 from torch.nn import functional as F
 
 from module import commons
-from module.modules import LayerNorm
 
+from typing import Optional
 
 class LayerNorm(nn.Module):
     def __init__(self, channels, eps=1e-5):
@@ -59,6 +59,7 @@ class Encoder(nn.Module):
         #  self.cond_layer = weight_norm(cond_layer, name='weight')
         #  self.gin_channels = 256
         self.cond_layer_idx = self.n_layers
+        self.spk_emb_linear = nn.Linear(256, self.hidden_channels)
         if "gin_channels" in kwargs:
             self.gin_channels = kwargs["gin_channels"]
             if self.gin_channels != 0:
@@ -98,22 +99,36 @@ class Encoder(nn.Module):
             )
             self.norm_layers_2.append(LayerNorm(hidden_channels))
 
-    def forward(self, x, x_mask, g=None):
+    # def forward(self, x, x_mask, g=None):
+    #     attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+    #     x = x * x_mask
+    #     for i in range(self.n_layers):
+    #         if i == self.cond_layer_idx and g is not None:
+    #             g = self.spk_emb_linear(g.transpose(1, 2))
+    #             g = g.transpose(1, 2)
+    #             x = x + g
+    #             x = x * x_mask
+    #         y = self.attn_layers[i](x, x, attn_mask)
+    #         y = self.drop(y)
+    #         x = self.norm_layers_1[i](x + y)
+
+    #         y = self.ffn_layers[i](x, x_mask)
+    #         y = self.drop(y)
+    #         x = self.norm_layers_2[i](x + y)
+    #     x = x * x_mask
+    #     return x
+    
+    def forward(self, x, x_mask):
         attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
         x = x * x_mask
-        for i in range(self.n_layers):
-            if i == self.cond_layer_idx and g is not None:
-                g = self.spk_emb_linear(g.transpose(1, 2))
-                g = g.transpose(1, 2)
-                x = x + g
-                x = x * x_mask
-            y = self.attn_layers[i](x, x, attn_mask)
+        for attn_layers,norm_layers_1,ffn_layers,norm_layers_2 in zip(self.attn_layers,self.norm_layers_1,self.ffn_layers,self.norm_layers_2):
+            y = attn_layers(x, x, attn_mask)
             y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
+            x = norm_layers_1(x + y)
 
-            y = self.ffn_layers[i](x, x_mask)
+            y = ffn_layers(x, x_mask)
             y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
+            x = norm_layers_2(x + y)
         x = x * x_mask
         return x
 
@@ -172,17 +187,18 @@ class MultiHeadAttention(nn.Module):
                 self.conv_k.weight.copy_(self.conv_q.weight)
                 self.conv_k.bias.copy_(self.conv_q.bias)
 
-    def forward(self, x, c, attn_mask=None):
+    def forward(self, x, c, attn_mask:Optional[torch.Tensor]=None):
         q = self.conv_q(x)
         k = self.conv_k(c)
         v = self.conv_v(c)
 
-        x, self.attn = self.attention(q, k, v, mask=attn_mask)
+        # x, self.attn = self.attention(q, k, v, mask=attn_mask)
+        x, _ = self.attention(q, k, v, mask=attn_mask)
 
         x = self.conv_o(x)
         return x
 
-    def attention(self, query, key, value, mask=None):
+    def attention(self, query, key, value, mask:Optional[torch.Tensor]=None):
         # reshape [b, d, t] -> [b, n_h, t, d_k]
         b, d, t_s, _ = (*key.size(), query.size(2))
         query = query.view(b, self.n_heads, self.k_channels, -1).transpose(2, 3)
@@ -304,7 +320,7 @@ class FFN(nn.Module):
         filter_channels,
         kernel_size,
         p_dropout=0.0,
-        activation=None,
+        activation="",
         causal=False,
     ):
         super().__init__()
@@ -316,10 +332,11 @@ class FFN(nn.Module):
         self.activation = activation
         self.causal = causal
 
-        if causal:
-            self.padding = self._causal_padding
-        else:
-            self.padding = self._same_padding
+        # 从上下文看这里一定是 False
+        # if causal:
+        #     self.padding = self._causal_padding
+        # else:
+        #     self.padding = self._same_padding
 
         self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
         self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
@@ -334,6 +351,9 @@ class FFN(nn.Module):
         x = self.drop(x)
         x = self.conv_2(self.padding(x * x_mask))
         return x * x_mask
+    
+    def padding(self, x):
+        return self._same_padding(x)
 
     def _causal_padding(self, x):
         if self.kernel_size == 1:
@@ -352,3 +372,35 @@ class FFN(nn.Module):
         padding = [[0, 0], [0, 0], [pad_l, pad_r]]
         x = F.pad(x, commons.convert_pad_shape(padding))
         return x
+
+
+class MRTE(nn.Module):
+    def __init__(
+        self,
+        content_enc_channels=192,
+        hidden_size=512,
+        out_channels=192,
+        kernel_size=5,
+        n_heads=4,
+        ge_layer=2,
+    ):
+        super(MRTE, self).__init__()
+        self.cross_attention = MultiHeadAttention(hidden_size, hidden_size, n_heads)
+        self.c_pre = nn.Conv1d(content_enc_channels, hidden_size, 1)
+        self.text_pre = nn.Conv1d(content_enc_channels, hidden_size, 1)
+        self.c_post = nn.Conv1d(hidden_size, out_channels, 1)
+
+    def forward(self, ssl_enc, ssl_mask, text, text_mask, ge):
+        attn_mask = text_mask.unsqueeze(2) * ssl_mask.unsqueeze(-1)
+
+        ssl_enc = self.c_pre(ssl_enc * ssl_mask)
+        text_enc = self.text_pre(text * text_mask)
+        x = (
+                self.cross_attention(
+                    ssl_enc * ssl_mask, text_enc * text_mask, attn_mask
+                )
+                + ssl_enc
+                + ge
+            )
+        x = self.c_post(x * ssl_mask)
+        return x

GPT_SoVITS/module/commons.py

@@ -13,10 +13,10 @@ def get_padding(kernel_size, dilation=1):
     return int((kernel_size * dilation - dilation) / 2)
 
 
-def convert_pad_shape(pad_shape):
-    l = pad_shape[::-1]
-    pad_shape = [item for sublist in l for item in sublist]
-    return pad_shape
+# def convert_pad_shape(pad_shape):
+#     l = pad_shape[::-1]
+#     pad_shape = [item for sublist in l for item in sublist]
+#     return pad_shape
 
 
 def intersperse(lst, item):

GPT_SoVITS/module/models_onnx.py

@@ -1,5 +1,6 @@
 import copy
 import math
+from typing import Optional
 import torch
 from torch import nn
 from torch.nn import functional as F
@@ -11,7 +12,6 @@ from module import attentions_onnx as attentions
 from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
 from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
 from module.commons import init_weights, get_padding
-from module.mrte_model import MRTE
 from module.quantize import ResidualVectorQuantizer
 # from text import symbols
 from text import symbols as symbols_v1
@@ -218,7 +218,7 @@ class TextEncoder(nn.Module):
             symbols = symbols_v2.symbols
         self.text_embedding = nn.Embedding(len(symbols), hidden_channels)
 
-        self.mrte = MRTE()
+        self.mrte = attentions.MRTE()
 
         self.encoder2 = attentions.Encoder(
             hidden_channels,
@@ -249,25 +249,6 @@ class TextEncoder(nn.Module):
         m, logs = torch.split(stats, self.out_channels, dim=1)
         return y, m, logs, y_mask
 
-    def extract_latent(self, x):
-        x = self.ssl_proj(x)
-        quantized, codes, commit_loss, quantized_list = self.quantizer(x)
-        return codes.transpose(0, 1)
-
-    def decode_latent(self, codes, y_mask, refer, refer_mask, ge):
-        quantized = self.quantizer.decode(codes)
-
-        y = self.vq_proj(quantized) * y_mask
-        y = self.encoder_ssl(y * y_mask, y_mask)
-
-        y = self.mrte(y, y_mask, refer, refer_mask, ge)
-
-        y = self.encoder2(y * y_mask, y_mask)
-
-        stats = self.proj(y) * y_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return y, m, logs, y_mask, quantized
-
 
 class ResidualCouplingBlock(nn.Module):
     def __init__(
@@ -448,7 +429,7 @@ class Generator(torch.nn.Module):
         if gin_channels != 0:
             self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
 
-    def forward(self, x, g=None):
+    def forward(self, x, g:Optional[torch.Tensor]=None):
         x = self.conv_pre(x)
         if g is not None:
             x = x + self.cond(g)
@@ -870,15 +851,15 @@ class SynthesizerTrn(nn.Module):
             upsample_kernel_sizes,
             gin_channels=gin_channels,
         )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
+        # self.enc_q = PosteriorEncoder(
+        #     spec_channels,
+        #     inter_channels,
+        #     hidden_channels,
+        #     5,
+        #     1,
+        #     16,
+        #     gin_channels=gin_channels,
+        # )
         self.flow = ResidualCouplingBlock(
             inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels
         )