Merge 4c091e34021f91e6b4898006ba7eade90d2edfbb into 42586e20f7e2d58026ed2fa68ac1ddf41fc48346

add RTF performence

GPT_SoVITS/export_torch_script.py

@@ -261,41 +261,21 @@ class T2SBlock:
 
         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 = 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 = attn.transpose(1, 2).reshape(batch_size, q_len, -1)
         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,
-            )
+        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):

GPT_SoVITS/stream_v2pro.py

@@ -0,0 +1,597 @@
+# 这是一个实验性质的实现，旨在探索 stream infer 的可能性。(xiao hai xie zhe wan de)
+from typing import List
+from export_torch_script import ExportERes2NetV2, SSLModel, T2SModel, VitsModel, get_raw_t2s_model, init_sv_cn, resamplex, sample, spectrogram_torch
+import export_torch_script
+from my_utils import load_audio
+import torch
+from torch import LongTensor, Tensor, nn
+from torch.nn import functional as F
+
+import soundfile
+from inference_webui import get_phones_and_bert
+import matplotlib.pyplot as plt
+
+
+class StreamT2SModel(nn.Module):
+    def __init__(self, t2s: T2SModel):
+        super(StreamT2SModel, self).__init__()
+        self.t2s = t2s
+
+    @torch.jit.export
+    def pre_infer(
+        self,
+        prompts: LongTensor,
+        ref_seq: LongTensor,
+        text_seq: LongTensor,
+        ref_bert: torch.Tensor,
+        text_bert: torch.Tensor,
+        top_k: int,
+    ) -> tuple[int, Tensor, Tensor, List[Tensor], List[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.t2s.ar_text_embedding(all_phoneme_ids)
+        x = x + self.t2s.bert_proj(bert.transpose(1, 2))
+        x: torch.Tensor = self.t2s.ar_text_position(x)
+
+        # [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.t2s.ar_audio_embedding(y)
+        y_len: int = y_emb.shape[1]
+        prefix_len = y.shape[1]
+        y_pos = self.t2s.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.t2s.num_head, -1, -1)
+            .view(bsz, self.t2s.num_head, src_len, src_len)
+            .to(device=x.device, dtype=torch.bool)
+        )
+
+        xy_dec, k_cache, v_cache = self.t2s.t2s_transformer.process_prompt(
+            xy_pos, xy_attn_mask, None
+        )
+
+        logits = self.t2s.ar_predict_layer(xy_dec[:, -1])
+        logits = logits[:, :-1]
+        samples = sample(
+            logits, y, top_k=top_k, top_p=1, repetition_penalty=1.35, temperature=1.0
+        )[0]
+        y = torch.concat([y, samples], dim=1)
+        y_emb: Tensor = self.t2s.ar_audio_embedding(y[:, -1:])
+        xy_pos: Tensor = (
+            y_emb * self.t2s.ar_audio_position.x_scale
+            + self.t2s.ar_audio_position.alpha
+            * self.t2s.ar_audio_position.pe[:, y_len].to(
+                dtype=y_emb.dtype, device=y_emb.device
+            )
+        )
+
+        return y_len, y, xy_pos, k_cache, v_cache
+
+    @torch.jit.export
+    def decode_next_token(
+        self,
+        idx: int,  # 记住从1开始 到1500
+        top_k: int,
+        y_len: int,
+        y: Tensor,
+        xy_pos: Tensor,
+        k_cache: List[Tensor],
+        v_cache: List[Tensor],
+    ) -> tuple[Tensor, Tensor, int, List[Tensor], List[Tensor]]:
+        # [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.t2s_transformer.decode_next_token(
+            xy_pos, k_cache, v_cache
+        )
+        logits = self.t2s.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=top_k, top_p=1, repetition_penalty=1.35, temperature=1.0
+        )[0]
+
+        y = torch.concat([y, samples], dim=1)
+        last_token = int(samples[0, 0])
+
+        # if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
+        #     stop = True
+        if torch.argmax(logits, dim=-1)[0] == self.t2s.EOS or samples[0, 0] == self.t2s.EOS:
+            return y[:,:-1], xy_pos, self.t2s.EOS, k_cache, v_cache
+
+        # if stop:
+        #     if y.shape[1] == 0:
+        #         y = torch.concat([y, torch.zeros_like(samples)], dim=1)
+        #     break
+
+        y_emb = self.t2s.ar_audio_embedding(y[:, -1:])
+        xy_pos = (
+            y_emb * self.t2s.ar_audio_position.x_scale
+            + self.t2s.ar_audio_position.alpha
+            * self.t2s.ar_audio_position.pe[:, y_len + idx].to(
+                dtype=y_emb.dtype, device=y_emb.device
+            )
+        )
+        return y, xy_pos, last_token, k_cache, v_cache
+
+    def forward(
+        self,
+        idx: int,  # 记住从1开始 到1500
+        top_k: int,
+        y_len: int,
+        y: Tensor,
+        xy_pos: Tensor,
+        k_cache: List[Tensor],
+        v_cache: List[Tensor],
+    ):
+        return self.decode_next_token(idx,top_k,y_len,y,xy_pos,k_cache,v_cache)
+
+
+class StepVitsModel(nn.Module):
+    def __init__(self, vits: VitsModel,sv_model:ExportERes2NetV2):
+        super().__init__()
+        self.hps = vits.hps
+        self.vq_model = vits.vq_model
+        self.hann_window = vits.hann_window
+        self.sv = sv_model
+
+    def ref_handle(self, ref_audio_32k):
+        refer = spectrogram_torch(
+            self.hann_window,
+            ref_audio_32k,
+            self.hps.data.filter_length,
+            self.hps.data.sampling_rate,
+            self.hps.data.hop_length,
+            self.hps.data.win_length,
+            center=False,
+        )
+        ref_audio_16k = resamplex(ref_audio_32k, 32000, 16000).to(ref_audio_32k.dtype).to(ref_audio_32k.device)
+        sv_emb = self.sv(ref_audio_16k)
+        return refer, sv_emb
+
+    def extract_latent(self, ssl_content):
+        codes = self.vq_model.extract_latent(ssl_content)
+        return codes[0]
+
+    def forward(self, pred_semantic, text_seq, refer, sv_emb=None):
+        return self.vq_model(
+            pred_semantic, text_seq, refer, speed=1.0, sv_emb=sv_emb
+        )[0, 0]
+
+
+@torch.jit.script
+def find_best_audio_offset_fast(reference_audio: Tensor, search_audio: Tensor):
+    ref_len = len(reference_audio)
+    search_len = len(search_audio)
+
+    if search_len < ref_len:
+        raise ValueError(
+            f"搜索音频长度 ({search_len}) 必须大于等于参考音频长度 ({ref_len})"
+        )
+
+    # 使用F.conv1d计算原始互相关
+    reference_flipped = reference_audio.unsqueeze(0).unsqueeze(0)
+    search_padded = search_audio.unsqueeze(0).unsqueeze(0)
+
+    # 计算点积
+    dot_products = F.conv1d(search_padded, reference_flipped).squeeze()
+
+    if len(dot_products.shape) == 0:
+        dot_products = dot_products.unsqueeze(0)
+
+    # 计算参考音频的平方和
+    ref_squared_sum = torch.sum(reference_audio**2)
+
+    # 计算搜索音频每个位置的平方和（滑动窗口）
+    search_squared = search_audio**2
+    search_squared_padded = search_squared.unsqueeze(0).unsqueeze(0)
+    ones_kernel = torch.ones(
+        1, 1, ref_len, dtype=search_audio.dtype, device=search_audio.device
+    )
+
+    segment_squared_sums = F.conv1d(search_squared_padded, ones_kernel).squeeze()
+
+    if len(segment_squared_sums.shape) == 0:
+        segment_squared_sums = segment_squared_sums.unsqueeze(0)
+
+    # 计算归一化因子
+    ref_norm = torch.sqrt(ref_squared_sum)
+    segment_norms = torch.sqrt(segment_squared_sums)
+
+    # 避免除零
+    epsilon = 1e-8
+    normalization_factor = ref_norm * segment_norms + epsilon
+
+    # 归一化互相关
+    correlation_scores = dot_products / normalization_factor
+
+    best_offset = torch.argmax(correlation_scores).item()
+
+    return best_offset, correlation_scores
+
+
+import time
+
+def test_stream(
+    gpt_path,
+    vits_path,
+    version,
+    ref_audio_path,
+    ref_text,
+    output_path,
+    device="cpu",
+    is_half=True,
+):
+    if export_torch_script.sv_cn_model == None:
+        init_sv_cn(device,is_half)
+
+    ref_audio = torch.tensor([load_audio(ref_audio_path, 16000)]).float()
+    ssl = SSLModel()
+
+    print(f"device: {device}")
+
+    ref_seq_id, ref_bert_T, ref_norm_text = get_phones_and_bert(
+        ref_text, "all_zh", "v2"
+    )
+    ref_seq = torch.LongTensor([ref_seq_id]).to(device)
+    ref_bert = ref_bert_T.T
+    if is_half:
+        ref_bert = ref_bert.half()
+    ref_bert = ref_bert.to(ref_seq.device)
+
+    text_seq_id, text_bert_T, norm_text = get_phones_and_bert(
+        "这是一个简单的示例，真没想到这么简单就完成了，真的神奇，接下来我们说说狐狸,可能这就是狐狸吧.它有长长的尾巴，尖尖的耳朵，传说中还有九条尾巴。你觉得狐狸神奇吗？", "auto", "v2"
+    )
+    text_seq = torch.LongTensor([text_seq_id]).to(device)
+    text_bert = text_bert_T.T
+    if is_half:
+        text_bert = text_bert.half()
+    text_bert = text_bert.to(text_seq.device)
+
+    ssl_content = ssl(ref_audio)
+    if is_half:
+        ssl_content = ssl_content.half()
+    ssl_content = ssl_content.to(device)
+
+    sv_model = ExportERes2NetV2(export_torch_script.sv_cn_model)
+
+    # vits_path = "SoVITS_weights_v2/xw_e8_s216.pth"
+    vits = VitsModel(vits_path, version,is_half=is_half,device=device)
+    vits.eval()
+
+    # gpt_path = "GPT_weights_v2/xw-e15.ckpt"
+    # dict_s1 = torch.load(gpt_path, map_location=device)
+    dict_s1 = torch.load(gpt_path, weights_only=False)
+    raw_t2s = get_raw_t2s_model(dict_s1).to(device)
+    print("#### get_raw_t2s_model ####")
+    print(raw_t2s.config)
+    if is_half:
+        raw_t2s = raw_t2s.half()
+    t2s_m = T2SModel(raw_t2s)
+    t2s_m.eval()
+    # t2s = torch.jit.script(t2s_m).to(device)
+    t2s = t2s_m
+    print("#### script t2s_m ####")
+
+    print("vits.hps.data.sampling_rate:", vits.hps.data.sampling_rate)
+
+    stream_t2s = StreamT2SModel(t2s).to(device)
+    stream_t2s = torch.jit.script(stream_t2s)
+
+    ref_audio_sr = resamplex(ref_audio, 16000, 32000)
+    if is_half:
+        ref_audio_sr = ref_audio_sr.half()
+    ref_audio_sr = ref_audio_sr.to(device)
+
+    top_k = 15
+
+    codes = vits.vq_model.extract_latent(ssl_content)
+    prompt_semantic = codes[0, 0]
+    prompts = prompt_semantic.unsqueeze(0)
+
+    audio_16k = resamplex(ref_audio_sr, 32000, 16000).to(ref_audio_sr.dtype)
+    sv_emb = sv_model(audio_16k)
+    print("text_seq",text_seq.shape)
+
+    refer = spectrogram_torch(
+        vits.hann_window,
+        ref_audio_sr,
+        vits.hps.data.filter_length,
+        vits.hps.data.sampling_rate,
+        vits.hps.data.hop_length,
+        vits.hps.data.win_length,
+        center=False,
+    )
+
+    st = time.time()
+    et = time.time()
+
+    y_len, y, xy_pos, k_cache, v_cache = stream_t2s.pre_infer(prompts, ref_seq, text_seq, ref_bert, text_bert, top_k)
+    idx = 1
+    last_idx = 0
+    audios = []
+    raw_audios = []
+    last_audio_ret = None
+    offset_index = []
+    full_audios = []
+    print("y.shape:", y.shape)
+    cut_id = 0
+    while True:
+        y, xy_pos, last_token, k_cache, v_cache = stream_t2s(idx, top_k, y_len, y, xy_pos, k_cache, v_cache)
+        # print("y.shape:", y.shape)
+        stop = last_token==t2s.EOS
+        print('idx:',idx , 'y.shape:', y.shape, y.shape[1]-idx)
+
+        if last_token < 50 and idx-last_idx > (len(audios)+1) * 25 and idx > cut_id:
+            cut_id = idx + 7
+            print('trigger:',idx, last_idx, y[:,-idx+last_idx:], y[:,-idx+last_idx:].shape)
+            # y = torch.cat([y, y[:,-1:]], dim=1)
+            # idx+=1
+
+        if stop :
+            idx -=1
+            print('stop')
+            print(idx, y[:,-idx+last_idx:])
+            print(idx,last_idx, y.shape)
+            print(y[:,-idx:-idx+20])
+
+
+        # 玄学这档子事说不清楚
+        if idx == cut_id or stop:
+            print(f"idx: {idx}, last_idx: {last_idx}, cut_id: {cut_id}, stop: {stop}")
+            audio = vits.vq_model(y[:,-idx:].unsqueeze(0), text_seq, refer, speed=1.0, sv_emb=sv_emb)[0, 0]
+            full_audios.append(audio)
+            if last_idx == 0:
+                last_audio_ret = audio[-1280*8:-1280*8+256]
+                audio = audio[:-1280*8]
+                raw_audios.append(audio)
+                et = time.time()
+            else:
+                if stop:
+                    audio_ = audio[last_idx*1280 -1280*8:]
+                    raw_audios.append(audio_)
+                    i, x = find_best_audio_offset_fast(last_audio_ret, audio_[:1280])
+                    offset_index.append(i)
+                    audio = audio_[i:]
+                else:
+                    audio_ = audio[last_idx*1280 -1280*8:-1280*8]
+                    raw_audios.append(audio_)
+                    i, x = find_best_audio_offset_fast(last_audio_ret, audio_[:1280])
+                    offset_index.append(i)
+                    last_audio_ret = audio[-1280*8:-1280*8+256]
+                    audio = audio_[i:]
+            last_idx = idx
+            # print(f'write {output_path}/out_{audio_index}')
+            # soundfile.write(f"{output_path}/out_{audio_index}.wav", audio.float().detach().cpu().numpy(), 32000)
+            audios.append(audio)
+        # print(idx,'/',1500 , y.shape, y[0,-1].item(), stop)
+        if idx>1500:
+            break
+
+        if stop:
+            break
+
+        idx+=1
+
+    at = time.time()
+
+    for (i,a) in enumerate(audios):
+        print(f'write {output_path}/out_{i}')
+        soundfile.write(f"{output_path}/out_{i}.wav", a.float().detach().cpu().numpy(), 32000)
+        
+    print(f"frist token: {et - st:.4f} seconds")
+    print(f"all token: {at - st:.4f} seconds")
+    audio = vits.vq_model(y[:,-idx:].unsqueeze(0), text_seq, refer, speed=1.0, sv_emb=sv_emb)[0, 0]
+    soundfile.write(f"{output_path}/out_final.wav", audio.float().detach().cpu().numpy(), 32000)
+    audio = torch.cat(audios, dim=0)
+    soundfile.write(f"{output_path}/out.wav", audio.float().detach().cpu().numpy(), 32000)
+    audio_raw = torch.cat(raw_audios, dim=0)
+    soundfile.write(f"{output_path}/out.raw.wav", audio_raw.float().detach().cpu().numpy(), 32000)
+    
+
+    colors = ['red', 'green', 'blue', 'orange', 'purple', 'cyan', 'magenta', 'yellow']
+
+    fig, axes = plt.subplots(len(full_audios)+2, 1, figsize=(10, 6))
+
+    max_duration = full_audios[-1].shape[0]
+
+    last_line = 0
+    for i,(ax,a) in enumerate(zip(axes[:-1],full_audios)):
+        ax.plot(a.float().detach().cpu().numpy(), color=colors[i], alpha=0.5, label=f"Audio {i}")
+        ax.axvline(x=last_line, color=colors[i], linestyle='--')
+        last_line = a.shape[0]-8*1280
+        ax.axvline(x=last_line, color=colors[i], linestyle='--')
+        ax.set_xlim(0, max_duration)
+        axes[-1].axvline(x=last_line, color=colors[i], linestyle='--')
+        axes[-2].axvline(x=last_line, color=colors[i], linestyle='--')
+    
+    axes[-2].plot(audio.float().detach().cpu().numpy(), color='black', label='Final Audio')
+    axes[-2].set_xlim(0, max_duration)
+
+    axes[-1].plot(audio_raw.float().detach().cpu().numpy(), color='black', label='Raw Audio')
+    axes[-1].set_xlim(0, max_duration)
+
+    for i,y in enumerate(y[0][-idx:]):
+        # axes[-1].text(i*1280, 0.05, str(int(y)), fontsize=12, ha='center')
+        axes[-1].axvline(x=i*1280, color='gray', linestyle=':', alpha=0.5)
+        
+
+    # plt.title('Overlapped Waveform Comparison')
+    # plt.xlabel('Sample Number')
+    # plt.ylabel('Amplitude')
+    # plt.tight_layout()
+    print("offset_index:", offset_index)
+    plt.show()
+
+
+def export_prov2(
+    gpt_path,
+    vits_path,
+    version,
+    ref_audio_path,
+    ref_text,
+    output_path,
+    device="cpu",
+    is_half=True,
+):
+    if export_torch_script.sv_cn_model == None:
+        init_sv_cn(device,is_half)
+
+    ref_audio = torch.tensor([load_audio(ref_audio_path, 16000)]).float()
+    ssl = SSLModel()
+
+    print(f"device: {device}")
+
+    ref_seq_id, ref_bert_T, ref_norm_text = get_phones_and_bert(
+        ref_text, "all_zh", "v2"
+    )
+    ref_seq = torch.LongTensor([ref_seq_id]).to(device)
+    ref_bert = ref_bert_T.T
+    if is_half:
+        ref_bert = ref_bert.half()
+    ref_bert = ref_bert.to(ref_seq.device)
+
+    text_seq_id, text_bert_T, norm_text = get_phones_and_bert(
+        "这是一个简单的示例，真没想到这么简单就完成了。真的神奇。接下来我们说说狐狸,可能这就是狐狸吧.它有长长的尾巴，尖尖的耳朵，传说中还有九条尾巴。你觉得狐狸神奇吗？。The King and His Stories.Once there was a king. He likes to write stories, but his stories were not good. As people were afraid of him, they all said his stories were good.After reading them, the writer at once turned to the soldiers and said: Take me back to prison, please.", "auto", "v2"
+    )
+    text_seq = torch.LongTensor([text_seq_id]).to(device)
+    text_bert = text_bert_T.T
+    if is_half:
+        text_bert = text_bert.half()
+    text_bert = text_bert.to(text_seq.device)
+
+    ssl_content = ssl(ref_audio)
+    if is_half:
+        ssl_content = ssl_content.half()
+    ssl_content = ssl_content.to(device)
+
+    sv_model = ExportERes2NetV2(export_torch_script.sv_cn_model)
+
+    # vits_path = "SoVITS_weights_v2/xw_e8_s216.pth"
+    vits = VitsModel(vits_path, version,is_half=is_half,device=device)
+    vits.eval()
+    vits = StepVitsModel(vits, sv_model)
+
+    # gpt_path = "GPT_weights_v2/xw-e15.ckpt"
+    # dict_s1 = torch.load(gpt_path, map_location=device)
+    dict_s1 = torch.load(gpt_path, weights_only=False)
+    raw_t2s = get_raw_t2s_model(dict_s1).to(device)
+    print("#### get_raw_t2s_model ####")
+    print(raw_t2s.config)
+    if is_half:
+        raw_t2s = raw_t2s.half()
+    t2s_m = T2SModel(raw_t2s)
+    t2s_m.eval()
+    # t2s = torch.jit.script(t2s_m).to(device)
+    t2s = t2s_m
+    print("#### script t2s_m ####")
+
+    print("vits.hps.data.sampling_rate:", vits.hps.data.sampling_rate)
+
+    stream_t2s = StreamT2SModel(t2s).to(device)
+    stream_t2s = torch.jit.script(stream_t2s)
+
+    ref_audio_sr = resamplex(ref_audio, 16000, 32000)
+    if is_half:
+        ref_audio_sr = ref_audio_sr.half()
+    ref_audio_sr = ref_audio_sr.to(device)
+
+    top_k = 15
+
+    prompts = vits.extract_latent(ssl_content)
+
+    audio_16k = resamplex(ref_audio_sr, 32000, 16000).to(ref_audio_sr.dtype)
+    sv_emb = sv_model(audio_16k)
+    print("text_seq",text_seq.shape)
+    # torch.jit.trace()
+
+    refer,sv_emb = vits.ref_handle(ref_audio_sr)
+
+    st = time.time()
+    et = time.time()
+
+    y_len, y, xy_pos, k_cache, v_cache = stream_t2s.pre_infer(prompts, ref_seq, text_seq, ref_bert, text_bert, top_k)
+    idx = 1
+    print("y.shape:", y.shape)
+    while True:
+        y, xy_pos, last_token, k_cache, v_cache = stream_t2s(idx, top_k, y_len, y, xy_pos, k_cache, v_cache)
+        # print("y.shape:", y.shape)
+
+        idx+=1
+        # print(idx,'/',1500 , y.shape, y[0,-1].item(), stop)
+        if idx>1500:
+            break
+
+        if last_token == t2s.EOS:
+            break
+
+    at = time.time()
+    print("EOS:",t2s.EOS)
+
+    print(f"frist token: {et - st:.4f} seconds")
+    print(f"all token: {at - st:.4f} seconds")
+    print("sv_emb", sv_emb.shape)
+    print("refer",refer.shape)
+    y = y[:,-idx:].unsqueeze(0)
+    print("y", y.shape)
+    audio = vits(y, text_seq, refer, sv_emb)
+    soundfile.write(f"{output_path}/out_final.wav", audio.float().detach().cpu().numpy(), 32000)
+
+    torch._dynamo.mark_dynamic(ssl_content, 2)
+    torch._dynamo.mark_dynamic(ref_audio_sr, 1)
+    torch._dynamo.mark_dynamic(ref_seq, 1)
+    torch._dynamo.mark_dynamic(text_seq, 1)
+    torch._dynamo.mark_dynamic(ref_bert, 0)
+    torch._dynamo.mark_dynamic(text_bert, 0)
+    torch._dynamo.mark_dynamic(refer, 2)
+    torch._dynamo.mark_dynamic(y, 2)
+
+    inputs = {
+        "forward": (y, text_seq, refer, sv_emb),
+        "extract_latent": ssl_content,
+        "ref_handle": ref_audio_sr,
+    }
+
+    stream_t2s.save(f"{output_path}/t2s.pt")
+    torch.jit.trace_module(vits, inputs=inputs, optimize=True).save(f"{output_path}/vits.pt")
+    torch.jit.script(find_best_audio_offset_fast, optimize=True).save(f"{output_path}/find_best_audio_offset_fast.pt")
+
+
+if __name__ == "__main__":
+    with torch.no_grad():
+        test_stream(
+            gpt_path="GPT_SoVITS/pretrained_models/s1v3.ckpt",
+            vits_path="GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro.pth",
+            version="v2Pro",
+            # ref_audio_path="/mnt/g/ad_ref.wav",
+            # ref_text="你这老坏蛋，我找了你这么久，真没想到在这里找到你。他说.",
+            ref_audio_path="output/denoise_opt/ht/ht.mp4_0000026560_0000147200.wav",
+            ref_text='说真的，这件衣服才配得上本小姐嘛',
+            output_path="streaming",
+            device="cuda",
+            is_half=True,
+        )

README.md

@@ -43,6 +43,11 @@ Unseen speakers few-shot fine-tuning demo:
 
 https://github.com/RVC-Boss/GPT-SoVITS/assets/129054828/05bee1fa-bdd8-4d85-9350-80c060ab47fb
 
+**RTF(inference speed) of GPT-SoVITS v2 ProPlus**:
+0.028 tested in 4060Ti, 0.014 tested in 4090 (1400words~=4min, inference time is 3.36s), 0.526 in M4 CPU. You can test our [huggingface demo](https://lj1995-gpt-sovits-proplus.hf.space/) (half H200) to experience high-speed inference .
+
+请不要尬黑GPT-SoVITS推理速度慢，谢谢！
+
 **User guide: [简体中文](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e) | [English](https://rentry.co/GPT-SoVITS-guide#/)**
 
 ## Installation