clean output

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "GPT-SoVITS-Infer"
-version = "0.2.0"
+version = "0.2.1"
 description = "Inference code for GPT-SoVITS"
 authors = [
     {name = "Yanli",email = "[email protected]"},

src/gpt_sovits/AR/models/t2s_model.py

@@ -1,7 +1,6 @@
 # modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_model.py
 # reference: https://github.com/lifeiteng/vall-e
 import torch
-from tqdm import tqdm
 
 from gpt_sovits.AR.models.utils import make_pad_mask
 from gpt_sovits.AR.models.utils import (
@@ -271,7 +270,7 @@ def infer(
         x_len = x.shape[1]
         x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool)
         stop = False
-        for _ in tqdm(range(1500)):
+        for _ in range(1500):
             y_emb = self.ar_audio_embedding(y)
             y_pos = self.ar_audio_position(y_emb)
             # x 和逐渐增长的 y 一起输入给模型
@@ -311,7 +310,6 @@ def infer(
                 if prompts.shape[1] == y.shape[1]:
                     y = torch.concat([y, torch.zeros_like(samples)], dim=1)
                     print("bad zero prediction")
-                print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]")
                 break
             # 本次生成的 semantic_ids 和之前的 y 构成新的 y
             # print(samples.shape)#[1,1]#第一个1是bs
@@ -390,7 +388,7 @@ def infer_panel(
         )
         xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to(x.device)
 
-        for idx in tqdm(range(1500)):
+        for idx in range(1500):
 
             xy_dec, _ = self.h((xy_pos, None), mask=xy_attn_mask, cache=cache)
             logits = self.ar_predict_layer(
@@ -425,7 +423,6 @@ def infer_panel(
                 if y.shape[1] == 0:
                     y = torch.concat([y, torch.zeros_like(samples)], dim=1)
                     print("bad zero prediction")
-                print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]")
                 break
 
             ####################### update next step ###################################

src/gpt_sovits/infer/__init__.py

@@ -1,3 +1,7 @@
 from gpt_sovits.infer.inference import GPTSoVITSInference
+from gpt_sovits.infer.inference_pool import GPTSoVITSInferencePool
 
-__all__ = ["GPTSoVITSInference"]
+__all__ = [
+    "GPTSoVITSInference",
+    "GPTSoVITSInferencePool",
+]

src/gpt_sovits/infer/inference_pool.py

@@ -134,8 +134,16 @@ def get_tts_wav(
         temperature=1,
     ):
         audio_list: List[Tuple[int, np.ndarray]] = []
+        total = len(clean_and_cut_text(text))
         for thing in tqdm.tqdm(
-            self.get_tts_wav_stream(text, text_language, top_k, top_p, temperature)
+            self.get_tts_wav_stream(
+                text,
+                text_language,
+                top_k,
+                top_p,
+                temperature,
+            ),
+            total=total,
         ):
             audio_list.append(thing)
         return audio_list[0][0], np.concatenate(

src/gpt_sovits/module/core_vq.py

@@ -36,7 +36,6 @@
 import torch
 from torch import nn
 import torch.nn.functional as F
-from tqdm import tqdm
 
 
 def default(val: tp.Any, d: tp.Any) -> tp.Any:
@@ -75,7 +74,7 @@ def kmeans(samples, num_clusters: int, num_iters: int = 10):
     means = sample_vectors(samples, num_clusters)
 
     print("kmeans start ... ")
-    for _ in tqdm(range(num_iters)):
+    for _ in range(num_iters):
         diffs = rearrange(samples, "n d -> n () d") - rearrange(means, "c d -> () c d")
         dists = -(diffs**2).sum(dim=-1)
 

src/gpt_sovits/utils.py

@@ -18,7 +18,6 @@
 
 MATPLOTLIB_FLAG = False
 
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
 logger = logging
 
 
@@ -64,14 +63,18 @@ def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False
     )
     return model, optimizer, learning_rate, iteration
 
+
 from time import time as ttime
 import shutil
-def my_save(fea,path):#####fix issue: torch.save doesn't support chinese path
-    dir=os.path.dirname(path)
-    name=os.path.basename(path)
-    tmp_path="%s.pth"%(ttime())
-    torch.save(fea,tmp_path)
-    shutil.move(tmp_path,"%s/%s"%(dir,name))
+
+
+def my_save(fea, path):  #####fix issue: torch.save doesn't support chinese path
+    dir = os.path.dirname(path)
+    name = os.path.basename(path)
+    tmp_path = "%s.pth" % (ttime())
+    torch.save(fea, tmp_path)
+    shutil.move(tmp_path, "%s/%s" % (dir, name))
+
 
 def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
     logger.info(