modified: .gitignore

modified:   GPT_SoVITS/AR/models/t2s_model.py
	modified:   GPT_SoVITS/AR/models/utils.py
	modified:   GPT_SoVITS/inference_webui.py

.gitignore

@@ -9,4 +9,6 @@ logs
 reference
 GPT_weights
 SoVITS_weights
-TEMP
+TEMP
+gweight.txt
+sweight.txt

GPT_SoVITS/AR/models/t2s_model.py

@@ -329,6 +329,7 @@ class Text2SemanticDecoder(nn.Module):
         bert_feature,
         top_k: int = -100,
         top_p: int = 100,
+        min_p: int = 80,
         early_stop_num: int = -1,
         temperature: float = 1.0,
     ):
@@ -397,7 +398,7 @@ class Text2SemanticDecoder(nn.Module):
             if(idx==0):###第一次跑不能EOS否则没有了
                 logits = logits[:, :-1]  ###刨除1024终止符号的概率
             samples = sample(
-                logits[0], y, top_k=top_k, top_p=top_p, repetition_penalty=1.35, temperature=temperature
+                logits[0], y, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=1.35, temperature=temperature
             )[0].unsqueeze(0)
             # 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

GPT_SoVITS/AR/models/utils.py

@@ -112,6 +112,7 @@ def logits_to_probs(
     previous_tokens: Optional[torch.Tensor] = None,
     temperature: float = 1.0,
     top_k: Optional[int] = None,
+    min_p: Optional[float] = None,
     top_p: Optional[int] = None,
     repetition_penalty: float = 1.0,
 ):
@@ -127,6 +128,12 @@ def logits_to_probs(
         )
         logits.scatter_(dim=0, index=previous_tokens, src=score)
 
+    # Min-p sampling, we sample tokens with a probability above the min_p * max_prob where max_prob is the maximum probability in the distribution
+    if min_p is not None and min_p < 1.0 and min_p > 0.0:
+        max_prob = torch.max(logits)
+        logits = torch.where(logits < min_p * max_prob, -float("Inf"), logits)
+
+    # Top-p sampling, we sample from the smallest set of tokens whose cumulative probability mass exceeds the threshold `p`
     if top_p is not None and top_p < 1.0:
         sorted_logits, sorted_indices = torch.sort(logits, descending=True)
         cum_probs = torch.cumsum(
@@ -139,8 +146,10 @@ def logits_to_probs(
         )
         logits = logits.masked_fill(indices_to_remove, -float("Inf"))
 
+    # Use temperature to smooth the distribution
     logits = logits / max(temperature, 1e-5)
 
+    # Top-k sampling, we sample only from the top k most likely tokens
     if top_k is not None:
         v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
         pivot = v.select(-1, -1).unsqueeze(-1)

GPT_SoVITS/inference_webui.py

@@ -311,7 +311,7 @@ def merge_short_text_in_array(texts, threshold):
             result[len(result) - 1] += text
     return result
 
-def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, top_p=0.6, temperature=0.6, ref_free = False):
+def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, min_p=0.8, top_p=1.0, temperature=0.6, ref_free = False):
     if prompt_text is None or len(prompt_text) == 0:
         ref_free = True
     t0 = ttime()
@@ -399,6 +399,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
                 bert,
                 # prompt_phone_len=ph_offset,
                 top_k=top_k,
+                min_p=min_p,
                 top_p=top_p,
                 temperature=temperature,
                 early_stop_num=hz * max_sec,
@@ -586,6 +587,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
             with gr.Row():
                 gr.Markdown(value=i18n("gpt采样参数(无参考文本时不要太低)："))
                 top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=5,interactive=True)
+                min_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("min_p"),value=0.8,interactive=True)
                 top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True)
                 temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True)
             inference_button = gr.Button(i18n("合成语音"), variant="primary")
@@ -593,7 +595,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
 
         inference_button.click(
             get_tts_wav,
-            [inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, top_p, temperature, ref_text_free],
+            [inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, min_p, top_p, temperature, ref_text_free],
             [output],
         )