Added support for inference text slicing in api.py

api.py

@@ -35,7 +35,8 @@ POST:
 ```json
 {
     "text": "先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。",
-    "text_language": "zh"
+    "text_language": "zh", 
+    "slice": "按标点符号切"
 }
 ```
 
@@ -120,7 +121,7 @@ RESP: 无
 
 
 import argparse
-import os
+import os, re
 import sys
 
 now_dir = os.getcwd()
@@ -166,6 +167,7 @@ parser.add_argument("-a", "--bind_addr", type=str, default="0.0.0.0", help="defa
 parser.add_argument("-p", "--port", type=int, default=g_config.api_port, help="default: 9880")
 parser.add_argument("-fp", "--full_precision", action="store_true", default=False, help="覆盖config.is_half为False, 使用全精度")
 parser.add_argument("-hp", "--half_precision", action="store_true", default=False, help="覆盖config.is_half为True, 使用半精度")
+#parser.add_argument("-sl", "--slice", type=str, default="No slice", help="文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。")
 # bool值的用法为 `python ./api.py -fp ...`
 # 此时 full_precision==True, half_precision==False
 
@@ -375,6 +377,19 @@ dict_language = {
     "多语种混合": "auto"
 }
 
+slice_option = {
+    "凑四句一切": "凑四句一切",
+    "凑50字一切": "凑50字一切",
+    "按中文句号。切": "按中文句号。切",
+    "按英文句号.切": "按英文句号.切",
+    "按标点符号切": "按标点符号切",
+    "per 4 sentences": "凑四句一切",
+    "per 50 letters": "凑50字一切",
+    "per period": "按英文句号.切",
+    "per punctuation mark": "按标点符号切",
+    None: "No slice"
+}
+
 dtype=torch.float16 if is_half == True else torch.float32
 def get_bert_inf(phones, word2ph, norm_text, language):
     language=language.replace("all_","")
@@ -447,13 +462,41 @@ def get_phones_and_bert(text,language):
 
     return phones,bert.to(dtype),norm_text
 
-def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language):
+def merge_short_text_in_array(texts, threshold):
+    if (len(texts)) < 2:
+        return texts
+    result = []
+    text = ""
+    for ele in texts:
+        text += ele
+        if len(text) >= threshold:
+            result.append(text)
+            text = ""
+    if (len(text) > 0):
+        if len(result) == 0:
+            result.append(text)
+        else:
+            result[len(result) - 1] += text
+    return result
+    
+def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut):
+    # not supporting ref_free
     t0 = ttime()
+    prompt_language = dict_language[prompt_language]
     prompt_text = prompt_text.strip("\n")
-    prompt_language, text = prompt_language, text.strip("\n")
+    if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
-    zero_wav = np.zeros(int(hps.data.sampling_rate * 0.3), dtype=np.float16 if is_half == True else np.float32)
+    print(f"实际输入的参考文本: {prompt_text}")
+    text = text.strip("\n")
+    if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text
+    print(f"实际输入的目标文本: {text}")
+
+    zero_wav = np.zeros(
+        int(hps.data.sampling_rate * 0.3), 
+        dtype=np.float16 if is_half == True else np.float32
+    )
     with torch.no_grad():
         wav16k, sr = librosa.load(ref_wav_path, sr=16000)
+        # neglected error checking for reference audio duration
         wav16k = torch.from_numpy(wav16k)
         zero_wav_torch = torch.from_numpy(zero_wav)
         if (is_half == True):
@@ -463,20 +506,48 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language)
             wav16k = wav16k.to(device)
             zero_wav_torch = zero_wav_torch.to(device)
         wav16k = torch.cat([wav16k, zero_wav_torch])
-        ssl_content = ssl_model.model(wav16k.unsqueeze(0))["last_hidden_state"].transpose(1, 2)  # .float()
+        ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
+            "last_hidden_state"
+            ].transpose(
+                1, 2
+            )  # .float()
         codes = vq_model.extract_latent(ssl_content)
+
         prompt_semantic = codes[0, 0]
     t1 = ttime()
-    prompt_language = dict_language[prompt_language]
+    
-    text_language = dict_language[text_language]
+    # 文本切句 
-    phones1, word2ph1, norm_text1 = clean_text(prompt_text, prompt_language)
+    # default to no slice if no argument is provided
-    phones1 = cleaned_text_to_sequence(phones1)
+    how_to_cut = slice_option[how_to_cut]
+    print(f"[INFO] 文本切句選項: {how_to_cut}")
+    if (how_to_cut == "凑四句一切"):
+            text = cut1(text)
+    elif (how_to_cut == "凑50字一切"):
+        text = cut2(text)
+    elif (how_to_cut == "按英文句号.切"):
+        text = cut3(text)
+    elif (how_to_cut == "按英文句号.切"):
+        text = cut4(text)
+    elif (how_to_cut == "按标点符号切"):
+        text = cut5(text)
+
+    while "\n\n" in text:
+        text = text.replace("\n\n", "\n")
+    print(f"实际输入的目标文本(切句后): {text}")
     texts = text.split("\n")
+    texts = merge_short_text_in_array(texts, 5)
     audio_opt = []
+
     phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language)
 
     for text in texts:
+        # 解决输入目标文本的空行导致报错的问题
+        if (len(text.strip()) == 0):
+            continue
+        if (text[-1] not in splits): text += "。" if text_language != "en" else "."
+        print(f"实际输入的目标文本(每句): {text}")
         phones2,bert2,norm_text2=get_phones_and_bert(text, text_language)
+        print(f"前端处理后的文本(每句): {norm_text2}")
         bert = torch.cat([bert1, bert2], 1)
         all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0)
 
@@ -496,23 +567,118 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language)
                 early_stop_num=hz * max_sec)
         t3 = ttime()
         # print(pred_semantic.shape,idx)
-        pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)  # .unsqueeze(0)#mq要多unsqueeze一次
+        pred_semantic = pred_semantic[:, -idx:].unsqueeze(
+            0
+        )  # .unsqueeze(0)#mq要多unsqueeze一次
         refer = get_spepc(hps, ref_wav_path)  # .to(device)
         if (is_half == True):
             refer = refer.half().to(device)
         else:
             refer = refer.to(device)
         # audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
-        audio = \
+        audio = (
-            vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0),
+            vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer
-                            refer).detach().cpu().numpy()[
+            )
-                0, 0]  ###试试重建不带上prompt部分
+                .detach()
+                .cpu()
+                .numpy()[0, 0]  
+                ) ###试试重建不带上prompt部分
+        max_audio=np.abs(audio).max()#简单防止16bit爆音
+        if max_audio>1:audio/=max_audio
         audio_opt.append(audio)
         audio_opt.append(zero_wav)
         t4 = ttime()
     print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
-    yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16)
+    yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(
+        np.int16
+    )
 
+splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", }
+
+def get_first(text):
+    pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
+    text = re.split(pattern, text)[0].strip()
+    return text
+
+def split(todo_text):
+    todo_text = todo_text.replace("……", "。").replace("——", "，")
+    if todo_text[-1] not in splits:
+        todo_text += "。"
+    i_split_head = i_split_tail = 0
+    len_text = len(todo_text)
+    todo_texts = []
+    while 1:
+        if i_split_head >= len_text:
+            break  # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入
+        if todo_text[i_split_head] in splits:
+            i_split_head += 1
+            todo_texts.append(todo_text[i_split_tail:i_split_head])
+            i_split_tail = i_split_head
+        else:
+            i_split_head += 1
+    return todo_texts
+
+def cut1(inp):
+    inp = inp.strip("\n")
+    inps = split(inp)
+    split_idx = list(range(0, len(inps), 4))
+    split_idx[-1] = None
+    if len(split_idx) > 1:
+        opts = []
+        for idx in range(len(split_idx) - 1):
+            opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]]))
+    else:
+        opts = [inp]
+    return "\n".join(opts)
+
+
+def cut2(inp):
+    inp = inp.strip("\n")
+    inps = split(inp)
+    if len(inps) < 2:
+        return inp
+    opts = []
+    summ = 0
+    tmp_str = ""
+    for i in range(len(inps)):
+        summ += len(inps[i])
+        tmp_str += inps[i]
+        if summ > 50:
+            summ = 0
+            opts.append(tmp_str)
+            tmp_str = ""
+    if tmp_str != "":
+        opts.append(tmp_str)
+    # print(opts)
+    if len(opts) > 1 and len(opts[-1]) < 50:  ##如果最后一个太短了，和前一个合一起
+        opts[-2] = opts[-2] + opts[-1]
+        opts = opts[:-1]
+    return "\n".join(opts)
+
+
+def cut3(inp):
+    inp = inp.strip("\n")
+    return "\n".join(["%s" % item for item in inp.strip("。").split("。")])
+
+
+def cut4(inp):
+    inp = inp.strip("\n")
+    return "\n".join(["%s" % item for item in inp.strip(".").split(".")])
+
+
+# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
+def cut5(inp):
+    # if not re.search(r'[^\w\s]', inp[-1]):
+    # inp += '。'
+    inp = inp.strip("\n")
+    punds = r'[,.;?!、，。？！;：…]'
+    items = re.split(f'({punds})', inp)
+    mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])]
+    # 在句子不存在符号或句尾无符号的时候保证文本完整
+    if len(items)%2 == 1:
+        mergeitems.append(items[-1])
+    opt = "\n".join(mergeitems)
+    return opt
 
 def handle_control(command):
     if command == "restart":
@@ -541,7 +707,7 @@ def handle_change(path, text, language):
     return JSONResponse({"code": 0, "message": "Success"}, status_code=200)
 
 
-def handle(refer_wav_path, prompt_text, prompt_language, text, text_language):
+def handle(refer_wav_path, prompt_text, prompt_language, text, text_language, slice):
     if (
             refer_wav_path == "" or refer_wav_path is None
             or prompt_text == "" or prompt_text is None
@@ -557,7 +723,7 @@ def handle(refer_wav_path, prompt_text, prompt_language, text, text_language):
 
     with torch.no_grad():
         gen = get_tts_wav(
-            refer_wav_path, prompt_text, prompt_language, text, text_language
+            refer_wav_path, prompt_text, prompt_language, text, text_language, slice
         )
         sampling_rate, audio_data = next(gen)
 
@@ -628,6 +794,7 @@ async def tts_endpoint(request: Request):
         json_post_raw.get("prompt_language"),
         json_post_raw.get("text"),
         json_post_raw.get("text_language"),
+        json_post_raw.get("slice"),
     )
 
 
@@ -638,8 +805,9 @@ async def tts_endpoint(
         prompt_language: str = None,
         text: str = None,
         text_language: str = None,
+        slice: str = None
 ):
-    return handle(refer_wav_path, prompt_text, prompt_language, text, text_language)
+    return handle(refer_wav_path, prompt_text, prompt_language, text, text_language, slice)
 
 
 if __name__ == "__main__":