Of course it didn't work. Now I get to go on another patching odyssey! Yay!

GPT_SoVITS/inference_webui.py

@@ -1,271 +1,271 @@
-# Based on GPT-SoVITS-fast-inference by ChasonJiang
-
-import random
-import os
-import torch
-import spaces
-
-if torch.cuda.is_available():
-    device = "cuda"
-else:
-    device = "cpu"
-
-import re, logging
-logging.getLogger("markdown_it").setLevel(logging.ERROR)
-logging.getLogger("urllib3").setLevel(logging.ERROR)
-logging.getLogger("httpcore").setLevel(logging.ERROR)
-logging.getLogger("httpx").setLevel(logging.ERROR)
-logging.getLogger("asyncio").setLevel(logging.ERROR)
-logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
-logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
-import pdb
-import json
-
-infer_ttswebui = os.environ.get("infer_ttswebui", 9872)
-infer_ttswebui = int(infer_ttswebui)
-is_share = os.environ.get("is_share", "False")
-is_share = eval(is_share)
-if "_CUDA_VISIBLE_DEVICES" in os.environ:
-    os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
-is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
-gpt_path=None
-sovits_path=None
-#gpt_path = os.environ.get("gpt_path", None)
-#sovits_path = os.environ.get("sovits_path", None)
-cnhubert_base_path = os.environ.get("cnhubert_base_path", None)
-bert_path = os.environ.get("bert_path", None)
-
-import gradio as gr
-from TTS_infer_pack.TTS import TTS, TTS_Config
-from TTS_infer_pack.text_segmentation_method import get_method
-
-
-dict_language = {
-    "ZH": "all_zh",#全部按中文识别
-    "EN": "en",#全部按英文识别#######不变
-    "JP": "all_ja",#全部按日文识别
-    "ZH/EN": "zh",#按中英混合识别####不变
-    "JP/EN": "ja",#按日英混合识别####不变
-    "Automatic": "auto",#多语种启动切分识别语种
-}
-
-cut_method = {
-    "None":"cut0",
-    "4 Sentences": "cut1",
-    "50 Characters": "cut2",
-    "ZH/JP Punctuation": "cut3",
-    "EN Punctuation": "cut4",
-    "All Punctuation": "cut5",
-}
-
-tts_config = TTS_Config("GPT_SoVITS/configs/tts_infer.yaml")
-tts_config.device = device
-tts_config.is_half = is_half
-if gpt_path is not None:
-    tts_config.t2s_weights_path = gpt_path
-if sovits_path is not None:
-    tts_config.vits_weights_path = sovits_path
-if cnhubert_base_path is not None:
-    tts_config.cnhuhbert_base_path = cnhubert_base_path
-if bert_path is not None:
-    tts_config.bert_base_path = bert_path
-
-print(tts_config)
-tts_pipeline = TTS(tts_config)
-gpt_path = tts_config.t2s_weights_path
-sovits_path = tts_config.vits_weights_path
-
-clm= ""
-
-@spaces.GPU()
-def inference(name, gptmp, svmp, sty, text, text_lang, 
-              ref_audio_path, prompt_text, 
-              prompt_lang, top_k, 
-              top_p, temperature, 
-              text_split_method, batch_size, 
-              speed_factor,
-              split_bucket,fragment_interval,
-              seed, keep_random, parallel_infer,
-              repetition_penalty
-              ):
-
-    global clm
-    #Live switching
-    if(not ref_audio_path):
-        ref_audio_path=f"referenceaudio/{name}/"+referencedata[name][0][sty]
-        prompt_text=referencedata[name][1][sty]
-    if clm!=name:
-        print(f"Switching to model {name}")
-        clm=name
-        tts_pipeline.init_t2s_weights(gptmp)
-        tts_pipeline.init_vits_weights(svmp)
-
-    seed = -1 if keep_random else seed
-    actual_seed = seed if seed not in [-1, "", None] else random.randrange(1 << 32)
-    print(f"TMP: {temperature} | SPDFCT: {speed_factor} | STY: {sty} | LANG: {text_lang}")
-    inputs={
-        "text": text,
-        "text_lang": dict_language[text_lang],
-        "ref_audio_path": ref_audio_path,
-        "prompt_text": prompt_text,
-        "prompt_lang": dict_language[prompt_lang],
-        "top_k": top_k,
-        "top_p": top_p,
-        "temperature": temperature,
-        "text_split_method": cut_method[text_split_method],
-        "batch_size":int(batch_size),
-        "speed_factor":float(speed_factor),
-        "split_bucket":split_bucket,
-        "return_fragment":False,
-        "fragment_interval":fragment_interval,
-        "seed":actual_seed,
-        "parallel_infer": parallel_infer,
-        "repetition_penalty": repetition_penalty,
-    }
-    for item in tts_pipeline.run(inputs):
-        yield item, actual_seed
-
-def custom_sort_key(s):
-    # 使用正则表达式提取字符串中的数字部分和非数字部分
-    parts = re.split('(\d+)', s)
-    # 将数字部分转换为整数，非数字部分保持不变
-    parts = [int(part) if part.isdigit() else part for part in parts]
-    return parts
-
-
-def change_choices():
-    SoVITS_names, GPT_names = get_weights_names()
-    return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names, key=custom_sort_key), "__type__": "update"}
-
-
-pretrained_sovits_name = "GPT_SoVITS/pretrained_models/s2G488k.pth"
-pretrained_gpt_name = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
-SoVITS_weight_root = "GPT_SoVITS/SoVITS_weights/"
-GPT_weight_root = "GPT_SoVITS/GPT_weights/"
-
-def get_weights_names():
-    SoVITS_names = [pretrained_sovits_name]
-    for name in os.listdir(SoVITS_weight_root):
-        if name.endswith(".pth"): SoVITS_names.append("%s/%s" % (SoVITS_weight_root, name))
-    GPT_names = [pretrained_gpt_name]
-    for name in os.listdir(GPT_weight_root):
-        if name.endswith(".ckpt"): GPT_names.append("%s/%s" % (GPT_weight_root, name))
-    return SoVITS_names, GPT_names
-
-def load_models():
-    print("Loading models...")
-    voices=[]
-    ustyles={}
-    with open("voicelist.json", "r", encoding="utf-8") as f:
-        voc_info = json.load(f)
-    for name, info in voc_info.items():
-        if not info['enable']:
-            continue
-        title= info['title']
-        gptmodelpath= "%s/%s" % (GPT_weight_root, info['gpt_model_path'])
-        sovitsmodelpath= "%s/%s" % (SoVITS_weight_root, info['sovits_model_path'])
-        author= info['modelauthor']
-        image = info['cover']
-        styles = info['styles']
-        #check that all styles properly exist
-        for s in styles.values():
-            if(not os.path.exists(f"referenceaudio/{name}/{s}")):
-                print(f"WARNING : Some defined preset styles do not exist for model {name}, skipping")
-                styles=None
-                break
-        styletrans = info['styletrans']
-        st=[styles, styletrans]
-        voices.append((name, title, gptmodelpath, sovitsmodelpath, author, image))
-        ustyles[name]=st
-        print(f"Indexed model {title}")
-    return voices, ustyles
-
-modeldata, referencedata = load_models()
-
-#Gradio preload
-text = gr.TextArea(label="Input Text", value="Hello there! This is test audio of a new text to speech tool.")
-text_language = gr.Dropdown(label="Language", choices=["EN", "JP", "ZH", "ZH/EN", "JP/EN", "Automatic"], value="EN")
-how_to_cut = gr.Dropdown(label="Slicing Method",
-    choices=["None", "4 Sentences", "50 Characters", "ZH/JP Punctuation", "EN Punctuation", "All Punctuation" ],
-    value="4 Sentences",
-    interactive=True,
-)
-top_k = gr.Slider(minimum=1,maximum=100,step=1,label="Top_k",value=5,interactive=True)
-top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label="Top_p",value=1,interactive=True)
-temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label="Temperature",value=0.7,interactive=True)
-batch_size = gr.Slider(minimum=1,maximum=200,step=1,label="Batch Size",value=20,interactive=True)             
-fragment_interval = gr.Slider(minimum=0.01,maximum=1,step=0.01,label="Fragment Interval",value=0.3,interactive=True)
-speed_factor = gr.Slider(minimum=0.50,maximum=2,step=0.05,label="Speed Factor",value=1.0,interactive=True)
-repetition_penalty = gr.Slider(minimum=0,maximum=2,step=0.05,label="Repetition Penalty",value=1.35,interactive=True)
-parallel_infer = gr.Checkbox(label="Parallel Infer", value=True, interactive=True, show_label=True)
-split_bucket = gr.Checkbox(label="Split Bucket", value=True, interactive=True, show_label=True)
-seed = gr.Number(label="Random Seed",value=-1, interactive=True, show_label=True)
-keep_random = gr.Checkbox(label="Use Randomized Seed", value=True, interactive=True, show_label=True)
-
-#Main gradio
-with gr.Blocks(title="Lemonfoot GPT-SoVITS") as app:
-    gr.Markdown(
-        "# Lemonfoot GPT-SoVITS 🚀🍋\n"
-        "### Space by Kit Lemonfoot / Noel Shirogane's High Flying Birds\n"
-        "Based on code originally by RVC_Boss and ChasonJiang\n\n"
-        "Do no evil.\n\n"
-    )
-    for (name, title, gptmodelpath, sovitsmodelpath, author, image) in modeldata:
-        with gr.TabItem(name):
-            with gr.Row():
-                with gr.Column():
-                    n = gr.Textbox(value=name, visible=False, interactive=False)
-                    gptmp = gr.Textbox(value=gptmodelpath, visible=False, interactive=False)
-                    svmp = gr.Textbox(value=sovitsmodelpath, visible=False, interactive=False)
-                    gr.Markdown(f"**{title}**\n\n Dataset author: {author}")
-                    gr.Image(f"images/{image}", label=None, show_label=False, width=300, show_download_button=False, container=False, show_share_button=False)
-                with gr.Column():
-                    #if there isn't any styles don't bother rendering the style window
-                    if(not referencedata[name][0]==None):
-                        rd = list(referencedata[name][0].keys())
-                        with gr.TabItem("Style using a preset"):
-                            sty = gr.Dropdown(
-                                label="Current style",
-                                choices=rd,
-                                value=rd[0],
-                                interactive=True
-                            )
-                    else:
-                        sty=gr.Textbox(value="none", visible=False, interactive=False)
-                    with gr.TabItem("Style using a different audio"):
-                        with gr.Column():
-                            ref_audio_path = gr.Audio(label="Reference Audio", type="filepath")
-                            prompt_text = gr.Textbox(label="Reference Audio Text", interactive=True, placeholder="Leave blank to use no-text reference mode.")
-                            prompt_language = gr.Dropdown(label="Reference Audio Language", choices=["EN", "JP", "ZH", "ZH/EN", "JP/EN", "Automatic"], value="EN")
-                with gr.Column():
-                    inference_button = gr.Button("Synthesize", variant="primary")
-                    output = gr.Audio(label="Output")
-
-                    inference_button.click(
-                        inference,
-                        inputs=[n, gptmp, svmp, sty, text, text_language, ref_audio_path, prompt_text, prompt_language, top_k, top_p, temperature, how_to_cut, batch_size, speed_factor, split_bucket, fragment_interval, seed, keep_random, parallel_infer, repetition_penalty],
-                        outputs=[output, seed]
-                    )
-
-    #bottom info
-    with gr.Row():
-        with gr.Column():
-            text.render()
-            text_language.render()
-            how_to_cut.render()
-        with gr.Column():
-            temperature.render()
-            speed_factor.render()
-            with gr.Accordion("Advanced Inference Parameters", open=False):
-                top_k.render()
-                top_p.render()
-                batch_size.render()
-                fragment_interval.render()
-                repetition_penalty.render()
-                parallel_infer.render()
-                split_bucket.render()
-                seed.render()
-                keep_random.render()
-
-
-app.queue().launch()
+# Based on GPT-SoVITS-fast-inference by ChasonJiang
+
+import random
+import os
+import torch
+
+if torch.cuda.is_available():
+    device = "cuda"
+else:
+    device = "cpu"
+
+import re, logging
+logging.getLogger("markdown_it").setLevel(logging.ERROR)
+logging.getLogger("urllib3").setLevel(logging.ERROR)
+logging.getLogger("httpcore").setLevel(logging.ERROR)
+logging.getLogger("httpx").setLevel(logging.ERROR)
+logging.getLogger("asyncio").setLevel(logging.ERROR)
+logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
+logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
+import pdb
+import json
+
+infer_ttswebui = os.environ.get("infer_ttswebui", 9872)
+infer_ttswebui = int(infer_ttswebui)
+is_share = os.environ.get("is_share", "False")
+is_share = eval(is_share)
+if "_CUDA_VISIBLE_DEVICES" in os.environ:
+    os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
+is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
+gpt_path=None
+sovits_path=None
+#gpt_path = os.environ.get("gpt_path", None)
+#sovits_path = os.environ.get("sovits_path", None)
+cnhubert_base_path = os.environ.get("cnhubert_base_path", None)
+bert_path = os.environ.get("bert_path", None)
+
+import gradio as gr
+from TTS_infer_pack.TTS import TTS, TTS_Config
+from TTS_infer_pack.text_segmentation_method import get_method
+
+import nltk
+nltk.download('averaged_perceptron_tagger_eng')
+
+dict_language = {
+    "ZH": "all_zh",#全部按中文识别
+    "EN": "en",#全部按英文识别#######不变
+    "JP": "all_ja",#全部按日文识别
+    "ZH/EN": "zh",#按中英混合识别####不变
+    "JP/EN": "ja",#按日英混合识别####不变
+    "Automatic": "auto",#多语种启动切分识别语种
+}
+
+cut_method = {
+    "None":"cut0",
+    "4 Sentences": "cut1",
+    "50 Characters": "cut2",
+    "ZH/JP Punctuation": "cut3",
+    "EN Punctuation": "cut4",
+    "All Punctuation": "cut5",
+}
+
+tts_config = TTS_Config("GPT_SoVITS/configs/tts_infer.yaml")
+tts_config.device = device
+tts_config.is_half = is_half
+if gpt_path is not None:
+    tts_config.t2s_weights_path = gpt_path
+if sovits_path is not None:
+    tts_config.vits_weights_path = sovits_path
+if cnhubert_base_path is not None:
+    tts_config.cnhuhbert_base_path = cnhubert_base_path
+if bert_path is not None:
+    tts_config.bert_base_path = bert_path
+
+print(tts_config)
+tts_pipeline = TTS(tts_config)
+gpt_path = tts_config.t2s_weights_path
+sovits_path = tts_config.vits_weights_path
+
+clm= ""
+
+def inference(name, gptmp, svmp, sty, text, text_lang, 
+              ref_audio_path, prompt_text, 
+              prompt_lang, top_k, 
+              top_p, temperature, 
+              text_split_method, batch_size, 
+              speed_factor,
+              split_bucket,fragment_interval,
+              seed, keep_random, parallel_infer,
+              repetition_penalty
+              ):
+
+    global clm
+    #Live switching
+    if(not ref_audio_path):
+        ref_audio_path=f"referenceaudio/{name}/"+referencedata[name][0][sty]
+        prompt_text=referencedata[name][1][sty]
+    if clm!=name:
+        print(f"Switching to model {name}")
+        clm=name
+        tts_pipeline.init_t2s_weights(gptmp)
+        tts_pipeline.init_vits_weights(svmp)
+
+    seed = -1 if keep_random else seed
+    actual_seed = seed if seed not in [-1, "", None] else random.randrange(1 << 32)
+    print(f"TMP: {temperature} | SPDFCT: {speed_factor} | STY: {sty} | LANG: {text_lang}")
+    inputs={
+        "text": text,
+        "text_lang": dict_language[text_lang],
+        "ref_audio_path": ref_audio_path,
+        "prompt_text": prompt_text,
+        "prompt_lang": dict_language[prompt_lang],
+        "top_k": top_k,
+        "top_p": top_p,
+        "temperature": temperature,
+        "text_split_method": cut_method[text_split_method],
+        "batch_size":int(batch_size),
+        "speed_factor":float(speed_factor),
+        "split_bucket":split_bucket,
+        "return_fragment":False,
+        "fragment_interval":fragment_interval,
+        "seed":actual_seed,
+        "parallel_infer": parallel_infer,
+        "repetition_penalty": repetition_penalty,
+    }
+    for item in tts_pipeline.run(inputs):
+        yield item, actual_seed
+
+def custom_sort_key(s):
+    # 使用正则表达式提取字符串中的数字部分和非数字部分
+    parts = re.split('(\d+)', s)
+    # 将数字部分转换为整数，非数字部分保持不变
+    parts = [int(part) if part.isdigit() else part for part in parts]
+    return parts
+
+
+def change_choices():
+    SoVITS_names, GPT_names = get_weights_names()
+    return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names, key=custom_sort_key), "__type__": "update"}
+
+
+pretrained_sovits_name = "GPT_SoVITS/pretrained_models/s2G488k.pth"
+pretrained_gpt_name = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
+SoVITS_weight_root = "GPT_SoVITS/SoVITS_weights/"
+GPT_weight_root = "GPT_SoVITS/GPT_weights/"
+
+def get_weights_names():
+    SoVITS_names = [pretrained_sovits_name]
+    for name in os.listdir(SoVITS_weight_root):
+        if name.endswith(".pth"): SoVITS_names.append("%s/%s" % (SoVITS_weight_root, name))
+    GPT_names = [pretrained_gpt_name]
+    for name in os.listdir(GPT_weight_root):
+        if name.endswith(".ckpt"): GPT_names.append("%s/%s" % (GPT_weight_root, name))
+    return SoVITS_names, GPT_names
+
+def load_models():
+    print("Loading models...")
+    voices=[]
+    ustyles={}
+    with open("voicelist.json", "r", encoding="utf-8") as f:
+        voc_info = json.load(f)
+    for name, info in voc_info.items():
+        if not info['enable']:
+            continue
+        title= info['title']
+        gptmodelpath= "%s/%s" % (GPT_weight_root, info['gpt_model_path'])
+        sovitsmodelpath= "%s/%s" % (SoVITS_weight_root, info['sovits_model_path'])
+        author= info['modelauthor']
+        image = info['cover']
+        styles = info['styles']
+        #check that all styles properly exist
+        for s in styles.values():
+            if(not os.path.exists(f"referenceaudio/{name}/{s}")):
+                print(f"WARNING : Some defined preset styles do not exist for model {name}, skipping")
+                styles=None
+                break
+        styletrans = info['styletrans']
+        st=[styles, styletrans]
+        voices.append((name, title, gptmodelpath, sovitsmodelpath, author, image))
+        ustyles[name]=st
+        print(f"Indexed model {title}")
+    return voices, ustyles
+
+modeldata, referencedata = load_models()
+
+#Gradio preload
+text = gr.TextArea(label="Input Text", value="Hello there! This is test audio of a new text to speech tool.")
+text_language = gr.Dropdown(label="Language", choices=["EN", "JP", "ZH", "ZH/EN", "JP/EN", "Automatic"], value="EN")
+how_to_cut = gr.Dropdown(label="Slicing Method",
+    choices=["None", "4 Sentences", "50 Characters", "ZH/JP Punctuation", "EN Punctuation", "All Punctuation" ],
+    value="4 Sentences",
+    interactive=True,
+)
+top_k = gr.Slider(minimum=1,maximum=100,step=1,label="Top_k",value=5,interactive=True)
+top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label="Top_p",value=1,interactive=True)
+temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label="Temperature",value=0.7,interactive=True)
+batch_size = gr.Slider(minimum=1,maximum=200,step=1,label="Batch Size",value=20,interactive=True)             
+fragment_interval = gr.Slider(minimum=0.01,maximum=1,step=0.01,label="Fragment Interval",value=0.3,interactive=True)
+speed_factor = gr.Slider(minimum=0.50,maximum=2,step=0.05,label="Speed Factor",value=1.0,interactive=True)
+repetition_penalty = gr.Slider(minimum=0,maximum=2,step=0.05,label="Repetition Penalty",value=1.35,interactive=True)
+parallel_infer = gr.Checkbox(label="Parallel Infer", value=True, interactive=True, show_label=True)
+split_bucket = gr.Checkbox(label="Split Bucket", value=True, interactive=True, show_label=True)
+seed = gr.Number(label="Random Seed",value=-1, interactive=True, show_label=True)
+keep_random = gr.Checkbox(label="Use Randomized Seed", value=True, interactive=True, show_label=True)
+
+#Main gradio
+with gr.Blocks(title="Lemonfoot GPT-SoVITS") as app:
+    gr.Markdown(
+        "# Lemonfoot GPT-SoVITS 🚀🍋\n"
+        "### Space by Kit Lemonfoot / Noel Shirogane's High Flying Birds\n"
+        "Based on code originally by RVC_Boss and ChasonJiang\n\n"
+        "Do no evil.\n\n"
+    )
+    for (name, title, gptmodelpath, sovitsmodelpath, author, image) in modeldata:
+        with gr.TabItem(name):
+            with gr.Row():
+                with gr.Column():
+                    n = gr.Textbox(value=name, visible=False, interactive=False)
+                    gptmp = gr.Textbox(value=gptmodelpath, visible=False, interactive=False)
+                    svmp = gr.Textbox(value=sovitsmodelpath, visible=False, interactive=False)
+                    gr.Markdown(f"**{title}**\n\n Dataset author: {author}")
+                    gr.Image(f"images/{image}", label=None, show_label=False, width=300, show_download_button=False, container=False, show_share_button=False)
+                with gr.Column():
+                    #if there isn't any styles don't bother rendering the style window
+                    if(not referencedata[name][0]==None):
+                        rd = list(referencedata[name][0].keys())
+                        with gr.TabItem("Style using a preset"):
+                            sty = gr.Dropdown(
+                                label="Current style",
+                                choices=rd,
+                                value=rd[0],
+                                interactive=True
+                            )
+                    else:
+                        sty=gr.Textbox(value="none", visible=False, interactive=False)
+                    with gr.TabItem("Style using a different audio"):
+                        with gr.Column():
+                            ref_audio_path = gr.Audio(label="Reference Audio", type="filepath")
+                            prompt_text = gr.Textbox(label="Reference Audio Text", interactive=True, placeholder="Leave blank to use no-text reference mode.")
+                            prompt_language = gr.Dropdown(label="Reference Audio Language", choices=["EN", "JP", "ZH", "ZH/EN", "JP/EN", "Automatic"], value="EN")
+                with gr.Column():
+                    inference_button = gr.Button("Synthesize", variant="primary")
+                    output = gr.Audio(label="Output")
+
+                    inference_button.click(
+                        inference,
+                        inputs=[n, gptmp, svmp, sty, text, text_language, ref_audio_path, prompt_text, prompt_language, top_k, top_p, temperature, how_to_cut, batch_size, speed_factor, split_bucket, fragment_interval, seed, keep_random, parallel_infer, repetition_penalty],
+                        outputs=[output, seed]
+                    )
+
+    #bottom info
+    with gr.Row():
+        with gr.Column():
+            text.render()
+            text_language.render()
+            how_to_cut.render()
+        with gr.Column():
+            temperature.render()
+            speed_factor.render()
+            with gr.Accordion("Advanced Inference Parameters", open=False):
+                top_k.render()
+                top_p.render()
+                batch_size.render()
+                fragment_interval.render()
+                repetition_penalty.render()
+                parallel_infer.render()
+                split_bucket.render()
+                seed.render()
+                keep_random.render()
+
+
+app.queue().launch()