Update app.py

app.py

@@ -1,7 +1,6 @@
 import json
 import os
 import re
-
 import librosa
 import numpy as np
 import torch
@@ -12,9 +11,15 @@ import gradio as gr
 from models import SynthesizerTrn
 from text import text_to_sequence, _clean_text
 from mel_processing import spectrogram_torch
+from huggingface_hub import hf_hub_download
 
-limitation = os.getenv("SYSTEM") == "spaces"  # limit text and audio length in huggingface spaces
+# Konfigurasi repositori sumber
+REPO_ID = "Plana-Archive/Plana-TTS"
+SUBFOLDER = "Prosekai-TTS/saved_model"
 
+# Pastikan berjalan di CPU untuk Space gratis
+device = torch.device("cpu")
+limitation = os.getenv("SYSTEM") == "spaces"
 
 def get_text(text, hps, is_phoneme):
     text_norm = text_to_sequence(text, hps.symbols, [] if is_phoneme else hps.data.text_cleaners)
@@ -23,7 +28,6 @@ def get_text(text, hps, is_phoneme):
     text_norm = LongTensor(text_norm)
     return text_norm
 
-
 def create_tts_fn(model, hps, speaker_ids):
     def tts_fn(text, speaker, speed, is_phoneme):
         if limitation:
@@ -31,143 +35,83 @@ def create_tts_fn(model, hps, speaker_ids):
             max_len = 500
             if is_phoneme:
                 max_len *= 3
-            else:
-                if len(hps.data.text_cleaners) > 0 and hps.data.text_cleaners[0] == "zh_ja_mixture_cleaners":
-                    text_len = len(re.sub("(\[ZH\]|\[JA\])", "", text))
             if text_len > max_len:
                 return "Error: Text is too long", None
 
         speaker_id = speaker_ids[speaker]
         stn_tst = get_text(text, hps, is_phoneme)
         with no_grad():
-            x_tst = stn_tst.cuda().unsqueeze(0)
-            x_tst_lengths = LongTensor([stn_tst.size(0)]).cuda()
-            sid = LongTensor([speaker_id]).cuda()
-            audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8,
-                                length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy()
+            x_tst = stn_tst.unsqueeze(0).to(device)
+            x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device)
+            sid = LongTensor([speaker_id]).to(device)
+            audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, 
+                                noise_scale_w=0.8, length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy()
         del stn_tst, x_tst, x_tst_lengths, sid
         return "Success", (hps.data.sampling_rate, audio)
-
     return tts_fn
 
-
-
-
-
 def create_to_phoneme_fn(hps):
     def to_phoneme_fn(text):
         return _clean_text(text, hps.data.text_cleaners) if text != "" else ""
-
     return to_phoneme_fn
 
-
 css = """
-        #advanced-btn {
-            color: white;
-            border-color: black;
-            background: black;
-            font-size: .7rem !important;
-            line-height: 19px;
-            margin-top: 24px;
-            margin-bottom: 12px;
-            padding: 2px 8px;
-            border-radius: 14px !important;
-        }
-        #advanced-options {
-            display: none;
-            margin-bottom: 20px;
-        }
+        #advanced-btn { color: white; border-color: black; background: black; font-size: .7rem !important; border-radius: 14px !important; }
+        #advanced-options { display: none; margin-bottom: 20px; }
 """
 
 if __name__ == '__main__':
-    models_tts = []
-    models_vc = []
-    models_soft_vc = []
-    # {"title": "ハミダシクリエイティブ", "lang": "日本語 (Japanese)", "example":  "こんにちは。", "type": "vits"}
-    name = 'プロセカ TTS'
-    lang = '日本語 (Japanese)'
-    example = 'こんにちは。'
-    config_path = f"saved_model/config.json"
-    model_path = f"saved_model/model.pth"
-    cover_path = f"saved_model/cover.png"
+    # Download file model dari Hugging Face Hub secara otomatis
+    print("[*] Downloading model assets from Hub...")
+    config_path = hf_hub_download(repo_id=REPO_ID, filename="config.json", subfolder=SUBFOLDER)
+    model_path = hf_hub_download(repo_id=REPO_ID, filename="model.pth", subfolder=SUBFOLDER)
+    cover_path = hf_hub_download(repo_id=REPO_ID, filename="cover.png", subfolder=SUBFOLDER)
+
     hps = utils.get_hparams_from_file(config_path)
     model = SynthesizerTrn(
         len(hps.symbols),
         hps.data.filter_length // 2 + 1,
         hps.train.segment_size // hps.data.hop_length,
         n_speakers=hps.data.n_speakers,
-        **hps.model).cuda()
+        **hps.model).to(device)
+    
     utils.load_checkpoint(model_path, model, None)
     model.eval()
+    
     speaker_ids = [sid for sid, name in enumerate(hps.speakers) if name != "None"]
     speakers = [name for sid, name in enumerate(hps.speakers) if name != "None"]
 
-    t = 'vits'
-    models_tts.append((name, cover_path, speakers, lang, example,
-                        hps.symbols, create_tts_fn(model, hps, speaker_ids),
-                        create_to_phoneme_fn(hps)))
-                               
+    models_tts = [('プロセカ TTS', cover_path, speakers, '日本語 (Japanese)', 'こんにちは。',
+                   hps.symbols, create_tts_fn(model, hps, speaker_ids),
+                   create_to_phoneme_fn(hps))]
 
     app = gr.Blocks(css=css)
-
     with app:
-        gr.Markdown("# Project Sekai TTS Using VITS Model\n\n"
-                    "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=kdrkdrkdr.ProsekaTTS)\n\n")
+        gr.Markdown("# Project Sekai TTS Using VITS Model (CPU Mode)\n\n")
         with gr.Tabs():
             with gr.TabItem("TTS"):
-                with gr.Tabs():
-                    for i, (name, cover_path, speakers, lang, example, symbols, tts_fn,
-                            to_phoneme_fn) in enumerate(models_tts):
-                        with gr.TabItem(f"Proseka"):
-                            with gr.Column():
-                                gr.Markdown(f"## {name}\n\n"
-                                            f"![cover](file/{cover_path})\n\n"
-                                            f"lang: {lang}")
-                                tts_input1 = gr.TextArea(label="Text (500 words limitation)", value=example,
-                                                         elem_id=f"tts-input{i}")
-                                tts_input2 = gr.Dropdown(label="Speaker", choices=speakers,
-                                                         type="index", value=speakers[0])
-                                tts_input3 = gr.Slider(label="Speed", value=1, minimum=0.1, maximum=2, step=0.1)
-                                with gr.Accordion(label="Advanced Options", open=False):
-                                    phoneme_input = gr.Checkbox(value=False, label="Phoneme input")
-                                    to_phoneme_btn = gr.Button("Covert text to phoneme")
-                                    phoneme_list = gr.Dataset(label="Phoneme list", components=[tts_input1],
-                                                              samples=[[x] for x in symbols],
-                                                              elem_id=f"phoneme-list{i}")
-                                    phoneme_list_json = gr.Json(value=symbols, visible=False)
-                                tts_submit = gr.Button("Generate", variant="primary")
-                                tts_output1 = gr.Textbox(label="Output Message")
-                                tts_output2 = gr.Audio(label="Output Audio")
-                                tts_submit.click(tts_fn, [tts_input1, tts_input2, tts_input3, phoneme_input],
-                                                 [tts_output1, tts_output2])
-                                to_phoneme_btn.click(to_phoneme_fn, [tts_input1], [tts_input1])
-                                phoneme_list.click(None, [phoneme_list, phoneme_list_json], [],
-                                                   _js=f"""
-                                (i,phonemes) => {{
-                                    let root = document.querySelector("body > gradio-app");
-                                    if (root.shadowRoot != null)
-                                        root = root.shadowRoot;
-                                    let text_input = root.querySelector("#tts-input{i}").querySelector("textarea");
-                                    let startPos = text_input.selectionStart;
-                                    let endPos = text_input.selectionEnd;
-                                    let oldTxt = text_input.value;
-                                    let result = oldTxt.substring(0, startPos) + phonemes[i] + oldTxt.substring(endPos);
-                                    text_input.value = result;
-                                    let x = window.scrollX, y = window.scrollY;
-                                    text_input.focus();
-                                    text_input.selectionStart = startPos + phonemes[i].length;
-                                    text_input.selectionEnd = startPos + phonemes[i].length;
-                                    text_input.blur();
-                                    window.scrollTo(x, y);
-                                    return [];
-                                }}""")
-
-            
-        gr.Markdown(
-            "Official User Page \n\n"
-            "- [https://github.com/kdrkdrkdr/ProsekaTTS](https://github.com/kdrkdrkdr/ProsekaTTS)\n\n"
-            "Reference \n\n"
-            "- [https://huggingface.co/spaces/skytnt/moe-tts](https://huggingface.co/spaces/skytnt/moe-tts)"
-            
-        )
-    app.queue(concurrency_count=3).launch(show_api=False)
+                for i, (name, cover, spks, lang, ex, syms, tts_fn, to_phoneme_fn) in enumerate(models_tts):
+                    with gr.TabItem("Proseka"):
+                        with gr.Column():
+                            gr.Markdown(f"## {name}\n\n![cover](file/{cover})\n\nlang: {lang}")
+                            tts_input1 = gr.TextArea(label="Text", value=ex, elem_id=f"tts-input{i}")
+                            tts_input2 = gr.Dropdown(label="Speaker", choices=spks, type="index", value=spks[0])
+                            tts_input3 = gr.Slider(label="Speed", value=1, minimum=0.1, maximum=2, step=0.1)
+                            
+                            with gr.Accordion(label="Advanced Options", open=False):
+                                phoneme_input = gr.Checkbox(value=False, label="Phoneme input")
+                                to_phoneme_btn = gr.Button("Convert text to phoneme")
+                                phoneme_list = gr.Dataset(label="Phoneme list", components=[tts_input1], 
+                                                          samples=[[x] for x in syms])
+                                phoneme_list_json = gr.Json(value=syms, visible=False)
+                                
+                            tts_submit = gr.Button("Generate", variant="primary")
+                            tts_output1 = gr.Textbox(label="Output Message")
+                            tts_output2 = gr.Audio(label="Output Audio")
+                            
+                            tts_submit.click(tts_fn, [tts_input1, tts_input2, tts_input3, phoneme_input], [tts_output1, tts_output2])
+                            to_phoneme_btn.click(to_phoneme_fn, [tts_input1], [tts_input1])
+
+        gr.Markdown("Official User Page: [Plana-Archive](https://huggingface.co/Plana-Archive)")
+
+    app.queue().launch(show_api=False)