updated

webui.py

@@ -1,256 +1,274 @@
-import gradio as gr
-import os
-import re
-import torch
-import numpy as np
-from scipy.io.wavfile import write
-from phonemizer.backend.espeak.wrapper import EspeakWrapper
-from safetensors.torch import load_file
-from tts import commons
-from tts import utils
-from tts.models import SynthesizerTrn
-from text.symbols import symbols
-from text import text_to_sequence
-
-_ESPEAK_LIBRARY = r"C:\Program Files\eSpeak NG\libespeak-ng.dll"
-if os.path.exists(_ESPEAK_LIBRARY):
-    EspeakWrapper.set_library(_ESPEAK_LIBRARY)
-    print(f"✅ Found eSpeak-ng: {_ESPEAK_LIBRARY}")
-
-MODEL_PATH = "checkpoints/sonya-tts.safetensors"
-CONFIG_PATH = "checkpoints/config.json"
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-
-def clean_text_for_vits(text):
-    text = text.strip()
-    text = text.replace("'", "'")
-    text = text.replace(""", '"').replace(""", '"')
-    text = text.replace("–", "-").replace("—", "-")
-    text = re.sub(r"[()\[\]{}<>]", "", text)
-    text = re.sub(r"[^a-zA-Z0-9\s.,!?'\-]", "", text)
-    text = re.sub(r"\s+", " ", text)
-    return text
-
-def get_text(text, hps):
-    text = clean_text_for_vits(text)
-    text_norm = text_to_sequence(text, hps.data.text_cleaners)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    return torch.LongTensor(text_norm)
-
-def split_sentences(text):
-    text = clean_text_for_vits(text)
-    if not text:
-        return []
-    return re.split(r'(?<=[.!?])\s+', text)
-
-
-print("🔄 Loading Sonya TTS Model...")
-hps = utils.get_hparams_from_file(CONFIG_PATH)
-
-net_g = SynthesizerTrn(
-    len(symbols),
-    hps.data.filter_length // 2 + 1,
-    hps.train.segment_size // hps.data.hop_length,
-    **hps.model
-).to(device)
-
-net_g.eval()
-
-if os.path.exists(MODEL_PATH):
-    state_dict = load_file(MODEL_PATH, device=device)
-    net_g.load_state_dict(state_dict)
-    print(f"✅ Loaded weights from {MODEL_PATH}")
-else:
-    raise FileNotFoundError(f"Could not find model at {MODEL_PATH}")
-
-def infer_short(text, noise_scale, noise_scale_w, length_scale):
-    if not text.strip():
-        return None
-        
-    stn_tst = get_text(text, hps)
-    
-    with torch.no_grad():
-        x_tst = stn_tst.to(device).unsqueeze(0)
-        x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(device)
-        
-        audio = net_g.infer(
-            x_tst, 
-            x_tst_lengths, 
-            noise_scale=noise_scale, 
-            noise_scale_w=noise_scale_w, 
-            length_scale=length_scale
-        )[0][0,0].data.cpu().float().numpy()
-        
-    return (hps.data.sampling_rate, audio)
-
-def infer_long(text, length_scale, noise_scale):
-    if not text.strip():
-        return None
-
-    sentences = split_sentences(text)
-    audio_chunks = []
-    
-    fixed_noise_w = 0.6 
-    base_pause = 0.3
-
-    for sent in sentences:
-        if len(sent.strip()) < 2: continue
-        
-        stn_tst = get_text(sent, hps)
-        with torch.no_grad():
-            x_tst = stn_tst.to(device).unsqueeze(0)
-            x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(device)
-            
-            audio = net_g.infer(
-                x_tst, 
-                x_tst_lengths, 
-                noise_scale=noise_scale, 
-                noise_scale_w=fixed_noise_w, 
-                length_scale=length_scale
-            )[0][0,0].data.cpu().float().numpy()
-            
-        if sent.endswith("?"):
-            pause_dur = base_pause + 0.2
-        elif sent.endswith("!"):
-            pause_dur = base_pause + 0.1
-        else:
-            pause_dur = base_pause
-            
-        silence = np.zeros(int(hps.data.sampling_rate * pause_dur))
-        
-        audio_chunks.append(audio)
-        audio_chunks.append(silence)
-        
-    final_audio = np.concatenate(audio_chunks)
-    return (hps.data.sampling_rate, final_audio)
-
-
-theme = gr.themes.Soft(
-    primary_hue="pink",
-    secondary_hue="rose",
-    neutral_hue="slate"
-).set(
-    button_primary_background_fill="linear-gradient(90deg, #ff69b4, #ff1493)",
-    button_primary_background_fill_hover="linear-gradient(90deg, #ff1493, #c71585)",
-    button_primary_text_color="white",
-)
-
-custom_css = """
-.banner-container {
-    width: 100%;
-    max-width: 100%;
-    margin: 0 auto 20px auto;
-    display: flex;
-    justify-content: center;
-    align-items: center;
-}
-
-.banner-container img {
-    width: 100%;
-    max-width: 1800px;
-    max-height: 120px;
-    height: auto;
-    object-fit: scale-down;  
-    object-position: center;
-    border-radius: 8px;
-}
-
-.main-title {
-    text-align: center;
-    color: #ff1493;
-    font-size: 2em;
-    font-weight: 700;
-    margin: 15px 0 8px 0;
-}
-
-.subtitle {
-    text-align: center;
-    color: white;
-    font-size: 1.1em;
-    margin-bottom: 25px;
-    font-weight: 400;
-}
-
-footer {
-    display: none !important;
-}
-"""
-
-with gr.Blocks(theme=theme, css=custom_css, title="Sonya TTS") as app:
-
-  
-    with gr.Row(elem_classes="banner-container"):
-        if os.path.exists("logo.png"):
-            gr.Image("logo.png", show_label=False, container=False, elem_classes="banner-img")
-    
-    
-    gr.HTML("""
-        <h1 class="main-title">✨ Sonya TTS — A Beautiful, Expressive Neural Voice Engine</h1>
-        <p class="subtitle">High-fidelity AI speech with emotion, rhythm, and audiobook mode</p>
-    """)
-
-    with gr.Tabs():
-
-        
-        with gr.TabItem("🎛️ Studio Mode"):
-            with gr.Row():
-                with gr.Column(scale=2):
-                    inp_short = gr.Textbox(
-                        label="💬 Input Text", 
-                        placeholder="Type something for Sonya to say...", 
-                        lines=4,
-                        value="Hello! I am Sonya, your AI voice."
-                    )
-
-                    with gr.Accordion("⚙️ Voice Controls", open=True):
-                        slider_ns = gr.Slider(0.1, 1.0, value=0.4, label="🎭 Emotion", info="Higher = more expressive")
-                        slider_nsw = gr.Slider(0.1, 1.0, value=0.5, label="🎵 Rhythm", info="Higher = looser timing")
-                        slider_ls = gr.Slider(0.5, 1.5, value=0.97, label="⏱ Speed", info="Lower = faster, Higher = slower")
-
-                    btn_short = gr.Button("✨ Generate Voice", variant="primary", size="lg")
-
-                with gr.Column(scale=1):
-                    out_short = gr.Audio(label="🔊 Sonya's Voice", type="numpy")
-
-            btn_short.click(
-                infer_short, 
-                inputs=[inp_short, slider_ns, slider_nsw, slider_ls], 
-                outputs=[out_short]
-            )
-
-   
-        with gr.TabItem("📖 Audiobook Mode"):
-            gr.Markdown(
-                """<p style='text-align: center; color: #666; font-size: 1.05em;'>
-                Paste long text. Sonya will read it beautifully with natural pauses.
-                </p>""",
-                elem_classes="audiobook-description"
-            )
-
-            with gr.Row():
-                with gr.Column(scale=2):
-                    inp_long = gr.Textbox(
-                        label="📜 Long Text Input", 
-                        placeholder="Paste your story or article here...", 
-                        lines=10
-                    )
-
-                    with gr.Accordion("⚙️ Narration Settings", open=False):
-                        long_ls = gr.Slider(0.5, 1.5, value=1.0, label="⏱ Reading Speed")
-                        long_ns = gr.Slider(0.1, 1.0, value=0.5, label="🎭 Tone Variation")
-
-                    btn_long = gr.Button("🎧 Read Aloud", variant="primary", size="lg")
-
-                with gr.Column(scale=1):
-                    out_long = gr.Audio(label="📢 Full Narration", type="numpy")
-
-            btn_long.click(
-                infer_long, 
-                inputs=[inp_long, long_ls, long_ns], 
-                outputs=[out_long]
-            )
-
-if __name__ == "__main__":
+import gradio as gr
+import os
+import re
+import torch
+import numpy as np
+from scipy.io.wavfile import write
+from phonemizer.backend.espeak.wrapper import EspeakWrapper
+from safetensors.torch import load_file
+from huggingface_hub import hf_hub_download
+
+from tts import commons
+from tts import utils
+from tts.models import SynthesizerTrn
+from text.symbols import symbols
+from text import text_to_sequence
+
+_ESPEAK_LIBRARY = r"C:\Program Files\eSpeak NG\libespeak-ng.dll"
+if os.path.exists(_ESPEAK_LIBRARY):
+    EspeakWrapper.set_library(_ESPEAK_LIBRARY)
+    print(f"✅ Found eSpeak-ng: {_ESPEAK_LIBRARY}")
+
+
+REPO_ID = "PatnaikAshish/Sonya-TTS"
+
+MODEL_FILENAME = "sonya-tts.safetensors"
+CONFIG_FILENAME = "config.json"
+
+LOCAL_MODEL_PATH = "checkpoints/sonya-tts.safetensors"
+LOCAL_CONFIG_PATH = "checkpoints/config.json"
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def clean_text_for_vits(text):
+    text = text.strip()
+    text = text.replace("'", "'")
+    text = text.replace(""", '"').replace(""", '"')
+    text = text.replace("–", "-").replace("—", "-")
+    text = re.sub(r"[()\[\]{}<>]", "", text)
+    text = re.sub(r"[^a-zA-Z0-9\s.,!?'\-]", "", text)
+    text = re.sub(r"\s+", " ", text)
+    return text
+
+
+def get_text(text, hps):
+    text = clean_text_for_vits(text)
+    text_norm = text_to_sequence(text, hps.data.text_cleaners)
+    if hps.data.add_blank:
+        text_norm = commons.intersperse(text_norm, 0)
+    return torch.LongTensor(text_norm)
+
+
+def split_sentences(text):
+    text = clean_text_for_vits(text)
+    if not text:
+        return []
+    return re.split(r'(?<=[.!?])\s+', text)
+
+
+print("🔄 Loading Sonya TTS Model...")
+
+if os.path.exists(LOCAL_MODEL_PATH) and os.path.exists(LOCAL_CONFIG_PATH):
+    print("✅ Loading Sonya TTS from local checkpoints...")
+    model_path = LOCAL_MODEL_PATH
+    config_path = LOCAL_CONFIG_PATH
+else:
+    print("🌍 Downloading Sonya TTS from Hugging Face...")
+    model_path = hf_hub_download(repo_id=REPO_ID, filename=MODEL_FILENAME)
+    config_path = hf_hub_download(repo_id=REPO_ID, filename=CONFIG_FILENAME)
+
+hps = utils.get_hparams_from_file(config_path)
+
+net_g = SynthesizerTrn(
+    len(symbols),
+    hps.data.filter_length // 2 + 1,
+    hps.train.segment_size // hps.data.hop_length,
+    **hps.model
+).to(device)
+
+net_g.eval()
+
+state_dict = load_file(model_path)
+net_g.load_state_dict(state_dict)
+print("🎉 Sonya TTS loaded successfully!")
+
+
+def infer_short(text, noise_scale, noise_scale_w, length_scale):
+    if not text.strip():
+        return None
+        
+    stn_tst = get_text(text, hps)
+    
+    with torch.no_grad():
+        x_tst = stn_tst.to(device).unsqueeze(0)
+        x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(device)
+        
+        audio = net_g.infer(
+            x_tst, 
+            x_tst_lengths, 
+            noise_scale=noise_scale, 
+            noise_scale_w=noise_scale_w, 
+            length_scale=length_scale
+        )[0][0,0].data.cpu().float().numpy()
+        
+    return (hps.data.sampling_rate, audio)
+
+
+def infer_long(text, length_scale, noise_scale):
+    if not text.strip():
+        return None
+
+    sentences = split_sentences(text)
+    audio_chunks = []
+    
+    fixed_noise_w = 0.6 
+    base_pause = 0.3
+
+    for sent in sentences:
+        if len(sent.strip()) < 2: 
+            continue
+        
+        stn_tst = get_text(sent, hps)
+        with torch.no_grad():
+            x_tst = stn_tst.to(device).unsqueeze(0)
+            x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(device)
+            
+            audio = net_g.infer(
+                x_tst, 
+                x_tst_lengths, 
+                noise_scale=noise_scale, 
+                noise_scale_w=fixed_noise_w, 
+                length_scale=length_scale
+            )[0][0,0].data.cpu().float().numpy()
+            
+        if sent.endswith("?"):
+            pause_dur = base_pause + 0.2
+        elif sent.endswith("!"):
+            pause_dur = base_pause + 0.1
+        else:
+            pause_dur = base_pause
+            
+        silence = np.zeros(int(hps.data.sampling_rate * pause_dur))
+        
+        audio_chunks.append(audio)
+        audio_chunks.append(silence)
+        
+    final_audio = np.concatenate(audio_chunks)
+    return (hps.data.sampling_rate, final_audio)
+
+
+theme = gr.themes.Soft(
+    primary_hue="pink",
+    secondary_hue="rose",
+    neutral_hue="slate"
+).set(
+    button_primary_background_fill="linear-gradient(90deg, #ff69b4, #ff1493)",
+    button_primary_background_fill_hover="linear-gradient(90deg, #ff1493, #c71585)",
+    button_primary_text_color="white",
+)
+
+custom_css = """
+.banner-container {
+    width: 100%;
+    max-width: 100%;
+    margin: 0 auto 20px auto;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+}
+
+.banner-container img {
+    width: 100%;
+    max-width: 1800px;
+    max-height: 120px;
+    height: auto;
+    object-fit: scale-down;  
+    object-position: center;
+    border-radius: 8px;
+}
+
+.main-title {
+    text-align: center;
+    color: #ff1493;
+    font-size: 2em;
+    font-weight: 700;
+    margin: 15px 0 8px 0;
+}
+
+.subtitle {
+    text-align: center;
+    color: white;
+    font-size: 1.1em;
+    margin-bottom: 25px;
+    font-weight: 400;
+}
+
+footer {
+    display: none !important;
+}
+"""
+
+
+with gr.Blocks(theme=theme, css=custom_css, title="Sonya TTS") as app:
+
+    with gr.Row(elem_classes="banner-container"):
+        if os.path.exists("logo.png"):
+            gr.Image("logo.png", show_label=False, container=False, elem_classes="banner-img")
+
+    gr.HTML("""
+        <h1 class="main-title">✨ Sonya TTS — A Beautiful, Expressive Neural Voice Engine</h1>
+        <p class="subtitle">High-fidelity AI speech with emotion, rhythm, and audiobook mode</p>
+    """)
+
+    with gr.Tabs():
+
+        with gr.TabItem("🎛️ Studio Mode"):
+            with gr.Row():
+                with gr.Column(scale=2):
+                    inp_short = gr.Textbox(
+                        label="💬 Input Text", 
+                        placeholder="Type something for Sonya to say...", 
+                        lines=4,
+                        value="Hello! I am Sonya, your AI voice."
+                    )
+
+                    with gr.Accordion("⚙️ Voice Controls", open=True):
+                        slider_ns = gr.Slider(0.1, 1.0, value=0.4, label="🎭 Emotion", info="Higher = more expressive")
+                        slider_nsw = gr.Slider(0.1, 1.0, value=0.5, label="🎵 Rhythm", info="Higher = looser timing")
+                        slider_ls = gr.Slider(0.5, 1.5, value=0.97, label="⏱ Speed", info="Lower = faster, Higher = slower")
+
+                    btn_short = gr.Button("✨ Generate Voice", variant="primary", size="lg")
+
+                with gr.Column(scale=1):
+                    out_short = gr.Audio(label="🔊 Sonya's Voice", type="numpy")
+
+            btn_short.click(
+                infer_short, 
+                inputs=[inp_short, slider_ns, slider_nsw, slider_ls], 
+                outputs=[out_short]
+            )
+
+        with gr.TabItem("📖 Audiobook Mode"):
+            gr.Markdown(
+                """<p style='text-align: center; color: #666; font-size: 1.05em;'>
+                Paste long text. Sonya will read it beautifully with natural pauses.
+                </p>""",
+                elem_classes="audiobook-description"
+            )
+
+            with gr.Row():
+                with gr.Column(scale=2):
+                    inp_long = gr.Textbox(
+                        label="📜 Long Text Input", 
+                        placeholder="Paste your story or article here...", 
+                        lines=10
+                    )
+
+                    with gr.Accordion("⚙️ Narration Settings", open=False):
+                        long_ls = gr.Slider(0.5, 1.5, value=1.0, label="⏱ Reading Speed")
+                        long_ns = gr.Slider(0.1, 1.0, value=0.5, label="🎭 Tone Variation")
+
+                    btn_long = gr.Button("🎧 Read Aloud", variant="primary", size="lg")
+
+                with gr.Column(scale=1):
+                    out_long = gr.Audio(label="📢 Full Narration", type="numpy")
+
+            btn_long.click(
+                infer_long, 
+                inputs=[inp_long, long_ls, long_ns], 
+                outputs=[out_long]
+            )
+
+if __name__ == "__main__":
     app.launch()