| 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 = "checkpoints/sonya-tts.safetensors" |
| CONFIG_FILENAME = "checkpoints/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() |
