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server.py

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+"""
+BgTTS-38M Web Server — Gradio Interface
+========================================
+Voice cloning TTS with Bulgarian + English support.
+"""
+
+import sys
+import os
+import torch
+import numpy as np
+import tempfile
+import time
+import soundfile as sf
+
+# Add parent dir to path for imports
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+from config import (
+    AUDIO_OFFSET, NUM_AUDIO_TOKENS, END_OF_SPEECH_TOKEN_ID,
+    START_OF_SPEECH_TOKEN_ID, CODEC_SAMPLE_RATE, CODEC_FRAME_RATE,
+)
+from tokenizer import TTSTokenizer
+from codec import CodecV6
+from model import load_for_inference
+from inference import generate, _split_text
+
+# ── Global state ──────────────────────────────────────────────
+MODEL = None
+TOKENIZER = None
+CODEC = None
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+CHECKPOINT_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "checkpoint_inference.pt")
+
+
+def load_model():
+    """Load model, tokenizer, codec once at startup."""
+    global MODEL, TOKENIZER, CODEC
+    print(f"Loading model from {CHECKPOINT_PATH} on {DEVICE}...")
+    MODEL = load_for_inference(CHECKPOINT_PATH, device=DEVICE)
+    TOKENIZER = TTSTokenizer()
+    CODEC = CodecV6(device=DEVICE)
+    print("Model loaded!")
+
+
+def synthesize_speech(text, ref_audio, temperature, top_k, top_p, rep_penalty):
+    """
+    Generate speech from text using reference audio for voice cloning.
+    
+    Returns: (sample_rate, audio_array) tuple for Gradio
+    """
+    if not text or not text.strip():
+        return None
+    
+    if ref_audio is None:
+        return None
+    
+    # Encode reference audio for speaker embedding
+    sr_ref, audio_ref = ref_audio
+    audio_ref = audio_ref.astype(np.float32)
+    if audio_ref.max() > 1.0 or audio_ref.min() < -1.0:
+        audio_ref = audio_ref / max(abs(audio_ref.max()), abs(audio_ref.min()))
+    
+    waveform = torch.from_numpy(audio_ref)
+    if waveform.dim() == 2:
+        waveform = waveform.mean(1)
+    
+    result = CODEC.encode_waveform(waveform, sr_ref)
+    speaker_emb = result['global_embedding'].to(DEVICE)
+    
+    # Split text into chunks
+    chunks = _split_text(text, TOKENIZER, max_len=250)
+    
+    t0 = time.time()
+    all_codes = []
+    for chunk in chunks:
+        codes = generate(
+            MODEL, TOKENIZER, chunk, speaker_emb,
+            max_new_tokens=512,
+            temperature=temperature,
+            top_k=int(top_k),
+            top_p=top_p,
+            rep_penalty=rep_penalty,
+            device=DEVICE
+        )
+        if codes is not None and len(codes) > 0:
+            all_codes.append(codes)
+    
+    gen_time = time.time() - t0
+    
+    if not all_codes:
+        return None
+    
+    codes = torch.cat(all_codes)
+    audio_dur = len(codes) / CODEC_FRAME_RATE
+    rtf = gen_time / audio_dur if audio_dur > 0 else float('inf')
+    
+    # Decode to waveform
+    wav = CODEC.decode(codes, speaker_emb)
+    wav_np = wav.numpy()
+    
+    info = f"✅ {len(codes)} tokens | {audio_dur:.1f}s audio | {gen_time:.1f}s gen | RTF: {rtf:.3f}"
+    
+    return (CODEC_SAMPLE_RATE, wav_np), info
+
+
+def build_ui():
+    """Build Gradio interface."""
+    import gradio as gr
+    
+    with gr.Blocks(
+        title="BgTTS-38M — Bulgarian Text-to-Speech",
+        theme=gr.themes.Soft(
+            primary_hue="blue",
+            secondary_hue="slate",
+        ),
+        css="""
+        .main-title { text-align: center; margin-bottom: 0.5em; }
+        .subtitle { text-align: center; color: #666; margin-bottom: 1.5em; }
+        """
+    ) as app:
+        gr.HTML('<h1 class="main-title">🎙️ BgTTS-38M</h1>')
+        gr.HTML('<p class="subtitle">Bulgarian + English Text-to-Speech with Voice Cloning | 38M params | 153MB</p>')
+        
+        with gr.Row():
+            with gr.Column(scale=2):
+                text_input = gr.Textbox(
+                    label="Текст / Text",
+                    placeholder="Въведете текст на български или английски...\nEnter text in Bulgarian or English...",
+                    lines=5,
+                    max_lines=15,
+                )
+                
+                ref_audio = gr.Audio(
+                    label="🎤 Reference Voice (за клониране на глас)",
+                    type="numpy",
+                    sources=["upload", "microphone"],
+                )
+                
+                with gr.Row():
+                    generate_btn = gr.Button("🔊 Генерирай / Generate", variant="primary", size="lg")
+                    clear_btn = gr.Button("🗑️ Изчисти", size="lg")
+                
+            with gr.Column(scale=1):
+                with gr.Accordion("⚙️ Настройки / Settings", open=False):
+                    temperature = gr.Slider(
+                        minimum=0.05, maximum=1.5, value=0.3, step=0.05,
+                        label="Temperature",
+                        info="По-ниска = по-чисто, по-висока = по-разнообразно"
+                    )
+                    top_k = gr.Slider(
+                        minimum=1, maximum=500, value=250, step=10,
+                        label="Top-K"
+                    )
+                    top_p = gr.Slider(
+                        minimum=0.1, maximum=1.0, value=0.95, step=0.05,
+                        label="Top-P (Nucleus)"
+                    )
+                    rep_penalty = gr.Slider(
+                        minimum=1.0, maximum=2.0, value=1.1, step=0.05,
+                        label="Repetition Penalty"
+                    )
+                
+                output_audio = gr.Audio(
+                    label="🔊 Резултат / Output",
+                    type="numpy",
+                    interactive=False,
+                )
+                
+                info_text = gr.Textbox(
+                    label="ℹ️ Информация",
+                    interactive=False,
+                    lines=2,
+                )
+        
+        # Examples
+        gr.Examples(
+            examples=[
+                ["Българският език е изключително богат и мелодичен."],
+                ["Artificial intelligence has reached a fascinating stage."],
+                ["Когато говорим за истински multitasking, способността ми да превключвам плавно между български и English е от огромно значение."],
+                ["Здравейте! Казвам се Ани и мога да говоря на български и английски."],
+                ["The quick brown fox jumps over the lazy dog."],
+            ],
+            inputs=[text_input],
+            label="📝 Примери / Examples",
+        )
+        
+        # Event handlers
+        generate_btn.click(
+            fn=synthesize_speech,
+            inputs=[text_input, ref_audio, temperature, top_k, top_p, rep_penalty],
+            outputs=[output_audio, info_text],
+        )
+        
+        clear_btn.click(
+            fn=lambda: (None, None, ""),
+            outputs=[text_input, output_audio, info_text],
+        )
+    
+    return app
+
+
+if __name__ == "__main__":
+    import argparse
+    p = argparse.ArgumentParser()
+    p.add_argument("--checkpoint", default=CHECKPOINT_PATH)
+    p.add_argument("--host", default="0.0.0.0")
+    p.add_argument("--port", type=int, default=7860)
+    p.add_argument("--share", action="store_true")
+    p.add_argument("--device", default=DEVICE)
+    args = p.parse_args()
+    
+    CHECKPOINT_PATH = args.checkpoint
+    DEVICE = args.device
+    
+    load_model()
+    app = build_ui()
+    app.launch(
+        server_name=args.host,
+        server_port=args.port,
+        share=args.share,
+    )