Upload app.py

app.py

@@ -0,0 +1,206 @@
+import os
+import json
+import random
+import torch
+import numpy as np
+import gradio as gr
+from chatterbox.tts import ChatterboxTTS
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+from torch import nn
+import re
+
+# === Einstellungen ===
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+MODEL_REPO = "SebastianBodza/Kartoffelbox-v0.1"
+T3_CHECKPOINT_FILE = "t3_kartoffelbox.safetensors"
+MAX_CHARS = 5000
+CHUNK_CHAR_LIMIT = 300
+SETTINGS_DIR = "settings"
+
+# === Init ===
+if not os.path.exists(SETTINGS_DIR):
+    os.makedirs(SETTINGS_DIR)
+
+MODEL = None
+print(f"🚀 Running on device: {DEVICE}")
+
+def get_or_load_model():
+    global MODEL
+    if MODEL is None:
+        print("Model not loaded, initializing...")
+        MODEL = ChatterboxTTS.from_pretrained(DEVICE)
+        checkpoint_path = hf_hub_download(
+            repo_id=MODEL_REPO,
+            filename=T3_CHECKPOINT_FILE,
+            token=os.environ.get("HUGGING_FACE_HUB_TOKEN", "")
+        )
+        t3_state = load_file(checkpoint_path, device="cpu")
+        MODEL.t3.load_state_dict(t3_state)
+
+        # Position Embeddings erweitern
+        pos_emb_module = MODEL.t3.text_pos_emb
+        old_pos = pos_emb_module.emb.num_embeddings
+        if MAX_CHARS > old_pos:
+            emb_dim = pos_emb_module.emb.embedding_dim
+            new_emb = nn.Embedding(MAX_CHARS, emb_dim)
+            with torch.no_grad():
+                new_emb.weight[:old_pos] = pos_emb_module.emb.weight
+            pos_emb_module.emb = new_emb
+            print(f"Expanded position embeddings: {old_pos} → {MAX_CHARS}")
+
+        MODEL.t3.to(DEVICE)
+        MODEL.s3gen.to(DEVICE)
+        print(f"Model loaded. Device: {MODEL.device}")
+    return MODEL
+
+try:
+    get_or_load_model()
+except Exception as e:
+    print(f"CRITICAL: Failed to load model: {e}")
+
+def set_seed(seed: int):
+    torch.manual_seed(seed)
+    if DEVICE == "cuda":
+        torch.cuda.manual_seed_all(seed)
+    random.seed(seed)
+    np.random.seed(seed)
+
+def split_text_into_chunks(text, max_length=CHUNK_CHAR_LIMIT):
+    sentences = re.split(r'(?<=[.!?]) +', text)
+    chunks = []
+    chunk = ""
+    for sentence in sentences:
+        if len(chunk) + len(sentence) < max_length:
+            chunk += " " + sentence
+        else:
+            if chunk:
+                chunks.append(chunk.strip())
+            chunk = sentence
+    if chunk:
+        chunks.append(chunk.strip())
+    return chunks
+
+# === Einstellungen speichern/laden ===
+def list_presets():
+    return [f[:-5] for f in os.listdir(SETTINGS_DIR) if f.endswith(".json") and f != "last.json"]
+
+def load_preset(name):
+    path = os.path.join(SETTINGS_DIR, name + ".json")
+    if os.path.exists(path):
+        with open(path, "r", encoding="utf-8") as f:
+            return json.load(f)
+    return None
+
+def save_preset(name, data):
+    path = os.path.join(SETTINGS_DIR, name + ".json")
+    with open(path, "w", encoding="utf-8") as f:
+        json.dump(data, f, indent=2)
+    save_preset("last", data)  # Als "zuletzt genutzt" speichern
+
+def generate_tts_audio(text_input, audio_prompt_path_input, exaggeration_input, temperature_input, seed_num_input, cfgw_input):
+    model = get_or_load_model()
+    if seed_num_input != 0:
+        set_seed(int(seed_num_input))
+
+    full_audio = []
+    chunks = split_text_into_chunks(text_input[:MAX_CHARS])
+    print(f"Text wird in {len(chunks)} Teile aufgeteilt…")
+
+    for i, chunk in enumerate(chunks):
+        print(f"▶️ Teil {i+1}/{len(chunks)}: {chunk[:60]}...")
+        wav = model.generate(
+            chunk,
+            audio_prompt_path=audio_prompt_path_input,
+            exaggeration=exaggeration_input,
+            temperature=temperature_input,
+            cfg_weight=cfgw_input,
+        )
+        full_audio.append(wav.squeeze(0).cpu().numpy())
+
+    audio_concat = np.concatenate(full_audio)
+    return (model.sr, audio_concat)
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        gr.Markdown("# 🥔 Kartoffel-TTS (Chatterbox)\nLangtext → Sprachstil mit Profilen")
+
+    with gr.Row():
+        with gr.Column():
+            preset_dropdown = gr.Dropdown(label="🔄 Preset wählen", choices=list_presets(), value=None)
+            preset_name = gr.Textbox(label="📝 Name zum Speichern", value="mein-profil")
+
+            text = gr.Textbox(
+                value="Hier kannst du einen längeren deutschen Text eingeben…",
+                label=f"Text (max {MAX_CHARS} Zeichen)",
+                max_lines=12
+            )
+            ref_wav = gr.Audio(
+                sources=["upload", "microphone"],
+                type="filepath",
+                label="Referenz-Audiodatei (optional)",
+                value="https://storage.googleapis.com/chatterbox-demo-samples/prompts/female_shadowheart4.flac"
+            )
+            exaggeration = gr.Slider(0.25, 2, step=.05, label="Exaggeration", value=.5)
+            cfg_weight = gr.Slider(0.2, 1, step=.05, label="CFG/Pace", value=0.3)
+
+            with gr.Accordion("Weitere Optionen", open=False):
+                seed_num = gr.Number(value=0, label="Zufalls-Seed (0 = zufällig)")
+                temp = gr.Slider(0.05, 5, step=.05, label="Temperature", value=.6)
+
+            save_btn = gr.Button("💾 Einstellungen speichern")
+            run_btn = gr.Button("🎤 Audio generieren")
+
+        with gr.Column():
+            audio_output = gr.Audio(label="🔊 Ergebnis")
+
+    # Funktionen zuweisen
+    def on_preset_selected(name):
+        if name:
+            p = load_preset(name)
+            if p:
+                return p["exaggeration"], p["temperature"], p["seed"], p["cfg"]
+        return gr.update(), gr.update(), gr.update(), gr.update()
+
+    preset_dropdown.change(
+        on_preset_selected,
+        inputs=[preset_dropdown],
+        outputs=[exaggeration, temp, seed_num, cfg_weight]
+    )
+
+    def save_current_settings(name, exaggeration, temperature, seed, cfg):
+        save_preset(name, {
+            "exaggeration": exaggeration,
+            "temperature": temperature,
+            "seed": seed,
+            "cfg": cfg
+        })
+        return gr.update(choices=list_presets())
+
+    save_btn.click(
+        fn=save_current_settings,
+        inputs=[preset_name, exaggeration, temp, seed_num, cfg_weight],
+        outputs=[preset_dropdown]
+    )
+
+    run_btn.click(
+        fn=generate_tts_audio,
+        inputs=[text, ref_wav, exaggeration, temp, seed_num, cfg_weight],
+        outputs=[audio_output],
+    )
+
+    # Letztes Profil beim Start laden
+    if os.path.exists(os.path.join(SETTINGS_DIR, "last.json")):
+        last = load_preset("last")
+        if last:
+            exaggeration.value = last["exaggeration"]
+            temp.value = last["temperature"]
+            seed_num.value = last["seed"]
+            cfg_weight.value = last["cfg"]
+
+# 👇 ROBUSTER START – wichtig für exe ohne Konsole!
+demo.launch(
+    quiet=True,
+    show_error=True,
+    prevent_thread_lock=False
+)