Update README.md

README.md

@@ -1,198 +1,16 @@
-# Silero v4 Indic — HuggingFace Space (Gradio) Backend
-
-This document contains a ready-to-run HuggingFace Space project that hosts **Silero v4_indic** as a free CPU Gradio app. Drop `v4_indic.pt` (downloaded from `https://models.silero.ai/models/tts/indic/v4_indic.pt`) into the project root, push to your Space, and it will provide a public `/` UI and a simple REST-style `/api/predict` endpoint.
-
----
-
-## Project structure
-
-```
-space-repo/
-├── app.py                # Gradio app + server endpoints (main)
-├── requirements.txt      # Python dependencies
-├── README.md             # This file (short)
-└── v4_indic.pt           # Put the downloaded model here (NOT included)
-```
-
----
-
-## requirements.txt
-
-```
-gradio==3.39.1
-torch==2.1.0
-soundfile==0.13.1
-onnxruntime==1.23.2
-numpy
-```
-
-> Use CPU-only torch (no GPU) in HF Spaces. The `torch` version should be compatible with the Space runtime; if HF Spaces provides `torch` preinstalled, you can remove it from requirements to speed deploy.
-
----
-
-## app.py
-
-```python
-import os
-import threading
-import tempfile
-import numpy as np
-import soundfile as sf
-import gradio as gr
-
-# Attempt to import torch; HF Spaces usually has CPU torch available.
-import torch
-
-MODEL_PATH = "v4_indic.pt"
-SAMPLE_RATE = 48000
-
-lock = threading.Lock()
-model = None
-
-
-def load_model():
-    global model
-    if model is not None:
-        return model
-    if not os.path.exists(MODEL_PATH):
-        raise FileNotFoundError(f"Model file not found: {MODEL_PATH}. Put v4_indic.pt in repo root.")
-
-    # Silero packaged model loader
-    print(f"Loading Silero model from {MODEL_PATH}...")
-    pkg = torch.package.PackageImporter(MODEL_PATH)
-    # The original package uses "tts_models" and object name "model"
-    model = pkg.load_pickle("tts_models", "model")
-    print("Model loaded into memory")
-    return model
-
-
-# Try to call model.apply_tts with flexible signature
-def synthesize_text(text: str, lang: str = "hi", speaker: int = 0, sample_rate: int = SAMPLE_RATE):
-    m = load_model()
-
-    # Normalize inputs
-    if not isinstance(text, str) or len(text.strip()) == 0:
-        raise ValueError("Empty text")
-
-    # Some Silero wrappers accept (text=..., lang_id=..., speaker_id=...),
-    # others accept (text=..., lang=..., speaker=...). Use try/except to support both.
-    try:
-        # Common high-level API
-        audio = m.apply_tts(text=text, speaker=speaker, lang_id=int(lang) if isinstance(lang, (int, np.integer)) else lang, sample_rate=sample_rate)
-    except TypeError:
-        try:
-            audio = m.apply_tts(text=text, speaker_id=int(speaker), lang_id=int(lang) if isinstance(lang, (int, np.integer)) else lang, sample_rate=sample_rate)
-        except Exception:
-            # Fallback: some versions accept (text, speaker, lang)
-            audio = m.apply_tts(text, speaker, lang, sample_rate)
-
-    # The returned audio can be numpy array or torch tensor
-    if isinstance(audio, torch.Tensor):
-        audio = audio.detach().cpu().numpy()
-    audio = np.asarray(audio)
-
-    # Ensure float32 in [-1,1]
-    if audio.dtype == np.int16:
-        audio = audio.astype('float32') / 32768.0
-    audio = audio.astype('float32')
-    max_abs = np.max(np.abs(audio))
-    if max_abs > 1.0:
-        audio = audio / max_abs
-
-    return audio, sample_rate
-
-
-# Gradio wrapper: returns file-like audio buffer
-def tts_gradio(text, lang_dropdown, speaker_slider):
-    # Map dropdown label to lang id or code expected by model
-    # You might need to adjust mapping depending on model internal language ids
-    lang_map = {
-        "Hindi (hi)": 0,
-        "Marathi (mr)": 1,
-        "Bengali (bn)": 2,
-        "Tamil (ta)": 3,
-        "Telugu (te)": 4,
-        "Kannada (kn)": 5,
-        "Malayalam (ml)": 6,
-        "Gujarati (gu)": 7,
-    }
-
-    lang_id = lang_map.get(lang_dropdown, 0)
-
-    # Prevent concurrent synth calls
-    with lock:
-        audio, sr = synthesize_text(text, lang=lang_id, speaker=int(speaker_slider))
-
-        # Write to temporary wav file and return its path (gradio will serve it)
-        tmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
-        sf.write(tmp.name, audio, sr)
-        tmp.flush()
-        tmp.close()
-        return tmp.name
-
-
-# Build Gradio UI
-def build_ui():
-    with gr.Blocks() as demo:
-        gr.Markdown("# Silero v4 Indic — TTS (HuggingFace Space)\nDrop `v4_indic.pt` in the repo root and reload the Space.")
-
-        with gr.Row():
-            with gr.Column(scale=3):
-                txt = gr.Textbox(lines=4, label="Text to synthesize", value="नमस्ते, यह एक परीक्षण है।")
-                lang = gr.Dropdown(list=["Hindi (hi)", "Marathi (mr)", "Bengali (bn)", "Tamil (ta)", "Telugu (te)", "Kannada (kn)", "Malayalam (ml)", "Gujarati (gu)"], label="Language")
-                speaker = gr.Slider(minimum=0, maximum=3, step=1, value=0, label="Speaker ID (if model supports multiple speakers)")
-                btn = gr.Button("Synthesize")
-
-            with gr.Column(scale=2):
-                out = gr.Audio(label="Generated audio")
-
-        btn.click(fn=tts_gradio, inputs=[txt, lang, speaker], outputs=[out])
-
-    return demo
-
-
-if __name__ == "__main__":
-    # Preload model at startup (keeps first request fast)
-    try:
-        load_model()
-    except Exception as e:
-        print("Model failed to load at startup:", e)
-
-    demo = build_ui()
-    demo.launch(server_name="0.0.0.0", server_port=7860)
-```
-
----
-
-## Notes & Deployment steps
-
-1. **Download model**: `wget https://models.silero.ai/models/tts/indic/v4_indic.pt -O v4_indic.pt` and place in repo root.
-2. **Create a new Space**: [https://huggingface.co/new-space](https://huggingface.co/new-space) → choose `Gradio` runtime and public/private as you wish.
-3. **Push repo**: Upload `app.py`, `requirements.txt`, `README.md`, and `v4_indic.pt` to the Space (via web UI drag & drop or via git).
-4. **Wait** until the Space builds; the model will be loaded on first startup.
-5. **API**: The Space exposes a Gradio UI and a `/api/predict` endpoint automatically (Gradio inference API). You can call it programmatically.
-
----
-
-## Tips & Troubleshooting
-
-* If the Space build fails due to `torch` version mismatch, remove `torch` from `requirements.txt` and let the Space use its preinstalled torch.
-* If you see `AttributeError` when calling `apply_tts`, some packaged model versions have slightly different API names. The wrapper `synthesize_text` attempts several common signatures; adapt if necessary.
-* The model file is ~34MB — fits in Space disk quota.
-* If multiple users will call TTS concurrently, consider a small rate limiter or queue: Silero v4 is CPU-bound but reasonably fast for short utterances.
-
----
-
-## Security
-
-* Avoid uploading private keys in the repo.
-* If you need to restrict usage, make the Space private and issue access tokens.
-
----
-
-If you want, I can now:
-
-* Generate a git-ready ZIP of this project (app.py + requirements + README) so you can upload directly.
-* Or produce a minimal `Dockerfile` if you prefer deploying elsewhere.
-
-Which would you like?
+---
+title: Silero v4 Indic TTS
+emoji: 🔊
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: "3.39.1"
+app_file: app.py
+pinned: false
+---
+
+# Silero v4 Indic TTS
+
+A HuggingFace Space that provides text-to-speech for Indic languages using Silero v4 model.
+
+Upload `v4_indic.pt` into the root directory and the Space will load it automatically.