Create app.py

app.py

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+# app.py
+from fastapi import FastAPI, UploadFile, File, Form
+from fastapi.responses import FileResponse, HTMLResponse
+import torch
+import torchaudio
+import os
+from pathlib import Path
+from TTS.tts.models.xtts import Xtts
+from TTS.tts.configs.xtts_config import XttsConfig
+import gradio as gr
+import uvicorn
+
+# ------------------------
+# Setup paths
+# ------------------------
+MODEL_DIR = "my_model"          # folder with config.json, vocab.json, model.pth
+OUTPUT_DIR = "outputs"
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# ------------------------
+# Load TTS model
+# ------------------------
+config = XttsConfig()
+config.load_json(os.path.join(MODEL_DIR, "config.json"))
+
+model = Xtts.init_from_config(config)
+model.load_checkpoint(
+    config,
+    checkpoint_dir=MODEL_DIR,
+    use_deepspeed=False,
+    vocab_path=os.path.join(MODEL_DIR, "vocab.json")
+)
+model.to(device)
+
+# ------------------------
+# TTS function
+# ------------------------
+def tts_arabic(text: str, audio_file: str) -> str:
+    gpt_cond_latent, speaker_embedding = model.get_conditioning_latents(audio_path=[audio_file])
+    out = model.inference(
+        text=text,
+        language="ar",
+        gpt_cond_latent=gpt_cond_latent,
+        speaker_embedding=speaker_embedding,
+        temperature=model.config.temperature,
+        top_k=model.config.top_k,
+        length_penalty=model.config.length_penalty,
+        repetition_penalty=model.config.repetition_penalty,
+        top_p=model.config.top_p,
+    )
+    output_wav = os.path.join(OUTPUT_DIR, "output.wav")
+    torchaudio.save(output_wav, torch.tensor(out["wav"]).unsqueeze(0), 24000)
+    return output_wav
+
+# ------------------------
+# FastAPI setup
+# ------------------------
+app = FastAPI(title="EGTTS TTS API")
+
+@app.get("/", response_class=HTMLResponse)
+def index():
+    """Return simple HTML that links to Gradio UI"""
+    return """
+    <h2>Welcome to EGTTS TTS API</h2>
+    <p>Swagger docs available at <a href="/docs">/docs</a></p>
+    <p>Try the Gradio interface at <a href="/gradio">/gradio</a></p>
+    """
+
+@app.post("/tts/")
+async def tts_endpoint(
+    text: str = Form(...),
+    audio_file: UploadFile = File(...)
+):
+    # Save uploaded file
+    file_path = os.path.join(OUTPUT_DIR, audio_file.filename)
+    with open(file_path, "wb") as f:
+        f.write(await audio_file.read())
+
+    output_wav = tts_arabic(text, file_path)
+    return FileResponse(output_wav, media_type="audio/wav", filename="output.wav")
+
+# ------------------------
+# Gradio interface
+# ------------------------
+def gradio_fn(text, audio_file):
+    return tts_arabic(text, audio_file.name)
+
+gradio_interface = gr.Interface(
+    fn=gradio_fn,
+    inputs=[
+        gr.Textbox(label="Arabic Text", placeholder="اكتب النص هنا..."),
+        gr.File(label="Speaker Audio (.wav)")
+    ],
+    outputs=gr.Audio(label="Generated Speech"),
+    live=True,
+    title="EGTTS Arabic TTS",
+    description="Generate Arabic speech from text using your fine-tuned EGTTS model."
+)
+
+# Mount Gradio inside FastAPI
+@app.get("/gradio", response_class=HTMLResponse)
+def gradio_ui():
+    return gradio_interface.launch(inline=True, share=False, prevent_thread_lock=True).read()
+
+# ------------------------
+# Run server
+# ------------------------
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)