Update app.py

app.py

@@ -1,54 +1,8 @@
 import gradio as gr
 import torch
 import numpy as np
-import sciimport gradio as gr
-import torch
-import numpy as np
-import tempfile
 import scipy.io.wavfile
 from transformers import VitsModel, AutoTokenizer
-
-# Load model and tokenizer
-model = VitsModel.from_pretrained("jellecali8/somali_tts_model")
-tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-som")
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device).eval()
-
-# Load custom speaker embedding
-try:
-    speaker_embedding = torch.tensor(np.load("new_speaker_embedding.npy")).unsqueeze(0).to(device)
-except Exception as e:
-    speaker_embedding = None
-    print(f"Embedding load error: {e}")
-
-def tts_fn(text):
-    try:
-        inputs = tokenizer(text, return_tensors="pt").to(device)
-
-        with torch.no_grad():
-            output = model(**inputs, speaker_embeddings=speaker_embedding)
-
-        # Check for empty waveform
-        if output.waveform is None or output.waveform.shape[-1] == 0:
-            return "❌ Model-ka ma soo saarin cod. Waxaa laga yaabaa in embedding uu cilad leeyahay."
-
-        audio = output.waveform.squeeze().cpu().numpy()
-
-        # Save audio to temp file
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
-            scipy.io.wavfile.write(f.name, rate=16000, data=(audio * 32767).astype(np.int16))
-            return f.name
-    except Exception as e:
-        return f"Error during synthesis: {str(e)}"
-
-gr.Interface(
-    fn=tts_fn,
-    inputs=gr.Textbox(label="Qor qoraalka Somali"),
-    outputs=gr.Audio(label="Codka la clone gareeyey"),
-    title="Cod Somali ah oo la clone gareeyay"
-).launch()
-py.io.wavfile
-from transformers import VitsModel, AutoTokenizer
 import re
 
 # Load model and tokenizer