Update app.py

app.py

@@ -1,26 +1,72 @@
+import gradio as gr
 import torch
+import numpy as np
+import scipy.io.wavfile
 from transformers import VitsModel, AutoTokenizer
-import gradio as gr
+import re
+
+model = VitsModel.from_pretrained("Somali-tts/somali_tts_model")
+tokenizer = AutoTokenizer.from_pretrained("saleolow/somali-mms-tts")
 
-# Load the fine-tuned model and tokenizer
-model = VitsModel.from_pretrained("HusseinBashir/fine_tuned_vits_som")
-tokenizer = AutoTokenizer.from_pretrained("HusseinBashir/fine_tuned_vits_som")
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
+model.to(device).eval()
+
+number_words = {
+    0: "eber", 1: "koow", 2: "labo", 3: "seddex", 4: "afar", 5: "shan",
+    6: "lix", 7: "todobo", 8: "sideed", 9: "sagaal", 10: "toban",
+    11: "toban iyo koow", 12: "toban iyo labo", 13: "toban iyo seddex",
+    14: "toban iyo afar", 15: "toban iyo shan", 16: "toban iyo lix",
+    17: "toban iyo todobo", 18: "toban iyo sideed", 19: "toban iyo sagaal",
+    20: "labaatan", 30: "sodon", 40: "afartan", 50: "konton",
+    60: "lixdan", 70: "todobaatan", 80: "sideetan", 90: "sagaashan",
+    100: "boqol", 1000: "kun"
+}
+
+def number_to_words(number):
+    number = int(number)
+    if number < 20:
+        return number_words[number]
+    elif number < 100:
+        tens, unit = divmod(number, 10)
+        return number_words[tens * 10] + (" iyo " + number_words[unit] if unit else "")
+    elif number < 1000:
+        hundreds, remainder = divmod(number, 100)
+        part = (number_words[hundreds] + " boqol") if hundreds > 1 else "boqol"
+        if remainder:
+            part += " iyo " + number_to_words(remainder)
+        return part
+    elif number < 1000000:
+        thousands, remainder = divmod(number, 1000)
+        words = []
+        if thousands == 1:
+            words.append("kun")
+        else:
+            words.append(number_to_words(thousands) + " kun")
+        if remainder:
+            words.append("iyo " + number_to_words(remainder))
+        return " ".join(words)
+    else:
+        return str(number)
+
+def normalize_text(text):
+    numbers = re.findall(r'\d+', text)
+    for num in numbers:
+        text = text.replace(num, number_to_words(num))
+    return text
 
-# Gradio TTS function
 def tts(text):
+    text = normalize_text(text)
     inputs = tokenizer(text, return_tensors="pt").to(device)
     with torch.no_grad():
-        output = model(**inputs).waveform.squeeze(1).cpu().numpy()
-    return output
+        waveform = model(**inputs).waveform.squeeze().cpu().numpy()
+    output_path = "output.wav"
+    scipy.io.wavfile.write(output_path, rate=model.config.sampling_rate, data=(waveform * 32767).astype(np.int16))
+    return output_path
 
-# Gradio interface for the TTS model
-iface = gr.Interface(
+gr.Interface(
     fn=tts,
-    inputs=gr.Textbox(label="Enter text"),
-    outputs=gr.Audio(label="Generated Speech"),
-    title="Fine-tuned VITS TTS",
-    description="Generate speech from text using the fine-tuned VITS model."
-)
-iface.launch()
+    inputs=gr.Textbox(label="Qor qoraalka af-Soomaaliga"),
+    outputs=gr.Audio(type="filepath", label="Codka TTS"),
+    title="Somali TTS API",
+    description="Ku qor qoraal si aad u maqasho codka af-Soomaaliga",
+).launch()