Update app.py

app.py

@@ -1,72 +1,46 @@
-import gradio as gr
-import torch
-import numpy as np
-import scipy.io.wavfile
+from fastapi import FastAPI
+from pydantic import BaseModel
 from transformers import VitsModel, AutoTokenizer
-import re
+import torch
+import base64
+import io
+import soundfile as sf
 
-model = VitsModel.from_pretrained("Somali-tts/somali_tts_model")
-tokenizer = AutoTokenizer.from_pretrained("saleolow/somali-mms-tts")
+app = FastAPI()
 
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device).eval()
+# Load model & tokenizer hal mar marka server-ka bilaabmo
+model_name = "Somali-tts/somali_tts_model"
+model = VitsModel.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-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"
-}
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()
 
-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)
+class TTSRequest(BaseModel):
+    text: str
 
-def normalize_text(text):
-    numbers = re.findall(r'\d+', text)
-    for num in numbers:
-        text = text.replace(num, number_to_words(num))
-    return text
+@app.post("/tts")
+async def tts(request: TTSRequest):
+    text = request.text
+    # Tokenize input text
+    tokens = tokenizer(text, return_tensors="pt")
+    tokens = {k: v.to(device) for k, v in tokens.items()}
 
-def tts(text):
-    text = normalize_text(text)
-    inputs = tokenizer(text, return_tensors="pt").to(device)
+    # Generate speech waveform tensor
     with torch.no_grad():
-        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
+        audio = model.generate_speech(tokens['input_ids'])
+
+    # Convert tensor to numpy array
+    audio_np = audio.squeeze().cpu().numpy()
+
+    # Write to WAV buffer
+    buffer = io.BytesIO()
+    sf.write(buffer, audio_np, samplerate=22050, format='WAV')
+    wav_bytes = buffer.getvalue()
+
+    # Encode wav bytes to base64
+    b64_audio = base64.b64encode(wav_bytes).decode('utf-8')
 
-gr.Interface(
-    fn=tts,
-    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()
+    # Return base64 audio string with wav header
+    return {"audio": f"data:audio/wav;base64,{b64_audio}"}