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import gradio as gr
import torch
import torchaudio
import re
import os
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
from speechbrain.pretrained import EncoderClassifier
device = "cuda" if torch.cuda.is_available() else "cpu"
# Load processor & vocoder
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)
# Load both TTS models
model_male = SpeechT5ForTextToSpeech.from_pretrained("HusseinBashir/xus23").to(device)
model_female = SpeechT5ForTextToSpeech.from_pretrained("Somalitts/8aad").to(device)
# Load speaker encoder model
speaker_model = EncoderClassifier.from_hparams(
source="speechbrain/spkrec-xvect-voxceleb",
run_opts={"device": device},
savedir="./spk_model"
)
# Auto-generate embedding
def get_embedding(wav_path, pt_path):
if os.path.exists(pt_path):
return torch.load(pt_path).to(device)
else:
audio, sr = torchaudio.load(wav_path)
audio = torchaudio.functional.resample(audio, sr, 16000).mean(dim=0).unsqueeze(0).to(device)
with torch.no_grad():
emb = speaker_model.encode_batch(audio)
emb = torch.nn.functional.normalize(emb, dim=2).squeeze()
torch.save(emb.cpu(), pt_path)
return emb
# Ensure embeddings are created or loaded
embedding_male = get_embedding("498-enhanced-v2.wav", "male_embedding.pt")
embedding_female = get_embedding("caasho.wav", "female_embedding.pt")
# Somali numbers to words
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):
if number < 20:
return number_words[number]
elif number < 100:
tens, unit = divmod(number, 10)
return number_words[tens * 10] + (" " + number_words[unit] if unit else "")
elif number < 1000:
hundreds, remainder = divmod(number, 100)
return (number_words[hundreds] + " boqol" if hundreds > 1 else "BOQOL") + (" " + number_to_words(remainder) if remainder else "")
elif number < 1000000:
thousands, remainder = divmod(number, 1000)
return (number_to_words(thousands) + " kun" if thousands > 1 else "KUN") + (" " + number_to_words(remainder) if remainder else "")
elif number < 1000000000:
millions, remainder = divmod(number, 1000000)
return number_to_words(millions) + " malyan" + (" " + number_to_words(remainder) if remainder else "")
elif number < 1000000000000:
billions, remainder = divmod(number, 1000000000)
return number_to_words(billions) + " milyaar" + (" " + number_to_words(remainder) if remainder else "")
else:
return str(number)
def replace_numbers_with_words(text):
return re.sub(r'\b\d+\b', lambda match: number_to_words(int(match.group())), text)
def normalize_text(text):
text = text.lower()
text = replace_numbers_with_words(text)
text = re.sub(r'[^\w\s]', '', text)
return text
# Main TTS function
def text_to_speech(text, voice):
text = normalize_text(text)
inputs = processor(text=text, return_tensors="pt").to(device)
if voice == "Male":
model = model_male
embedding = embedding_male
else:
model = model_female
embedding = embedding_female
with torch.no_grad():
speech = model.generate_speech(inputs["input_ids"], embedding.unsqueeze(0), vocoder=vocoder)
return (16000, speech.cpu().numpy())
# Gradio Interface
iface = gr.Interface(
fn=text_to_speech,
inputs=[
gr.Textbox(label="Geli qoraalka Af-Soomaaliga", placeholder="Tusaale: Baro aqoonta casriga ah..."),
gr.Radio(["Male", "Female"], label="Dooro Codka", value="Female")
],
outputs=gr.Audio(label="Codka la abuuray", type="numpy"),
title="Somali TTS (Lab & Dhedig)",
description="Dooro codka aad rabto, geli qoraal af-soomaali ah, codka ayaa la abuuri doonaa adigoo isticmaalaya Somali TTS (SpeechT5)."
)
iface.launch()
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