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| from fastapi import FastAPI, Request | |
| from fastapi.responses import FileResponse | |
| import torch | |
| import numpy as np | |
| import scipy.io.wavfile | |
| from transformers import VitsModel, AutoTokenizer | |
| import re | |
| app = FastAPI() | |
| # Load model and tokenizer | |
| model = VitsModel.from_pretrained("Somali-tts/somali_tts_model") | |
| tokenizer = AutoTokenizer.from_pretrained("saleolow/somali-mms-tts") | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model.to(device) | |
| model.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 = [number_to_words(thousands) + " kun" if thousands != 1 else "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)) | |
| text = text.replace("KH", "qa").replace("Z", "S") | |
| text = text.replace("SH", "SHa'a").replace("DH", "Dha'a") | |
| text = text.replace("ZamZam", "SamSam") | |
| return text | |
| async def tts(request: Request): | |
| data = await request.json() | |
| text = normalize_text(data["text"]) | |
| inputs = tokenizer(text, return_tensors="pt").to(device) | |
| with torch.no_grad(): | |
| waveform = model(**inputs).waveform.squeeze().cpu().numpy() | |
| filename = "output.wav" | |
| scipy.io.wavfile.write(filename, rate=model.config.sampling_rate, data=(waveform * 32767).astype(np.int16)) | |
| return FileResponse(filename, media_type="audio/wav") | |