Create app.py

app.py

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+import os
+import re
+import uuid
+import torch
+import torchaudio
+import soundfile as sf
+from fastapi import FastAPI
+from fastapi.responses import FileResponse
+from pydantic import BaseModel
+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
+from speechbrain.inference.speaker import EncoderClassifier
+
+app = FastAPI()
+device = "cuda" if torch.cuda.is_available() else "cpu"
+CACHE_DIR = "/tmp/hf-cache"
+
+# Load models
+processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts", cache_dir=CACHE_DIR)
+vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan", cache_dir=CACHE_DIR).to(device)
+model_male = SpeechT5ForTextToSpeech.from_pretrained("Somalitts/5aad", cache_dir=CACHE_DIR).to(device)
+model_female = SpeechT5ForTextToSpeech.from_pretrained("Somalitts/8aad", cache_dir=CACHE_DIR).to(device)
+
+# Speaker encoder
+speaker_model = EncoderClassifier.from_hparams(
+    source="speechbrain/spkrec-xvect-voxceleb",
+    run_opts={"device": device},
+    savedir="/tmp/spk_model"
+)
+
+# Load speaker embeddings
+def get_embedding(wav_path, pt_path):
+    if os.path.exists(pt_path):
+        return torch.load(pt_path).to(device)
+    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
+
+embedding_male = get_embedding("Hussein.wav", "/tmp/male_embedding.pt")
+embedding_female = get_embedding("caasho.wav", "/tmp/female_embedding.pt")
+
+# Text normalization
+number_words = {
+    0: "eber", 1: "koow", 2: "labo", 3: "seddex", 4: "afar", 5: "shan",
+    6: "lix", 7: "todobo", 8: "sideed", 9: "sagaal", 10: "toban",
+    20: "labaatan", 30: "sodon", 40: "afartan", 50: "konton",
+    60: "lixdan", 70: "todobaatan", 80: "sideetan", 90: "sagaashan",
+    100: "boqol", 1000: "kun"
+}
+
+def number_to_words(n):
+    if n < 20:
+        return number_words.get(n, str(n))
+    elif n < 100:
+        tens, unit = divmod(n, 10)
+        return number_words[tens * 10] + (" " + number_words[unit] if unit else "")
+    elif n < 1000:
+        hundreds, rem = divmod(n, 100)
+        return (number_words[hundreds] + " boqol" if hundreds > 1 else "boqol") + (" " + number_to_words(rem) if rem else "")
+    elif n < 1_000_000:
+        th, rem = divmod(n, 1000)
+        return (number_to_words(th) + " kun") + (" " + number_to_words(rem) if rem else "")
+    else:
+        return str(n)
+
+def replace_numbers_with_words(text):
+    return re.sub(r'\b\d+\b', lambda m: number_to_words(int(m.group())), text)
+
+def normalize_text(text):
+    text = text.lower()
+    text = replace_numbers_with_words(text)
+    text = re.sub(r'[^\w\s]', '', text)
+    return text
+
+# API request schema
+class TTSRequest(BaseModel):
+    text: str
+    voice: str  # "Male" or "Female"
+
+@app.post("/speak")
+def speak(payload: TTSRequest):
+    clean_text = normalize_text(payload.text)
+    inputs = processor(text=clean_text, return_tensors="pt").to(device)
+    model = model_male if payload.voice.lower() == "male" else model_female
+    embedding = embedding_male if payload.voice.lower() == "male" else embedding_female
+
+    with torch.no_grad():
+        waveform = model.generate_speech(inputs["input_ids"], embedding.unsqueeze(0), vocoder=vocoder)
+
+    out_path = f"/tmp/{uuid.uuid4().hex}.wav"
+    sf.write(out_path, waveform.cpu().numpy(), 16000)
+    return FileResponse(out_path, media_type="audio/wav", filename="voice.wav")