Create app.py

app.py

@@ -0,0 +1,144 @@
+import os
+
+os.environ["HF_HOME"] = "/tmp"
+os.environ["TRANSFORMERS_CACHE"] = "/tmp"
+os.environ["TORCH_HOME"] = "/tmp"
+os.environ["XDG_CACHE_HOME"] = "/tmp"
+
+import io
+import re
+import math
+import numpy as np
+import scipy.io.wavfile
+import torch
+from fastapi import FastAPI, Query
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+from transformers import VitsModel, AutoTokenizer
+
+app = FastAPI()
+
+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: int) -> str:
+    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)
+    elif number < 1000000000:
+        millions, remainder = divmod(number, 1000000)
+        words = []
+        if millions == 1:
+            words.append("milyan")
+        else:
+            words.append(number_to_words(millions) + " milyan")
+        if remainder:
+            words.append(number_to_words(remainder))
+        return " ".join(words)
+    else:
+        return str(number)
+
+def normalize_text(text: str) -> str:
+    numbers = re.findall(r'\d+', text)
+    for num in numbers:
+        text = text.replace(num, number_to_words(int(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
+
+def waveform_to_wav_bytes(waveform: torch.Tensor, sample_rate: int = 22050) -> bytes:
+    np_waveform = waveform.cpu().numpy()
+    if np_waveform.ndim == 3:
+        np_waveform = np_waveform[0]
+    if np_waveform.ndim == 2:
+        np_waveform = np_waveform.mean(axis=0)
+    np_waveform = np.clip(np_waveform, -1.0, 1.0).astype(np.float32)
+    pcm_waveform = (np_waveform * 32767).astype(np.int16)
+    buf = io.BytesIO()
+    scipy.io.wavfile.write(buf, rate=sample_rate, data=pcm_waveform)
+    buf.seek(0)
+    return buf.read()
+
+class TextIn(BaseModel):
+    inputs: str
+
+@app.post("/synthesize")
+async def synthesize_post(data: TextIn):
+    text = normalize_text(data.inputs)
+    inputs = tokenizer(text, return_tensors="pt").to(device)
+    with torch.no_grad():
+        output = model(**inputs)
+    if hasattr(output, "waveform"):
+        waveform = output.waveform
+    elif isinstance(output, dict) and "waveform" in output:
+        waveform = output["waveform"]
+    elif isinstance(output, (tuple, list)):
+        waveform = output[0]
+    else:
+        return {"error": "Waveform not found in model output"}
+    sample_rate = getattr(model.config, "sampling_rate", 22050)
+    wav_bytes = waveform_to_wav_bytes(waveform, sample_rate=sample_rate)
+    return StreamingResponse(io.BytesIO(wav_bytes), media_type="audio/wav")
+
+@app.get("/synthesize")
+async def synthesize_get(text: str = Query(..., description="Text to synthesize"), test: bool = Query(False)):
+    if test:
+        duration_s = 2.0
+        sample_rate = 22050
+        t = np.linspace(0, duration_s, int(sample_rate * duration_s), endpoint=False)
+        freq = 440
+        waveform = 0.5 * np.sin(2 * math.pi * freq * t).astype(np.float32)
+        pcm_waveform = (waveform * 32767).astype(np.int16)
+        buf = io.BytesIO()
+        scipy.io.wavfile.write(buf, rate=sample_rate, data=pcm_waveform)
+        buf.seek(0)
+        return StreamingResponse(buf, media_type="audio/wav")
+    normalized = normalize_text(text)
+    inputs = tokenizer(normalized, return_tensors="pt").to(device)
+    with torch.no_grad():
+        output = model(**inputs)
+    if hasattr(output, "waveform"):
+        waveform = output.waveform
+    elif isinstance(output, dict) and "waveform" in output:
+        waveform = output["waveform"]
+    elif isinstance(output, (tuple, list)):
+        waveform = output[0]
+    else:
+        return {"error": "Waveform not found in model output"}
+    sample_rate = getattr(model.config, "sampling_rate", 22050)
+    wav_bytes = waveform_to_wav_bytes(waveform, sample_rate=sample_rate)
+    return StreamingResponse(io.BytesIO(wav_bytes), media_type="audio/wav")