diff --git a/.gitignore b/.gitignore
index a94cc36..fad8b0b 100755
--- a/.gitignore
+++ b/.gitignore
@@ -8,6 +8,9 @@ __pycache__/
 # Custom
 data/model_small/
 data/model_large/
+data/v4_ru.pt
+wav_files/
+vocal.wav
 
 # C extensions
 *.so
diff --git a/poetry.lock b/poetry.lock
index 3aa6fae..8775a38 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -701,25 +701,6 @@ files = [
     {file = "kiwisolver-1.4.5.tar.gz", hash = "sha256:e57e563a57fb22a142da34f38acc2fc1a5c864bc29ca1517a88abc963e60d6ec"},
 ]
 
-[[package]]
-name = "lazy-loader"
-version = "0.4"
-description = "Makes it easy to load subpackages and functions on demand."
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "lazy_loader-0.4-py3-none-any.whl", hash = "sha256:342aa8e14d543a154047afb4ba8ef17f5563baad3fc610d7b15b213b0f119efc"},
-    {file = "lazy_loader-0.4.tar.gz", hash = "sha256:47c75182589b91a4e1a85a136c074285a5ad4d9f39c63e0d7fb76391c4574cd1"},
-]
-
-[package.dependencies]
-packaging = "*"
-
-[package.extras]
-dev = ["changelist (==0.5)"]
-lint = ["pre-commit (==3.7.0)"]
-test = ["pytest (>=7.4)", "pytest-cov (>=4.1)"]
-
 [[package]]
 name = "levenshtein"
 version = "0.23.0"
@@ -842,34 +823,32 @@ rapidfuzz = ">=3.1.0,<4.0.0"
 
 [[package]]
 name = "librosa"
-version = "0.10.1"
+version = "0.8.1"
 description = "Python module for audio and music processing"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.6"
 files = [
-    {file = "librosa-0.10.1-py3-none-any.whl", hash = "sha256:7ab91d9f5fcb75ea14848a05d3b1f825cf8d0c42ca160d19ae6874f2de2d8223"},
-    {file = "librosa-0.10.1.tar.gz", hash = "sha256:832f7d150d6dd08ed2aa08c0567a4be58330635c32ddd2208de9bc91300802c7"},
+    {file = "librosa-0.8.1-py3-none-any.whl", hash = "sha256:fd381e2d7067d4d4cf7691f2ef3620ef62a8aa6445dcf407e3328254692f742a"},
+    {file = "librosa-0.8.1.tar.gz", hash = "sha256:c53d05e768ae4a3e553ae21c2e5015293e5efbfd5c12d497f1104cb519cca6b3"},
 ]
 
 [package.dependencies]
-audioread = ">=2.1.9"
-decorator = ">=4.3.0"
+audioread = ">=2.0.0"
+decorator = ">=3.0.0"
 joblib = ">=0.14"
-lazy-loader = ">=0.1"
-msgpack = ">=1.0"
-numba = ">=0.51.0"
-numpy = ">=1.20.3,<1.22.0 || >1.22.0,<1.22.1 || >1.22.1,<1.22.2 || >1.22.2"
+numba = ">=0.43.0"
+numpy = ">=1.15.0"
+packaging = ">=20.0"
 pooch = ">=1.0"
-scikit-learn = ">=0.20.0"
-scipy = ">=1.2.0"
-soundfile = ">=0.12.1"
-soxr = ">=0.3.2"
-typing-extensions = ">=4.1.1"
+resampy = ">=0.2.2"
+scikit-learn = ">=0.14.0,<0.19.0 || >0.19.0"
+scipy = ">=1.0.0"
+soundfile = ">=0.10.2"
 
 [package.extras]
-display = ["matplotlib (>=3.3.0)"]
-docs = ["ipython (>=7.0)", "matplotlib (>=3.3.0)", "mir-eval (>=0.5)", "numba (>=0.51)", "numpydoc", "presets", "sphinx (!=1.3.1)", "sphinx-gallery (>=0.7)", "sphinx-multiversion (>=0.2.3)", "sphinx-rtd-theme (>=1.2.0)", "sphinxcontrib-svg2pdfconverter"]
-tests = ["matplotlib (>=3.3.0)", "packaging (>=20.0)", "pytest", "pytest-cov", "pytest-mpl", "resampy (>=0.2.2)", "samplerate", "types-decorator"]
+display = ["matplotlib (>=1.5)"]
+docs = ["matplotlib (>=2.0.0,<3.3)", "numba (<0.50)", "numpydoc", "presets", "sphinx (!=1.3.1)", "sphinx-gallery (>=0.7)", "sphinx-multiversion (>=0.2.3)", "sphinx-rtd-theme (==0.5.*)", "spinxcontrib-svg2pdfconverter"]
+tests = ["contextlib2", "matplotlib (>=3.0)", "pytest", "pytest-cov", "pytest-mpl", "samplerate", "soxr"]
 
 [[package]]
 name = "llvmlite"
@@ -1107,71 +1086,6 @@ docs = ["sphinx"]
 gmpy = ["gmpy2 (>=2.1.0a4)"]
 tests = ["pytest (>=4.6)"]
 
-[[package]]
-name = "msgpack"
-version = "1.0.8"
-description = "MessagePack serializer"
-optional = false
-python-versions = ">=3.8"
-files = [
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-]
-
 [[package]]
 name = "networkx"
 version = "3.3"
@@ -1935,6 +1849,26 @@ urllib3 = ">=1.21.1,<3"
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
+[[package]]
+name = "resampy"
+version = "0.4.3"
+description = "Efficient signal resampling"
+optional = false
+python-versions = "*"
+files = [
+    {file = "resampy-0.4.3-py3-none-any.whl", hash = "sha256:ad2ed64516b140a122d96704e32bc0f92b23f45419e8b8f478e5a05f83edcebd"},
+    {file = "resampy-0.4.3.tar.gz", hash = "sha256:a0d1c28398f0e55994b739650afef4e3974115edbe96cd4bb81968425e916e47"},
+]
+
+[package.dependencies]
+numba = ">=0.53"
+numpy = ">=1.17"
+
+[package.extras]
+design = ["optuna (>=2.10.0)"]
+docs = ["numpydoc", "sphinx (!=1.3.1)"]
+tests = ["pytest (<8)", "pytest-cov", "scipy (>=1.1)"]
+
 [[package]]
 name = "rich"
 version = "13.7.1"
@@ -2144,52 +2078,6 @@ cffi = ">=1.0"
 [package.extras]
 numpy = ["numpy"]
 
-[[package]]
-name = "soxr"
-version = "0.3.7"
-description = "High quality, one-dimensional sample-rate conversion library"
-optional = false
-python-versions = ">=3.6"
-files = [
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-    {file = "soxr-0.3.7.tar.gz", hash = "sha256:436ddff00c6eb2c75b79c19cfdca7527b1e31b5fad738652f044045ba6258593"},
-]
-
-[package.dependencies]
-numpy = "*"
-
-[package.extras]
-docs = ["linkify-it-py", "myst-parser", "sphinx", "sphinx-book-theme"]
-test = ["pytest"]
-
 [[package]]
 name = "srt"
 version = "3.5.3"
@@ -2415,6 +2303,19 @@ srt = "*"
 tqdm = "*"
 websockets = "*"
 
+[[package]]
+name = "webrtcvad"
+version = "2.0.10"
+description = "Python interface to the Google WebRTC Voice Activity Detector (VAD)"
+optional = false
+python-versions = "*"
+files = [
+    {file = "webrtcvad-2.0.10.tar.gz", hash = "sha256:f1bed2fb25b63fb7b1a55d64090c993c9c9167b28485ae0bcdd81cf6ede96aea"},
+]
+
+[package.extras]
+dev = ["check-manifest", "memory_profiler", "nose", "psutil", "unittest2", "zest.releaser"]
+
 [[package]]
 name = "websockets"
 version = "12.0"
@@ -2499,4 +2400,4 @@ files = [
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11"
-content-hash = "f3829914195ce01bfac29dea5117fb3f28f095ecc6081a77eddb6bacc895718b"
+content-hash = "c86def154e0a021a940b80bedcb08103ec9c0762a80432a58bf81d31c6a9244f"
diff --git a/pyproject.toml b/pyproject.toml
index 3923d61..3ecdc46 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -28,6 +28,7 @@ ollama = "^0.1.6"
 ruff = "^0.4.2"
 noisereduce = "^3.0.2"
 environs = "^11.0.0"
+webrtcvad = "^2.0.10"
 
 
 [[tool.poetry.source]]
diff --git a/test.py b/test.py
index e69de29..79213a6 100644
--- a/test.py
+++ b/test.py
@@ -0,0 +1,41 @@
+import os
+
+import requests
+from bs4 import BeautifulSoup
+
+# URL веб-страницы, которую нужно спарсить
+url = 'https://theportalwiki.com/wiki/GLaDOS_voice_lines/ru'
+
+# Получаем содержимое страницы
+response = requests.get(url)
+response.raise_for_status()  # Проверка на успешный запрос
+
+# Парсим HTML
+soup = BeautifulSoup(response.text, 'html.parser')
+
+# Находим все теги <a>
+links = soup.find_all('a')
+
+# Фильтруем ссылки, которые заканчиваются на .wav
+wav_links = [
+    link.get('href') for link in links if link.get('href') and link.get('href').endswith('.wav')
+]
+
+# Создаем директорию для сохранения файлов, если её нет
+os.makedirs('wav_files', exist_ok=True)
+
+# Скачиваем каждый wav-файл
+for wav_link in wav_links:
+    # Получаем имя файла из URL
+    filename = wav_link.split('/')[-1]
+    file_path = os.path.join('wav_files', filename)
+
+    # Скачиваем файл
+    response = requests.get(wav_link)
+    response.raise_for_status()
+
+    # Сохраняем файл
+    with open(file_path, 'wb') as file:
+        file.write(response.content)
+
+    print(f"Downloaded {filename}")
diff --git a/utils/tts.py b/utils/tts.py
new file mode 100644
index 0000000..49cb978
--- /dev/null
+++ b/utils/tts.py
@@ -0,0 +1,57 @@
+import os
+
+import torch
+import torchaudio
+
+
+def load_data(audio_folder):
+    audios = []
+    texts = []
+    for audio_file in os.listdir(audio_folder):
+        if audio_file.endswith('.wav'):
+            audio_path = os.path.join(audio_folder, audio_file)
+            waveform, sample_rate = torchaudio.load(audio_path)
+            text_path = audio_path.replace('.wav', '.txt')
+            with open(text_path) as f:
+                text = f.read().strip()
+            audios.append((waveform, sample_rate))
+            texts.append(text)
+    return audios, texts
+
+
+def train(model, audios, texts, epochs=3, learning_rate=1e-4):
+    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
+    criterion = torch.nn.MSELoss()  # Вам нужно будет настроить эту функцию под вашу задачу
+
+    model.train()
+    for epoch in range(epochs):
+        total_loss = 0
+        for waveform, text in zip(audios, texts):
+            optimizer.zero_grad()
+            # Предполагается, что модель принимает текст и возвращает аудио
+            predicted_waveform = model(text)
+            loss = criterion(predicted_waveform, waveform)
+            loss.backward()
+            optimizer.step()
+            total_loss += loss.item()
+        average_loss = total_loss / len(audios)
+        print(f'Epoch {epoch + 1}: Average Loss = {average_loss}')
+
+
+def main():
+    model_path = 'data/v4_ru.pt'
+    model = torch.load(model_path)
+    model.eval()
+    audio_folder = 'wav_files'
+    audios, texts = load_data(audio_folder)
+    train(model, audios, texts)
+    torch.save(model.state_dict(), 'fine_tuned_model.pth')
+    model.eval()
+    sample_text = "Пример текста для синтеза."
+    with torch.no_grad():
+        generated_waveform = model(sample_text)
+        torchaudio.save('output_audio.wav', generated_waveform, 16000)
+
+
+if __name__ == '__main__':
+    main()