Add VachanaTTS2 support with wrapper, documentation, and dependency

Co-authored-by: wannaphong <8536487+wannaphong@users.noreply.github.com>

Author: Copilot

README.md

@@ -24,6 +24,26 @@ file = tts.tts("ภาษาไทย ง่าย มาก มาก", filenam
 wave = tts.tts("ภาษาไทย ง่าย มาก มาก",return_type="waveform") # It will get waveform.
 ```
 
+### Using Different TTS Models
+
+PyThaiTTS supports multiple TTS models. You can specify which model to use:
+
+```python
+from pythaitts import TTS
+
+# Use VachanaTTS (default voices: th_f_1, th_m_1, th_f_2, th_m_2)
+tts = TTS(pretrained="vachana")
+file = tts.tts("สวัสดีครับ", speaker_idx="th_f_1", filename="output.wav")
+
+# Use Lunarlist ONNX (default)
+tts = TTS(pretrained="lunarlist_onnx")
+file = tts.tts("ภาษาไทย ง่าย มาก", filename="output.wav")
+
+# Use KhanomTan
+tts = TTS(pretrained="khanomtan")
+file = tts.tts("ภาษาไทย", speaker_idx="Linda", filename="output.wav")
+```
+
 ### Text Preprocessing
 
 PyThaiTTS includes automatic text preprocessing to improve TTS quality:

pythaitts/__init__.py

@@ -10,10 +10,10 @@
 class TTS:
     def __init__(self, pretrained="lunarlist_onnx", mode="last_checkpoint", version="1.0", device:str="cpu") -> None:
         """
-        :param str pretrained: TTS pretrained (lunarlist_onnx, khanomtan, lunarlist)
-        :param str mode: pretrained mode (lunarlist_onnx don't support)
+        :param str pretrained: TTS pretrained (lunarlist_onnx, khanomtan, lunarlist, vachana)
+        :param str mode: pretrained mode (lunarlist_onnx and vachana don't support)
         :param str version: model version (default is 1.0 or 1.1)
-        :param str device: device for running model. (lunarlist_onnx support CPU only.)
+        :param str device: device for running model. (lunarlist_onnx and vachana support CPU only.)
 
         **Options for mode**
             * *last_checkpoint* (default) - last checkpoint of model
@@ -28,6 +28,8 @@ def __init__(self, pretrained="lunarlist_onnx", mode="last_checkpoint", version=
         For lunarlist_onnx tts model, \
         You can see more about lunarlist tts at `https://github.com/PyThaiNLP/thaitts-onnx <https://github.com/PyThaiNLP/thaitts-onnx>`_
 
+        For vachana tts model, \
+        You can see more about vachana tts at `https://github.com/VYNCX/VachanaTTS2 <https://github.com/VYNCX/VachanaTTS2>`_
 
         
         """
@@ -49,6 +51,9 @@ def load_pretrained(self,version):
         elif self.pretrained == "lunarlist":
             from pythaitts.pretrained.lunarlist_model import LunarlistModel
             self.model = LunarlistModel(mode=self.mode, device=self.device)
+        elif self.pretrained == "vachana":
+            from pythaitts.pretrained.vachana_tts import VachanaTTS
+            self.model = VachanaTTS()
         else:
             raise NotImplemented(
                 "PyThaiTTS doesn't support %s pretrained." % self.pretrained
@@ -59,7 +64,7 @@ def tts(self, text: str, speaker_idx: str = "Linda", language_idx: str = "th-th"
         speech synthesis
 
         :param str text: text
-        :param str speaker_idx: speaker (default is Linda)
+        :param str speaker_idx: speaker (default is Linda for khanomtan, th_f_1 for vachana)
         :param str language_idx: language (default is th-th)
         :param str return_type: return type (default is file)
         :param str filename: path filename for save wav file if return_type is file.
@@ -72,6 +77,8 @@ def tts(self, text: str, speaker_idx: str = "Linda", language_idx: str = "th-th"
 
         if self.pretrained == "lunarlist" or self.pretrained == "lunarlist_onnx":
             return self.model(text=text,return_type=return_type,filename=filename)
+        elif self.pretrained == "vachana":
+            return self.model(text=text,speaker_idx=speaker_idx,return_type=return_type,filename=filename)
         return self.model(
             text=text,
             speaker_idx=speaker_idx,

pythaitts/pretrained/vachana_tts.py

@@ -0,0 +1,95 @@
+# -*- coding: utf-8 -*-
+"""
+VachanaTTS2 model
+
+VachanaTTS2 is a Thai text-to-speech model built on VITS architecture.
+It supports multiple Thai voices and is optimized for both CPU and GPU usage.
+
+See more: https://github.com/VYNCX/VachanaTTS2
+"""
+import tempfile
+
+
+class VachanaTTS:
+    def __init__(self) -> None:
+        """
+        Initialize VachanaTTS model.
+        The model will be automatically downloaded from HuggingFace on first use.
+        """
+        try:
+            from vachanatts import TTS as VachanaTTS_TTS
+            self.tts_func = VachanaTTS_TTS
+        except ImportError:
+            raise ImportError(
+                "vachanatts is not installed. Please install it with: pip install vachanatts"
+            )
+
+    def __call__(self, text: str, speaker_idx: str = "th_f_1", return_type: str = "file", filename: str = None, **kwargs):
+        """
+        Generate speech from text using VachanaTTS.
+
+        :param str text: Input text to synthesize
+        :param str speaker_idx: Voice to use (th_f_1, th_m_1, th_f_2, th_m_2). Default is "th_f_1"
+        :param str return_type: Return type ("file" or "waveform")
+        :param str filename: Output filename for the generated audio
+        :param kwargs: Additional parameters (volume, speed, noise_scale, noise_w_scale)
+        :return: File path if return_type is "file", otherwise audio waveform data
+        """
+        # Extract additional parameters with defaults
+        volume = kwargs.get('volume', 1.0)
+        speed = kwargs.get('speed', 1.0)
+        noise_scale = kwargs.get('noise_scale', 0.667)
+        noise_w_scale = kwargs.get('noise_w_scale', 0.8)
+
+        if return_type == "waveform":
+            # For waveform return, we need to generate to a temp file then read it
+            import wave
+            import numpy as np
+            with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
+                temp_filename = fp.name
+            
+            # Generate the audio file
+            self.tts_func(
+                text,
+                voice=speaker_idx,
+                output=temp_filename,
+                volume=volume,
+                speed=speed,
+                noise_scale=noise_scale,
+                noise_w_scale=noise_w_scale
+            )
+            
+            # Read the waveform from the file
+            with wave.open(temp_filename, 'rb') as wav_file:
+                n_frames = wav_file.getnframes()
+                audio_data = wav_file.readframes(n_frames)
+                # Convert bytes to numpy array
+                import struct
+                sample_width = wav_file.getsampwidth()
+                if sample_width == 2:
+                    waveform = np.frombuffer(audio_data, dtype=np.int16)
+                else:
+                    waveform = np.frombuffer(audio_data, dtype=np.int8)
+            
+            # Clean up temp file
+            import os
+            os.unlink(temp_filename)
+            
+            return waveform
+        else:
+            # File output
+            if filename is None:
+                with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
+                    filename = fp.name
+            
+            self.tts_func(
+                text,
+                voice=speaker_idx,
+                output=filename,
+                volume=volume,
+                speed=speed,
+                noise_scale=noise_scale,
+                noise_w_scale=noise_w_scale
+            )
+            
+            return filename

requirements.txt

@@ -1,3 +1,4 @@
 huggingface_hub
 numpy>=1.22
-onnxruntime
+onnxruntime
+vachanatts