init

.gitignore

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+venv/

app.py

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+"""
+ONNX-based TTS Gradio Application for Japanese
+PyTorch-free implementation using ONNX Runtime
+"""
+
+import glob
+import os
+import tempfile
+from time import perf_counter
+from typing import Optional
+
+import gradio as gr
+import numpy as np
+import onnxruntime as ort
+import pyopenjtalk
+import soundfile as sf
+
+# ============================================================================
+# Configuration
+# ============================================================================
+
+# Get script directory
+SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+MODELS_DIR = os.path.join(SCRIPT_DIR, "models")
+DEFAULT_MODEL = "g003_ep5709.onnx"
+MODEL_PATH = os.getenv("MODEL_PATH", os.path.join(MODELS_DIR, DEFAULT_MODEL))
+VOCODER_PATH = os.getenv("VOCODER_PATH", None)
+USE_GPU = os.getenv("USE_GPU", "false").lower() == "true"
+SAMPLE_RATE = 22050
+
+
+def get_available_models():
+    """Get list of available ONNX models from models directory"""
+    if not os.path.exists(MODELS_DIR):
+        return [DEFAULT_MODEL]
+
+    models = glob.glob(os.path.join(MODELS_DIR, "*.onnx"))
+    model_names = [os.path.basename(m) for m in models]
+
+    if not model_names:
+        return [DEFAULT_MODEL]
+
+    return sorted(model_names)
+
+# ============================================================================
+# Text Processing (PyTorch-free)
+# ============================================================================
+
+# Load symbols from matcha
+_pad = "_"
+_punctuation = ';:,.!?¡¿—…"«»"" '
+_letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
+_letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
+
+symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa)
+_symbol_to_id = {s: i for i, s in enumerate(symbols)}
+
+
+def text_to_sequence(text):
+    """Convert text to sequence of IDs"""
+    sequence = []
+    for symbol in text:
+        if symbol in _symbol_to_id:
+            sequence.append(_symbol_to_id[symbol])
+        else:
+            sequence.append(0)  # Unknown symbol
+    return sequence
+
+
+def intersperse(sequence, token):
+    """Intersperse token between elements of sequence"""
+    result = [token] * (len(sequence) * 2 + 1)
+    result[1::2] = sequence
+    return result
+
+
+def process_japanese_text(text: str):
+    """Process Japanese text to phoneme sequence"""
+    if not text.strip():
+        raise ValueError("Text cannot be empty")
+
+    # Phonemize using pyopenjtalk
+    phonemes = pyopenjtalk.g2p(text, kana=False)
+    phonemes = phonemes.replace(" ", "")
+    phonemes = phonemes.replace("pau", " ")
+
+    print(f"Input: {text}")
+    print(f"Phonemes: {phonemes}")
+
+    # Text to sequence
+    sequence = text_to_sequence(phonemes)
+
+    # Intersperse with padding
+    sequence = intersperse(sequence, 0)
+
+    # Convert to numpy
+    x = np.array(sequence, dtype=np.int64)[np.newaxis, :]
+    x_lengths = np.array([x.shape[-1]], dtype=np.int64)
+
+    return x, x_lengths
+
+
+# ============================================================================
+# ONNX Model Manager
+# ============================================================================
+
+class ONNXModelManager:
+    """Manages ONNX model loading and inference"""
+
+    def __init__(self, model_path: str, vocoder_path: Optional[str] = None, use_gpu: bool = False):
+        self.model_path = model_path
+        self.vocoder_path = vocoder_path
+        self.use_gpu = use_gpu
+
+        # Select execution providers
+        if use_gpu:
+            self.providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
+        else:
+            self.providers = ["CPUExecutionProvider"]
+
+        self.model = None
+        self.vocoder = None
+        self.is_multi_speaker = False
+        self.has_vocoder_embedded = False
+
+        self._load_model()
+
+    def _load_model(self):
+        """Load ONNX model(s)"""
+        print(f"Loading model from {self.model_path} with providers {self.providers}")
+        self.model = ort.InferenceSession(self.model_path, providers=self.providers)
+
+        model_inputs = self.model.get_inputs()
+        model_outputs = list(self.model.get_outputs())
+
+        self.is_multi_speaker = len(model_inputs) == 4
+        self.has_vocoder_embedded = model_outputs[0].name == "wav"
+
+        print(f"Model loaded: multi_speaker={self.is_multi_speaker}, "
+              f"vocoder_embedded={self.has_vocoder_embedded}")
+
+        # Load external vocoder if needed
+        if not self.has_vocoder_embedded and self.vocoder_path:
+            print(f"Loading external vocoder from {self.vocoder_path}")
+            self.vocoder = ort.InferenceSession(self.vocoder_path, providers=self.providers)
+
+    def synthesize(
+        self,
+        x: np.ndarray,
+        x_lengths: np.ndarray,
+        scales: np.ndarray,
+        spks: Optional[np.ndarray] = None
+    ):
+        """Run ONNX inference"""
+        inputs = {
+            "x": x,
+            "x_lengths": x_lengths,
+            "scales": scales,
+        }
+
+        if self.is_multi_speaker and spks is not None:
+            inputs["spks"] = spks
+
+        # Run Matcha inference
+        outputs = self.model.run(None, inputs)
+
+        if self.has_vocoder_embedded:
+            # End-to-end: model outputs waveform directly
+            return outputs[0], outputs[1]  # wav, wav_lengths
+        else:
+            # Model outputs mel spectrogram
+            mels, mel_lengths = outputs[0], outputs[1]
+
+            if self.vocoder is not None:
+                # Run external vocoder
+                vocoder_inputs = {self.vocoder.get_inputs()[0].name: mels}
+                wavs = self.vocoder.run(None, vocoder_inputs)[0]
+                wavs = wavs.squeeze(1)
+                wav_lengths = mel_lengths * 256
+                return wavs, wav_lengths
+            else:
+                # No vocoder available, return mel
+                return mels, mel_lengths
+
+
+# Initialize model managers (one per model)
+model_managers = {}
+current_model = None
+
+
+def get_model_manager(model_name: str) -> ONNXModelManager:
+    """Get or create model manager for specified model"""
+    global model_managers, current_model
+
+    model_path = os.path.join(MODELS_DIR, model_name)
+
+    if model_name not in model_managers:
+        print(f"Loading new model: {model_name}")
+        model_managers[model_name] = ONNXModelManager(
+            model_path=model_path,
+            vocoder_path=VOCODER_PATH,
+            use_gpu=USE_GPU
+        )
+
+    current_model = model_name
+    return model_managers[model_name]
+
+
+# Initialize default model
+get_model_manager(DEFAULT_MODEL)
+
+# ============================================================================
+# Gradio Interface Functions
+# ============================================================================
+
+
+def synthesise(
+    text: str,
+    model_name: str,
+    speaker_id: int,
+    temperature: float,
+    speaking_rate: float,
+):
+    """
+    Synthesize speech from Japanese text
+
+    Args:
+        text: Japanese text input
+        model_name: Model filename
+        speaker_id: Speaker ID (for multi-speaker models)
+        temperature: Sampling temperature
+        speaking_rate: Speaking rate multiplier
+
+    Returns:
+        Tuple of (audio_path, phonemes_text)
+    """
+    t0 = perf_counter()
+
+    try:
+        # Get model manager
+        manager = get_model_manager(model_name)
+
+        # Process text
+        x, x_lengths = process_japanese_text(text)
+
+        # Prepare scales
+        scales = np.array([temperature, speaking_rate], dtype=np.float32)
+
+        # Prepare speaker ID
+        spks = None
+        if manager.is_multi_speaker and speaker_id >= 0:
+            spks = np.array([speaker_id], dtype=np.int64)
+
+        # Run inference
+        outputs, output_lengths = manager.synthesize(x, x_lengths, scales, spks)
+
+        # Extract single result
+        audio = outputs[0][:output_lengths[0]]
+        inference_time = perf_counter() - t0
+
+        # Calculate RTF
+        audio_duration_sec = len(audio) / SAMPLE_RATE
+        rtf = inference_time / audio_duration_sec
+
+        print(f"Inference time: {inference_time:.3f}s, "
+              f"Audio duration: {audio_duration_sec:.3f}s, "
+              f"RTF: {rtf:.3f}")
+
+        # Save to temporary file
+        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
+            sf.write(fp.name, audio, SAMPLE_RATE, "PCM_24")
+            audio_path = fp.name
+
+        # Get phonemes for display
+        phonemes = pyopenjtalk.g2p(text, kana=False)
+        phonemes = phonemes.replace(" ", "")
+        phonemes = phonemes.replace("pau", " ")
+
+        info = f"Model: {model_name}\n"
+        info += f"Speaker ID: {speaker_id if manager.is_multi_speaker else 'N/A (Single speaker)'}\n"
+        info += f"Phonemes: {phonemes}\n"
+        info += f"RTF: {rtf:.3f}"
+
+        return audio_path, info
+
+    except Exception as e:
+        print(f"Error: {e}")
+        raise
+
+
+# ============================================================================
+# Gradio Application
+# ============================================================================
+
+def create_gradio_interface():
+    """Create Gradio interface"""
+
+    # Get available models
+    available_models = get_available_models()
+
+    with gr.Blocks(
+        title="🍵 Matcha-TTS ONNX (Japanese)",
+    ) as demo:
+        gr.Markdown(
+            """
+            # 🍵 Matcha-TTS ONNX - Japanese Text-to-Speech
+
+            ### PyTorch-free implementation using ONNX Runtime
+            """
+        )
+
+        with gr.Row():
+            with gr.Column():
+                # Model Selection
+                model_dropdown = gr.Dropdown(
+                    label="モデル / Model",
+                    choices=available_models,
+                    value=DEFAULT_MODEL if DEFAULT_MODEL in available_models else available_models[0],
+                    interactive=True
+                )
+
+                text_input = gr.Textbox(
+                    label="日本語テキスト / Japanese Text",
+                    value="こんにちは、世界！",
+                    lines=3,
+                    placeholder="日本語のテキストを入力してください..."
+                )
+
+                # Speaker ID
+                speaker_id = gr.Number(
+                    label="Speaker ID (スピーカーID)",
+                    value=0,
+                    minimum=0,
+                    maximum=99,
+                    precision=0,
+                    info="単一スピーカーモデルでは無視されます"
+                )
+
+                with gr.Row():
+                    temperature = gr.Slider(
+                        label="Temperature (温度)",
+                        minimum=0.0,
+                        maximum=1.0,
+                        step=0.01,
+                        value=0.667,
+                        info="サンプリングのランダム性"
+                    )
+
+                    speaking_rate = gr.Slider(
+                        label="Speaking Rate (話速)",
+                        minimum=0.1,
+                        maximum=5.0,
+                        step=0.1,
+                        value=1.0,
+                        info="1.0 = 標準速度"
+                    )
+
+                with gr.Row():
+                    synthesise_btn = gr.Button(
+                        "🎵 音声生成 / Synthesize",
+                        variant="primary",
+                        size="lg"
+                    )
+                    clear_btn = gr.Button(
+                        "クリア / Clear",
+                        variant="secondary"
+                    )
+
+            with gr.Column():
+                audio_output = gr.Audio(
+                    label="生成音声 / Generated Audio",
+                    type="filepath"
+                )
+
+                info_output = gr.Textbox(
+                    label="情報 / Information",
+                    lines=5,
+                    interactive=False
+                )
+
+        # Examples
+        gr.Examples(
+            examples=[
+                ["こんにちは、世界！", "g003_ep5709.onnx", 0, 0.667, 1.0],
+                ["本日は晴天なり。", "g003_ep5709.onnx", 0, 0.667, 1.0],
+                ["日本語の音声合成をテストしています。", "g003_ep5709.onnx", 0, 0.667, 1.0],
+                ["人工知能の進化は目覚ましいものがあります。", "g003_ep5709.onnx", 0, 0.667, 1.0],
+            ],
+            inputs=[text_input, model_dropdown, speaker_id, temperature, speaking_rate],
+            label="例文 / Examples"
+        )
+
+        # Event handlers
+        synthesise_btn.click(
+            fn=synthesise,
+            inputs=[text_input, model_dropdown, speaker_id, temperature, speaking_rate],
+            outputs=[audio_output, info_output]
+        )
+
+        clear_btn.click(
+            fn=lambda: (None, None, ""),
+            outputs=[audio_output, info_output]
+        )
+
+        gr.Markdown(
+            """
+            ---
+            ### 情報 / Information
+
+            - **モデル**: ONNX (PyTorch-free)
+            - **サンプルレート**: 22050 Hz
+            - **音素化**: pyopenjtalk
+            - **推論**: ONNX Runtime
+            - **モデル自動切り替え**: 選択したモデルを自動的にロード
+
+            ### Speaker ID について
+            - **単一スピーカーモデル**: Speaker ID は無視されます
+            - **マルチスピーカーモデル**: Speaker ID で話者を切り替え
+            """
+        )
+
+    return demo
+
+
+# ============================================================================
+# Main
+# ============================================================================
+
+if __name__ == "__main__":
+    demo = create_gradio_interface()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False,
+        show_error=True
+    )

models/g003_ep5709.onnx

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+size 140821217

models/g003_ep5709_qint8.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1980f50cf9e30b728fc6c10075d698b8aee8d63144e619090502c95185467bf2
+size 43394106

requirements.txt

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+gradio
+numpy
+onnxruntime-gpu
+pyopenjtalk
+soundfile