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import os
import json
import numpy as np
import scipy.io.wavfile as wavfile
from onnxruntime import InferenceSession
from phonemizer import phonemize
# === Step 1: Load phoneme-to-ID vocabulary ===
CONFIG_PATH = "./config_kokoro.json" # Download this from Hugging Face: Kokoro-82M/config.json
with open(CONFIG_PATH, "r", encoding="utf-8") as f:
config = json.load(f)
phoneme_to_id = config["vocab"]
# === Step 2: Convert text to phonemes using espeak-ng ===
text = "Hi how are you, what is your name. tell me something"
phonemes = phonemize(
text,
language="en-us",
backend="espeak",
strip=True,
preserve_punctuation=True,
with_stress=True
)
# === Step 3: Filter out unsupported phonemes and convert to token IDs ===
phonemes = "".join(p for p in phonemes if p in phoneme_to_id)
print("Phonemes:", phonemes)
tokens = [phoneme_to_id[p] for p in phonemes]
print("Token IDs:", tokens)
# === Step 4: Prepare style embedding and input IDs ===
assert len(tokens) <= 510, "Token sequence too long (max 510 phonemes)"
voices = np.fromfile('./voices/af.bin', dtype=np.float32).reshape(-1, 1, 256)
ref_s = voices[len(tokens)] # Select style vector based on token length
tokens = [[0, *tokens, 0]] # Add padding tokens at the beginning and end
# === Step 5: Run ONNX model inference ===
model_name = 'model.onnx'
sess = InferenceSession(os.path.join('onnx', model_name))
audio = sess.run(None, {
'input_ids': tokens,
'style': ref_s,
'speed': np.ones(1, dtype=np.float32),
})[0]
# === Step 6: Save output audio as a 24kHz WAV file ===
wavfile.write('audio.wav', 24000, audio[0])
print("✅ Audio saved to audio.wav")
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