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import json
import os
import sys
import tempfile
import time
import types
import numpy as np
import torch
import soundfile as sf
from huggingface_hub import hf_hub_download
from kanade_tokenizer import KanadeModel, load_audio, load_vocoder, vocode
from kokoro_onnx import Kokoro
from kokoro_onnx.config import MAX_PHONEME_LENGTH, SAMPLE_RATE
from misaki import espeak
from misaki.espeak import EspeakG2P
from core.chunked_convert import chunked_voice_conversion
class KokoClone:
def __init__(self, kanade_model="frothywater/kanade-12.5hz", hf_repo="PatnaikAshish/kokoclone"):
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Initializing KokoClone on: {self.device.type.upper()}")
self.hf_repo = hf_repo
print("Loading Kanade model...")
self.kanade = KanadeModel.from_pretrained(kanade_model).to(self.device).eval()
self.vocoder = load_vocoder(self.kanade.config.vocoder_name).to(self.device)
self.sample_rate = self.kanade.config.sample_rate
self.kokoro_cache = {}
def _get_vocab_config(self, lang):
if lang in {"zh", "ja"}:
zh_vocab = os.path.join("model", "config-v1.1-zh.json")
if not os.path.exists(zh_vocab):
print("Downloading missing file 'config-v1.1-zh.json' from hexgrad/Kokoro-82M-v1.1-zh...")
hf_hub_download(
repo_id="hexgrad/Kokoro-82M-v1.1-zh",
filename="config.json",
local_dir=".",
)
downloaded = os.path.join("config.json")
if os.path.exists(downloaded):
os.replace(downloaded, zh_vocab)
if os.path.exists(zh_vocab):
return zh_vocab
local_config = os.path.join("model", "config.json")
if os.path.exists(local_config):
try:
with open(local_config, encoding="utf-8") as fp:
config = json.load(fp)
if isinstance(config, dict) and "vocab" in config:
return local_config
print("Warning: model/config.json is missing 'vocab'; using packaged kokoro_onnx config instead")
except (OSError, json.JSONDecodeError) as exc:
print(f"Warning: could not read model/config.json ({exc}); using packaged kokoro_onnx config instead")
return str(importlib.resources.files("kokoro_onnx").joinpath("config.json"))
def _patch_kokoro_compat(self, kokoro):
input_types = {input_meta.name: input_meta.type for input_meta in kokoro.sess.get_inputs()}
if input_types.get("speed") != "tensor(float)" or "input_ids" not in input_types:
return kokoro
def _create_audio_compat(instance, phonemes, voice, speed):
if len(phonemes) > MAX_PHONEME_LENGTH:
phonemes = phonemes[:MAX_PHONEME_LENGTH]
start_t = time.time()
tokens = np.array(instance.tokenizer.tokenize(phonemes), dtype=np.int64)
assert len(tokens) <= MAX_PHONEME_LENGTH, (
f"Context length is {MAX_PHONEME_LENGTH}, but leave room for the pad token 0 at start & end"
)
voice_style = voice[len(tokens)]
inputs = {
"input_ids": [[0, *tokens, 0]],
"style": np.array(voice_style, dtype=np.float32),
"speed": np.array([speed], dtype=np.float32),
}
audio = instance.sess.run(None, inputs)[0]
audio_duration = len(audio) / SAMPLE_RATE
create_duration = time.time() - start_t
if audio_duration > 0:
_ = create_duration / audio_duration
return audio, SAMPLE_RATE
kokoro._create_audio = types.MethodType(_create_audio_compat, kokoro)
return kokoro
def _ensure_file(self, folder, filename):
filepath = os.path.join(folder, filename)
if not os.path.exists(filepath):
print(f"Downloading missing file '{filename}' from {self.hf_repo}...")
hf_hub_download(
repo_id=self.hf_repo,
filename=f"{folder}/{filename}",
local_dir="."
)
return filepath
def _create_en_callable(self):
en_g2p = EspeakG2P(language="en-us")
def en_callable(text):
try:
phonemes, _ = en_g2p(text)
return phonemes
except Exception:
return text
return en_callable
def _get_config(self, lang):
model_file = self._ensure_file("model", "kokoro.onnx")
voices_file = self._ensure_file("voice", "voices-v1.0.bin")
vocab = None
g2p = None
en_callable = None
if lang == "en":
voice = "af_bella"
elif lang == "hi":
g2p = EspeakG2P(language="hi")
voice = "hf_alpha"
elif lang == "fr":
g2p = EspeakG2P(language="fr-fr")
voice = "ff_siwis"
elif lang == "it":
g2p = EspeakG2P(language="it")
voice = "im_nicola"
elif lang == "es":
g2p = EspeakG2P(language="es")
voice = "im_nicola"
elif lang == "pt":
g2p = EspeakG2P(language="pt-br")
voice = "pf_dora"
elif lang == "ja":
from misaki import ja
import unidic
import subprocess
if not os.path.exists(unidic.DICDIR):
print("Downloading Japanese dictionary...")
subprocess.run([sys.executable, "-m", "unidic", "download"], check=True)
g2p = ja.JAG2P()
voice = "jf_alpha"
vocab = self._get_vocab_config(lang)
en_callable = self._create_en_callable()
elif lang == "zh":
from misaki import zh
import re
base_g2p = zh.ZHG2P(version="1.1")
en_callable = self._create_en_callable()
def mixed_g2p(text):
parts = re.split(r'([a-zA-Z]+)', text)
phonemes_list = []
for part in parts:
if part and part[0].isalpha() and part[0].isascii():
phonemes_list.append(en_callable(part))
else:
if part:
ph, _ = base_g2p(part)
phonemes_list.append(ph)
return "".join(phonemes_list), text
g2p = mixed_g2p
voice = "zf_001"
model_file = self._ensure_file("model", "kokoro-v1.1-zh.onnx")
voices_file = self._ensure_file("voice", "voices-v1.1-zh.bin")
vocab = self._get_vocab_config(lang)
else:
raise ValueError(f"Language '{lang}' not supported.")
return model_file, voices_file, vocab, g2p, voice, en_callable
def _load_kokoro(self, model_file, voices_file, vocab):
"""Charge Kokoro avec cache."""
if model_file not in self.kokoro_cache:
kokoro = Kokoro(model_file, voices_file, vocab_config=vocab) if vocab else Kokoro(model_file, voices_file)
self.kokoro_cache[model_file] = self._patch_kokoro_compat(kokoro)
return self.kokoro_cache[model_file]
def speak(self, text, lang, output_path="output.wav", speed=1.0):
"""
TTS simple avec la voix Kokoro par défaut — sans clonage.
Supporte toutes les langues disponibles.
"""
model_file, voices_file, vocab, g2p, voice, en_callable = self._get_config(lang)
kokoro = self._load_kokoro(model_file, voices_file, vocab)
print(f"Synthesizing text ({lang.upper()}) with voice '{voice}'...")
if g2p:
phonemes, _ = g2p(text)
samples, sr = kokoro.create(phonemes, voice=voice, speed=speed, is_phonemes=True)
else:
samples, sr = kokoro.create(text, voice=voice, speed=speed, lang="en-us")
sf.write(output_path, samples, sr)
print(f"Success! Saved: {output_path}")
def generate(self, text, lang, reference_audio, output_path="output.wav"):
"""TTS + voice cloning via Kanade."""
model_file, voices_file, vocab, g2p, voice, en_callable = self._get_config(lang)
kokoro = self._load_kokoro(model_file, voices_file, vocab)
print(f"Synthesizing text ({lang.upper()})...")
if g2p:
phonemes, _ = g2p(text)
samples, sr = kokoro.create(phonemes, voice=voice, speed=1.0, is_phonemes=True)
else:
samples, sr = kokoro.create(text, voice=voice, speed=0.9, lang="en-us")
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_audio:
temp_path = temp_audio.name
sf.write(temp_path, samples, sr)
try:
print("Applying Voice Clone...")
source_wav = load_audio(temp_path, sample_rate=self.sample_rate).to(self.device)
ref_wav = load_audio(reference_audio, sample_rate=self.sample_rate).to(self.device)
with torch.inference_mode():
converted_wav = chunked_voice_conversion(
kanade=self.kanade,
vocoder_model=self.vocoder,
source_wav=source_wav,
ref_wav=ref_wav,
sample_rate=self.sample_rate
)
sf.write(output_path, converted_wav.numpy(), self.sample_rate)
print(f"Success! Saved: {output_path}")
finally:
if os.path.exists(temp_path):
os.remove(temp_path)
def convert(self, source_audio, reference_audio, output_path="output.wav"):
"""Re-voicing : source audio → voix de référence."""
print("Applying Voice Conversion...")
source_wav = load_audio(source_audio, sample_rate=self.sample_rate).to(self.device)
ref_wav = load_audio(reference_audio, sample_rate=self.sample_rate).to(self.device)
with torch.inference_mode():
converted_wav = chunked_voice_conversion(
kanade=self.kanade,
vocoder_model=self.vocoder,
source_wav=source_wav,
ref_wav=ref_wav,
sample_rate=self.sample_rate
)
sf.write(output_path, converted_wav.numpy(), self.sample_rate)
print(f"Success! Saved: {output_path}") |