tambahannya
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bipproduction
245
xclone.py
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245
xclone.py
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import io
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import os
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import sys
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import base64
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import argparse
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import torch
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import torchaudio as ta
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import torchaudio.functional as F
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from chatterbox.tts import ChatterboxTTS
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from huggingface_hub import hf_hub_download
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from safetensors.torch import load_file
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# =========================
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# KONFIGURASI MODEL - DISESUAIKAN UNTUK NATURALNESS
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# =========================
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MODEL_REPO = "grandhigh/Chatterbox-TTS-Indonesian"
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CHECKPOINT = "t3_cfg.safetensors"
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DEVICE = "cpu"
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# Parameter dioptimasi untuk suara lebih natural dan mirip source
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TEMPERATURE = 0.65
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TOP_P = 0.88
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REPETITION_PENALTY = 1.25
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AUDIO_GAIN_DB = 0.8
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PROMPT_FOLDER = "prompt_source"
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os.makedirs(PROMPT_FOLDER, exist_ok=True)
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# =========================
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# Enhance audio dengan fokus pada naturalness
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# =========================
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def enhance_audio(wav, sr):
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peak = wav.abs().max()
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if peak > 0:
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wav = wav / (peak + 1e-8) * 0.95
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wav = F.highpass_biquad(wav, sr, cutoff_freq=60)
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wav = F.lowpass_biquad(wav, sr, cutoff_freq=10000)
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wav = F.bass_biquad(wav, sr, gain=1.5, central_freq=200, Q=0.7)
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wav = F.treble_biquad(wav, sr, gain=-1.2, central_freq=6000, Q=0.7)
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threshold = 0.6
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ratio = 2.5
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knee = 0.1
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abs_wav = wav.abs()
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mask_hard = abs_wav > (threshold + knee)
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mask_knee = (abs_wav > (threshold - knee)) & (abs_wav <= (threshold + knee))
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compressed = torch.where(
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mask_hard,
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torch.sign(wav) * (threshold + (abs_wav - threshold) / ratio),
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wav
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)
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knee_factor = ((abs_wav - (threshold - knee)) / (2 * knee)) ** 2
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knee_compressed = torch.sign(wav) * (
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threshold - knee + knee_factor * (2 * knee) + (abs_wav - threshold) / ratio * knee_factor
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)
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compressed = torch.where(mask_knee, knee_compressed, compressed)
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wav = compressed
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saturation_amount = 0.08
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wav = torch.tanh(wav * (1 + saturation_amount)) / (1 + saturation_amount)
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pink_noise = generate_pink_noise(wav.shape, wav.device) * 0.0003
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wav = wav + pink_noise
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wav = torch.tanh(wav * 1.1) * 0.92
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wav = F.gain(wav, gain_db=AUDIO_GAIN_DB)
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peak = wav.abs().max().item()
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if peak > 0:
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wav = wav / peak * 0.88
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return wav
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def generate_pink_noise(shape, device):
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white = torch.randn(shape, device=device)
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if len(shape) > 1:
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pink = torch.zeros_like(white)
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for i in range(shape[0]):
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b = torch.zeros(7)
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for j in range(shape[1]):
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white_val = white[i, j].item()
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b[0] = 0.99886 * b[0] + white_val * 0.0555179
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b[1] = 0.99332 * b[1] + white_val * 0.0750759
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b[2] = 0.96900 * b[2] + white_val * 0.1538520
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b[3] = 0.86650 * b[3] + white_val * 0.3104856
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b[4] = 0.55000 * b[4] + white_val * 0.5329522
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b[5] = -0.7616 * b[5] - white_val * 0.0168980
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pink[i, j] = (b[0] + b[1] + b[2] + b[3] + b[4] + b[5] + b[6] + white_val * 0.5362) * 0.11
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b[6] = white_val * 0.115926
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else:
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pink = torch.zeros_like(white)
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b = torch.zeros(7)
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for j in range(shape[0]):
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white_val = white[j].item()
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b[0] = 0.99886 * b[0] + white_val * 0.0555179
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b[1] = 0.99332 * b[1] + white_val * 0.0750759
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b[2] = 0.96900 * b[2] + white_val * 0.1538520
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b[3] = 0.86650 * b[3] + white_val * 0.3104856
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b[4] = 0.55000 * b[4] + white_val * 0.5329522
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b[5] = -0.7616 * b[5] - white_val * 0.0168980
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pink[j] = (b[0] + b[1] + b[2] + b[3] + b[4] + b[5] + b[6] + white_val * 0.5362) * 0.11
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b[6] = white_val * 0.115926
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return pink * 0.1
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# Load model sekali
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print("Loading model...")
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model = ChatterboxTTS.from_pretrained(device=DEVICE)
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ckpt = hf_hub_download(repo_id=MODEL_REPO, filename=CHECKPOINT)
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state = load_file(ckpt, device=DEVICE)
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model.t3.to(DEVICE).load_state_dict(state)
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model.t3.eval()
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for module in model.t3.modules():
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if hasattr(module, "training"):
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module.training = False
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for module in model.t3.modules():
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if isinstance(module, torch.nn.Dropout):
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module.p = 0
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print("Model ready with enhanced settings.")
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# ======= Fungsi CLI =======
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def register_prompt_base64(prompt_name, base64_audio):
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filename = f"{prompt_name}.wav"
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path = os.path.join(PROMPT_FOLDER, filename)
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raw = base64.b64decode(base64_audio)
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with open(path, "wb") as f:
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f.write(raw)
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print(f"Registered base64 prompt as {filename}")
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def register_prompt_file(src_path, prompt_name=None):
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if prompt_name is None:
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prompt_name = os.path.splitext(os.path.basename(src_path))[0]
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save_path = os.path.join(PROMPT_FOLDER, f"{prompt_name}.wav")
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with open(src_path, "rb") as src_file:
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data = src_file.read()
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with open(save_path, "wb") as dst_file:
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dst_file.write(data)
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print(f"Registered prompt file as {prompt_name}.wav")
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def list_prompt():
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files = os.listdir(PROMPT_FOLDER)
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wav_files = [f for f in files if f.lower().endswith(".wav")]
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print(f"Total prompts: {len(wav_files)}")
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for f in wav_files:
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print(f"- {f}")
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def delete_prompt(prompt_name):
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file_path = os.path.join(PROMPT_FOLDER, f"{prompt_name}.wav")
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if not os.path.exists(file_path):
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print(f"Prompt '{prompt_name}' not found.", file=sys.stderr)
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sys.exit(1)
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os.remove(file_path)
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print(f"Deleted prompt {prompt_name}.wav")
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def rename_prompt(old_name, new_name):
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old_path = os.path.join(PROMPT_FOLDER, f"{old_name}.wav")
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new_path = os.path.join(PROMPT_FOLDER, f"{new_name}.wav")
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if not os.path.exists(old_path):
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print(f"Old prompt '{old_name}' not found.", file=sys.stderr)
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sys.exit(1)
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if os.path.exists(new_path):
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print(f"New prompt name '{new_name}' already exists.", file=sys.stderr)
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sys.exit(1)
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os.rename(old_path, new_path)
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print(f"Renamed prompt '{old_name}' to '{new_name}'")
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def tts(text, prompt, output_path="output.wav", temperature=TEMPERATURE, top_p=TOP_P, repetition_penalty=REPETITION_PENALTY):
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prompt_path = os.path.join(PROMPT_FOLDER, f"{prompt}.wav")
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if not os.path.exists(prompt_path):
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print(f"Prompt '{prompt}' not found.", file=sys.stderr)
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sys.exit(1)
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try:
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wav = model.generate(
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text,
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audio_prompt_path=prompt_path,
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temperature=temperature,
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top_p=top_p,
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repetition_penalty=repetition_penalty,
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)
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except Exception as e:
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print(f"Failed to generate audio: {e}", file=sys.stderr)
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sys.exit(1)
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wav = enhance_audio(wav.cpu(), model.sr)
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ta.save(output_path, wav, model.sr, format="wav")
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print(f"TTS output saved to {output_path}")
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def main():
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parser = argparse.ArgumentParser(description="Chatterbox TTS CLI")
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subparsers = parser.add_subparsers(dest="command")
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# register base64
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p_register_base64 = subparsers.add_parser("register-base64", help="Register prompt from base64 string")
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p_register_base64.add_argument("prompt_name")
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p_register_base64.add_argument("base64_audio")
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# register file
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p_register_file = subparsers.add_parser("register-file", help="Register prompt from wav file")
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p_register_file.add_argument("src_path")
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p_register_file.add_argument("--name", default=None)
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# list prompt
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p_list = subparsers.add_parser("list", help="List all prompt wav files")
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# delete prompt
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p_delete = subparsers.add_parser("delete", help="Delete prompt wav file")
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p_delete.add_argument("prompt_name")
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# rename prompt
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p_rename = subparsers.add_parser("rename", help="Rename prompt wav file")
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p_rename.add_argument("old_name")
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p_rename.add_argument("new_name")
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# tts generate
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p_tts = subparsers.add_parser("tts", help="Generate TTS wav file")
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p_tts.add_argument("text")
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p_tts.add_argument("prompt")
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p_tts.add_argument("--output", default="output.wav")
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p_tts.add_argument("--temperature", type=float, default=TEMPERATURE)
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p_tts.add_argument("--top_p", type=float, default=TOP_P)
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p_tts.add_argument("--repetition_penalty", type=float, default=REPETITION_PENALTY)
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args = parser.parse_args()
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if args.command == "register-base64":
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register_prompt_base64(args.prompt_name, args.base64_audio)
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elif args.command == "register-file":
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register_prompt_file(args.src_path, args.name)
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elif args.command == "list":
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list_prompt()
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elif args.command == "delete":
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delete_prompt(args.prompt_name)
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elif args.command == "rename":
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rename_prompt(args.old_name, args.new_name)
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elif args.command == "tts":
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tts(args.text, args.prompt, args.output, args.temperature, args.top_p, args.repetition_penalty)
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else:
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parser.print_help()
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sys.exit(1)
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if __name__ == "__main__":
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main()
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