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import os
import torch
import gradio as gr
from huggingface_hub import hf_hub_download
import langid
from openvoice.api import BaseSpeakerTTS, ToneColorConverter
import openvoice.se_extractor as se_extractor
# Constants
CKPT_BASE_PATH = "checkpoints"
EN_SUFFIX = f"{CKPT_BASE_PATH}/base_speakers/EN"
CONVERTER_SUFFIX = f"{CKPT_BASE_PATH}/converter"
OUTPUT_DIR = "outputs/"
os.makedirs(OUTPUT_DIR, exist_ok=True)
# Download necessary files
def download_from_hf_hub(filename, local_dir="./"):
os.makedirs(local_dir, exist_ok=True)
hf_hub_download(repo_id="myshell-ai/OpenVoice", filename=filename, local_dir=local_dir)
for file in [f"{CONVERTER_SUFFIX}/checkpoint.pth", f"{CONVERTER_SUFFIX}/config.json",
f"{EN_SUFFIX}/checkpoint.pth", f"{EN_SUFFIX}/config.json",
f"{EN_SUFFIX}/en_default_se.pth", f"{EN_SUFFIX}/en_style_se.pth"]:
download_from_hf_hub(file)
# Initialize models
pt_device = "cpu"
en_base_speaker_tts = BaseSpeakerTTS(f"{EN_SUFFIX}/config.json", device=pt_device)
en_base_speaker_tts.load_ckpt(f"{EN_SUFFIX}/checkpoint.pth")
tone_color_converter = ToneColorConverter(f"{CONVERTER_SUFFIX}/config.json", device=pt_device)
tone_color_converter.load_ckpt(f"{CONVERTER_SUFFIX}/checkpoint.pth")
en_source_default_se = torch.load(f"{EN_SUFFIX}/en_default_se.pth")
en_source_style_se = torch.load(f"{EN_SUFFIX}/en_style_se.pth")
# Main prediction function
def predict(prompt, style, audio_file_pth, tau):
if len(prompt) < 2 or len(prompt) > 200:
return "Text should be between 2 and 200 characters.", None
try:
target_se, _ = se_extractor.get_se(audio_file_pth, tone_color_converter, target_dir=OUTPUT_DIR, vad=True)
except Exception as e:
return f"Error getting target tone color: {str(e)}", None
src_path = f"{OUTPUT_DIR}/tmp.wav"
en_base_speaker_tts.tts(prompt, src_path, speaker=style, language="English")
save_path = f"{OUTPUT_DIR}/output.wav"
tone_color_converter.convert(
audio_src_path=src_path,
src_se=en_source_style_se if style != "default" else en_source_default_se,
tgt_se=target_se,
output_path=save_path,
tau=tau
)
return "Voice cloning completed successfully.", save_path
# Gradio interface
def create_demo():
with gr.Blocks() as demo:
gr.Markdown("# OpenVoice: Instant Voice Cloning with fine-tuning")
with gr.Row():
input_text = gr.Textbox(label="Text to speak", placeholder="Enter text here (2-200 characters)")
style = gr.Dropdown(
label="Style",
choices=["default", "whispering", "cheerful", "terrified", "angry", "sad", "friendly"],
value="default"
)
with gr.Row():
reference_audio = gr.Audio(label="Reference Audio", type="filepath")
tau_slider = gr.Slider(minimum=0.1, maximum=1.0, value=0.7, label="Tau (Voice similarity)", info="Higher values make the output more similar to the reference voice")
submit_button = gr.Button("Generate Voice")
output_text = gr.Textbox(label="Status")
output_audio = gr.Audio(label="Generated Audio")
submit_button.click(
predict,
inputs=[input_text, style, reference_audio, tau_slider],
outputs=[output_text, output_audio]
)
return demo
# Launch the demo
if __name__ == "__main__":
demo = create_demo()
demo.launch() |