File size: 4,143 Bytes
37835db |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
import os
import torch
import gradio as gr
from openvoice import se_extractor
from openvoice.api import ToneColorConverter
from transformers import pipeline
import scipy
from pathlib import Path
# Output directory setup
output_dir = './openvoice_outputs'
os.makedirs(output_dir, exist_ok=True)
# Function to get model names from a directory
def get_model_names(model_dir):
model_paths = Path(model_dir).glob('*')
return [model_path.name for model_path in model_paths if model_path.is_dir()]
def generate_speech(text, model_path):
synthesiser = pipeline("text-to-speech", model_path, device=0 if torch.cuda.is_available() else -1)
speech = synthesiser(text)
# Resample to 48kHz if needed
if speech["sampling_rate"] != 48000:
resampled_audio = scipy.signal.resample(speech["audio"][0], int(len(speech["audio"][0]) * 48000 / speech["sampling_rate"]))
sampling_rate = 48000
else:
resampled_audio = speech["audio"][0]
sampling_rate = speech["sampling_rate"]
return sampling_rate, resampled_audio
def save_audio(sampling_rate, audio_data, filename="output.wav"):
scipy.io.wavfile.write(filename, rate=sampling_rate, data=audio_data)
return filename
def voice_cloning(base_speaker, reference_speaker, model_version, device_choice, vad_select):
try:
# Determine paths and device
ckpt_converter = f'./OPENVOICE_MODELS/{model_version}'
device = "cuda:0" if device_choice == "GPU" and torch.cuda.is_available() else "cpu"
print(f"Device: {device}")
# Load the ToneColorConverter
tone_color_converter = ToneColorConverter(f'{ckpt_converter}/config.json', device=device)
tone_color_converter.load_ckpt(f'{ckpt_converter}/checkpoint.pth')
# Extract speaker embeddings
source_se, _ = se_extractor.get_se(base_speaker, tone_color_converter, vad=vad_select)
target_se, _ = se_extractor.get_se(reference_speaker, tone_color_converter, vad=vad_select)
# Define output file paths
save_path = f'{output_dir}/output_cloned.wav'
# Perform tone color conversion
tone_color_converter.convert(
audio_src_path=base_speaker,
src_se=source_se,
tgt_se=target_se,
output_path=save_path,
)
return save_path, "Voice cloning successful!"
except Exception as e:
return None, f"Error: {str(e)}"
def ui_fn(text, model_dir, model_name, clone, reference_speaker, model_version, device_choice, vad_select):
model_path = os.path.join(model_dir, model_name)
sampling_rate, audio_data = generate_speech(text, model_path)
audio_file = save_audio(sampling_rate, audio_data)
if clone:
cloned_audio_file, status = voice_cloning(audio_file, reference_speaker, model_version, device_choice, vad_select)
return cloned_audio_file, status
else:
return audio_file, "Speech generation successful!"
if __name__ == "__main__":
#model_dir = "./models_mms"
#model_names = get_model_names(model_dir)
iface = gr.Interface(
fn=ui_fn,
inputs=[
gr.Textbox(label="Text to Synthesize"),
gr.Textbox(label="Model Path or Id", value="VIZINTZOR/MMS-TTS-THAI-MALE-NARRATOR"),
#gr.Dropdown(model_names, label="Model"),
gr.Checkbox(label="Clone Voice", value=False),
gr.Audio(label="Reference Speaker (Target Voice)", type="filepath"),
gr.Dropdown(["v1", "v2"], value="v2", label="Model Version"),
gr.Dropdown(["CPU", "GPU"], value="GPU" if torch.cuda.is_available() else "CPU", label="Device"),
gr.Checkbox(value=False, label="VAD", interactive=True)
],
outputs=[
gr.Audio(label="Generated Audio", type="filepath"),
gr.Textbox(label="Status", interactive=False)
],
title="Text-to-Speech Synthesizer with Voice Cloning",
description="Enter text and model path to generate speech. Optionally, clone the voice using a reference speaker."
)
iface.launch()
|