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import os
import tempfile
from openai import OpenAI
from tts_voice import tts_order_voice
import edge_tts
import anyio
import torch
import torchaudio
import gradio as gr
from scipy.io import wavfile
from scipy.io.wavfile import write
# 创建 KNN-VC 模型
knn_vc = torch.hub.load('bshall/knn-vc', 'knn_vc', prematched=True, trust_repo=True, pretrained=True, device='cpu')
# 初始化 language_dict
language_dict = tts_order_voice
# 异步文字转语音函数
async def text_to_speech_edge(text, language_code):
voice = language_dict[language_code]
communicate = edge_tts.Communicate(text, voice)
with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
tmp_path = tmp_file.name
await communicate.save(tmp_path)
return "语音合成完成:{}".format(text), tmp_path
# 声音更改函数
#def voice_change(audio_in, audio_ref):
#samplerate1, data1 = wavfile.read(audio_in)
#samplerate2, data2 = wavfile.read(audio_ref)
#write("./audio_in.wav", samplerate1, data1)
#write("./audio_ref.wav", samplerate2, data2)
#query_seq = knn_vc.get_features("./audio_in.wav")
#matching_set = knn_vc.get_matching_set(["./audio_ref.wav"])
#out_wav = knn_vc.match(query_seq, matching_set, topk=4)
#torchaudio.save('output.wav', out_wav[None], 16000)
#return 'output.wav'
# def voice_change(audio_in, audio_ref):
# samplerate1, data1 = wavfile.read(audio_in)
# samplerate2, data2 = wavfile.read(audio_ref)
# with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_audio_in, \
# tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_audio_ref:
# audio_in_path = tmp_audio_in.name
# audio_ref_path = tmp_audio_ref.name
# write(audio_in_path, samplerate1, data1)
# write(audio_ref_path, samplerate2, data2)
# query_seq = knn_vc.get_features(audio_in_path)
# matching_set = knn_vc.get_matching_set([audio_ref_path])
# out_wav = knn_vc.match(query_seq, matching_set, topk=4)
# output_path = 'output.wav'
# torchaudio.save(output_path, out_wav[None], 16000)
# return output_path
def voice_change(audio_in, audio_ref):
samplerate1, data1 = wavfile.read(audio_in)
samplerate2, data2 = wavfile.read(audio_ref)
# 强制匹配音频文件的长度
max_length = max(data1.shape[0], data2.shape[0])
if data1.shape[0] < max_length:
data1 = np.pad(data1, (0, max_length - data1.shape[0]), mode='constant')
if data2.shape[0] < max_length:
data2 = np.pad(data2, (0, max_length - data2.shape[0]), mode='constant')
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_audio_in, \
tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_audio_ref:
audio_in_path = tmp_audio_in.name
audio_ref_path = tmp_audio_ref.name
wavfile.write(audio_in_path, samplerate1, data1)
wavfile.write(audio_ref_path, samplerate2, data2)
query_seq = knn_vc.get_features(audio_in_path)
matching_set = knn_vc.get_matching_set([audio_ref_path])
out_wav = knn_vc.match(query_seq, matching_set, topk=4)
output_path = 'output.wav'
torchaudio.save(output_path, torch.tensor(out_wav)[None], 16000)
return output_path
# 文字转语音(OpenAI)
def tts(text, model, voice, api_key):
if len(text) > 300:
raise gr.Error('您输入的文本字符多于300个,请缩短您的文本')
if api_key == '':
raise gr.Error('请填写您的 中转API Key')
try:
client = OpenAI(api_key=api_key, base_url='https://lmzh.top/v1')
response = client.audio.speech.create(
model=model,
voice=voice,
input=text,
)
except Exception as error:
raise gr.Error(f"生成语音时出错:{error}")
with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as temp_file:
temp_file.write(response.content)
return temp_file.name
# Gradio 前端设计
app = gr.Blocks()
with app:
gr.Markdown("# <center>OpenAI TTS + 3秒实时AI变声+需要使用中转key</center>")
gr.Markdown("### <center>中转key购买地址https://buy.sipola.cn</center>")
with gr.Tab("TTS"):
with gr.Row(variant='panel'):
api_key = gr.Textbox(type='password', label='API Key', placeholder='请在此填写您的API Key')
model = gr.Dropdown(choices=['tts-1','tts-1-hd'], label='请选择模型(tts-1推理更快,tts-1-hd音质更好)', value='tts-1')
voice = gr.Dropdown(choices=['alloy', 'echo', 'fable', 'onyx', 'nova', 'shimmer'], label='请选择一个说话人', value='alloy')
with gr.Row():
with gr.Column():
inp_text = gr.Textbox(label="请填写您想生成的文本(中英文皆可)", placeholder="想说却还没说的 还很多 攒着是因为想写成歌", lines=5)
btn_text = gr.Button("一键开启真实拟声吧", variant="primary")
with gr.Column():
inp1 = gr.Audio(type="filepath", label="OpenAI TTS真实拟声", interactive=False)
inp2 = gr.Audio(type="filepath", label="请上传AI变声的参照音频(决定变声后的语音音色)")
btn1 = gr.Button("一键开启AI变声吧", variant="primary")
with gr.Column():
out1 = gr.Audio(type="filepath", label="AI变声后的专属音频")
btn_text.click(tts, [inp_text, model, voice, api_key], inp1)
btn1.click(voice_change, [inp1, inp2], out1)
with gr.Tab("⚡ Edge TTS"):
with gr.Row():
input_text = gr.Textbox(lines=5, placeholder="想说却还没说的 还很多 攒着是因为想写成歌", label="请填写您想生成的文本(中英文皆可)")
default_language = list(language_dict.keys())[15]
language = gr.Dropdown(choices=list(language_dict.keys()), value=default_language, label="请选择文本对应的语言")
btn_edge = gr.Button("一键开启真实拟声吧", variant="primary")
output_text = gr.Textbox(label="输出文本", visible=False)
output_audio = gr.Audio(type="filepath", label="Edge TTS真实拟声")
with gr.Row():
inp_vc = gr.Audio(type="filepath", label="请上传AI变声的参照音频(决定变声后的语音音色)")
btn_vc = gr.Button("一键开启AI变声吧", variant="primary")
out_vc = gr.Audio(type="filepath", label="AI变声后的专属音频")
btn_edge.click(lambda text, lang: anyio.run(text_to_speech_edge, text, lang), [input_text, language], [output_text, output_audio])
btn_vc.click(voice_change, [output_audio, inp_vc], out_vc)
gr.Markdown("### <center>注意获取中转API Key [here](https://buy.sipola.cn).</center>")
gr.HTML('''
<div class="footer">
<center><p>Power by sipola </p></center>
</div>
''')
app.launch(show_error=True)
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