Delete test.py

test.py

@@ -1,87 +0,0 @@
-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
-import numpy as np
-
-# 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
-
-# 音频填充函数
-def pad_audio(data, target_length):
-    if len(data) < target_length:
-        pad_length = target_length - len(data)
-        data = np.pad(data, (0, pad_length), mode='constant')
-    return data
-
-# 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)
-
-    # 使两个音频长度一致
-    target_length = max(len(data1), len(data2))
-    data1 = pad_audio(data1, target_length)
-    data2 = pad_audio(data2, target_length)
-
-    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])
-    print("query_seq shape:", query_seq.shape)
-    print("matching_set shape:", matching_set.shape)
-    
-    # 确保 query_seq 和 matching_set 维度一致
-    if query_seq.shape[0] > matching_set.shape[1]:
-        query_seq = query_seq[:matching_set.shape[1]]
-    elif query_seq.shape[0] < matching_set.shape[1]:
-        matching_set = matching_set[:, :query_seq.shape[0], :]
-
-    out_wav = knn_vc.match(query_seq, matching_set, topk=4)
-
-    # 确保 out_wav 是二维张量
-    if len(out_wav.shape) == 1:
-        out_wav = out_wav.unsqueeze(0)
-
-    output_path = 'output.wav'
-    torchaudio.save(output_path, out_wav, 16000)
-    return output_path
-
-# 示例使用 gradio 界面
-def gradio_interface(audio_in, audio_ref):
-    return voice_change(audio_in, audio_ref)
-
-# 创建 Gradio 界面
-iface = gr.Interface(fn=gradio_interface, 
-                     inputs=["audio", "audio"], 
-                     outputs="audio", 
-                     title="KNN-VC Voice Changer")
-
-if __name__ == "__main__":
-    iface.launch()