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积极的屁孩
commited on
Commit
·
cc7434e
1
Parent(s):
29b1e08
adjustments
Browse files
app.py
CHANGED
@@ -236,7 +236,7 @@ def vevo_style(content_wav, style_wav):
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# 检查并处理音频数据
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if content_wav is None or style_wav is None:
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raise ValueError("
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# 处理音频格式
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if isinstance(content_wav, tuple) and len(content_wav) == 2:
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@@ -260,7 +260,7 @@ def vevo_style(content_wav, style_wav):
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# 归一化音量
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content_tensor = content_tensor / (torch.max(torch.abs(content_tensor)) + 1e-6) * 0.95
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else:
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raise ValueError("
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if isinstance(style_wav, tuple) and len(style_wav) == 2:
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# 确保正确的顺序 (data, sample_rate)
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@@ -272,11 +272,11 @@ def vevo_style(content_wav, style_wav):
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if style_tensor.ndim == 1:
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style_tensor = style_tensor.unsqueeze(0) # 添加通道维度
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else:
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raise ValueError("
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# 打印debug信息
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print(f"
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print(f"
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# 保存音频
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torchaudio.save(temp_content_path, content_tensor, content_sr)
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@@ -296,17 +296,17 @@ def vevo_style(content_wav, style_wav):
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# 检查生成音频是否为数值异常
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if torch.isnan(gen_audio).any() or torch.isinf(gen_audio).any():
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print("
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gen_audio = torch.nan_to_num(gen_audio, nan=0.0, posinf=0.95, neginf=-0.95)
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print(f"
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# 保存生成的音频
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save_audio(gen_audio, output_path=output_path)
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return output_path
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except Exception as e:
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print(f"
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import traceback
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traceback.print_exc()
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raise e
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@@ -318,7 +318,7 @@ def vevo_timbre(content_wav, reference_wav):
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# 检查并处理音频数据
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if content_wav is None or reference_wav is None:
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raise ValueError("
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# 处理内容音频格式
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if isinstance(content_wav, tuple) and len(content_wav) == 2:
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@@ -342,7 +342,7 @@ def vevo_timbre(content_wav, reference_wav):
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# 归一化音量
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content_tensor = content_tensor / (torch.max(torch.abs(content_tensor)) + 1e-6) * 0.95
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else:
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raise ValueError("
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# 处理参考音频格式
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if isinstance(reference_wav, tuple) and len(reference_wav) == 2:
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@@ -366,11 +366,11 @@ def vevo_timbre(content_wav, reference_wav):
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# 归一化音量
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reference_tensor = reference_tensor / (torch.max(torch.abs(reference_tensor)) + 1e-6) * 0.95
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else:
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raise ValueError("
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# 打印debug信息
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print(f"
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print(f"
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# 保存上传的音频
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torchaudio.save(temp_content_path, content_tensor, content_sr)
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@@ -389,29 +389,30 @@ def vevo_timbre(content_wav, reference_wav):
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# 检查生成音频是否为数值异常
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if torch.isnan(gen_audio).any() or torch.isinf(gen_audio).any():
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print("
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gen_audio = torch.nan_to_num(gen_audio, nan=0.0, posinf=0.95, neginf=-0.95)
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print(f"
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# 保存生成的音频
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save_audio(gen_audio, output_path=output_path)
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return output_path
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except Exception as e:
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print(f"
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import traceback
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traceback.print_exc()
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raise e
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def vevo_voice(content_wav,
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temp_content_path = "wav/temp_content.wav"
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output_path = "wav/output_vevovoice.wav"
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# 检查并处理音频数据
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if content_wav is None or
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raise ValueError("
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# 处理内容音频格式
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if isinstance(content_wav, tuple) and len(content_wav) == 2:
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@@ -435,39 +436,65 @@ def vevo_voice(content_wav, reference_wav):
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# 归一化音量
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content_tensor = content_tensor / (torch.max(torch.abs(content_tensor)) + 1e-6) * 0.95
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else:
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raise ValueError("
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#
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if isinstance(
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if isinstance(
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else:
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-
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# 确保是单声道
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if len(
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-
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# 重采样到24kHz
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if
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-
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-
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-
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else:
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-
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# 归一化音量
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else:
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raise ValueError("
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# 打印debug信息
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print(f"
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print(f"
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# 保存上传的音频
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torchaudio.save(temp_content_path, content_tensor, content_sr)
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torchaudio.save(
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try:
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# 获取管道
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@@ -477,23 +504,23 @@ def vevo_voice(content_wav, reference_wav):
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gen_audio = pipeline.inference_ar_and_fm(
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src_wav_path=temp_content_path,
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src_text=None,
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style_ref_wav_path=
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timbre_ref_wav_path=
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)
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# 检查生成音频是否为数值异常
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if torch.isnan(gen_audio).any() or torch.isinf(gen_audio).any():
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print("
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gen_audio = torch.nan_to_num(gen_audio, nan=0.0, posinf=0.95, neginf=-0.95)
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print(f"
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# 保存生成的音频
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save_audio(gen_audio, output_path=output_path)
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return output_path
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except Exception as e:
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print(f"
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import traceback
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traceback.print_exc()
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raise e
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@@ -505,7 +532,7 @@ def vevo_tts(text, ref_wav, timbre_ref_wav=None, src_language="en", ref_language
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# 检查并处理音频数据
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if ref_wav is None:
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raise ValueError("
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# 处理参考音频格式
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if isinstance(ref_wav, tuple) and len(ref_wav) == 2:
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@@ -529,10 +556,10 @@ def vevo_tts(text, ref_wav, timbre_ref_wav=None, src_language="en", ref_language
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# 归一化音量
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ref_tensor = ref_tensor / (torch.max(torch.abs(ref_tensor)) + 1e-6) * 0.95
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else:
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raise ValueError("
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# 打印debug信息
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print(f"
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# 保存上传的音频
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torchaudio.save(temp_ref_path, ref_tensor, ref_sr)
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@@ -559,10 +586,10 @@ def vevo_tts(text, ref_wav, timbre_ref_wav=None, src_language="en", ref_language
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# 归一化音量
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timbre_tensor = timbre_tensor / (torch.max(torch.abs(timbre_tensor)) + 1e-6) * 0.95
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print(f"
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torchaudio.save(temp_timbre_path, timbre_tensor, timbre_sr)
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else:
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raise ValueError("
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else:
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temp_timbre_path = temp_ref_path
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@@ -583,74 +610,75 @@ def vevo_tts(text, ref_wav, timbre_ref_wav=None, src_language="en", ref_language
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# 检查生成音频是否为数值异常
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if torch.isnan(gen_audio).any() or torch.isinf(gen_audio).any():
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print("
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gen_audio = torch.nan_to_num(gen_audio, nan=0.0, posinf=0.95, neginf=-0.95)
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print(f"
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# 保存生成的音频
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save_audio(gen_audio, output_path=output_path)
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return output_path
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except Exception as e:
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print(f"
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import traceback
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traceback.print_exc()
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raise e
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# 创建Gradio界面
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with gr.Blocks(title="VEVO
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gr.Markdown("# VEVO
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gr.Markdown("##
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with gr.Tab("
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gr.Markdown("### Vevo-
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with gr.Row():
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with gr.Column():
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with gr.Column():
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-
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with gr.Tab("
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gr.Markdown("### Vevo-
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with gr.Row():
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with gr.Column():
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with gr.Column():
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with gr.Tab("
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gr.Markdown("### Vevo-
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with gr.Row():
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with gr.Column():
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with gr.Column():
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with gr.Tab("
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gr.Markdown("### Vevo-TTS:
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with gr.Row():
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with gr.Column():
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tts_text = gr.Textbox(label="
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tts_src_language = gr.Dropdown(["en", "zh", "de", "fr", "ja", "ko"], label="
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tts_reference = gr.Audio(label="
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tts_ref_language = gr.Dropdown(["en", "zh", "de", "fr", "ja", "ko"], label="
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with gr.Accordion("
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tts_timbre_reference = gr.Audio(label="
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tts_button = gr.Button("
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with gr.Column():
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tts_output = gr.Audio(label="
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tts_button.click(
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vevo_tts,
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@@ -659,14 +687,14 @@ with gr.Blocks(title="VEVO Demo") as demo:
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)
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gr.Markdown("""
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##
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VEVO
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1. **Vevo-Style**:
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2. **Vevo-Timbre**:
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3. **Vevo-Voice**:
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4. **Vevo-TTS**:
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-
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""")
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# 启动应用
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# 检查并处理音频数据
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if content_wav is None or style_wav is None:
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raise ValueError("Please upload audio files")
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# 处理音频格式
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if isinstance(content_wav, tuple) and len(content_wav) == 2:
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# 归一化音量
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content_tensor = content_tensor / (torch.max(torch.abs(content_tensor)) + 1e-6) * 0.95
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else:
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raise ValueError("Invalid content audio format")
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if isinstance(style_wav, tuple) and len(style_wav) == 2:
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# 确保正确的顺序 (data, sample_rate)
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if style_tensor.ndim == 1:
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style_tensor = style_tensor.unsqueeze(0) # 添加通道维度
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else:
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raise ValueError("Invalid style audio format")
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# 打印debug信息
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print(f"Content audio shape: {content_tensor.shape}, sample rate: {content_sr}")
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print(f"Style audio shape: {style_tensor.shape}, sample rate: {style_sr}")
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# 保存音频
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torchaudio.save(temp_content_path, content_tensor, content_sr)
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# 检查生成音频是否为数值异常
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if torch.isnan(gen_audio).any() or torch.isinf(gen_audio).any():
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print("Warning: Generated audio contains NaN or Inf values")
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gen_audio = torch.nan_to_num(gen_audio, nan=0.0, posinf=0.95, neginf=-0.95)
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print(f"Generated audio shape: {gen_audio.shape}, max: {torch.max(gen_audio)}, min: {torch.min(gen_audio)}")
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# 保存生成的音频
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save_audio(gen_audio, output_path=output_path)
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return output_path
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except Exception as e:
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print(f"Error during processing: {e}")
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import traceback
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traceback.print_exc()
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raise e
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# 检查并处理音频数据
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if content_wav is None or reference_wav is None:
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raise ValueError("Please upload audio files")
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# 处理内容音频格式
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if isinstance(content_wav, tuple) and len(content_wav) == 2:
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# 归一化音量
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content_tensor = content_tensor / (torch.max(torch.abs(content_tensor)) + 1e-6) * 0.95
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else:
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raise ValueError("Invalid content audio format")
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# 处理参考音频格式
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if isinstance(reference_wav, tuple) and len(reference_wav) == 2:
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# 归一化音量
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reference_tensor = reference_tensor / (torch.max(torch.abs(reference_tensor)) + 1e-6) * 0.95
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else:
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raise ValueError("Invalid reference audio format")
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# 打印debug信息
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print(f"Content audio shape: {content_tensor.shape}, sample rate: {content_sr}")
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print(f"Reference audio shape: {reference_tensor.shape}, sample rate: {reference_sr}")
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# 保存上传的音频
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torchaudio.save(temp_content_path, content_tensor, content_sr)
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# 检查生成音频是否为数值异常
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if torch.isnan(gen_audio).any() or torch.isinf(gen_audio).any():
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print("Warning: Generated audio contains NaN or Inf values")
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gen_audio = torch.nan_to_num(gen_audio, nan=0.0, posinf=0.95, neginf=-0.95)
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print(f"Generated audio shape: {gen_audio.shape}, max: {torch.max(gen_audio)}, min: {torch.min(gen_audio)}")
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# 保存生成的音频
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save_audio(gen_audio, output_path=output_path)
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return output_path
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except Exception as e:
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print(f"Error during processing: {e}")
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import traceback
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traceback.print_exc()
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raise e
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def vevo_voice(content_wav, style_reference_wav, timbre_reference_wav):
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temp_content_path = "wav/temp_content.wav"
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temp_style_path = "wav/temp_style.wav"
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temp_timbre_path = "wav/temp_timbre.wav"
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output_path = "wav/output_vevovoice.wav"
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# 检查并处理音频数据
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if content_wav is None or style_reference_wav is None or timbre_reference_wav is None:
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raise ValueError("Please upload all required audio files")
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# 处理内容音频格式
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if isinstance(content_wav, tuple) and len(content_wav) == 2:
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# 归一化音量
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content_tensor = content_tensor / (torch.max(torch.abs(content_tensor)) + 1e-6) * 0.95
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else:
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raise ValueError("Invalid content audio format")
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# 处理风格参考音频格式
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if isinstance(style_reference_wav, tuple) and len(style_reference_wav) == 2:
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if isinstance(style_reference_wav[0], np.ndarray):
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style_data, style_sr = style_reference_wav
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else:
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style_sr, style_data = style_reference_wav
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# 确保是单声道
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if len(style_data.shape) > 1 and style_data.shape[1] > 1:
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style_data = np.mean(style_data, axis=1)
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# 重采样到24kHz
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if style_sr != 24000:
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style_tensor = torch.FloatTensor(style_data).unsqueeze(0)
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style_tensor = torchaudio.functional.resample(style_tensor, style_sr, 24000)
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style_sr = 24000
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else:
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style_tensor = torch.FloatTensor(style_data).unsqueeze(0)
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# 归一化音量
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style_tensor = style_tensor / (torch.max(torch.abs(style_tensor)) + 1e-6) * 0.95
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else:
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raise ValueError("Invalid style reference audio format")
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# 处理音色参考音频格式
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if isinstance(timbre_reference_wav, tuple) and len(timbre_reference_wav) == 2:
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467 |
+
if isinstance(timbre_reference_wav[0], np.ndarray):
|
468 |
+
timbre_data, timbre_sr = timbre_reference_wav
|
469 |
+
else:
|
470 |
+
timbre_sr, timbre_data = timbre_reference_wav
|
471 |
+
|
472 |
+
# 确保是单声道
|
473 |
+
if len(timbre_data.shape) > 1 and timbre_data.shape[1] > 1:
|
474 |
+
timbre_data = np.mean(timbre_data, axis=1)
|
475 |
+
|
476 |
+
# 重采样到24kHz
|
477 |
+
if timbre_sr != 24000:
|
478 |
+
timbre_tensor = torch.FloatTensor(timbre_data).unsqueeze(0)
|
479 |
+
timbre_tensor = torchaudio.functional.resample(timbre_tensor, timbre_sr, 24000)
|
480 |
+
timbre_sr = 24000
|
481 |
+
else:
|
482 |
+
timbre_tensor = torch.FloatTensor(timbre_data).unsqueeze(0)
|
483 |
+
|
484 |
+
# 归一化音量
|
485 |
+
timbre_tensor = timbre_tensor / (torch.max(torch.abs(timbre_tensor)) + 1e-6) * 0.95
|
486 |
else:
|
487 |
+
raise ValueError("Invalid timbre reference audio format")
|
488 |
|
489 |
# 打印debug信息
|
490 |
+
print(f"Content audio shape: {content_tensor.shape}, sample rate: {content_sr}")
|
491 |
+
print(f"Style reference audio shape: {style_tensor.shape}, sample rate: {style_sr}")
|
492 |
+
print(f"Timbre reference audio shape: {timbre_tensor.shape}, sample rate: {timbre_sr}")
|
493 |
|
494 |
# 保存上传的音频
|
495 |
torchaudio.save(temp_content_path, content_tensor, content_sr)
|
496 |
+
torchaudio.save(temp_style_path, style_tensor, style_sr)
|
497 |
+
torchaudio.save(temp_timbre_path, timbre_tensor, timbre_sr)
|
498 |
|
499 |
try:
|
500 |
# 获取管道
|
|
|
504 |
gen_audio = pipeline.inference_ar_and_fm(
|
505 |
src_wav_path=temp_content_path,
|
506 |
src_text=None,
|
507 |
+
style_ref_wav_path=temp_style_path,
|
508 |
+
timbre_ref_wav_path=temp_timbre_path,
|
509 |
)
|
510 |
|
511 |
# 检查生成音频是否为数值异常
|
512 |
if torch.isnan(gen_audio).any() or torch.isinf(gen_audio).any():
|
513 |
+
print("Warning: Generated audio contains NaN or Inf values")
|
514 |
gen_audio = torch.nan_to_num(gen_audio, nan=0.0, posinf=0.95, neginf=-0.95)
|
515 |
|
516 |
+
print(f"Generated audio shape: {gen_audio.shape}, max: {torch.max(gen_audio)}, min: {torch.min(gen_audio)}")
|
517 |
|
518 |
# 保存生成的音频
|
519 |
save_audio(gen_audio, output_path=output_path)
|
520 |
|
521 |
return output_path
|
522 |
except Exception as e:
|
523 |
+
print(f"Error during processing: {e}")
|
524 |
import traceback
|
525 |
traceback.print_exc()
|
526 |
raise e
|
|
|
532 |
|
533 |
# 检查并处理音频数据
|
534 |
if ref_wav is None:
|
535 |
+
raise ValueError("Please upload a reference audio file")
|
536 |
|
537 |
# 处理参考音频格式
|
538 |
if isinstance(ref_wav, tuple) and len(ref_wav) == 2:
|
|
|
556 |
# 归一化音量
|
557 |
ref_tensor = ref_tensor / (torch.max(torch.abs(ref_tensor)) + 1e-6) * 0.95
|
558 |
else:
|
559 |
+
raise ValueError("Invalid reference audio format")
|
560 |
|
561 |
# 打印debug信息
|
562 |
+
print(f"Reference audio shape: {ref_tensor.shape}, sample rate: {ref_sr}")
|
563 |
|
564 |
# 保存上传的音频
|
565 |
torchaudio.save(temp_ref_path, ref_tensor, ref_sr)
|
|
|
586 |
# 归一化音量
|
587 |
timbre_tensor = timbre_tensor / (torch.max(torch.abs(timbre_tensor)) + 1e-6) * 0.95
|
588 |
|
589 |
+
print(f"Timbre reference audio shape: {timbre_tensor.shape}, sample rate: {timbre_sr}")
|
590 |
torchaudio.save(temp_timbre_path, timbre_tensor, timbre_sr)
|
591 |
else:
|
592 |
+
raise ValueError("Invalid timbre reference audio format")
|
593 |
else:
|
594 |
temp_timbre_path = temp_ref_path
|
595 |
|
|
|
610 |
|
611 |
# 检查生成音频是否为数值异常
|
612 |
if torch.isnan(gen_audio).any() or torch.isinf(gen_audio).any():
|
613 |
+
print("Warning: Generated audio contains NaN or Inf values")
|
614 |
gen_audio = torch.nan_to_num(gen_audio, nan=0.0, posinf=0.95, neginf=-0.95)
|
615 |
|
616 |
+
print(f"Generated audio shape: {gen_audio.shape}, max: {torch.max(gen_audio)}, min: {torch.min(gen_audio)}")
|
617 |
|
618 |
# 保存生成的音频
|
619 |
save_audio(gen_audio, output_path=output_path)
|
620 |
|
621 |
return output_path
|
622 |
except Exception as e:
|
623 |
+
print(f"Error during processing: {e}")
|
624 |
import traceback
|
625 |
traceback.print_exc()
|
626 |
raise e
|
627 |
|
628 |
# 创建Gradio界面
|
629 |
+
with gr.Blocks(title="VEVO DEMO") as demo:
|
630 |
+
gr.Markdown("# VEVO DEMO")
|
631 |
+
gr.Markdown("## Controllable Zero-Shot Voice Conversion and Style Transfer")
|
632 |
|
633 |
+
with gr.Tab("Vevo-Timbre"):
|
634 |
+
gr.Markdown("### Vevo-Timbre: Maintain style but transfer timbre")
|
635 |
with gr.Row():
|
636 |
with gr.Column():
|
637 |
+
timbre_content = gr.Audio(label="Content Audio", type="numpy")
|
638 |
+
timbre_reference = gr.Audio(label="Timbre Reference", type="numpy")
|
639 |
+
timbre_button = gr.Button("Generate")
|
640 |
with gr.Column():
|
641 |
+
timbre_output = gr.Audio(label="Result")
|
642 |
+
timbre_button.click(vevo_timbre, inputs=[timbre_content, timbre_reference], outputs=timbre_output)
|
643 |
|
644 |
+
with gr.Tab("Vevo-Voice"):
|
645 |
+
gr.Markdown("### Vevo-Voice: Transfer both style and timbre with separate references")
|
646 |
with gr.Row():
|
647 |
with gr.Column():
|
648 |
+
voice_content = gr.Audio(label="Content Audio", type="numpy")
|
649 |
+
voice_style_reference = gr.Audio(label="Style Reference", type="numpy")
|
650 |
+
voice_timbre_reference = gr.Audio(label="Timbre Reference", type="numpy")
|
651 |
+
voice_button = gr.Button("Generate")
|
652 |
with gr.Column():
|
653 |
+
voice_output = gr.Audio(label="Result")
|
654 |
+
voice_button.click(vevo_voice, inputs=[voice_content, voice_style_reference, voice_timbre_reference], outputs=voice_output)
|
655 |
|
656 |
+
with gr.Tab("Vevo-Style"):
|
657 |
+
gr.Markdown("### Vevo-Style: Maintain timbre but transfer style (accent, emotion, etc.)")
|
658 |
with gr.Row():
|
659 |
with gr.Column():
|
660 |
+
style_content = gr.Audio(label="Content Audio", type="numpy")
|
661 |
+
style_reference = gr.Audio(label="Style Reference", type="numpy")
|
662 |
+
style_button = gr.Button("Generate")
|
663 |
with gr.Column():
|
664 |
+
style_output = gr.Audio(label="Result")
|
665 |
+
style_button.click(vevo_style, inputs=[style_content, style_reference], outputs=style_output)
|
666 |
|
667 |
+
with gr.Tab("Vevo-TTS"):
|
668 |
+
gr.Markdown("### Vevo-TTS: Text-to-speech with controllable style and timbre")
|
669 |
with gr.Row():
|
670 |
with gr.Column():
|
671 |
+
tts_text = gr.Textbox(label="Input Text", placeholder="Enter text to synthesize...", lines=3)
|
672 |
+
tts_src_language = gr.Dropdown(["en", "zh", "de", "fr", "ja", "ko"], label="Text Language", value="en")
|
673 |
+
tts_reference = gr.Audio(label="Style Reference", type="numpy")
|
674 |
+
tts_ref_language = gr.Dropdown(["en", "zh", "de", "fr", "ja", "ko"], label="Reference Audio Language", value="en")
|
675 |
|
676 |
+
with gr.Accordion("Advanced Options", open=False):
|
677 |
+
tts_timbre_reference = gr.Audio(label="Timbre Reference (Optional)", type="numpy")
|
678 |
|
679 |
+
tts_button = gr.Button("Generate")
|
680 |
with gr.Column():
|
681 |
+
tts_output = gr.Audio(label="Result")
|
682 |
|
683 |
tts_button.click(
|
684 |
vevo_tts,
|
|
|
687 |
)
|
688 |
|
689 |
gr.Markdown("""
|
690 |
+
## About VEVO
|
691 |
+
VEVO is a versatile voice synthesis and conversion model that offers four main functionalities:
|
692 |
+
1. **Vevo-Style**: Maintains timbre but transfers style (accent, emotion, etc.)
|
693 |
+
2. **Vevo-Timbre**: Maintains style but transfers timbre
|
694 |
+
3. **Vevo-Voice**: Transfers both style and timbre simultaneously
|
695 |
+
4. **Vevo-TTS**: Text-to-speech with controllable style and timbre
|
696 |
+
|
697 |
+
For more information, visit the [Amphion project](https://github.com/open-mmlab/Amphion)
|
698 |
""")
|
699 |
|
700 |
# 启动应用
|