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on
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Running
on
Zero
Update app_v1v2.py
Browse files- app_v1v2.py +242 -174
app_v1v2.py
CHANGED
@@ -1,175 +1,243 @@
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import spaces
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import gradio as gr
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import torch
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import yaml
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import argparse
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from seed_vc_wrapper import SeedVCWrapper
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torch._inductor.config.
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gr.
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gr.Slider(minimum=
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gr.Slider(minimum=0.
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gr.Slider(minimum=
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gr.Checkbox(label="
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main(args)
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import spaces
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import gradio as gr
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import torch
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import yaml
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import argparse
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from seed_vc_wrapper import SeedVCWrapper
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from modules.v2.vc_wrapper import VoiceConversionWrapper
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# Set up device and torch configurations
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if torch.cuda.is_available():
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device = torch.device("cuda")
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elif torch.backends.mps.is_available():
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device = torch.device("mps")
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else:
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device = torch.device("cpu")
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torch._inductor.config.coordinate_descent_tuning = True
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torch._inductor.config.triton.unique_kernel_names = True
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if hasattr(torch._inductor.config, "fx_graph_cache"):
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# Experimental feature to reduce compilation times, will be on by default in future
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torch._inductor.config.fx_graph_cache = True
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dtype = torch.float16
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# Global variables to store model instances
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vc_wrapper_v1 = None
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vc_wrapper_v2 = None
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def load_v2_models(args):
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from hydra.utils import instantiate
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from omegaconf import DictConfig
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cfg = DictConfig(yaml.safe_load(open("configs/v2/vc_wrapper.yaml", "r")))
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vc_wrapper = instantiate(cfg)
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vc_wrapper.load_checkpoints()
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vc_wrapper.to(device)
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vc_wrapper.eval()
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vc_wrapper.setup_ar_caches(max_batch_size=1, max_seq_len=4096, dtype=dtype, device=device)
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if args.compile:
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vc_wrapper.compile_ar()
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# vc_wrapper.compile_cfm()
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return vc_wrapper
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@spaces.GPU
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def convert_voice_v1_wrapper(source_audio_path, target_audio_path, diffusion_steps=10,
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length_adjust=1.0, inference_cfg_rate=0.7, f0_condition=False,
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auto_f0_adjust=True, pitch_shift=0, stream_output=True):
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"""
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Wrapper function for vc_wrapper.convert_voice that can be decorated with @spaces.GPU
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"""
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global vc_wrapper_v1
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if vc_wrapper_v1 is None:
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vc_wrapper_v1 = SeedVCWrapper()
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# Use yield from to properly handle the generator
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yield from vc_wrapper_v1.convert_voice(
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source=source_audio_path,
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target=target_audio_path,
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diffusion_steps=diffusion_steps,
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length_adjust=length_adjust,
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inference_cfg_rate=inference_cfg_rate,
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f0_condition=f0_condition,
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auto_f0_adjust=auto_f0_adjust,
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pitch_shift=pitch_shift,
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stream_output=stream_output
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)
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@spaces.GPU
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def convert_voice_v2_wrapper(source_audio_path, target_audio_path, diffusion_steps=30,
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length_adjust=1.0, intelligebility_cfg_rate=0.7, similarity_cfg_rate=0.7,
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top_p=0.7, temperature=0.7, repetition_penalty=1.5,
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convert_style=False, anonymization_only=False, stream_output=True):
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"""
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Wrapper function for vc_wrapper.convert_voice_with_streaming that can be decorated with @spaces.GPU
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"""
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global vc_wrapper_v2
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if vc_wrapper_v2 is None:
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# Initialize with default arguments
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parser = argparse.ArgumentParser()
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parser.add_argument("--compile", type=bool, default=True)
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args = parser.parse_args([])
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vc_wrapper_v2 = load_v2_models(args)
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# Use yield from to properly handle the generator
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yield from vc_wrapper_v2.convert_voice_with_streaming(
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source_audio_path=source_audio_path,
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target_audio_path=target_audio_path,
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diffusion_steps=diffusion_steps,
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length_adjust=length_adjust,
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intelligebility_cfg_rate=intelligebility_cfg_rate,
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similarity_cfg_rate=similarity_cfg_rate,
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top_p=top_p,
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temperature=temperature,
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repetition_penalty=repetition_penalty,
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convert_style=convert_style,
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anonymization_only=anonymization_only,
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device=device,
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dtype=dtype,
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stream_output=stream_output
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)
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def create_v1_interface():
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# Set up Gradio interface
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description = (
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"Zero-shot voice conversion with in-context learning. For local deployment please check [GitHub repository](https://github.com/Plachtaa/seed-vc) "
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"for details and updates.<br>Note that any reference audio will be forcefully clipped to 25s if beyond this length.<br> "
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"If total duration of source and reference audio exceeds 30s, source audio will be processed in chunks.<br> "
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"无需训练的 zero-shot 语音/歌声转换模型,若需本地部署查看[GitHub页面](https://github.com/Plachtaa/seed-vc)<br>"
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"请注意,参考音频若超过 25 秒,则会被自动裁剪至此长度。<br>若源音频和参考音频的总时长超过 30 秒,源音频将被分段处理。")
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inputs = [
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gr.Audio(type="filepath", label="Source Audio / 源音频"),
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gr.Audio(type="filepath", label="Reference Audio / 参考音频"),
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gr.Slider(minimum=1, maximum=200, value=10, step=1, label="Diffusion Steps / 扩散步数",
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info="10 by default, 50~100 for best quality / 默认为 10,50~100 为最佳质量"),
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gr.Slider(minimum=0.5, maximum=2.0, step=0.1, value=1.0, label="Length Adjust / 长度调整",
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info="<1.0 for speed-up speech, >1.0 for slow-down speech / <1.0 加速语速,>1.0 减慢语速"),
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gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0.7, label="Inference CFG Rate",
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info="has subtle influence / 有微小影响"),
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gr.Checkbox(label="Use F0 conditioned model / 启用F0输入", value=False,
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info="Must set to true for singing voice conversion / 歌声转换时必须勾选"),
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gr.Checkbox(label="Auto F0 adjust / 自动F0调整", value=True,
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info="Roughly adjust F0 to match target voice. Only works when F0 conditioned model is used. / 粗略调整 F0 以匹配目标音色,仅在勾选 '启用F0输入' 时生效"),
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gr.Slider(label='Pitch shift / 音调变换', minimum=-24, maximum=24, step=1, value=0,
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info="Pitch shift in semitones, only works when F0 conditioned model is used / 半音数的音高变换,仅在勾选 '启用F0输入' 时生效"),
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]
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examples = [
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["examples/source/yae_0.wav", "examples/reference/dingzhen_0.wav", 25, 1.0, 0.7, False, True, 0],
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["examples/source/jay_0.wav", "examples/reference/azuma_0.wav", 25, 1.0, 0.7, True, True, 0],
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["examples/source/Wiz Khalifa,Charlie Puth - See You Again [vocals]_[cut_28sec].wav",
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"examples/reference/teio_0.wav", 100, 1.0, 0.7, True, False, 0],
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["examples/source/TECHNOPOLIS - 2085 [vocals]_[cut_14sec].wav",
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"examples/reference/trump_0.wav", 50, 1.0, 0.7, True, False, -12],
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]
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outputs = [
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gr.Audio(label="Stream Output Audio / 流式输出", streaming=True, format='mp3'),
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gr.Audio(label="Full Output Audio / 完整输出", streaming=False, format='wav')
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]
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return gr.Interface(
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fn=convert_voice_v1_wrapper,
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description=description,
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inputs=inputs,
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outputs=outputs,
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title="Seed Voice Conversion V1 (Voice & Singing Voice Conversion)",
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examples=examples,
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cache_examples=False,
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)
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def create_v2_interface(vc_wrapper):
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# Set up Gradio interface
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description = (
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"Zero-shot voice/style conversion with in-context learning. For local deployment please check [GitHub repository](https://github.com/Plachtaa/seed-vc) "
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"for details and updates.<br>Note that any reference audio will be forcefully clipped to 25s if beyond this length.<br> "
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"If total duration of source and reference audio exceeds 30s, source audio will be processed in chunks.<br> "
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"Please click the 'convert style/emotion/accent' checkbox to convert the style, emotion, or accent of the source audio, or else only timbre conversion will be performed.<br> "
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"Click the 'anonymization only' checkbox will ignore reference audio but convert source to an 'average voice' determined by model itself.<br> "
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"无需训练的 zero-shot 语音/口音转换模型,若需本地部署查看[GitHub页面](https://github.com/Plachtaa/seed-vc)<br>"
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"请注意,参考音频若超过 25 秒,则会被自动裁剪至此长度。<br>若源音频和参考音频的总时长超过 30 秒,源音频将被分段处理。"
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"<br>请勾选 'convert style/emotion/accent' 以转换源音频的风格、情感或口音,否则仅执行音色转换。<br>"
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"勾选 'anonymization only' 会无视参考音频而将源音频转换为某种由模型自身决定的 '平均音色'。<br>"
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"Credits to [Vevo](https://github.com/open-mmlab/Amphion/tree/main/models/vc/vevo)"
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)
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inputs = [
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gr.Audio(type="filepath", label="Source Audio / 源音频"),
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gr.Audio(type="filepath", label="Reference Audio / 参考音频"),
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gr.Slider(minimum=1, maximum=200, value=30, step=1, label="Diffusion Steps / 扩散步数",
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info="30 by default, 50~100 for best quality / 默认为 30,50~100 为最佳质量"),
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gr.Slider(minimum=0.5, maximum=2.0, step=0.1, value=1.0, label="Length Adjust / 长度调整",
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info="<1.0 for speed-up speech, >1.0 for slow-down speech / <1.0 加速语速,>1.0 减慢语速"),
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gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0.0, label="Intelligibility CFG Rate",
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info="controls pronunciation intelligibility / 控制发音清晰度"),
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gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0.7, label="Similarity CFG Rate",
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info="controls similarity to reference audio / 控制与参考音频的相似度"),
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gr.Slider(minimum=0.1, maximum=1.0, step=0.1, value=0.9, label="Top-p",
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info="AR model sampling top P"),
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gr.Slider(minimum=0.1, maximum=2.0, step=0.1, value=1.0, label="Temperature",
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info="AR model sampling temperature"),
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gr.Slider(minimum=1.0, maximum=3.0, step=0.1, value=1.0, label="Repetition Penalty",
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info="AR model sampling repetition penalty"),
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gr.Checkbox(label="convert style/emotion/accent", value=False),
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gr.Checkbox(label="anonymization only", value=False),
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]
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examples = [
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["examples/source/yae_0.wav", "examples/reference/dingzhen_0.wav", 25, 1.0, 0.0, 0.7, 0.9, 1.0, 1.0, False,
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False],
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["examples/source/jay_0.wav", "examples/reference/azuma_0.wav", 25, 1.0, 0.0, 0.7, 0.9, 1.0, 1.0, False, False],
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]
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outputs = [
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gr.Audio(label="Stream Output Audio / 流式输���", streaming=True, format='mp3'),
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gr.Audio(label="Full Output Audio / 完整输出", streaming=False, format='wav')
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]
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return gr.Interface(
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fn=convert_voice_v2_wrapper,
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description=description,
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inputs=inputs,
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outputs=outputs,
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title="Seed Voice Conversion V2 (Voice & Style Conversion)",
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examples=examples,
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cache_examples=False,
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)
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def main(args):
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# Load V2 models
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+
vc_wrapper_v2 = load_v2_models(args)
|
219 |
+
|
220 |
+
# Create interfaces
|
221 |
+
v1_interface = create_v1_interface()
|
222 |
+
v2_interface = create_v2_interface(vc_wrapper_v2)
|
223 |
+
|
224 |
+
# Create tabs
|
225 |
+
with gr.Blocks(title="Seed Voice Conversion") as demo:
|
226 |
+
gr.Markdown("# Seed Voice Conversion")
|
227 |
+
gr.Markdown("Choose between V1 (Voice & Singing Voice Conversion) or V2 (Voice & Style Conversion)")
|
228 |
+
|
229 |
+
with gr.Tabs():
|
230 |
+
with gr.TabItem("V2 - Voice & Style Conversion"):
|
231 |
+
v2_interface.render()
|
232 |
+
with gr.TabItem("V1 - Voice & Singing Voice Conversion"):
|
233 |
+
v1_interface.render()
|
234 |
+
|
235 |
+
# Launch the combined interface
|
236 |
+
demo.launch()
|
237 |
+
|
238 |
+
|
239 |
+
if __name__ == "__main__":
|
240 |
+
parser = argparse.ArgumentParser()
|
241 |
+
parser.add_argument("--compile", type=bool, default=True)
|
242 |
+
args = parser.parse_args()
|
243 |
main(args)
|