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README.md

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 # seed-vc-edge-tts
-seed vc使用edge tts生成音频再进行推理，实现seed vc的文本转语音功能
+seed vc 使用 edge tts 生成音频再进行推理，实现 seed vc 的文本转语音功能
+
+基于`app.py`修改而来
+
+## 使用方法
+下载`seed_vc_edge_tts.py`到 seed vc 项目根目录，
+
+安装`requirements.txt`所需依赖，
+
+运行，
+
+然后在浏览器中打开 `http://localhost:7860/` 使用 Web 界面
+
+## 你需要关注以下修改
+edge tts 新版本需要网络代理才能访问，所以需要指定网络代理
+```python
+edge_proxy = "输入你的代理地址"
+
+例：edge_proxy = "http://192.168.0.1:7890"
+```
+```python
+edge_tts.Communicate(tts_text, "-".join(tts_choice.split("-")[:-1]), rate=speed_str, pitch=pitch_str, proxy=edge_proxy)
+```
+## 效果
+![效果](./doc/seed_vc_edge_tts_webui.jpg)

requirements.txt

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+edge_tts==6.1.13

seed_vc_edge_tts.py

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+import gradio as gr
+import torch
+import torchaudio
+import librosa
+from modules.commons import build_model, load_checkpoint, recursive_munch
+import yaml
+from hf_utils import load_custom_model_from_hf
+import numpy as np
+from pydub import AudioSegment
+
+# edge tts
+import asyncio
+import traceback
+import edge_tts
+
+# edge tts需要外网访问，这里设置你的网络代理
+edge_proxy = "http://192.168.31.69:7890"
+
+# Load model and configuration
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+dit_checkpoint_path, dit_config_path = load_custom_model_from_hf("Plachta/Seed-VC",
+                                                                 "DiT_seed_v2_uvit_whisper_small_wavenet_bigvgan_pruned.pth",
+                                                                 "config_dit_mel_seed_uvit_whisper_small_wavenet.yml")
+config = yaml.safe_load(open(dit_config_path, 'r'))
+model_params = recursive_munch(config['model_params'])
+model = build_model(model_params, stage='DiT')
+hop_length = config['preprocess_params']['spect_params']['hop_length']
+sr = config['preprocess_params']['sr']
+
+# Load checkpoints
+model, _, _, _ = load_checkpoint(model, None, dit_checkpoint_path,
+                                 load_only_params=True, ignore_modules=[], is_distributed=False)
+for key in model:
+    model[key].eval()
+    model[key].to(device)
+model.cfm.estimator.setup_caches(max_batch_size=1, max_seq_length=8192)
+
+# Load additional modules
+from modules.campplus.DTDNN import CAMPPlus
+
+campplus_ckpt_path = load_custom_model_from_hf("funasr/campplus", "campplus_cn_common.bin", config_filename=None)
+campplus_model = CAMPPlus(feat_dim=80, embedding_size=192)
+campplus_model.load_state_dict(torch.load(campplus_ckpt_path, map_location="cpu"))
+campplus_model.eval()
+campplus_model.to(device)
+
+from modules.bigvgan import bigvgan
+
+bigvgan_model = bigvgan.BigVGAN.from_pretrained('nvidia/bigvgan_v2_22khz_80band_256x', use_cuda_kernel=False)
+
+# remove weight norm in the model and set to eval mode
+bigvgan_model.remove_weight_norm()
+bigvgan_model = bigvgan_model.eval().to(device)
+
+# whisper
+from transformers import AutoFeatureExtractor, WhisperModel
+
+whisper_name = model_params.speech_tokenizer.whisper_name if hasattr(model_params.speech_tokenizer,
+                                                                     'whisper_name') else "openai/whisper-small"
+whisper_model = WhisperModel.from_pretrained(whisper_name, torch_dtype=torch.float16).to(device)
+del whisper_model.decoder
+whisper_feature_extractor = AutoFeatureExtractor.from_pretrained(whisper_name)
+
+# Generate mel spectrograms
+mel_fn_args = {
+    "n_fft": config['preprocess_params']['spect_params']['n_fft'],
+    "win_size": config['preprocess_params']['spect_params']['win_length'],
+    "hop_size": config['preprocess_params']['spect_params']['hop_length'],
+    "num_mels": config['preprocess_params']['spect_params']['n_mels'],
+    "sampling_rate": sr,
+    "fmin": 0,
+    "fmax": None,
+    "center": False
+}
+from modules.audio import mel_spectrogram
+
+to_mel = lambda x: mel_spectrogram(x, **mel_fn_args)
+
+# f0 conditioned model
+dit_checkpoint_path, dit_config_path = load_custom_model_from_hf("Plachta/Seed-VC",
+                                                                 "DiT_seed_v2_uvit_whisper_base_f0_44k_bigvgan_pruned_ft_ema.pth",
+                                                                 "config_dit_mel_seed_uvit_whisper_base_f0_44k.yml")
+
+config = yaml.safe_load(open(dit_config_path, 'r'))
+model_params = recursive_munch(config['model_params'])
+model_f0 = build_model(model_params, stage='DiT')
+hop_length = config['preprocess_params']['spect_params']['hop_length']
+sr = config['preprocess_params']['sr']
+
+# Load checkpoints
+model_f0, _, _, _ = load_checkpoint(model_f0, None, dit_checkpoint_path,
+                                    load_only_params=True, ignore_modules=[], is_distributed=False)
+for key in model_f0:
+    model_f0[key].eval()
+    model_f0[key].to(device)
+model_f0.cfm.estimator.setup_caches(max_batch_size=1, max_seq_length=8192)
+
+# f0 extractor
+from modules.rmvpe import RMVPE
+
+model_path = load_custom_model_from_hf("lj1995/VoiceConversionWebUI", "rmvpe.pt", None)
+rmvpe = RMVPE(model_path, is_half=False, device=device)
+
+mel_fn_args_f0 = {
+    "n_fft": config['preprocess_params']['spect_params']['n_fft'],
+    "win_size": config['preprocess_params']['spect_params']['win_length'],
+    "hop_size": config['preprocess_params']['spect_params']['hop_length'],
+    "num_mels": config['preprocess_params']['spect_params']['n_mels'],
+    "sampling_rate": sr,
+    "fmin": 0,
+    "fmax": None,
+    "center": False
+}
+to_mel_f0 = lambda x: mel_spectrogram(x, **mel_fn_args_f0)
+bigvgan_44k_model = bigvgan.BigVGAN.from_pretrained('nvidia/bigvgan_v2_44khz_128band_512x', use_cuda_kernel=False)
+
+# remove weight norm in the model and set to eval mode
+bigvgan_44k_model.remove_weight_norm()
+bigvgan_44k_model = bigvgan_44k_model.eval().to(device)
+
+
+def adjust_f0_semitones(f0_sequence, n_semitones):
+    factor = 2 ** (n_semitones / 12)
+    return f0_sequence * factor
+
+
+def crossfade(chunk1, chunk2, overlap):
+    fade_out = np.cos(np.linspace(0, np.pi / 2, overlap)) ** 2
+    fade_in = np.cos(np.linspace(np.pi / 2, 0, overlap)) ** 2
+    if len(chunk2) < overlap:
+        chunk2[:overlap] = chunk2[:overlap] * fade_in[:len(chunk2)] + (chunk1[-overlap:] * fade_out)[:len(chunk2)]
+    else:
+        chunk2[:overlap] = chunk2[:overlap] * fade_in + chunk1[-overlap:] * fade_out
+    return chunk2
+
+
+# streaming and chunk processing related params
+overlap_frame_len = 16
+bitrate = "320k"
+
+
+@torch.no_grad()
+@torch.inference_mode()
+def voice_conversion(tts_text, tts_choice, speed, pitch, target, diffusion_steps, length_adjust, inference_cfg_rate,
+                     f0_condition,
+                     auto_f0_adjust,
+                     pitch_shift):
+    print(length_adjust)
+    edge_audio = None
+    speed_str = f"{speed:+d}%"
+    pitch_str = f"{pitch:+d}Hz"
+    try:
+        print(tts_choice)
+        asyncio.run(
+            edge_tts.Communicate(
+                tts_text, "-".join(tts_choice.split("-")[:-1]), rate=speed_str, pitch=pitch_str, proxy=edge_proxy
+            ).save(edge_output_filename)
+        )
+        edge_audio = gr.Audio(value=edge_output_filename)
+    except EOFError:
+        yield None, None, None
+    except:
+        info = traceback.format_exc()
+        print(info)
+        yield None, None, None
+
+    inference_module = model if not f0_condition else model_f0
+    mel_fn = to_mel if not f0_condition else to_mel_f0
+    bigvgan_fn = bigvgan_model if not f0_condition else bigvgan_44k_model
+    sr = 22050 if not f0_condition else 44100
+    hop_length = 256 if not f0_condition else 512
+    max_context_window = sr // hop_length * 30
+    overlap_wave_len = overlap_frame_len * hop_length
+    # Load audio
+    source_audio = librosa.load(edge_output_filename, sr=sr)[0]
+    ref_audio = librosa.load(target, sr=sr)[0]
+
+    # Process audio
+    source_audio = torch.tensor(source_audio).unsqueeze(0).float().to(device)
+    ref_audio = torch.tensor(ref_audio[:sr * 25]).unsqueeze(0).float().to(device)
+
+    # Resample
+    ref_waves_16k = torchaudio.functional.resample(ref_audio, sr, 16000)
+    converted_waves_16k = torchaudio.functional.resample(source_audio, sr, 16000)
+    # if source audio less than 30 seconds, whisper can handle in one forward
+    if converted_waves_16k.size(-1) <= 16000 * 30:
+        alt_inputs = whisper_feature_extractor([converted_waves_16k.squeeze(0).cpu().numpy()],
+                                               return_tensors="pt",
+                                               return_attention_mask=True,
+                                               sampling_rate=16000)
+        alt_input_features = whisper_model._mask_input_features(
+            alt_inputs.input_features, attention_mask=alt_inputs.attention_mask).to(device)
+        alt_outputs = whisper_model.encoder(
+            alt_input_features.to(whisper_model.encoder.dtype),
+            head_mask=None,
+            output_attentions=False,
+            output_hidden_states=False,
+            return_dict=True,
+        )
+        S_alt = alt_outputs.last_hidden_state.to(torch.float32)
+        S_alt = S_alt[:, :converted_waves_16k.size(-1) // 320 + 1]
+    else:
+        overlapping_time = 5  # 5 seconds
+        S_alt_list = []
+        buffer = None
+        traversed_time = 0
+        while traversed_time < converted_waves_16k.size(-1):
+            if buffer is None:  # first chunk
+                chunk = converted_waves_16k[:, traversed_time:traversed_time + 16000 * 30]
+            else:
+                chunk = torch.cat(
+                    [buffer, converted_waves_16k[:, traversed_time:traversed_time + 16000 * (30 - overlapping_time)]],
+                    dim=-1)
+            alt_inputs = whisper_feature_extractor([chunk.squeeze(0).cpu().numpy()],
+                                                   return_tensors="pt",
+                                                   return_attention_mask=True,
+                                                   sampling_rate=16000)
+            alt_input_features = whisper_model._mask_input_features(
+                alt_inputs.input_features, attention_mask=alt_inputs.attention_mask).to(device)
+            alt_outputs = whisper_model.encoder(
+                alt_input_features.to(whisper_model.encoder.dtype),
+                head_mask=None,
+                output_attentions=False,
+                output_hidden_states=False,
+                return_dict=True,
+            )
+            S_alt = alt_outputs.last_hidden_state.to(torch.float32)
+            S_alt = S_alt[:, :chunk.size(-1) // 320 + 1]
+            if traversed_time == 0:
+                S_alt_list.append(S_alt)
+            else:
+                S_alt_list.append(S_alt[:, 50 * overlapping_time:])
+            buffer = chunk[:, -16000 * overlapping_time:]
+            traversed_time += 30 * 16000 if traversed_time == 0 else chunk.size(-1) - 16000 * overlapping_time
+        S_alt = torch.cat(S_alt_list, dim=1)
+
+    ori_waves_16k = torchaudio.functional.resample(ref_audio, sr, 16000)
+    ori_inputs = whisper_feature_extractor([ori_waves_16k.squeeze(0).cpu().numpy()],
+                                           return_tensors="pt",
+                                           return_attention_mask=True)
+    ori_input_features = whisper_model._mask_input_features(
+        ori_inputs.input_features, attention_mask=ori_inputs.attention_mask).to(device)
+    with torch.no_grad():
+        ori_outputs = whisper_model.encoder(
+            ori_input_features.to(whisper_model.encoder.dtype),
+            head_mask=None,
+            output_attentions=False,
+            output_hidden_states=False,
+            return_dict=True,
+        )
+    S_ori = ori_outputs.last_hidden_state.to(torch.float32)
+    S_ori = S_ori[:, :ori_waves_16k.size(-1) // 320 + 1]
+
+    mel = mel_fn(source_audio.to(device).float())
+    mel2 = mel_fn(ref_audio.to(device).float())
+
+    target_lengths = torch.LongTensor([int(mel.size(2) * length_adjust)]).to(mel.device)
+    target2_lengths = torch.LongTensor([mel2.size(2)]).to(mel2.device)
+
+    feat2 = torchaudio.compliance.kaldi.fbank(ref_waves_16k,
+                                              num_mel_bins=80,
+                                              dither=0,
+                                              sample_frequency=16000)
+    feat2 = feat2 - feat2.mean(dim=0, keepdim=True)
+    style2 = campplus_model(feat2.unsqueeze(0))
+
+    if f0_condition:
+        F0_ori = rmvpe.infer_from_audio(ref_waves_16k[0], thred=0.03)
+        F0_alt = rmvpe.infer_from_audio(converted_waves_16k[0], thred=0.03)
+
+        F0_ori = torch.from_numpy(F0_ori).to(device)[None]
+        F0_alt = torch.from_numpy(F0_alt).to(device)[None]
+
+        voiced_F0_ori = F0_ori[F0_ori > 1]
+        voiced_F0_alt = F0_alt[F0_alt > 1]
+
+        log_f0_alt = torch.log(F0_alt + 1e-5)
+        voiced_log_f0_ori = torch.log(voiced_F0_ori + 1e-5)
+        voiced_log_f0_alt = torch.log(voiced_F0_alt + 1e-5)
+        median_log_f0_ori = torch.median(voiced_log_f0_ori)
+        median_log_f0_alt = torch.median(voiced_log_f0_alt)
+
+        # shift alt log f0 level to ori log f0 level
+        shifted_log_f0_alt = log_f0_alt.clone()
+        if auto_f0_adjust:
+            shifted_log_f0_alt[F0_alt > 1] = log_f0_alt[F0_alt > 1] - median_log_f0_alt + median_log_f0_ori
+        shifted_f0_alt = torch.exp(shifted_log_f0_alt)
+        if pitch_shift != 0:
+            shifted_f0_alt[F0_alt > 1] = adjust_f0_semitones(shifted_f0_alt[F0_alt > 1], pitch_shift)
+    else:
+        F0_ori = None
+        F0_alt = None
+        shifted_f0_alt = None
+
+    # Length regulation
+    cond, _, codes, commitment_loss, codebook_loss = inference_module.length_regulator(S_alt, ylens=target_lengths,
+                                                                                       n_quantizers=3,
+                                                                                       f0=shifted_f0_alt)
+    prompt_condition, _, codes, commitment_loss, codebook_loss = inference_module.length_regulator(S_ori,
+                                                                                                   ylens=target2_lengths,
+                                                                                                   n_quantizers=3,
+                                                                                                   f0=F0_ori)
+
+    max_source_window = max_context_window - mel2.size(2)
+    # split source condition (cond) into chunks
+    processed_frames = 0
+    generated_wave_chunks = []
+    # generate chunk by chunk and stream the output
+    while processed_frames < cond.size(1):
+        chunk_cond = cond[:, processed_frames:processed_frames + max_source_window]
+        is_last_chunk = processed_frames + max_source_window >= cond.size(1)
+        cat_condition = torch.cat([prompt_condition, chunk_cond], dim=1)
+        with torch.autocast(device_type=device.type, dtype=torch.float16):
+            # Voice Conversion
+            vc_target = inference_module.cfm.inference(cat_condition,
+                                                       torch.LongTensor([cat_condition.size(1)]).to(mel2.device),
+                                                       mel2, style2, None, diffusion_steps,
+                                                       inference_cfg_rate=inference_cfg_rate)
+            vc_target = vc_target[:, :, mel2.size(-1):]
+        vc_wave = bigvgan_fn(vc_target.float())[0]
+        if processed_frames == 0:
+            if is_last_chunk:
+                output_wave = vc_wave[0].cpu().numpy()
+                generated_wave_chunks.append(output_wave)
+                output_wave = (output_wave * 32768.0).astype(np.int16)
+                mp3_bytes = AudioSegment(
+                    output_wave.tobytes(), frame_rate=sr,
+                    sample_width=output_wave.dtype.itemsize, channels=1
+                ).export(format="mp3", bitrate=bitrate).read()
+                yield edge_audio, mp3_bytes, (sr, np.concatenate(generated_wave_chunks))
+                break
+            output_wave = vc_wave[0, :-overlap_wave_len].cpu().numpy()
+            generated_wave_chunks.append(output_wave)
+            previous_chunk = vc_wave[0, -overlap_wave_len:]
+            processed_frames += vc_target.size(2) - overlap_frame_len
+            output_wave = (output_wave * 32768.0).astype(np.int16)
+            mp3_bytes = AudioSegment(
+                output_wave.tobytes(), frame_rate=sr,
+                sample_width=output_wave.dtype.itemsize, channels=1
+            ).export(format="mp3", bitrate=bitrate).read()
+            yield edge_audio, mp3_bytes, None
+        elif is_last_chunk:
+            output_wave = crossfade(previous_chunk.cpu().numpy(), vc_wave[0].cpu().numpy(), overlap_wave_len)
+            generated_wave_chunks.append(output_wave)
+            processed_frames += vc_target.size(2) - overlap_frame_len
+            output_wave = (output_wave * 32768.0).astype(np.int16)
+            mp3_bytes = AudioSegment(
+                output_wave.tobytes(), frame_rate=sr,
+                sample_width=output_wave.dtype.itemsize, channels=1
+            ).export(format="mp3", bitrate=bitrate).read()
+            yield edge_audio, mp3_bytes, (sr, np.concatenate(generated_wave_chunks))
+            break
+        else:
+            output_wave = crossfade(previous_chunk.cpu().numpy(), vc_wave[0, :-overlap_wave_len].cpu().numpy(),
+                                    overlap_wave_len)
+            generated_wave_chunks.append(output_wave)
+            previous_chunk = vc_wave[0, -overlap_wave_len:]
+            processed_frames += vc_target.size(2) - overlap_frame_len
+            output_wave = (output_wave * 32768.0).astype(np.int16)
+            mp3_bytes = AudioSegment(
+                output_wave.tobytes(), frame_rate=sr,
+                sample_width=output_wave.dtype.itemsize, channels=1
+            ).export(format="mp3", bitrate=bitrate).read()
+            yield edge_audio, mp3_bytes, None
+
+
+# edge tts
+edge_output_filename = "edge_output.mp3"
+
+# 如果不使用下面的默认音色，可以通过以下代码获取全部edge tts音色，需要外网访问
+# tts_voices_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices(proxy=edge_proxy))
+# tts_speakers = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voices_list]
+
+# 默认的edge tts音色
+tts_speakers = ['zh-HK-HiuGaaiNeural-Female', 'zh-HK-HiuMaanNeural-Female', 'zh-HK-WanLungNeural-Male',
+                'zh-CN-XiaoxiaoNeural-Female', 'zh-CN-XiaoyiNeural-Female', 'zh-CN-YunjianNeural-Male',
+                'zh-CN-YunxiNeural-Male', 'zh-CN-YunxiaNeural-Male', 'zh-CN-YunyangNeural-Male',
+                'zh-CN-liaoning-XiaobeiNeural-Female', 'zh-TW-HsiaoChenNeural-Female', 'zh-TW-YunJheNeural-Male',
+                'zh-TW-HsiaoYuNeural-Female', 'zh-CN-shaanxi-XiaoniNeural-Female', 'en-AU-NatashaNeural-Female',
+                'en-AU-WilliamNeural-Male', 'en-CA-ClaraNeural-Female', 'en-CA-LiamNeural-Male', 'en-HK-SamNeural-Male',
+                'en-HK-YanNeural-Female', 'en-IN-NeerjaExpressiveNeural-Female', 'en-IN-NeerjaNeural-Female',
+                'en-IN-PrabhatNeural-Male', 'en-IE-ConnorNeural-Male', 'en-IE-EmilyNeural-Female',
+                'en-KE-AsiliaNeural-Female', 'en-KE-ChilembaNeural-Male', 'en-NZ-MitchellNeural-Male',
+                'en-NZ-MollyNeural-Female', 'en-NG-AbeoNeural-Male', 'en-NG-EzinneNeural-Female',
+                'en-PH-JamesNeural-Male', 'en-PH-RosaNeural-Female', 'en-SG-LunaNeural-Female',
+                'en-SG-WayneNeural-Male',
+                'en-US-AvaMultilingualNeural-Female', 'en-US-AndrewMultilingualNeural-Male',
+                'en-US-EmmaMultilingualNeural-Female', 'en-US-BrianMultilingualNeural-Male', 'en-US-AvaNeural-Female',
+                'en-US-AndrewNeural-Male', 'en-US-EmmaNeural-Female', 'en-US-BrianNeural-Male',
+                'en-ZA-LeahNeural-Female',
+                'en-ZA-LukeNeural-Male', 'en-TZ-ElimuNeural-Male', 'en-TZ-ImaniNeural-Female',
+                'en-GB-LibbyNeural-Female',
+                'en-GB-MaisieNeural-Female', 'en-GB-RyanNeural-Male', 'en-GB-SoniaNeural-Female',
+                'en-GB-ThomasNeural-Male', 'en-US-AnaNeural-Female', 'en-US-AriaNeural-Female',
+                'en-US-ChristopherNeural-Male', 'en-US-EricNeural-Male', 'en-US-GuyNeural-Male',
+                'en-US-JennyNeural-Female', 'en-US-MichelleNeural-Female', 'en-US-RogerNeural-Male',
+                'en-US-SteffanNeural-Male', 'ko-KR-HyunsuMultilingualNeural-Male', 'ko-KR-InJoonNeural-Male',
+                'ko-KR-SunHiNeural-Female']
+
+
+def app():
+    with gr.Blocks(title="Seed VC Edge TTS") as demo:
+        with gr.Row():
+            gr.Markdown(value="""<h1>Seed VC Edge TTS</h1>
+            <p>输入文本，使用Edge TTS获取待处理音频，再进行推理</p>""")
+        with gr.Row():
+            with gr.Column():
+                tts_text = gr.Textbox(label="Input Text / 输入文本", lines=5, value="这是一个示例文本")
+                tts_choice = gr.Dropdown(
+                    label="Edge TTS Speaker / Edge TTS 音色",
+                    choices=tts_speakers,
+                    allow_custom_value=False,
+                    value="zh-CN-YunjianNeural-Male"
+                )
+                tts_speed = gr.Slider(
+                    minimum=-100,
+                    maximum=100,
+                    label="Edge TTS Speed(%) / Edge TTS 语速(%)",
+                    value=-10,
+                    step=5,
+                    interactive=True
+                )
+                tts_pitch = gr.Slider(
+                    minimum=-20,
+                    maximum=20,
+                    value=0,
+                    label="Pitch Adjustment / 音调调整",
+                    step=1
+                )
+                diffusion_steps = gr.Slider(minimum=1, maximum=200, value=10, step=1,
+                                            label="Diffusion Steps / 扩散步数",
+                                            info="10 by default, 50~100 for best quality / 默认为 10，50~100 为最佳质量")
+                length_adjust = gr.Slider(minimum=0.5, maximum=2.0, step=0.1, value=1.0,
+                                          label="Length Adjust / 长度调整",
+                                          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")
+                inference_cfg_rate = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0.7,
+                                               label="Inference CFG Rate",
+                                               info="has subtle influence / 有微小影响")
+                f0_condition = gr.Checkbox(label="Use F0 conditioned model / 启用F0输入", value=False,
+                                           info="Must set to true for singing voice conversion / 歌声转换时必须勾选")
+                auto_f0_adjust = gr.Checkbox(label="Auto F0 adjust / 自动F0调整", value=True,
+                                             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")
+                pitch_shift = gr.Slider(label='Pitch shift / 音调变换', minimum=-24, maximum=24, step=1, value=0,
+                                        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")
+            with gr.Column():
+                reference_audio = gr.Audio(type="filepath", label="Reference Audio / 参考音频")
+                edge_tts_output = gr.Audio(type="filepath", label="Edge TTS Audio / Edge TTS 音频")
+                stream_audio_output = gr.Audio(label="Stream Output Audio / 流式输出", streaming=True, format='mp3')
+                full_audio_output = gr.Audio(label="Full Output Audio / 完整输出", streaming=False, format='wav')
+                submit_btn = gr.Button(value="推理", variant='primary')
+
+        submit_btn.click(voice_conversion,
+                         inputs=[tts_text, tts_choice, tts_speed, tts_pitch, reference_audio, diffusion_steps,
+                                 length_adjust,
+                                 inference_cfg_rate,
+                                 f0_condition, auto_f0_adjust,
+                                 pitch_shift], outputs=[edge_tts_output, stream_audio_output, full_audio_output])
+
+    demo.queue(api_open=True).launch(debug=True, show_error=True)
+
+
+if __name__ == "__main__":
+    app()