diff --git a/README.md b/README.md index af73dc4..17e4c5f 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,26 @@ # 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) \ No newline at end of file diff --git a/doc/seed_vc_edge_tts_webui.jpg b/doc/seed_vc_edge_tts_webui.jpg new file mode 100644 index 0000000..b703aa6 Binary files /dev/null and b/doc/seed_vc_edge_tts_webui.jpg differ diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..2dbafcb --- /dev/null +++ b/requirements.txt @@ -0,0 +1 @@ +edge_tts==6.1.13 diff --git a/seed_vc_edge_tts.py b/seed_vc_edge_tts.py new file mode 100644 index 0000000..35baf45 --- /dev/null +++ b/seed_vc_edge_tts.py @@ -0,0 +1,464 @@ +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="""

Seed VC Edge TTS

+

输入文本，使用Edge TTS获取待处理音频，再进行推理

""") + 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()