convert the gradio app into a uvicorn http server

import os
os.environ['HF_HUB_CACHE'] = './checkpoints/hf_cache'
import gradio as gr
import torch
import torchaudio
import librosa
from modules.commons import build_model, load_checkpoint, recursive_munch, str2bool
import yaml
from hf_utils import load_custom_model_from_hf
import numpy as np
from pydub import AudioSegment
import argparse
from pathlib import Path
# Load model and configuration

fp16 = False
device = None
def load_models(args):
    global sr, hop_length, fp16
    fp16 = args.fp16
    ckpt_root = Path(__file__).parent / "checkpoints"
    print(f"Using device: {device}")
    print(f"Using fp16: {fp16}")
    # f0 conditioned model
    if args.checkpoint_path is None or args.checkpoint_path == "":
        # 检测模型
        dit_checkpoint_path = ckpt_root / "seed_vc" / "svc_model" / "DiT_seed_v2_uvit_whisper_base_f0_44k_bigvgan_pruned_ft_ema_v2.pth"
        dit_config_path = ckpt_root / "seed_vc" / "svc_model" / "config_dit_mel_seed_uvit_whisper_base_f0_44k.yml"
        if not dit_checkpoint_path.exists() or not dit_config_path.exists():
            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_v2.pth",
                                                                            "config_dit_mel_seed_uvit_whisper_base_f0_44k.yml")
    else:
        print(f"Using custom checkpoint: {args.checkpoint_path}")
        dit_checkpoint_path = args.checkpoint_path
        dit_config_path = args.config_path
    config = yaml.safe_load(open(dit_config_path, "r"))
    model_params = recursive_munch(config["model_params"])
    model_params.dit_type = 'DiT'
    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 = ckpt_root / "campplus" / "campplus_cn_common.bin"
    if not campplus_ckpt_path.exists():
        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)

    vocoder_type = model_params.vocoder.type

    if vocoder_type == 'bigvgan':
        from modules.bigvgan import bigvgan
        bigvgan_name = model_params.vocoder.name
        bigvgan_model = bigvgan.BigVGAN.from_pretrained(bigvgan_name, 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)
        vocoder_fn = bigvgan_model
    elif vocoder_type == 'hifigan':
        from modules.hifigan.generator import HiFTGenerator
        from modules.hifigan.f0_predictor import ConvRNNF0Predictor
        hift_config = yaml.safe_load(open('configs/hifigan.yml', 'r'))
        hift_gen = HiFTGenerator(**hift_config['hift'], f0_predictor=ConvRNNF0Predictor(**hift_config['f0_predictor']))
        hift_gen.load_state_dict(torch.load(str(ckpt_root / "cosy_hifigan" / "hift.pt"), map_location='cpu'))
        hift_gen.eval()
        hift_gen.to(device)
        vocoder_fn = hift_gen
    elif vocoder_type == "vocos":
        vocos_config = yaml.safe_load(open(model_params.vocoder.vocos.config, 'r'))
        vocos_path = model_params.vocoder.vocos.path
        vocos_model_params = recursive_munch(vocos_config['model_params'])
        vocos = build_model(vocos_model_params, stage='mel_vocos')
        vocos_checkpoint_path = vocos_path
        vocos, _, _, _ = load_checkpoint(vocos, None, vocos_checkpoint_path,
                                         load_only_params=True, ignore_modules=[], is_distributed=False)
        _ = [vocos[key].eval().to(device) for key in vocos]
        _ = [vocos[key].to(device) for key in vocos]
        total_params = sum(sum(p.numel() for p in vocos[key].parameters() if p.requires_grad) for key in vocos.keys())
        print(f"Vocoder model total parameters: {total_params / 1_000_000:.2f}M")
        vocoder_fn = vocos.decoder
    else:
        raise ValueError(f"Unknown vocoder type: {vocoder_type}")

    speech_tokenizer_type = model_params.speech_tokenizer.type
    if speech_tokenizer_type == 'whisper':
        # whisper
        from transformers import AutoFeatureExtractor, WhisperModel
        whisper_name = model_params.speech_tokenizer.name
        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)

        def semantic_fn(waves_16k):
            ori_inputs = whisper_feature_extractor([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[:, :waves_16k.size(-1) // 320 + 1]
            return S_ori
    elif speech_tokenizer_type == 'cnhubert':
        from transformers import (
            Wav2Vec2FeatureExtractor,
            HubertModel,
        )
        hubert_model_name = config['model_params']['speech_tokenizer']['name']
        hubert_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(hubert_model_name)
        hubert_model = HubertModel.from_pretrained(hubert_model_name)
        hubert_model = hubert_model.to(device)
        hubert_model = hubert_model.eval()
        hubert_model = hubert_model.half()

        def semantic_fn(waves_16k):
            ori_waves_16k_input_list = [
                waves_16k[bib].cpu().numpy()
                for bib in range(len(waves_16k))
            ]
            ori_inputs = hubert_feature_extractor(ori_waves_16k_input_list,
                                                  return_tensors="pt",
                                                  return_attention_mask=True,
                                                  padding=True,
                                                  sampling_rate=16000).to(device)
            with torch.no_grad():
                ori_outputs = hubert_model(
                    ori_inputs.input_values.half(),
                )
            S_ori = ori_outputs.last_hidden_state.float()
            return S_ori
    elif speech_tokenizer_type == 'xlsr':
        from transformers import (
            Wav2Vec2FeatureExtractor,
            Wav2Vec2Model,
        )
        model_name = config['model_params']['speech_tokenizer']['name']
        output_layer = config['model_params']['speech_tokenizer']['output_layer']
        wav2vec_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name)
        wav2vec_model = Wav2Vec2Model.from_pretrained(model_name)
        wav2vec_model.encoder.layers = wav2vec_model.encoder.layers[:output_layer]
        wav2vec_model = wav2vec_model.to(device)
        wav2vec_model = wav2vec_model.eval()
        wav2vec_model = wav2vec_model.half()

        def semantic_fn(waves_16k):
            ori_waves_16k_input_list = [
                waves_16k[bib].cpu().numpy()
                for bib in range(len(waves_16k))
            ]
            ori_inputs = wav2vec_feature_extractor(ori_waves_16k_input_list,
                                                   return_tensors="pt",
                                                   return_attention_mask=True,
                                                   padding=True,
                                                   sampling_rate=16000).to(device)
            with torch.no_grad():
                ori_outputs = wav2vec_model(
                    ori_inputs.input_values.half(),
                )
            S_ori = ori_outputs.last_hidden_state.float()
            return S_ori
    else:
        raise ValueError(f"Unknown speech tokenizer type: {speech_tokenizer_type}")
    # 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": config['preprocess_params']['spect_params'].get('fmin', 0),
        "fmax": None if config['preprocess_params']['spect_params'].get('fmax', "None") == "None" else 8000,
        "center": False
    }
    from modules.audio import mel_spectrogram

    to_mel = lambda x: mel_spectrogram(x, **mel_fn_args)
    # f0 extractor
    from modules.rmvpe import RMVPE
    rmvpe_path = ckpt_root / "rmvpe" / "rmvpe.pt"
    if not rmvpe_path.exists():
        rmvpe_path = load_custom_model_from_hf("lj1995/VoiceConversionWebUI", "rmvpe.pt", None)
    rmvpe = RMVPE(rmvpe_path, is_half=False, device=device)
    f0_fn = rmvpe.infer_from_audio

    return (
        model,
        semantic_fn,
        vocoder_fn,
        campplus_model,
        to_mel,
        mel_fn_args,
        f0_fn,
    )

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
    chunk2[:overlap] = chunk2[:overlap] * fade_in + chunk1[-overlap:] * fade_out
    return chunk2

# streaming and chunk processing related params
# max_context_window = sr // hop_length * 30
# overlap_frame_len = 16
# overlap_wave_len = overlap_frame_len * hop_length
bitrate = "320k"

model_f0, semantic_fn, vocoder_fn, campplus_model, to_mel_f0, mel_fn_args = None, None, None, None, None, None
f0_fn = None
overlap_wave_len = None
max_context_window = None
sr = None
hop_length = None
overlap_frame_len = 16

@torch.no_grad()
@torch.inference_mode()
def voice_conversion(source, target, diffusion_steps, length_adjust, inference_cfg_rate, auto_f0_adjust, pitch_shift):
    inference_module = model_f0
    mel_fn = to_mel_f0
    # Load audio
    source_audio = librosa.load(source, 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:
        S_alt = semantic_fn(converted_waves_16k)
    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)
            S_alt = semantic_fn(chunk)
            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)
    S_ori = semantic_fn(ori_waves_16k)

    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))

    F0_ori = f0_fn(ref_waves_16k[0], thred=0.03)
    F0_alt = f0_fn(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)

    # 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)
    interpolated_shifted_f0_alt = torch.nn.functional.interpolate(shifted_f0_alt.unsqueeze(1), size=cond.size(1),
                                                                  mode='nearest').squeeze(1)
    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]
        chunk_f0 = interpolated_shifted_f0_alt[:, 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 if fp16 else torch.float32):
            # 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 = vocoder_fn(vc_target.float()).squeeze().cpu()
        if vc_wave.ndim == 1:
            vc_wave = vc_wave.unsqueeze(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 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 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 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 mp3_bytes, None


def main(args):
    global model_f0, semantic_fn, vocoder_fn, campplus_model, to_mel_f0, mel_fn_args, f0_fn
    global overlap_wave_len, max_context_window, sr, hop_length
    model_f0, semantic_fn, vocoder_fn, campplus_model, to_mel_f0, mel_fn_args, f0_fn = load_models(args)
    # streaming and chunk processing related params
    max_context_window = sr // hop_length * 30
    overlap_wave_len = overlap_frame_len * hop_length
    description = ("Zero-shot voice conversion with in-context learning. For local deployment please check [GitHub repository](https://github.com/Plachtaa/seed-vc) "
                   "for details and updates.<br>Note that any reference audio will be forcefully clipped to 25s if beyond this length.<br> "
                   "If total duration of source and reference audio exceeds 30s, source audio will be processed in chunks.<br> "
                   "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>"
                   "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。")
    inputs = [
        gr.Audio(type="filepath", label="Source Audio / 源音频"),
        gr.Audio(type="filepath", label="Reference Audio / 参考音频"),
        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"),
        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"),
        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"),
        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"),
        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"),
    ]

    examples = [["examples/source/yae_0.wav", "examples/reference/dingzhen_0.wav", 25, 1.0, 0.7, True, 0],
                ["examples/source/jay_0.wav", "examples/reference/azuma_0.wav", 25, 1.0, 0.7, True, 0],
                ["examples/source/Wiz Khalifa,Charlie Puth - See You Again [vocals]_[cut_28sec].wav",
                 "examples/reference/teio_0.wav", 50, 1.0, 0.7, False, 0],
                ["examples/source/TECHNOPOLIS - 2085 [vocals]_[cut_14sec].wav",
                 "examples/reference/trump_0.wav", 50, 1.0, 0.7, False, -12],
                ]

    outputs = [gr.Audio(label="Stream Output Audio / 流式输出", streaming=True, format='mp3'),
               gr.Audio(label="Full Output Audio / 完整输出", streaming=False, format='wav')]

    gr.Interface(fn=voice_conversion,
                 description=description,
                 inputs=inputs,
                 outputs=outputs,
                 title="Seed Voice Conversion",
                 examples=examples,
                 cache_examples=False,
                 ).launch(share=args.share,)

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--checkpoint-path", type=str, help="Path to the checkpoint file", default=None)
    parser.add_argument("--config-path", type=str, help="Path to the config file", default=None)
    parser.add_argument("--share", type=str2bool, nargs="?", const=True, default=False, help="Whether to share the app")
    parser.add_argument("--fp16", type=str2bool, nargs="?", const=True, help="Whether to use fp16", default=True)
    parser.add_argument("--gpu", type=int, help="Which GPU id to use", default=0)
    args = parser.parse_args()
    cuda_target = f"cuda:{args.gpu}" if args.gpu else "cuda" 
    device = torch.device(cuda_target if torch.cuda.is_available() else "cpu")
    main(args)