TTS: vits voice conversion fail [Bug]

Describe the bug

The following error occurs when I use vits for voice conversion :

RuntimeError: Expected tensor for argument #1 ‘indices’ to have one of the following scalar types: Long, Int; but got torch.FloatTensor instead (while checking arguments for embedding)

To Reproduce

import os

from trainer import Trainer, TrainerArgs

from TTS.config.shared_configs import BaseAudioConfig
from TTS.tts.configs.shared_configs import BaseDatasetConfig
from TTS.tts.configs.vits_config import VitsConfig
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.vits import Vits,CharactersConfig,VitsArgs
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
from TTS.tts.utils.speakers import SpeakerManager

output_path = os.path.dirname(os.path.abspath(__file__))
dataset_config = BaseDatasetConfig(
    name="baker_old_2", path="/datasets/temp-bznsyp", language="zh-cn"
)
audio_config = BaseAudioConfig(
    sample_rate=48000,
    win_length=1024,
    hop_length=256,
    num_mels=80,
    preemphasis=0.0,
    ref_level_db=20,
    log_func="np.log",
    do_trim_silence=True,
    trim_db=45,
    mel_fmin=0,
    mel_fmax=None,
    spec_gain=1.0,
    signal_norm=False,
    do_amp_to_db_linear=False,
)

vitsArgs = VitsArgs(
    use_speaker_embedding=True,
    use_sdp=False,
    use_speaker_encoder_as_loss=True,
    speaker_encoder_config_path="/TTS/models/tts_models--multilingual--multi-dataset--your_tts/config_se.json",
    speaker_encoder_model_path="/TTS/models/tts_models--multilingual--multi-dataset--your_tts/model_se.pth",
)

config = VitsConfig(
    model_args=vitsArgs,
    audio=audio_config,
    run_name="vits_baker_temp",
    batch_size=48,
    eval_batch_size=24,
    batch_group_size=5,
    num_loader_workers=0,
    num_eval_loader_workers=8,
    run_eval=True,
    test_delay_epochs=-1,
    epochs=1000,
    text_cleaner="chinese_mandarin_cleaners",
    use_phonemes=True,
    phoneme_language="zh-cn",
    phonemizer="zh_cn_phonemizer",
    add_blank=False,
    phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
    compute_input_seq_cache=False,
    print_step=25,
    print_eval=True,
    mixed_precision=True,
    output_path=output_path,
    datasets=[dataset_config],
    characters=CharactersConfig(
        characters_class=None,
        vocab_dict=None,
        pad="_",
        eos="~",
        bos="^",
        blank=None,
        characters="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!'(),.:;? ",
        punctuations="\uff0c\u3002\uff1f\uff01\uff5e\uff1a\uff1b*\u2014\u2014-\uff08\uff09\u3010\u3011!'(),-.:;? “”",
        phonemes="12345giy\u0268\u0289\u026fu\u026a\u028f\u028ae\u00f8\u0258\u0259\u0275\u0264o\u025b\u0153\u025c\u025e\u028c\u0254\u00e6\u0250a\u0276\u0251\u0252\u1d7b\u0298\u0253\u01c0\u0257\u01c3\u0284\u01c2\u0260\u01c1\u029bpbtd\u0288\u0256c\u025fk\u0261q\u0262\u0294\u0274\u014b\u0272\u0273n\u0271m\u0299r\u0280\u2c71\u027e\u027d\u0278\u03b2fv\u03b8\u00f0sz\u0283\u0292\u0282\u0290\u00e7\u029dx\u0263\u03c7\u0281\u0127\u0295h\u0266\u026c\u026e\u028b\u0279\u027bj\u0270l\u026d\u028e\u029f\u02c8\u02cc\u02d0\u02d1\u028dw\u0265\u029c\u02a2\u02a1\u0255\u0291\u027a\u0267\u025a\u02de\u026b",
        is_unique=False,
        is_sorted=True
    ),
    test_sentences=[
        ["你在做什么？", "baker", None, "zh-cn"],
        ["篮球场上没有人", "baker", None, "zh-cn"],
    ],
)

# INITIALIZE THE AUDIO PROCESSOR
# Audio processor is used for feature extraction and audio I/O.
# It mainly serves to the dataloader and the training loggers.
ap = AudioProcessor.init_from_config(config)

# INITIALIZE THE TOKENIZER
# Tokenizer is used to convert text to sequences of token IDs.
# config is updated with the default characters if not defined in the config.
tokenizer, config = TTSTokenizer.init_from_config(config)

# LOAD DATA SAMPLES
# Each sample is a list of ```[text, audio_file_path, speaker_name]```
# You can define your custom sample loader returning the list of samples.
# Or define your custom formatter and pass it to the `load_tts_samples`.
# Check `TTS.tts.datasets.load_tts_samples` for more details.
train_samples, eval_samples = load_tts_samples(
    dataset_config,
    eval_split=True,
    eval_split_max_size=config.eval_split_max_size,
    eval_split_size=config.eval_split_size,
)

speaker_manager = SpeakerManager()
speaker_manager.use_cuda = True
speaker_manager.set_ids_from_data(train_samples + eval_samples, parse_key="speaker_name")
config.model_args.num_speakers = speaker_manager.num_speakers

# init model
model = Vits(config, ap, tokenizer, speaker_manager=speaker_manager)

# init the trainer and 
trainer = Trainer(
    TrainerArgs(),
    config,
    output_path,
    model=model,
    train_samples=train_samples,
    eval_samples=eval_samples,
)
trainer.fit()

voice conversion command:

tts  --model_path ./vits_baker_temp-June-20-2022_02+48PM-0000000/best_model.pth --config_path ./vits_baker_temp-June-20-2022_02+48PM-0000000/config.json --speaker_idx "baker" --out_path output.wav --reference_wav  006637.wav

Expected behavior

voice conversion success!

Logs

/opt/conda/lib/python3.8/site-packages/torch/functional.py:695: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at  ../aten/src/ATen/native/SpectralOps.cpp:798.)
  return _VF.stft(input, n_fft, hop_length, win_length, window,  # type: ignore[attr-defined]
Traceback (most recent call last):
  File "/opt/conda/bin/tts", line 33, in <module>
    sys.exit(load_entry_point('TTS', 'console_scripts', 'tts')())
  File "/TTS/TTS/bin/synthesize.py", line 309, in main
    wav = synthesizer.tts(
  File "/TTS/TTS/utils/synthesizer.py", line 339, in tts
    outputs = transfer_voice(
  File "/TTS/TTS/tts/utils/synthesis.py", line 304, in transfer_voice
    model_outputs = _func(reference_wav, speaker_id, d_vector, reference_speaker_id, reference_d_vector)
  File "/opt/conda/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/TTS/TTS/tts/models/vits.py", line 1140, in inference_voice_conversion
    wav, _, _ = self.voice_conversion(y, y_lengths, speaker_cond_src, speaker_cond_tgt)
  File "/TTS/TTS/tts/models/vits.py", line 1157, in voice_conversion
    g_src = self.emb_g(speaker_cond_src).unsqueeze(-1)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/sparse.py", line 158, in forward
    return F.embedding(
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/functional.py", line 2183, in embedding
    return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
RuntimeError: Expected tensor for argument #1 'indices' to have one of the following scalar types: Long, Int; but got torch.FloatTensor instead (while checking arguments for embedding)

Environment

{
    "CUDA": {
        "GPU": [
            "NVIDIA GeForce RTX 3090",
            "NVIDIA GeForce RTX 3090",
            "NVIDIA GeForce RTX 3090",
            "NVIDIA GeForce RTX 3090"
        ],
        "available": true,
        "version": "11.3"
    },
    "Packages": {
        "PyTorch_debug": false,
        "PyTorch_version": "1.11.0+cu113",
        "TTS": "0.6.2",
        "numpy": "1.21.6"
    },
    "System": {
        "OS": "Linux",
        "architecture": [
            "64bit",
            ""
        ],
        "processor": "x86_64",
        "python": "3.8.12",
        "version": "#91-Ubuntu SMP Thu Jul 15 19:09:17 UTC 2021"
    }
}

Additional context

No response