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---
license: cc
language:
- zh
pipeline_tag: text-to-speech
---
# Model Card for [Your VITS Model Name]
## Model Details
- **Model Name**: [Your VITS Model Name]
- **Model Type**: TTS (Text-to-Speech)
- **Architecture**: VITS (Variational Inference Text-to-Speech)
- **Author**: [Your Name or Organization]
- **Repository**: [Link to your Huggingface repository]
- **Paper**: [Link to the original VITS paper, if applicable]
## Model Description
VITS (Variational Inference Text-to-Speech) 是一種新穎的 TTS 模型架構，能夠生成高質量且自然的語音。本模型基於 VITS 架構，旨在提供高效的語音合成功能，適用於多種應用場景。
## Usage
### Inference
要使用此模型進行語音合成，您可以使用以下代碼示例：
```python
from transformers import Wav2Vec2Processor, VITSModel
processor = Wav2Vec2Processor.from_pretrained("[Your Huggingface Model Repository]")
model = VITSModel.from_pretrained("[Your Huggingface Model Repository]")
inputs = processor("要合成的文本", return_tensors="pt")
with torch.no_grad():
speech = model.generate_speech(inputs.input_values)
# Save or play the generated speech
with open("output.wav", "wb") as f:
f.write(speech)
```
### Training
如果您需要訓練此模型，請參考以下的代碼示例：
```python
from transformers import VITSConfig, VITSForSpeechSynthesis, Trainer, TrainingArguments
config = VITSConfig()
model = VITSForSpeechSynthesis(config)
training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
num_train_epochs=3,
save_steps=10_000,
save_total_limit=2,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=your_train_dataset,
eval_dataset=your_eval_dataset,
)
trainer.train()
```
## Model Performance
- **Training Dataset**: 描述用於訓練模型的數據集。
- **Evaluation Metrics**: 描述模型性能評估所使用的指標，如 MOS (Mean Opinion Score) 或 PESQ (Perceptual Evaluation of Speech Quality)。
- **Results**: 提供模型在測試數據集上的性能數據。
## Limitations and Bias
- **Known Limitations**: 描述模型的已知限制，如對某些語言或口音的支持較差。
- **Potential Bias**: 描述模型可能存在的偏見和倫理問題。
## Citation
如果您在研究中使用了此模型，請引用以下文獻：
```
@inproceedings{vits2021,
title={Variational Inference Text-to-Speech},
author={Your Name and Co-Authors},
booktitle={Conference on Your Conference Name},
year={2021}
}
```
## Acknowledgements
感謝 [Your Team or Collaborators] 對此模型開發的支持和貢獻。
--- |