README.md

metadata

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：

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：

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。

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