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base_model:
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- rhasspy/piper-voices
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pipeline_tag: text-to-audio
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base_model:
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- rhasspy/piper-voices
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pipeline_tag: text-to-audio
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---
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# Model Card for [Model Name]
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## Model Details
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- **Model Name**: [Your Model Name]
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- **Model Type**: Text-to-Audio
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- **Base Model**: [rhasspy/piper-voices](https://huggingface.co/rhasspy/piper-voices)
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- **Version**: [e.g., v1.0]
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- **License**: Apache-2.0
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- **Developers**: [Your Name or Organization]
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- **Release Date**: [e.g., YYYY-MM-DD]
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- **Contact**: [e.g., email, GitHub, or Hugging Face profile]
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- **Languages**: English (en)
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- **Pipeline Tag**: text-to-audio
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## Model Description
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This model is a [brief description, e.g., text-to-audio model designed to generate high-quality audio from textual input]. It is built upon the [rhasspy/piper-voices](https://huggingface.co/rhasspy/piper-voices) base model and fine-tuned for [specific use case or improvements, e.g., improved clarity, multilingual support, or domain-specific audio generation].
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- **Intended Use**: [e.g., Generating audio for accessibility, virtual assistants, or content creation]
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- **Primary Users**: [e.g., Developers, researchers, or content creators]
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- **Out-of-Scope Use Cases**: [e.g., Uses not intended, such as generating misleading audio or unethical applications]
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## Training Details
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- **Training Data**: [Describe the dataset(s) used, e.g., public datasets, proprietary data, or synthetic data. Include size, source, and any preprocessing steps.]
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- **Training Procedure**: [e.g., Fine-tuning details, epochs, hardware used, training time]
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- **Hyperparameters**: [e.g., Learning rate, batch size, optimizer]
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- **Compute Infrastructure**: [e.g., GPU/TPU type, cloud provider, or local setup]
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## Evaluation
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- **Metrics**: [e.g., Mean Opinion Score (MOS), Word Error Rate (WER), or other audio quality metrics]
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- **Results**: [Summarize performance, e
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