Instructions to use lithish2602/speecht5_tts_ta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lithish2602/speecht5_tts_ta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="lithish2602/speecht5_tts_ta")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("lithish2602/speecht5_tts_ta") model = AutoModelForTextToSpectrogram.from_pretrained("lithish2602/speecht5_tts_ta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files
README.md
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# speecht5_tts_ta
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the common_voice_17_0 dataset.
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## Model description
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- training_steps: 4000
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- mixed_precision_training: Native AMP
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### Framework versions
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- Transformers 4.49.0.dev0
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# speecht5_tts_ta
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the common_voice_17_0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5390
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## Model description
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- training_steps: 4000
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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| 0.357 | 500.0 | 1000 | 0.5013 |
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| 0.324 | 1000.0 | 2000 | 0.4800 |
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| 0.2914 | 1500.0 | 3000 | 0.4997 |
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| 0.283 | 2000.0 | 4000 | 0.5390 |
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### Framework versions
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- Transformers 4.49.0.dev0
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