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--- |
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license: mit |
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tags: |
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- unsloth |
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- trl |
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- sft |
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language: |
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- en |
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base_model: |
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- SparkAudio/Spark-TTS-0.5B |
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pipeline_tag: text-to-speech |
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--- |
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# Spark-TTS 0.5B Fine-Tuned Model (16-bit Merged) |
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This repository hosts a fine-tuned Spark-TTS 0.5B model optimized for speech synthesis using the Unsloth and TRL libraries. The model is saved and shared in a merged 16-bit format for efficient storage and faster inference while maintaining high-quality outputs. |
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## Model Details |
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- **Architecture:** Transformer-based Text-to-Speech (Spark-TTS) |
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- **Model Size:** 0.5 Billion parameters |
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- **Precision:** 16-bit merged weights (optimized for inference) |
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- **Fine-tuning:** Full fine-tuning enabled with LoRA adapters (bfloat16 precision) |
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- **Training Framework:** Unsloth & TRL (Supervised Fine-Tuning) |
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- **Tokenizer:** Compatible tokenizer included |
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## Intended Use |
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This model is intended for research and development in text-to-speech synthesis tasks, especially where GPU memory efficiency and long context handling are priorities. |
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## Usage |
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```python |
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from unsloth import FastModel |
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import torch |
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# Load the fine-tuned Spark-TTS model and tokenizer from Hugging Face Hub |
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model, tokenizer = FastModel.from_pretrained( |
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"sureshbeekhani/spark-tts-0.5b-finetune-16bit", |
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max_seq_length=2048, # Adjust based on your needs |
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dtype=torch.bfloat16, # Use bfloat16 for LoRA compatibility and efficiency |
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full_finetuning=False, # Set to False if you want to use the model for inference only |
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) |
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# Example text input for speech synthesis |
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text = "Hello, welcome to the Spark-TTS fine-tuned model demo!" |
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# Tokenize the input text |
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inputs = tokenizer(text, return_tensors="pt") |
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# Generate speech output from the model |
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# Note: Adjust this to your model’s specific generate method if applicable |
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outputs = model.generate(**inputs) |
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# Process or save outputs as needed (e.g., convert to audio waveform) |
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# This part depends on your model’s output format and synthesis pipeline |
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print("Inference completed successfully.") |
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# Limitations |
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LoRA fine-tuning is supported only with bfloat16 precision. |
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Designed primarily for speech synthesis; may not perform well for unrelated NLP tasks. |
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Usage in production should be tested carefully for latency and quality trade-offs. |
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#License |
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This model is licensed under the MIT License. |
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If you want, I can help generate a README.md file or add badges and additional sections! |
