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--- |
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language: |
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- ht |
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tags: |
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- 'speecht5 ' |
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- TTS |
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--- |
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# Fine-tuned SpeechT5 TTS Model for Haitian Creole |
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This model is a fine-tuned version of [microsoft/speecht5-tts](https://huggingface.co/microsoft/speecht5-tts) for the Haitian Creole language. It was fine-tuned on the CMU Haitian dataset. |
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## Model Description |
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The model is based on the SpeechT5 architecture, which is a variant of the T5 (Text-to-Text Transfer Transformer) model designed specifically for text-to-speech tasks. The model is capable of converting input text in Haitian Creole into corresponding speech. |
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## Intended Uses & Limitations |
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The model is intended for text-to-speech (TTS) applications in Haitian Creole language processing. It can be used for generating speech from written text, enabling applications such as audiobook narration, voice assistants, and more. |
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However, there are some limitations to be aware of: |
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- The model's performance heavily depends on the quality and diversity of the training data. Fine-tuning on more diverse and specific datasets might improve its performance. |
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- Like all machine learning models, this model may produce inaccuracies or errors in speech synthesis, especially for complex sentences or domain-specific jargon. |
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## Training and Evaluation Data |
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The model was fine-tuned on the CMU Haitian dataset, which contains text and corresponding audio samples in Haitian Creole. The dataset was split into training and evaluation sets to assess the model's performance. |
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## Training Procedure |
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### Training Hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- per_device_train_batch_size: 16 |
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- gradient_accumulation_steps: 2 |
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- warmup_steps: 500 |
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- max_steps: 4000 |
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- gradient_checkpointing: True |
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- fp16: True |
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- evaluation_strategy: no |
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- per_device_eval_batch_size: 8 |
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- save_steps: 1000 |
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- logging_steps: 25 |
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- report_to: ["tensorboard"] |
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- greater_is_better: False |
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### Training Results |
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The training progress and evaluation results are as follows: |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.5147 | 2.42 | 1000 | 0.4753 | |
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| 0.4932 | 4.84 | 2000 | 0.4629 | |
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| 0.4926 | 7.26 | 3000 | 0.4566 | |
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| 0.4907 | 9.69 | 4000 | 0.4542 | |
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| 0.4839 | 12.11 | 5000 | 0.4532 | |
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### Training Output |
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The training was completed with the following output: |
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- Global Step: 4000 |
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- Training Loss: 0.3344 |
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- Training Runtime: 7123.63 seconds |
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- Training Samples per Second: 17.97 |
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- Training Steps per Second: 0.562 |
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- Total FLOPs: 1.1690e+16 |
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## Framework Versions |
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- Transformers 4.31.0 |
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- PyTorch 2.0.1+cu118 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
