Create README.md

README.md

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+---
+language:
+- ht
+pipeline_tag: text-to-speech
+tags:
+- 'speecht5 '
+- TTS
+---
+
+
+# Fine-tuned SpeechT5 TTS Model for Haitian Creole
+
+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.
+
+## Model Description
+
+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.
+
+## Intended Uses & Limitations
+
+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.
+
+However, there are some limitations to be aware of:
+- 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.
+- Like all machine learning models, this model may produce inaccuracies or errors in speech synthesis, especially for complex sentences or domain-specific jargon.
+
+## Training and Evaluation Data
+
+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.
+
+## Training Procedure
+
+### Training Hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- per_device_train_batch_size: 16
+- gradient_accumulation_steps: 2
+- warmup_steps: 500
+- max_steps: 4000
+- gradient_checkpointing: True
+- fp16: True
+- evaluation_strategy: no
+- per_device_eval_batch_size: 8
+- save_steps: 1000
+- logging_steps: 25
+- report_to: ["tensorboard"]
+- greater_is_better: False
+
+### Training Results
+
+The training progress and evaluation results are as follows:
+
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| 0.5147        | 2.42  | 1000 | 0.4753          |
+| 0.4932        | 4.84  | 2000 | 0.4629          |
+| 0.4926        | 7.26  | 3000 | 0.4566          |
+| 0.4907        | 9.69  | 4000 | 0.4542          |
+| 0.4839        | 12.11 | 5000 | 0.4532          |
+
+### Training Output
+
+The training was completed with the following output:
+- Global Step: 4000
+- Training Loss: 0.3344
+- Training Runtime: 7123.63 seconds
+- Training Samples per Second: 17.97
+- Training Steps per Second: 0.562
+- Total FLOPs: 1.1690e+16
+
+## Framework Versions
+
+- Transformers 4.31.0
+- PyTorch 2.0.1+cu118
+- Datasets 2.13.1
+- Tokenizers 0.13.3