# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForTextToSpectrogram
processor = AutoProcessor.from_pretrained("AddisuSeteye/speecht5_tts_amharic")
model = AutoModelForTextToSpectrogram.from_pretrained("AddisuSeteye/speecht5_tts_amharic", device_map="auto")Quick Links
SpeechT5 TTS amharic
This model is a fine-tuned version of microsoft/speecht5_tts on the alfaa dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 100
Training results
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="AddisuSeteye/speecht5_tts_amharic")