Instructions to use nsnihal/braj-speech-to-text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nsnihal/braj-speech-to-text with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/seamless-m4t-v2-large") model = PeftModel.from_pretrained(base_model, "nsnihal/braj-speech-to-text") - Transformers
How to use nsnihal/braj-speech-to-text with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nsnihal/braj-speech-to-text", dtype="auto") - Notebooks
- Google Colab
- Kaggle
braj-speech-to-text
This model is a fine-tuned version of facebook/seamless-m4t-v2-large on the None 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP
Framework versions
- PEFT 0.16.0
- Transformers 4.53.2
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.2
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Base model
facebook/seamless-m4t-v2-large