Instructions to use Archan/hindi-turn-detector-with-random-pauses with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Archan/hindi-turn-detector-with-random-pauses with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Archan/hindi-turn-detector-with-random-pauses")# Load model directly from transformers import TurnDetector model = TurnDetector.from_pretrained("Archan/hindi-turn-detector-with-random-pauses", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Archan/hindi-turn-detector-with-random-pauses: direct link, hf CLI and curl.
- Browser
- Download file 1.31 kB
-
https://huggingface.co/Archan/hindi-turn-detector-with-random-pauses/resolve/ea28c95e3382cee1cd65814d42e0debc2cea1338/README.md
- Command line
-
hf download hf://Archan/hindi-turn-detector-with-random-pauses@ea28c95e3382cee1cd65814d42e0debc2cea1338/README.md
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curl -L -o README.md https://huggingface.co/Archan/hindi-turn-detector-with-random-pauses/resolve/ea28c95e3382cee1cd65814d42e0debc2cea1338/README.md
1.31 kB
metadata
library_name: transformers
tags:
- audio-classification
- turn-detection
- hindi
- generated_from_trainer
model-index:
- name: hindi-turn-detector-with-random-pauses
results: []
hindi-turn-detector-with-random-pauses
This model is a fine-tuned version of on an unknown 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 40
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 5.16.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.23.1