Instructions to use HamdanXI/Wav2vec2_Single_Utt_Test_Two with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HamdanXI/Wav2vec2_Single_Utt_Test_Two with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="HamdanXI/Wav2vec2_Single_Utt_Test_Two")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("HamdanXI/Wav2vec2_Single_Utt_Test_Two") model = AutoModelForCTC.from_pretrained("HamdanXI/Wav2vec2_Single_Utt_Test_Two", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from HamdanXI/Wav2vec2_Single_Utt_Test_Two: direct link, hf CLI and curl.
- Browser
- Download file 1.71 kB
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https://huggingface.co/HamdanXI/Wav2vec2_Single_Utt_Test_Two/resolve/main/README.md
- Command line
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hf download hf://HamdanXI/Wav2vec2_Single_Utt_Test_Two/README.md
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curl -L -o README.md https://huggingface.co/HamdanXI/Wav2vec2_Single_Utt_Test_Two/resolve/main/README.md
1.71 kB
metadata
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base-960h
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Wav2vec2_Single_Utt_Test_Two
results: []
Wav2vec2_Single_Utt_Test_Two
This model is a fine-tuned version of facebook/wav2vec2-base-960h on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.4727
- Wer: 1.0
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: 0.0001
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 24
- 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: 500
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.8088 | 3.2051 | 1000 | 1.4820 | 1.0 |
| 2.6287 | 6.4103 | 2000 | 0.9560 | 1.0 |
| 1.2689 | 9.6154 | 3000 | 0.9546 | 1.0 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu128
- Datasets 4.3.0
- Tokenizers 0.22.1