Instructions to use scasutt/wav2vec2-base_toy_train_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scasutt/wav2vec2-base_toy_train_data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="scasutt/wav2vec2-base_toy_train_data")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("scasutt/wav2vec2-base_toy_train_data") model = AutoModelForCTC.from_pretrained("scasutt/wav2vec2-base_toy_train_data", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from scasutt/wav2vec2-base_toy_train_data: direct link, hf CLI and curl.
- Browser
- Download file 1.78 kB
-
https://huggingface.co/scasutt/wav2vec2-base_toy_train_data/resolve/main/README.md
- Command line
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hf download hf://scasutt/wav2vec2-base_toy_train_data/README.md
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curl -L -o README.md https://huggingface.co/scasutt/wav2vec2-base_toy_train_data/resolve/main/README.md
1.78 kB
metadata
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base_toy_train_data
results: []
wav2vec2-base_toy_train_data
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.2522
- Wer: 0.7297
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.0033 | 4.2 | 500 | 2.7702 | 1.0 |
| 1.055 | 8.4 | 1000 | 1.2671 | 0.8667 |
| 0.6628 | 12.6 | 1500 | 1.1952 | 0.7883 |
| 0.5023 | 16.8 | 2000 | 1.1435 | 0.7659 |
| 0.4535 | 21.01 | 2500 | 1.1889 | 0.7458 |
| 0.3604 | 25.21 | 3000 | 1.2650 | 0.7378 |
| 0.3175 | 29.41 | 3500 | 1.2522 | 0.7297 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1