Instructions to use 7ocho/WMACv2.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 7ocho/WMACv2.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="7ocho/WMACv2.1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("7ocho/WMACv2.1") model = AutoModelForSpeechSeq2Seq.from_pretrained("7ocho/WMACv2.1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from 7ocho/WMACv2.1: direct link, hf CLI and curl.
- Browser
- Download file 1.55 kB
-
https://huggingface.co/7ocho/WMACv2.1/resolve/main/README.md
- Command line
-
hf download hf://7ocho/WMACv2.1/README.md
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curl -L -H "Authorization: Bearer $HF_TOKEN" -o README.md https://huggingface.co/7ocho/WMACv2.1/resolve/main/README.md
1.55 kB
metadata
library_name: transformers
base_model: 7ocho/WMACv2
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: WMACv2.1
results: []
WMACv2.1
This model is a fine-tuned version of 7ocho/WMACv2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3403
- Wer: 53.6579
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: 8
- eval_batch_size: 24
- seed: 42
- 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: cosine
- lr_scheduler_warmup_steps: 200
- training_steps: 2000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.4811 | 0.2338 | 1000 | 0.3687 | 54.9924 |
| 0.4310 | 0.4675 | 2000 | 0.3403 | 53.6579 |
Framework versions
- Transformers 5.2.0
- Pytorch 2.10.0+cu128
- Datasets 3.6.0
- Tokenizers 0.22.2