Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Divehi
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use vonewman/whisper-small-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vonewman/whisper-small-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="vonewman/whisper-small-dv")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("vonewman/whisper-small-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("vonewman/whisper-small-dv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from vonewman/whisper-small-dv: direct link, hf CLI and curl.
- Browser
- Download file 1.92 kB
-
https://huggingface.co/vonewman/whisper-small-dv/resolve/main/README.md
- Command line
-
hf download hf://vonewman/whisper-small-dv/README.md
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curl -L -o README.md https://huggingface.co/vonewman/whisper-small-dv/resolve/main/README.md
1.92 kB
metadata
language:
- dv
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Small Dv - Abdoulaye DIALLO
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 13
type: mozilla-foundation/common_voice_13_0
config: dv
split: test
args: dv
metrics:
- name: Wer
type: wer
value: 13.434989741628126
Whisper Small Dv - Abdoulaye DIALLO
This model is a fine-tuned version of openai/whisper-small on the Common Voice 13 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1725
- Wer Ortho: 63.0267
- Wer: 13.4350
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 0.1208 | 1.6287 | 500 | 0.1725 | 63.0267 | 13.4350 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1