Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Swahili
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use mn720/inctraining4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mn720/inctraining4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mn720/inctraining4")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mn720/inctraining4") model = AutoModelForSpeechSeq2Seq.from_pretrained("mn720/inctraining4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from mn720/inctraining4: direct link, hf CLI and curl.
- Browser
- Download file 2.35 kB
-
https://huggingface.co/mn720/inctraining4/resolve/main/README.md
- Command line
-
hf download hf://mn720/inctraining4/README.md
-
curl -L -H "Authorization: Bearer $HF_TOKEN" -o README.md https://huggingface.co/mn720/inctraining4/resolve/main/README.md
2.35 kB
metadata
language:
- sw
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_15_0
metrics:
- wer
model-index:
- name: Incremental Swahili Luganda
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Mix data
type: mozilla-foundation/common_voice_15_0
config: lg
split: validation
args: 'config: lu, split: test'
metrics:
- name: Wer
type: wer
value: 30.713561255969235
Incremental Swahili Luganda
This model is a fine-tuned version of openai/whisper-small on the Mix data dataset. It achieves the following results on the evaluation set:
- Loss: 0.3391
- Wer: 30.7136
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: 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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1672 | 0.0998 | 500 | 0.3706 | 33.9074 |
| 0.172 | 0.1996 | 1000 | 0.3714 | 33.9803 |
| 0.2094 | 0.2995 | 1500 | 0.3640 | 33.2407 |
| 0.1927 | 0.3993 | 2000 | 0.3577 | 32.5233 |
| 0.1905 | 0.4991 | 2500 | 0.3521 | 31.8810 |
| 0.1735 | 0.5989 | 3000 | 0.3470 | 31.3189 |
| 0.157 | 0.6987 | 3500 | 0.3428 | 31.0052 |
| 0.1784 | 0.7986 | 4000 | 0.3391 | 30.7136 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1