Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Marathi
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use simran14/mr-val-j with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use simran14/mr-val-j with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="simran14/mr-val-j")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("simran14/mr-val-j") model = AutoModelForSpeechSeq2Seq.from_pretrained("simran14/mr-val-j", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from simran14/mr-val-j: direct link, hf CLI and curl.
- Browser
- Download file 1.96 kB
-
https://huggingface.co/simran14/mr-val-j/resolve/main/README.md
- Command line
-
hf download hf://simran14/mr-val-j/README.md
-
curl -L -o README.md https://huggingface.co/simran14/mr-val-j/resolve/main/README.md
1.96 kB
metadata
library_name: transformers
language:
- mr
base_model: simran14/mr-val-i
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: simrank14 Whisper small val j
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 17.0
type: mozilla-foundation/common_voice_17_0
config: mr
split: test
args: mr
metrics:
- name: Wer
type: wer
value: 0.9319317080444345
simrank14 Whisper small val j
This model is a fine-tuned version of simran14/mr-val-i on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2062
- Wer: 0.9319
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: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1987 | 1.0152 | 200 | 0.1995 | 1.0835 |
| 0.1482 | 2.0305 | 400 | 0.2078 | 1.0115 |
| 0.0869 | 3.0457 | 600 | 0.2062 | 0.9319 |
Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1