language:
- hi
license: apache-2.0
base_model: openai/whisper-medium
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Medium finetuned Hindi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: hi
split: test
args: hi
metrics:
- name: Wer
type: wer
value: 99.8077099166743
Fine-tuned Whisper Medium for Hindi Language
Model Description
This model is a fine-tuned version of OpenAI's Whisper medium model, specifically optimized for the Hindi language. The fine-tuning process has led to an improvement in accuracy by 2.5% compared to the original Whisper model.
Performance
After fine-tuning, the model shows a 2.5% increase in transcription accuracy for Hindi language audio compared to the base Whisper medium model.
How to Use
You can use this model directly with a simple API call in Hugging Face. Here is a Python code snippet for using the model:
python
Copy code
from transformers import AutoModelForCTC, Wav2Vec2Processor
model = AutoModelForCTC.from_pretrained("your-username/your-model-name") processor = Wav2Vec2Processor.from_pretrained("your-username/your-model-name")
Replace 'path_to_audio_file' with the path to your Hindi audio file
input_audio = processor(path_to_audio_file, return_tensors="pt", padding=True)
Perform the transcription
transcription = model.generate(**input_audio) print("Transcription:", transcription)
Citation
If you use this model in your research, please cite it as follows:
bibtex @misc{your-model, author = {Your Name}, title = {Fine-tuned Whisper Medium for Hindi Language}, year = {2024}, publisher = {Hugging Face}, journal = {Hugging Face Model Hub} }
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0