Automatic Speech Recognition
Transformers
Safetensors
Telugu
whisper
hf-asr-leaderboard
Generated from Trainer
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("April01524/cmomay")
model = AutoModelForSpeechSeq2Seq.from_pretrained("April01524/cmomay")Quick Links
Whisper Small te - arthink
This model is a fine-tuned version of openai/whisper-small on the Google Fleurs dataset.
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: 4
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 100
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.3.0+cpu
- Datasets 2.19.0
- Tokenizers 0.19.1
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Model tree for April01524/cmomay
Base model
openai/whisper-small
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="April01524/cmomay")