Whisper Medium Malayalam
This model is a fine-tuned version of openai/whisper-medium on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
- WER: 38.6207
- CER: 7.3256
Note that Whisper's normalization has major issues for languages like Malayalam, so the above scores are evaluated without using normalization. With normalization (for a fair comparison with other models on this platform), the results are instead:
- WER: 11.49
This Colab can be used as a starting point to further finetune the model.
Usage instructions
Given an audio sample audio (this can be anything from a numpy array to a filepath), the following code generates transcriptions:
from transformers import pipeline, WhisperProcessor
processor = WhisperProcessor.from_pretrained("thennal/whisper-medium-ml")
forced_decoder_ids = processor.get_decoder_prompt_ids(language="ml", task="transcribe")
asr = pipeline(
"automatic-speech-recognition", model="thennal/whisper-medium-ml", device=0,
)
transcription = asr(audio, chunk_length_s=30, max_new_tokens=448, return_timestamps=False, generate_kwargs={
"forced_decoder_ids": forced_decoder_ids,
"do_sample": True,
})
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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 8000
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2