.ipynb_checkpoints/README-checkpoint.md

metadata

language:
  - th
license: apache-2.0
library_name: transformers
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_13_0
  - google/fleurs
metrics:
  - wer
base_model: openai/whisper-medium
model-index:
  - name: Whisper Medium Thai Combined V4 - biodatlab
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: mozilla-foundation/common_voice_13_0 th
          type: mozilla-foundation/common_voice_13_0
          config: th
          split: test
          args: th
        metrics:
          - type: wer
            value: 7.42
            name: Wer

Whisper Medium (Thai): Combined V3

This model is a fine-tuned version of openai/whisper-medium on augmented versions of the mozilla-foundation/common_voice_13_0 th, google/fleurs, and curated datasets. It achieves the following results on the common-voice-13 test set:

• WER: 7.42 (with Deepcut Tokenizer)

Model description

Use the model with huggingface's transformers as follows:

from transformers import pipeline

MODEL_NAME = "biodatlab/whisper-th-medium-combined"  # specify the model name
lang = "th"  # change to Thai langauge

device = 0 if torch.cuda.is_available() else "cpu"

pipe = pipeline(
    task="automatic-speech-recognition",
    model=MODEL_NAME,
    chunk_length_s=30,
    device=device,
)
pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(
  language=lang,
  task="transcribe"
)
text = pipe("audio.mp3")["text"] # give audio mp3 and transcribe text

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• training_steps: 10000
• mixed_precision_training: Native AMP

Framework versions

• Transformers 4.37.2
• Pytorch 2.1.0
• Datasets 2.16.1
• Tokenizers 0.15.1

Citation

Cite using Bibtex:

@misc {thonburian_whisper_med,
    author       = { Atirut Boribalburephan, Zaw Htet Aung, Knot Pipatsrisawat, Titipat Achakulvisut },
    title        = { Thonburian Whisper: A fine-tuned Whisper model for Thai automatic speech recognition },
    year         = 2022,
    url          = { https://huggingface.co./biodatlab/whisper-th-medium-combined },
    doi          = { 10.57967/hf/0226 },
    publisher    = { Hugging Face }
}