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--- |
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language: |
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- th |
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license: apache-2.0 |
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tags: |
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- whisper-event |
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- generated_from_trainer |
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datasets: |
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- mozilla-foundation/common_voice_13_0 |
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- google/fleurs |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Medium Thai Combined V3 - biodatlab |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: mozilla-foundation/common_voice_11_0 th |
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type: mozilla-foundation/common_voice_11_0 |
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config: th |
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split: test |
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args: th |
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metrics: |
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- name: Wer |
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type: wer |
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value: 8.44 |
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library_name: transformers |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Whisper Medium (Thai): Combined V3 |
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on augmented versions of the mozilla-foundation/common_voice_13_0 th, google/fleurs, and curated datasets. |
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It achieves the following results (NOT-UP-TO-DATE) on the common-voice-11 evaluation set: |
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- Loss: 0.1475 |
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- WER: 13.03 (without Tokenizer) |
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- WER: 8.44 (with Deepcut Tokenizer) |
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## Model description |
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Use the model with huggingface's `transformers` as follows: |
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```py |
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from transformers import pipeline |
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MODEL_NAME = "biodatlab/whisper-medium-th-combined" # specify the model name |
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lang = "th" # change to Thai langauge |
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device = 0 if torch.cuda.is_available() else "cpu" |
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pipe = pipeline( |
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task="automatic-speech-recognition", |
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model=MODEL_NAME, |
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chunk_length_s=30, |
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device=device, |
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) |
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pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids( |
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language=lang, |
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task="transcribe" |
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) |
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text = pipe("audio.mp3")["text"] # give audio mp3 and transcribe text |
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``` |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 5000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:| |
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| 0.0679 | 2.09 | 5000 | 0.1475 | 13.03 | |
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### Framework versions |
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- Transformers 4.31.0.dev0 |
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- Pytorch 2.1.0 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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## Citation |
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Cite using Bibtex: |
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``` |
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@misc {thonburian_whisper_med, |
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author = { Atirut Boribalburephan, Zaw Htet Aung, Knot Pipatsrisawat, Titipat Achakulvisut }, |
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title = { Thonburian Whisper: A fine-tuned Whisper model for Thai automatic speech recognition }, |
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year = 2022, |
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url = { https://huggingface.co/biodatlab/whisper-th-medium-combined }, |
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doi = { 10.57967/hf/0226 }, |
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publisher = { Hugging Face } |
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} |
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``` |
