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--- |
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license: apache-2.0 |
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base_model: biodatlab/whisper-th-small-combined |
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tags: |
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- generated_from_trainer |
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datasets: |
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- common_voice_17_0 |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper-small-thai |
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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: common_voice_17_0 |
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type: common_voice_17_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: 55.432891743610334 |
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language: |
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- th |
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pipeline_tag: automatic-speech-recognition |
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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-small-thai |
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This model is a fine-tuned version of [biodatlab/whisper-th-small-combined](https://huggingface.co/biodatlab/whisper-th-small-combined) on the common_voice_17_0 dataset. |
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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 = "FILM6912/Whisper-small-thai" # 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: 16 |
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- eval_batch_size: 8 |
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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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### Framework versions |
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- Transformers 4.43.3 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
