111 lines
2.8 KiB
Markdown
111 lines
2.8 KiB
Markdown
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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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library_name: transformers
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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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base_model: openai/whisper-large-v3
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model-index:
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- name: Whisper Large V3 Thai Combined V1 - biodatlab
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results:
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- task:
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type: automatic-speech-recognition
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name: Automatic Speech Recognition
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dataset:
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name: mozilla-foundation/common_voice_13_0 th
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type: mozilla-foundation/common_voice_13_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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- type: wer
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value: 6.59
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name: Wer
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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 Large V3 (Thai): Combined V1
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-large-v3) 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 on the common-voice-13 test set:
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- WER: 6.59 (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-th-large-v3-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: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: AdamW 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: 10000
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- mixed_precision_training: Native AMP
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### Framework versions
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- Transformers 4.37.2
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- Pytorch 2.1.0
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- Datasets 2.16.1
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- Tokenizers 0.15.1
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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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```
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