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Model: biodatlab/whisper-th-large-v3-combined
Source: Original Platform

README.md

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+---
+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-large-v3
+model-index:
+- name: Whisper Large V3 Thai Combined V1 - 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: 6.59
+      name: Wer
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# Whisper Large V3 (Thai): Combined V1
+
+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.
+It achieves the following results on the common-voice-13 test set:
+- WER: 6.59 (with Deepcut Tokenizer)
+
+## Model description
+
+Use the model with huggingface's `transformers` as follows:
+
+```py
+from transformers import pipeline
+
+MODEL_NAME = "biodatlab/whisper-th-large-v3-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 }
+}
+```