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---
license: apache-2.0
language:
- th
- en
base_model:
- openai/whisper-large-v3
pipeline_tag: automatic-speech-recognition
library_name: transformers
metrics:
- wer
---
# Pathumma Whisper Large V3 (Th)
## Model Description
Additional information is needed
## Quickstart
You can transcribe audio files using the [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline) class with the following code snippet:
```python
import torch
from transformers import pipeline
device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32
lang = "th"
task = "transcribe"
pipe = pipeline(
task="automatic-speech-recognition",
model="nectec/Pathumma-whisper-th-large-v3",
torch_dtype=torch_dtype,
device=device,
)
pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task=task)
text = pipe("audio_path.wav")["text"]
print(text)
```
<!-- ## Evaluation Performance
WER calculated with newmm tokenizer for Thai word segmentation.
| Model | CV18 (WER) | Gowejee (WER) | LOTUS-TRD (WER) | Thai Dialect (WER) | Elderly (WER) | Gigaspeech2 (WER) | Fleurs (WER) | Distant Meeting (WER) | Podcast (WER) |
|:----------------------------------------|:----------------------:|:-------------------------:|:----------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|
| whisper-large-v3 | 18.75 | 46.59 | 48.14 | 57.82 | 12.27 | 33.26 | 24.08 | 72.57 | 41.24 |
| airesearch-wav2vec2-large-xlsr-53-th | 8.49 | 17.28 | 63.01 | 48.53 | 11.29 | 52.72 | 37.32 | 85.11 | 65.12 |
| thonburian-whisper-th-large-v3-combined | 7.62 | 22.06 | 41.95 | 26.53 | 1.63 | 25.22 | 13.90 | 64.68 | 32.42 |
| monsoon-whisper-medium-gigaspeech2 | 11.66 | 20.50 | 41.04 | 42.06 | 7.57 | 21.40 | 21.54 | 51.65 | 38.89 |
| pathumma-whisper-th-large-v3 | 8.68 | 9.84 | 15.47 | 19.85 | 1.53 | 21.66 | 15.65 | 51.56 | 36.47 |
**Note:** Other models not target fine-tuned on dialect datasets may be less representative of dialect performance. -->
## Limitations and Future Work
Additional information is needed
## Acknowledgements
We extend our appreciation to the research teams engaged in the creation of the open speech model, including AIResearch, BiodatLab, Looloo Technology, SCB 10X, and OpenAI. We would like to express our gratitude to Dr. Titipat Achakulwisut of BiodatLab for the evaluation pipeline. We express our gratitude to ThaiSC, or NSTDA Supercomputer Centre, for supplying the LANTA used for model training, fine-tuning, and evaluation.
## Pathumma Audio Team
*Pattara Tipaksorn*, Wayupuk Sommuang, Oatsada Chatthong, *Kwanchiva Thangthai*
## Citation
```
@misc{tipaksorn2024PathummaWhisper,
title = { {Pathumma Whisper Large V3 (TH)} },
author = { Pattara Tipaksorn and Wayupuk Sommuang and Oatsada Chatthong and Kwanchiva Thangthai },
url = { https://huggingface.co/nectec/Pathumma-whisper-th-large-v3 },
publisher = { Hugging Face },
year = { 2024 },
}
```